From 3e845c1f08f8440cfee4f00bd84ecfb51787d513 Mon Sep 17 00:00:00 2001
From: fw00355 <fw00355@surrey.ac.uk>
Date: Wed, 22 May 2024 14:20:53 +0100
Subject: [PATCH] Changes before submission

---
 .runner_system_id             |    1 -
 README.md                     |   10 +
 app/.runner_system_id         |    1 -
 confusion_matrix.csv          |    6 -
 fredRNN.ipynb                 | 1156 ---------------------------------
 predictions_with_accuracy.csv |   33 -
 6 files changed, 10 insertions(+), 1197 deletions(-)
 delete mode 100644 .runner_system_id
 delete mode 100644 app/.runner_system_id
 delete mode 100644 confusion_matrix.csv
 delete mode 100644 fredRNN.ipynb
 delete mode 100644 predictions_with_accuracy.csv

diff --git a/.runner_system_id b/.runner_system_id
deleted file mode 100644
index e69e2f9..0000000
--- a/.runner_system_id
+++ /dev/null
@@ -1 +0,0 @@
-s_f301446f19c0
\ No newline at end of file
diff --git a/README.md b/README.md
index 96143db..6732b9f 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,13 @@
 # COM3029 CW
 
 Open app/app.ipynb to use the group work application.
+
+
+.gitlab-ci.yml is needed for CI/CD pipelines, though settingup GitLab Runner is also needed.
+
+Requirements:
+Docker
+NVIDEA GPU
+
+
+This project was only tested on Windows machines
diff --git a/app/.runner_system_id b/app/.runner_system_id
deleted file mode 100644
index e69e2f9..0000000
--- a/app/.runner_system_id
+++ /dev/null
@@ -1 +0,0 @@
-s_f301446f19c0
\ No newline at end of file
diff --git a/confusion_matrix.csv b/confusion_matrix.csv
deleted file mode 100644
index 0fafe03..0000000
--- a/confusion_matrix.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-,<pad>,B-O,B-AC,B-LF,I-LF
-<pad>,1549,0,0,0,0
-B-O,0,22,0,32,2
-B-AC,0,0,8,23,0
-B-LF,0,6,7,1010,13
-I-LF,0,1,0,27,20
diff --git a/fredRNN.ipynb b/fredRNN.ipynb
deleted file mode 100644
index 41005bc..0000000
--- a/fredRNN.ipynb
+++ /dev/null
@@ -1,1156 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "import torch\n",
-    "import torchtext\n",
-    "import torch.nn as nn\n",
-    "import torch.nn.functional as F\n",
-    "import torch.optim as optim\n",
-    "import numpy as np\n",
-    "import random\n",
-    "from datasets import load_dataset\n",
-    "torch.backends.cudnn.deterministic = True\n",
-    "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "import pandas as pd\n",
-    "import gc\n",
-    "from matplotlib import pyplot as plt\n",
-    "from tqdm import tqdm\n",
-    "from datasets import  load_metric\n",
-    "gc.collect()\n",
-    "torch.cuda.empty_cache()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torchtext.data.functional import to_map_style_dataset\n",
-    "dataset = load_dataset(\"surrey-nlp/PLOD-CW\")\n",
-    "# This might take a while\n",
-    "\n",
-    "\n",
-    "\n",
-    "train_data = to_map_style_dataset(dataset['train'])\n",
-    "val_data = to_map_style_dataset(dataset['validation'])\n",
-    "test_data = to_map_style_dataset(dataset['test'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of training examples: 1072\n",
-      "Number of validation examples: 126\n",
-      "Number of testing examples: 153\n",
-      "{'tokens': ['Abbreviations', ':', 'GEMS', ',', 'Global', 'Enteric', 'Multicenter', 'Study', ';', 'VIP', ',', 'ventilated', 'improved', 'pit', '.'], 'pos_tags': ['NOUN', 'PUNCT', 'PROPN', 'PUNCT', 'PROPN', 'PROPN', 'PROPN', 'PROPN', 'PUNCT', 'PROPN', 'PUNCT', 'VERB', 'ADJ', 'NOUN', 'PUNCT'], 'ner_tags': ['B-O', 'B-O', 'B-AC', 'B-O', 'B-LF', 'I-LF', 'I-LF', 'I-LF', 'B-O', 'B-AC', 'B-O', 'B-LF', 'I-LF', 'I-LF', 'B-O']}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f'Number of training examples: {len(train_data)}')\n",
-    "print(f'Number of validation examples: {len(val_data)}')\n",
-    "print(f'Number of testing examples: {len(test_data)}')\n",
-    "print(test_data[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Unique tokens in text vocabulary: 9135\n",
-      "Unique tokens in POS vocabulary: 19\n",
-      "Unique tokens in label vocabulary: 4\n",
-      "['<unk>', '<pad>', ',', '(', ')', 'the', '.', 'of', 'and', '-']\n",
-      "{'B-O': 0, 'B-AC': 1, 'B-LF': 2, 'I-LF': 3}\n",
-      "['<unk>', '<pad>', 'NOUN', 'PUNCT', 'PROPN', 'ADP', 'ADJ', 'VERB', 'DET', 'NUM']\n"
-     ]
-    }
-   ],
-   "source": [
-    "from torchtext.vocab import build_vocab_from_iterator, vocab\n",
-    "from torchtext.data.utils import get_tokenizer\n",
-    "from collections import OrderedDict\n",
-    "\n",
-    "MAX_VOCAB_SIZE = 100_000\n",
-    "\n",
-    "def _process_texts_for_vocab(data):\n",
-    "    for item in data:\n",
-    "        yield item['tokens']\n",
-    "def _process_pos_for_vocab(data):\n",
-    "    for item in data:\n",
-    "        yield item['pos_tags']\n",
-    "\n",
-    "text_vocab = build_vocab_from_iterator(_process_texts_for_vocab(train_data), specials=('<unk>', '<pad>'), max_tokens=MAX_VOCAB_SIZE)\n",
-    "pos_vocab = build_vocab_from_iterator(_process_pos_for_vocab(train_data), specials=('<unk>', '<pad>'), min_freq=1)\n",
-    "\n",
-    "diction = OrderedDict([(\"<pad>\", 0),(\"B-O\",1),(\"B-AC\",2),(\"B-LF\",3),(\"I-LF\",4)])\n",
-    "label_vocab = vocab(diction)\n",
-    "text_vocab.set_default_index(text_vocab[\"<unk>\"])\n",
-    "pos_vocab.set_default_index(text_vocab[\"<unk>\"])\n",
-    "label_vocab.set_default_index(0)\n",
-    "print(f\"Unique tokens in text vocabulary: {len(text_vocab)}\")\n",
-    "print(f\"Unique tokens in POS vocabulary: {len(pos_vocab)}\")\n",
-    "print(f\"Unique tokens in label vocabulary: {len(label_vocab)}\")\n",
-    "\n",
-    "\n",
-    "print(text_vocab.get_itos()[:10])\n",
-    "print(label_vocab.get_stoi())\n",
-    "print(pos_vocab.get_itos()[:10])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(',', 2268), ('(', 1583), (')', 1575), ('the', 1136), ('.', 1073), ('of', 995), ('and', 903), ('-', 901), (';', 691), ('in', 585), ('to', 449), (']', 367), ('[', 358), ('a', 346), ('with', 297), ('for', 221), ('were', 201), ('was', 194), (':', 180), ('by', 178)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from collections import Counter\n",
-    "\n",
-    "\n",
-    "counter = Counter()\n",
-    "for data_point in train_data:\n",
-    "    tokens = data_point['tokens']\n",
-    "    counter.update(tokens)\n",
-    "most_common_tokens = counter.most_common(20)\n",
-    "print(most_common_tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import Dataset, DataLoader\n",
-    "from torch.nn.utils.rnn import pad_sequence\n",
-    "class NERDataset(Dataset):\n",
-    "    def __init__(self, data, text_vocab, tag_vocab):\n",
-    "        self.data = data\n",
-    "        self.text_vocab = text_vocab\n",
-    "        self.tag_vocab = tag_vocab\n",
-    "    \n",
-    "    def __len__(self):\n",
-    "        return len(self.data)\n",
-    "    \n",
-    "    def __getitem__(self, idx):\n",
-    "        tokens = self.data[idx]['tokens']\n",
-    "        ner_tags = self.data[idx]['ner_tags']\n",
-    "        text_numerical = [self.text_vocab[token] for token in tokens]\n",
-    "        tag_numerical = [self.tag_vocab[tag] for tag in ner_tags]\n",
-    "        \n",
-    "        return torch.tensor(text_numerical, dtype=torch.long), torch.tensor(tag_numerical, dtype=torch.long)\n",
-    "    \n",
-    "def collate_fn(batch):\n",
-    "    tokens, tags = zip(*batch)\n",
-    "    tokens_padded = pad_sequence(tokens, batch_first=True, padding_value=text_vocab['<pad>'])\n",
-    "    tags_padded = pad_sequence(tags, batch_first=True, padding_value=label_vocab['<pad>']) # Assuming you have a pad token in your label vocab\n",
-    "\n",
-    "    return tokens_padded, tags_padded\n",
-    "\n",
-    "# Create instances of the NERDataset for training, validation, and test data\n",
-    "train_dataset = NERDataset(train_data, text_vocab, label_vocab)\n",
-    "val_dataset = NERDataset(val_data, text_vocab, label_vocab)\n",
-    "test_dataset = NERDataset(test_data, text_vocab, label_vocab)\n",
-    "\n",
-    "# Create DataLoaders\n",
-    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, collate_fn=collate_fn)\n",
-    "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, collate_fn=collate_fn)\n",
-    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, collate_fn=collate_fn)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([32, 100]) torch.Size([32, 100])\n"
-     ]
-    }
-   ],
-   "source": [
-    "from torch.utils.data import Dataset, DataLoader\n",
-    "from torch.nn.utils.rnn import pad_sequence\n",
-    "class NERDatasetPOS(Dataset):\n",
-    "    def __init__(self, data, text_vocab, pos_vocab, tag_vocab):\n",
-    "        self.data = data\n",
-    "        self.text_vocab = text_vocab\n",
-    "        self.pos_vocab = pos_vocab\n",
-    "        self.tag_vocab = tag_vocab\n",
-    "\n",
-    "    def __len__(self):\n",
-    "        return len(self.data)\n",
-    "\n",
-    "    def __getitem__(self, idx):\n",
-    "        tokens = self.data[idx]['tokens']\n",
-    "        pos_tags = self.data[idx]['pos_tags']\n",
-    "        ner_tags = self.data[idx]['ner_tags']\n",
-    "        \n",
-    "        # Concatenate token and POS tag\n",
-    "        tokens_with_pos = ['{}_{}'.format(token, pos) for token, pos in zip(tokens, pos_tags)]\n",
-    "\n",
-    "        text_numerical = [self.text_vocab.get(token_with_pos, self.text_vocab['<unk>']) for token_with_pos in tokens_with_pos]\n",
-    "        tag_numerical = [self.tag_vocab[tag] for tag in ner_tags]\n",
-    "\n",
-    "        return torch.tensor(text_numerical, dtype=torch.long), torch.tensor(tag_numerical, dtype=torch.long)\n",
-    "    \n",
-    "def collate_fn(batch):\n",
-    "    tokens, tags = zip(*batch)\n",
-    "    tokens_padded = pad_sequence(tokens, batch_first=True, padding_value=text_vocab['<pad>'])\n",
-    "    tags_padded = pad_sequence(tags, batch_first=True, padding_value=label_vocab['<pad>']) # Assuming you have a pad token in your label vocab\n",
-    "\n",
-    "    return tokens_padded, tags_padded\n",
-    "\n",
-    "train_dataset_pos = NERDatasetPOS(train_data, text_vocab, pos_vocab, label_vocab)\n",
-    "val_dataset_pos = NERDatasetPOS(val_data, text_vocab, pos_vocab, label_vocab)\n",
-    "test_dataset_pos = NERDatasetPOS(test_data, text_vocab, pos_vocab, label_vocab)\n",
-    "\n",
-    "# Create DataLoaders as before\n",
-    "train_loader_pos = DataLoader(train_dataset, batch_size=32, shuffle=True, collate_fn=collate_fn)\n",
-    "val_loader_pos = DataLoader(val_dataset, batch_size=32, shuffle=False, collate_fn=collate_fn)\n",
-    "test_loader_pos = DataLoader(test_dataset, batch_size=32, shuffle=False, collate_fn=collate_fn)\n",
-    "for batch in train_loader_pos:\n",
-    "    print(batch[0].shape, batch[1].shape)\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class RNN(nn.Module):\n",
-    "    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, dropout):\n",
-    "        super().__init__()\n",
-    "        self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
-    "        self.rnn = nn.RNN(embedding_dim, hidden_dim, batch_first=True)\n",
-    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
-    "        self.dropout = nn.Dropout(dropout)\n",
-    "\n",
-    "    def forward(self, text):\n",
-    "        # text = [batch size, sent len]\n",
-    "        embedded = self.dropout(self.embedding(text))\n",
-    "        # embedded = [batch size, sent len, emb dim]\n",
-    "        outputs, _ = self.rnn(embedded)\n",
-    "        # outputs = [batch size, sent len, hid dim]\n",
-    "        predictions = self.fc(self.dropout(outputs))\n",
-    "        # predictions = [batch size, sent len, output dim]\n",
-    "        return predictions\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def train(model, iterator, optimizer, criterion, device):\n",
-    "    model.train()\n",
-    "    epoch_loss = 0\n",
-    "    \n",
-    "    for batch in iterator:\n",
-    "        text, tags = batch\n",
-    "        text, tags = text.to(device), tags.to(device)\n",
-    "        \n",
-    "        optimizer.zero_grad()\n",
-    "        predictions = model(text)\n",
-    "        \n",
-    "        # Reshape for calculating loss\n",
-    "        predictions = predictions.view(-1, predictions.shape[-1])\n",
-    "        tags = tags.view(-1)\n",
-    "        \n",
-    "        loss = criterion(predictions, tags)\n",
-    "        loss.backward()\n",
-    "        optimizer.step()\n",
-    "        epoch_loss += loss.item()\n",
-    "    \n",
-    "    return epoch_loss / len(iterator)\n",
-    "def evaluate(model, iterator, criterion, device):\n",
-    "    model.eval()\n",
-    "    epoch_loss = 0\n",
-    "    \n",
-    "    with torch.no_grad():\n",
-    "        for batch in iterator:\n",
-    "            text, tags = batch\n",
-    "            text, tags = text.to(device), tags.to(device)\n",
-    "            predictions = model(text)\n",
-    "            predictions = predictions.view(-1, predictions.shape[-1])\n",
-    "            tags = tags.view(-1)\n",
-    "            loss = criterion(predictions, tags)\n",
-    "            epoch_loss += loss.item()\n",
-    "    \n",
-    "    return epoch_loss / len(iterator)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "THIS IS THE BASE TRAINING CODE, THE MODEL PARAMETERS ARE WHAT WERE FOUND VIA TRIAL AND ERROR AS A DECENT STARTING POINT. THEY ARE THE DEFAULT PARAMETERS FOR MY EXPERIMENTATION\n",
-    "INPUT_DIM = len(text_vocab)\n",
-    "EMBEDDING_DIM = 4096\n",
-    "HIDDEN_DIM = 128\n",
-    "OUTPUT_DIM = len(label_vocab)\n",
-    "DROPOUT = 0.8\n",
-    "\n",
-    "Training: 100%|██████████| 100/100 [02:04<00:00,  1.25s/it, Epoch 100/100, Train Loss: 0.0928, Val Loss: 0.1511]\n",
-    "\n",
-    "CPU times: total: 2min 4s\n",
-    "\n",
-    "Wall time: 2min 5s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\Main\\anaconda3\\envs\\COM3029\\lib\\site-packages\\transformers\\utils\\generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
-      "  _torch_pytree._register_pytree_node(\n",
-      "Training: 100%|██████████| 100/100 [01:52<00:00,  1.13s/it, Epoch 100/100, Train Loss: 0.0928, Val Loss: 0.1875]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "saved:  BASE/4096dim_128hidden_100epochs_08drop.pth\n",
-      "CPU times: total: 1min 53s\n",
-      "Wall time: 1min 55s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "# Model parameters\n",
-    "INPUT_DIM = len(text_vocab)\n",
-    "EMBEDDING_DIM = 4096\n",
-    "HIDDEN_DIM = 128\n",
-    "OUTPUT_DIM = len(label_vocab)\n",
-    "DROPOUT = 0.8\n",
-    "\n",
-    "# Instantiate the model with the corrected RNNforNER class parameters\n",
-    "model = RNN(INPUT_DIM, EMBEDDING_DIM, HIDDEN_DIM, OUTPUT_DIM, DROPOUT)\n",
-    "model = model.to(DEVICE)\n",
-    "\n",
-    "# Optimizer and loss function\n",
-    "optimizer = optim.Adam(model.parameters())\n",
-    "# Ensure you have a padding index for your labels. Replace '<pad>' with your actual padding token index if necessary\n",
-    "criterion = nn.CrossEntropyLoss() #ignore_index=label_vocab['<pad>']\n",
-    "criterion = criterion.to(DEVICE)\n",
-    "\n",
-    "train_loss_tracking = []\n",
-    "val_loss_tracking = []\n",
-    "# Training loop\n",
-    "N_EPOCHS = 100\n",
-    "pbar = tqdm(range(N_EPOCHS),desc=\"Training\")\n",
-    "for epoch in pbar:\n",
-    "    train_loss = train(model, train_loader, optimizer, criterion, DEVICE)\n",
-    "    valid_loss = evaluate(model, val_loader, criterion, DEVICE)\n",
-    "    train_loss_tracking.append(train_loss)\n",
-    "    val_loss_tracking.append(valid_loss)\n",
-    "    pbar.set_postfix_str(f'Epoch {epoch+1}/{N_EPOCHS}, Train Loss: {train_loss:.4f}, Val Loss: {valid_loss:.4f}')\n",
-    "model_name = \"{0}dim_{1}hidden_{2}epochs_{3}drop.pth\".format(EMBEDDING_DIM,HIDDEN_DIM,N_EPOCHS,str(DROPOUT).replace(\".\",\"\"))\n",
-    "torch.save(model.state_dict(),model_name)\n",
-    "print(\"saved: \",model_name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "THIS IS THE CODE FOR TRAINING THE POS_TAGGED MODEL\n",
-    "\n",
-    "\n",
-    "Training: 100%|██████████| 100/100 [02:08<00:00,  1.28s/it, Epoch 100/100, Train Loss: 0.0958, Val Loss: 0.1714]\n",
-    "\n",
-    "\n",
-    "saved:  4096dim_128hidden_100epochs_08drop_POS_tagged.pth\n",
-    "\n",
-    "\n",
-    "\n",
-    "CPU times: total: 2min 7s\n",
-    "\n",
-    "\n",
-    "Wall time: 2min 8s\n",
-    "\n",
-    "\n",
-    "This has slightly worse Validation loss, however, there is a smaller gap between validation loss and training loss, suggesting a reduction in overfitting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Training:  32%|███▏      | 32/100 [00:38<01:23,  1.23s/it, Epoch 32/100, Train Loss: 0.1715, Val Loss: 0.2057]"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "# Model parameters\n",
-    "INPUT_DIM = len(text_vocab)\n",
-    "EMBEDDING_DIM = 4096\n",
-    "HIDDEN_DIM = 128\n",
-    "OUTPUT_DIM = len(label_vocab)\n",
-    "DROPOUT = 0.8\n",
-    "\n",
-    "# Instantiate the model with the corrected RNNforNER class parameters\n",
-    "model = RNN(INPUT_DIM, EMBEDDING_DIM, HIDDEN_DIM, OUTPUT_DIM, DROPOUT)\n",
-    "model = model.to(DEVICE)\n",
-    "\n",
-    "# Optimizer and loss function\n",
-    "optimizer = optim.Adam(model.parameters())\n",
-    "# Ensure you have a padding index for your labels. Replace '<pad>' with your actual padding token index if necessary\n",
-    "criterion = nn.CrossEntropyLoss() #ignore_index=label_vocab['<pad>']\n",
-    "criterion = criterion.to(DEVICE)\n",
-    "\n",
-    "train_loss_tracking = []\n",
-    "val_loss_tracking = []\n",
-    "# Training loop\n",
-    "N_EPOCHS = 100\n",
-    "pbar = tqdm(range(N_EPOCHS),desc=\"Training\")\n",
-    "for epoch in pbar:\n",
-    "    train_loss = train(model, train_loader_pos, optimizer, criterion, DEVICE)\n",
-    "    valid_loss = evaluate(model, val_loader_pos, criterion, DEVICE)\n",
-    "    train_loss_tracking.append(train_loss)\n",
-    "    val_loss_tracking.append(valid_loss)\n",
-    "    pbar.set_postfix_str(f'Epoch {epoch+1}/{N_EPOCHS}, Train Loss: {train_loss:.4f}, Val Loss: {valid_loss:.4f}')\n",
-    "model_name = \"{0}dim_{1}hidden_{2}epochs_{3}drop_POS_tagged.pth\".format(EMBEDDING_DIM,HIDDEN_DIM,N_EPOCHS,str(DROPOUT).replace(\".\",\"\"))\n",
-    "torch.save(model.state_dict(),model_name)\n",
-    "print(\"saved: \",model_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.manifold import TSNE\n",
-    "import matplotlib.pyplot as plt\n",
-    "import gensim.downloader as api\n",
-    "word2vec_model = api.load('word2vec-google-news-300')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([32, 93, 300]) torch.Size([32, 93])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\Main\\AppData\\Local\\Temp\\ipykernel_13756\\2545187020.py:17: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\torch\\csrc\\utils\\tensor_new.cpp:278.)\n",
-      "  return torch.tensor(embeddings, dtype=torch.float), torch.tensor(tag_numerical, dtype=torch.long)\n"
-     ]
-    }
-   ],
-   "source": [
-    "class Word2VecDataset(Dataset):\n",
-    "    def __init__(self, data, word2vec_model, tag_vocab):\n",
-    "        self.data = data\n",
-    "        self.word2vec_model = word2vec_model\n",
-    "        self.tag_vocab = tag_vocab\n",
-    "    \n",
-    "    def __len__(self):\n",
-    "        return len(self.data)\n",
-    "    \n",
-    "    def __getitem__(self, idx):\n",
-    "        tokens = self.data[idx]['tokens']\n",
-    "        ner_tags = self.data[idx]['ner_tags']\n",
-    "\n",
-    "        embeddings = [self.word2vec_model[token] if token in self.word2vec_model else np.zeros(300) for token in tokens]\n",
-    "        tag_numerical = [self.tag_vocab[tag] for tag in ner_tags]\n",
-    "        \n",
-    "        return torch.tensor(embeddings, dtype=torch.float), torch.tensor(tag_numerical, dtype=torch.long)\n",
-    "def collate_fn(batch):\n",
-    "    embeddings, tags = zip(*batch)\n",
-    "    lengths = [len(seq) for seq in embeddings]\n",
-    "    embeddings_padded = pad_sequence(embeddings, batch_first=True)\n",
-    "    tags_padded = pad_sequence(tags, batch_first=True, padding_value=label_vocab['<pad>'])\n",
-    "    lengths = torch.tensor(lengths, dtype=torch.long)\n",
-    "    \n",
-    "    return embeddings_padded, tags_padded\n",
-    "\n",
-    "train_dataset_w2v = Word2VecDataset(train_data, word2vec_model, label_vocab)\n",
-    "val_dataset_w2v = Word2VecDataset(val_data, word2vec_model, label_vocab)\n",
-    "test_dataset_w2v = Word2VecDataset(test_data, word2vec_model, label_vocab)\n",
-    "\n",
-    "\n",
-    "train_loader_w2v = DataLoader(train_dataset_w2v, batch_size=32, shuffle=True, collate_fn=collate_fn)\n",
-    "val_loader_w2v = DataLoader(val_dataset_w2v, batch_size=32, shuffle=False, collate_fn=collate_fn)\n",
-    "test_loader_w2v = DataLoader(test_dataset_w2v, batch_size=32, shuffle=False, collate_fn=collate_fn)\n",
-    "\n",
-    "# To check the shape of a batch\n",
-    "for embeddings_padded, tags_padded in train_loader_w2v:\n",
-    "    print(embeddings_padded.shape, tags_padded.shape)\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "THIS IS THE CODE FOR TEXT2VEC USES DEFAULT PARAMETERS, CHANGES INPUT TO A WORD2VEC REPRESENTATION RATHER THAN VOCAB"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class RNNW2V(nn.Module): #needs to rewrite this, since it is already embeded by word2vec\n",
-    "    def __init__(self, embedding_dim, hidden_dim, output_dim, dropout):\n",
-    "        super().__init__()\n",
-    "        self.rnn = nn.RNN(embedding_dim, hidden_dim, batch_first=True)\n",
-    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
-    "        self.dropout = nn.Dropout(dropout)\n",
-    "\n",
-    "    def forward(self, embedded):\n",
-    "        embedded = self.dropout(embedded)\n",
-    "        outputs, _ = self.rnn(embedded)\n",
-    "        predictions = self.fc(self.dropout(outputs))\n",
-    "        return predictions\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Training:   0%|          | 0/100 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Training: 100%|██████████| 100/100 [03:48<00:00,  2.29s/it, Epoch 100/100, Train Loss: 0.1972, Val Loss: 0.1396]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "saved:  300dim_128hidden_100epochs_08drop_w2v.pth\n",
-      "CPU times: total: 14min 55s\n",
-      "Wall time: 3min 48s\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "# Model parameters\n",
-    "EMBEDDING_DIM = 300 #this is the embedding dimension of gensim's word2vec model\n",
-    "HIDDEN_DIM = 128\n",
-    "OUTPUT_DIM = len(label_vocab)\n",
-    "DROPOUT = 0.8\n",
-    "\n",
-    "# Instantiate the model with the corrected RNNforNER class parameters\n",
-    "model = RNNW2V(EMBEDDING_DIM, HIDDEN_DIM, OUTPUT_DIM, DROPOUT)\n",
-    "model = model.to(DEVICE)\n",
-    "\n",
-    "# Optimizer and loss function\n",
-    "optimizer = optim.Adam(model.parameters())\n",
-    "# Ensure you have a padding index for your labels. Replace '<pad>' with your actual padding token index if necessary\n",
-    "criterion = nn.CrossEntropyLoss() #ignore_index=label_vocab['<pad>']\n",
-    "criterion = criterion.to(DEVICE)\n",
-    "\n",
-    "train_loss_tracking = []\n",
-    "val_loss_tracking = []\n",
-    "# Training loop\n",
-    "N_EPOCHS = 100\n",
-    "pbar = tqdm(range(N_EPOCHS),desc=\"Training\")\n",
-    "for epoch in pbar:\n",
-    "    train_loss = train(model, train_loader_w2v, optimizer, criterion, DEVICE)\n",
-    "    valid_loss = evaluate(model, val_loader_w2v, criterion, DEVICE)\n",
-    "    train_loss_tracking.append(train_loss)\n",
-    "    val_loss_tracking.append(valid_loss)\n",
-    "    pbar.set_postfix_str(f'Epoch {epoch+1}/{N_EPOCHS}, Train Loss: {train_loss:.4f}, Val Loss: {valid_loss:.4f}')\n",
-    "model_name = \"{0}dim_{1}hidden_{2}epochs_{3}drop_w2v.pth\".format(EMBEDDING_DIM,HIDDEN_DIM,N_EPOCHS,str(DROPOUT).replace(\".\",\"\"))\n",
-    "torch.save(model.state_dict(),model_name)\n",
-    "print(\"saved: \",model_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "api.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(train_loss_tracking, label='Training loss')\n",
-    "plt.plot(val_loss_tracking, label='Validation loss')\n",
-    "plt.legend()\n",
-    "plt.xlabel('Epoch')\n",
-    "plt.ylabel('Loss')\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "division by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[28], line 99\u001b[0m\n\u001b[0;32m     95\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m accuracies\n\u001b[0;32m     98\u001b[0m \u001b[38;5;66;03m# Call this function after or during your training loop, passing in the label_vocab and text_vocab\u001b[39;00m\n\u001b[1;32m---> 99\u001b[0m predictions_df, confusion_matrix_df \u001b[38;5;241m=\u001b[39m \u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mDEVICE\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel_vocab\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext_vocab\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    100\u001b[0m \u001b[38;5;28mprint\u001b[39m(confusion_matrix_df\u001b[38;5;241m.\u001b[39mhead())\n\u001b[0;32m    101\u001b[0m predictions_df\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredictions_with_accuracy_\u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(model_name)\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.pth\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m),index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
-      "Cell \u001b[1;32mIn[28], line 34\u001b[0m, in \u001b[0;36mpredict\u001b[1;34m(model, iterator, device, label_vocab, text_vocab)\u001b[0m\n\u001b[0;32m     31\u001b[0m true_tags \u001b[38;5;241m=\u001b[39m [[label_vocab\u001b[38;5;241m.\u001b[39mget_itos()[index] \u001b[38;5;28;01mfor\u001b[39;00m index \u001b[38;5;129;01min\u001b[39;00m sentence] \u001b[38;5;28;01mfor\u001b[39;00m sentence \u001b[38;5;129;01min\u001b[39;00m tags]\n\u001b[0;32m     33\u001b[0m \u001b[38;5;66;03m# Compute accuracy for this batch\u001b[39;00m\n\u001b[1;32m---> 34\u001b[0m batch_accuracy_noPad \u001b[38;5;241m=\u001b[39m \u001b[43mcompute_accuracy_modified\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     35\u001b[0m batch_accuracy \u001b[38;5;241m=\u001b[39m compute_accuracy(predictions, tags)\n\u001b[0;32m     36\u001b[0m accuracy_list\u001b[38;5;241m.\u001b[39mextend(batch_accuracy_noPad)\n",
-      "Cell \u001b[1;32mIn[28], line 93\u001b[0m, in \u001b[0;36mcompute_accuracy_modified\u001b[1;34m(predictions, tags)\u001b[0m\n\u001b[0;32m     91\u001b[0m         \u001b[38;5;28;01melif\u001b[39;00m p\u001b[38;5;241m==\u001b[39mt \u001b[38;5;129;01mand\u001b[39;00m p\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m     92\u001b[0m             count \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m---> 93\u001b[0m     accuracy \u001b[38;5;241m=\u001b[39m \u001b[43mcorrect\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrue\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mcount\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(true) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m     94\u001b[0m     accuracies\u001b[38;5;241m.\u001b[39mappend(accuracy)\n\u001b[0;32m     95\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m accuracies\n",
-      "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import torch\n",
-    "from sklearn.metrics import confusion_matrix, f1_score\n",
-    "\n",
-    "def predict(model, iterator, device, label_vocab, text_vocab):\n",
-    "    model.eval()\n",
-    "    \n",
-    "    text_list = []\n",
-    "    true_labels_list = []\n",
-    "    predicted_labels_list = []\n",
-    "    accuracy_list = []\n",
-    "    all_true_labels = []\n",
-    "    all_predicted_labels = []\n",
-    "    accuracy_list_pad = []\n",
-    "\n",
-    "    with torch.no_grad():\n",
-    "        for batch in iterator:\n",
-    "            text, tags = batch\n",
-    "            text, tags = text.to(device), tags.to(device)\n",
-    "            predictions = model(text)\n",
-    "            \n",
-    "            # Convert predictions to labels\n",
-    "            predictions = predictions.argmax(dim=2)\n",
-    "            \n",
-    "            # Convert tensors to lists for easier handling\n",
-    "            predictions = predictions.tolist()\n",
-    "            tags = tags.tolist()\n",
-    "            \n",
-    "            # Convert indices to strings (NER tags)\n",
-    "            predictions_tags = [[label_vocab.get_itos()[index] for index in sentence] for sentence in predictions]\n",
-    "            true_tags = [[label_vocab.get_itos()[index] for index in sentence] for sentence in tags]\n",
-    "            \n",
-    "            # Compute accuracy for this batch\n",
-    "            batch_accuracy_noPad = compute_accuracy_modified(predictions, tags)\n",
-    "            batch_accuracy = compute_accuracy(predictions, tags)\n",
-    "            accuracy_list.extend(batch_accuracy_noPad)\n",
-    "            accuracy_list_pad.extend(batch_accuracy)\n",
-    "\n",
-    "            # Convert indices to text\n",
-    "            text_tokens = [[text_vocab.get_itos()[index] for index in sentence] for sentence in text.tolist()]\n",
-    "            \n",
-    "            # Append to lists\n",
-    "            for i in range(len(predictions_tags)):  \n",
-    "                text_list.append(text_tokens[i])\n",
-    "                true_labels_list.append(true_tags[i])\n",
-    "                predicted_labels_list.append(predictions_tags[i])\n",
-    "                all_true_labels.extend(true_tags[i])\n",
-    "                all_predicted_labels.extend(predictions_tags[i])\n",
-    "\n",
-    "            break  # Only show the first batch or part of it\n",
-    "            \n",
-    "    # Create DataFrame\n",
-    "    df = pd.DataFrame({\n",
-    "        'Text': text_list, \n",
-    "        'True Labels': true_labels_list, \n",
-    "        'Predicted Labels': predicted_labels_list, \n",
-    "        'Accuracy (excl. Padding)': accuracy_list, \n",
-    "        'Accuracy (incl. Padding)': accuracy_list_pad\n",
-    "    })\n",
-    "    \n",
-    "    \n",
-    "    cm = confusion_matrix(all_true_labels, all_predicted_labels)\n",
-    "    cm_df = pd.DataFrame(cm, index=label_vocab.get_itos(), columns=label_vocab.get_itos())\n",
-    "    \n",
-    "    non_pad_labels = [\"B-O\",\"B-AC\",\"B-LF\",\"I-LF\"]  # Assuming 0 is <pad>\n",
-    "    f1_no_pad = f1_score(all_true_labels, all_predicted_labels, labels=non_pad_labels, average='weighted')\n",
-    "    f1_pad = f1_score(all_true_labels, all_predicted_labels, average='weighted')\n",
-    "\n",
-    "\n",
-    "    df['F1 Score (excl. Padding)'] = f1_no_pad\n",
-    "    df[\"F1 Score (inc. Padding)\"] = f1_pad\n",
-    "    return df, cm_df\n",
-    "\n",
-    "def compute_accuracy(predictions, tags):\n",
-    "    accuracies = []\n",
-    "    for pred, true in zip(predictions, tags):\n",
-    "        correct = sum(p == t for p, t in zip(pred, true))\n",
-    "        accuracy = correct / len(true) if len(true) > 0 else 0\n",
-    "        accuracies.append(accuracy)\n",
-    "    return accuracies\n",
-    "\n",
-    "def compute_accuracy_modified(predictions, tags):\n",
-    "    accuracies = []\n",
-    "    for pred, true in zip(predictions, tags):\n",
-    "        #correct = sum(p == t for p, t in zip(pred, true))\n",
-    "        correct = 0\n",
-    "        count = 0        \n",
-    "        for p,t in zip(pred,true):\n",
-    "            if p == t and p != 0: #0 == <pad>\n",
-    "                correct += 1\n",
-    "            elif p==t and p==0:\n",
-    "                count += 1\n",
-    "        accuracy = correct / (len(true)-count) if len(true) > 0 and len(true)-count != 0 else 0\n",
-    "        accuracies.append(accuracy)\n",
-    "    return accuracies\n",
-    "\n",
-    "\n",
-    "# Call this function after or during your training loop, passing in the label_vocab and text_vocab\n",
-    "predictions_df, confusion_matrix_df = predict(model, test_loader, DEVICE, label_vocab, text_vocab)\n",
-    "print(confusion_matrix_df.head())\n",
-    "predictions_df.to_csv(\"predictions_with_accuracy_{0}.csv\".format(model_name).replace(\".pth\",\"\"),index=False)\n",
-    "confusion_matrix_df.to_csv(\"confusion_matrix_{0}.csv\".format(model_name.replace(\".pth\",\"\")),index=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "CODE FOR BERT"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\Main\\anaconda3\\envs\\COM3029\\lib\\site-packages\\transformers\\utils\\generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
-      "  _torch_pytree._register_pytree_node(\n",
-      "c:\\Users\\Main\\anaconda3\\envs\\COM3029\\lib\\site-packages\\transformers\\utils\\generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
-      "  _torch_pytree._register_pytree_node(\n",
-      "c:\\Users\\Main\\anaconda3\\envs\\COM3029\\lib\\site-packages\\transformers\\utils\\generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
-      "  _torch_pytree._register_pytree_node(\n",
-      "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
-      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
-      "Token indices sequence length is longer than the specified maximum sequence length for this model (542 > 512). Running this sequence through the model will result in indexing errors\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['[CLS]', 'for', 'this', 'purpose', 'the', 'gothenburg', 'young', 'persons', 'empowerment', 'scale', '(', 'g', '##ype', '##s', ')', 'was', 'developed', '.', '[SEP]']\n"
-     ]
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
-    "model = AutoModelForTokenClassification.from_pretrained(\"bert-base-uncased\", num_labels=4)\n",
-    "short_dataset = dataset[\"train\"]\n",
-    "val_dataset = dataset[\"validation\"]\n",
-    "test_dataset = dataset[\"test\"]\n",
-    "tokenized_input = tokenizer(short_dataset[\"tokens\"], is_split_into_words=True)\n",
-    "\n",
-    "# Example single sentence example.\n",
-    "for token in tokenized_input[\"input_ids\"]:\n",
-    "    print(tokenizer.convert_ids_to_tokens(token))\n",
-    "    break\n",
-    "label_encoding = {\"B-O\": 0, \"B-AC\": 1, \"B-LF\": 2, \"I-LF\": 3}\n",
-    "\n",
-    "label_list = []\n",
-    "for sample in short_dataset[\"ner_tags\"]:\n",
-    "    label_list.append([label_encoding[tag] for tag in sample])\n",
-    "\n",
-    "val_label_list = []\n",
-    "for sample in val_dataset[\"ner_tags\"]:\n",
-    "    val_label_list.append([label_encoding[tag] for tag in sample])\n",
-    "\n",
-    "test_label_list = []\n",
-    "for sample in test_dataset[\"ner_tags\"]:\n",
-    "    test_label_list.append([label_encoding[tag] for tag in sample])\n",
-    "\n",
-    "def tokenize_and_align_labels(short_dataset, list_name):\n",
-    "    tokenized_inputs = tokenizer(short_dataset[\"tokens\"], truncation=True, is_split_into_words=True) ## For some models, you may need to set max_length to approximately 500.\n",
-    "\n",
-    "    labels = []\n",
-    "    for i, label in enumerate(list_name):\n",
-    "        word_ids = tokenized_inputs.word_ids(batch_index=i)\n",
-    "        previous_word_idx = None\n",
-    "        label_ids = []\n",
-    "        for word_idx in word_ids:\n",
-    "            # Special tokens have a word id that is None. We set the label to -100 so they are automatically\n",
-    "            # ignored in the loss function.\n",
-    "            if word_idx is None:\n",
-    "                label_ids.append(-100)\n",
-    "            # We set the label for the first token of each word.\n",
-    "            elif word_idx != previous_word_idx:\n",
-    "                label_ids.append(label[word_idx])\n",
-    "            # For the other tokens in a word, we set the label to either the current label or -100, depending on\n",
-    "            # the label_all_tokens flag.\n",
-    "            else:\n",
-    "                label_ids.append(label[word_idx])\n",
-    "            previous_word_idx = word_idx\n",
-    "\n",
-    "        labels.append(label_ids)\n",
-    "\n",
-    "    tokenized_inputs[\"labels\"] = labels\n",
-    "    return tokenized_inputs\n",
-    "tokenized_datasets = tokenize_and_align_labels(short_dataset, label_list)\n",
-    "tokenized_val_datasets = tokenize_and_align_labels(val_dataset, val_label_list)\n",
-    "tokenized_test_datasets = tokenize_and_align_labels(test_dataset, test_label_list)\n",
-    "# print(tokenized_datasets)\n",
-    "# BERT's tokenizer returns the dataset in the form of a dictionary of lists (sentences). \n",
-    "# we have to convert it into a list of dictionaries for training.\n",
-    "def turn_dict_to_list_of_dict(d):\n",
-    "    new_list = []\n",
-    "\n",
-    "    for labels, inputs in zip(d[\"labels\"], d[\"input_ids\"]):\n",
-    "        entry = {\"input_ids\": inputs, \"labels\": labels}\n",
-    "        new_list.append(entry)\n",
-    "\n",
-    "    return new_list\n",
-    "tokenised_train = turn_dict_to_list_of_dict(tokenized_datasets)\n",
-    "tokenised_val = turn_dict_to_list_of_dict(tokenized_val_datasets)\n",
-    "tokenised_test = turn_dict_to_list_of_dict(tokenized_test_datasets)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\Main\\anaconda3\\envs\\COM3029\\lib\\site-packages\\datasets\\load.py:756: FutureWarning: The repository for seqeval contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.18.0/metrics/seqeval/seqeval.py\n",
-      "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
-      "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
-      "  warnings.warn(\n",
-      "c:\\Users\\Main\\anaconda3\\envs\\COM3029\\lib\\site-packages\\accelerate\\accelerator.py:432: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
-      "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False)\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "from transformers import DataCollatorForTokenClassification\n",
-    "data_collator = DataCollatorForTokenClassification(tokenizer)\n",
-    "import numpy as np\n",
-    "\n",
-    "metric = load_metric(\"seqeval\")\n",
-    "def compute_metrics(p):\n",
-    "    predictions, labels = p\n",
-    "    predictions = np.argmax(predictions, axis=2)\n",
-    "\n",
-    "    # Remove ignored index (special tokens)\n",
-    "    true_predictions = [\n",
-    "        [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n",
-    "        for prediction, label in zip(predictions, labels)\n",
-    "    ]\n",
-    "    true_labels = [\n",
-    "        [label_list[l] for (p, l) in zip(prediction, label) if l != -100]\n",
-    "        for prediction, label in zip(predictions, labels)\n",
-    "    ]\n",
-    "\n",
-    "    results = metric.compute(predictions=true_predictions, references=true_labels)\n",
-    "    return {\n",
-    "        \"precision\": results[\"overall_precision\"],\n",
-    "        \"recall\": results[\"overall_recall\"],\n",
-    "        \"f1\": results[\"overall_f1\"],\n",
-    "        \"accuracy\": results[\"overall_accuracy\"],\n",
-    "    }\n",
-    "from transformers import TrainingArguments, Trainer, EarlyStoppingCallback\n",
-    "\n",
-    "# Training arguments (feel free to play arround with these values)\n",
-    "model_name = \"bert-base-uncased\"\n",
-    "epochs = 5\n",
-    "batch_size = 4\n",
-    "learning_rate = 2e-5\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"BERT-finetuned-NER\",\n",
-    "    # evaluation_strategy = \"epoch\", ## Instead of focusing on loss and accuracy, we will focus on the F1 score\n",
-    "    evaluation_strategy ='steps',\n",
-    "    eval_steps = 7000,\n",
-    "    save_total_limit = 3,\n",
-    "    learning_rate=learning_rate,\n",
-    "    per_device_train_batch_size=batch_size,\n",
-    "    per_device_eval_batch_size=batch_size,\n",
-    "    num_train_epochs=epochs,\n",
-    "    weight_decay=0.001,\n",
-    "    save_steps=35000,\n",
-    "    metric_for_best_model = 'f1',\n",
-    "    load_best_model_at_end=True,\n",
-    "    logging_dir='logs',\n",
-    "    logging_steps=500,\n",
-    ")\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenised_train,\n",
-    "    eval_dataset=tokenised_val,\n",
-    "    data_collator = data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    callbacks = [EarlyStoppingCallback(early_stopping_patience=3)]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "05efc63bbdbd41ab943752bef926cb0c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1340 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'loss': 0.0071, 'learning_rate': 1.2537313432835823e-05, 'epoch': 1.87}\n",
-      "{'loss': 0.0036, 'learning_rate': 5.074626865671642e-06, 'epoch': 3.73}\n",
-      "{'train_runtime': 595.4785, 'train_samples_per_second': 9.001, 'train_steps_per_second': 2.25, 'train_loss': 0.0044074685715917335, 'epoch': 5.0}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "TrainOutput(global_step=1340, training_loss=0.0044074685715917335, metrics={'train_runtime': 595.4785, 'train_samples_per_second': 9.001, 'train_steps_per_second': 2.25, 'train_loss': 0.0044074685715917335, 'epoch': 5.0})"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2e4ad56ce7ce4c82a6072a110a483010",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/39 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 3, 3, 0, 1, 0, 0, 0, 0, 0, 0, 2, 3, 3, 1, 0, 1, 0, 0, 0, 0, 0, 0]': {'precision': 0.726027397260274,\n",
-       "  'recall': 0.7940074906367042,\n",
-       "  'f1': 0.7584973166368516,\n",
-       "  'number': 267},\n",
-       " '0, 0, 0, 0, 2, 3, 3, 3, 3, 0, 1, 0, 0, 0, 0]': {'precision': 0.6468590831918506,\n",
-       "  'recall': 0.710820895522388,\n",
-       "  'f1': 0.6773333333333333,\n",
-       "  'number': 536},\n",
-       " '0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 3, 0, 1, 0, 0, 0, 0, 2, 3, 3, 0, 1, 0, 0, 0, 0, 0]': {'precision': 0.6932515337423313,\n",
-       "  'recall': 0.7583892617449665,\n",
-       "  'f1': 0.7243589743589743,\n",
-       "  'number': 149},\n",
-       " '1, 0, 2, 3, 3, 0]': {'precision': 0.6644736842105263,\n",
-       "  'recall': 0.7829457364341085,\n",
-       "  'f1': 0.7188612099644129,\n",
-       "  'number': 129},\n",
-       " 'overall_precision': 0.6747491638795987,\n",
-       " 'overall_recall': 0.7465309898242368,\n",
-       " 'overall_f1': 0.7088274044795785,\n",
-       " 'overall_accuracy': 0.9245226281762141}"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Prepare the test data for evaluation in the same format as the training data\n",
-    "\n",
-    "predictions, labels, _ = trainer.predict(tokenised_test)\n",
-    "predictions = np.argmax(predictions, axis=2)\n",
-    "\n",
-    "# Remove the predictions for the [CLS] and [SEP] tokens \n",
-    "true_predictions = [\n",
-    "    [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n",
-    "    for prediction, label in zip(predictions, labels)\n",
-    "]\n",
-    "true_labels = [\n",
-    "    [label_list[l] for (p, l) in zip(prediction, label) if l != -100]\n",
-    "    for prediction, label in zip(predictions, labels)\n",
-    "]\n",
-    "\n",
-    "# Compute multiple metrics on the test restuls\n",
-    "results = metric.compute(predictions=true_predictions, references=true_labels)\n",
-    "results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "overall_precision\t0.674749164\t\t\t\t\n",
-    "overall_recall\t\t0.74653099\t\t\t\n",
-    "overall_f1\t\t\t0.708827404\t\t\n",
-    "overall_accuracy\t0.924522628\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = []\n",
-    "for sequence, metrics in results.items():\n",
-    "    if 'overall' in sequence:\n",
-    "        # Overall metrics don't have a sequence or number\n",
-    "        data.append({\n",
-    "            'Sequence': sequence,\n",
-    "            'Precision': metrics if sequence == 'overall_precision' else '',\n",
-    "            'Recall': metrics if sequence == 'overall_recall' else '',\n",
-    "            'F1': metrics if sequence == 'overall_f1' else '',\n",
-    "            'Number': '',\n",
-    "            'Accuracy': metrics if sequence == 'overall_accuracy' else ''\n",
-    "        })\n",
-    "    else:\n",
-    "        data.append({\n",
-    "            'Sequence': sequence,\n",
-    "            'Precision': metrics['precision'],\n",
-    "            'Recall': metrics['recall'],\n",
-    "            'F1': metrics['f1'],\n",
-    "            'Number': metrics['number'],\n",
-    "            'Accuracy': '' \n",
-    "        })\n",
-    "\n",
-    "# Create a pandas DataFrame\n",
-    "df = pd.DataFrame(data)\n",
-    "\n",
-    "# Save the DataFrame to a CSV file\n",
-    "df.to_csv('BERT/results.csv', index=False)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "COM3029",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/predictions_with_accuracy.csv b/predictions_with_accuracy.csv
deleted file mode 100644
index d65d5f6..0000000
--- a/predictions_with_accuracy.csv
+++ /dev/null
@@ -1,33 +0,0 @@
-Text,True Labels,Predicted Labels,Accuracy,F1 Score
-"['Abbreviations', ':', '<unk>', ',', 'Global', '<unk>', '<unk>', 'Study', ';', 'VIP', ',', '<unk>', 'improved', '<unk>', '.', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-AC', 'B-O', 'B-LF', 'I-LF', 'I-LF', 'I-LF', 'B-O', 'B-AC', 'B-O', 'B-LF', 'I-LF', 'I-LF', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-AC', 'B-O', 'B-LF', 'I-LF', 'I-LF', 'I-LF', 'B-O', 'B-AC', 'B-O', 'B-LF', 'I-LF', 'I-LF', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']",1.0,0.9541633921844966
-"['<unk>', 'from', 'FPLC', 'purification', 'were', 'treated', 'with', '<unk>', 'buffer', '[', '<unk>', ']', 'with', '10', 'mM', '<unk>', '-', 'dithiothreitol', '(', '<unk>', ')', 'and', '<unk>', 'for', '5', 'm', 'at', '<unk>', '°', 'C', 'then', 'analyzed', 'on', 'a', '4', '%', 'to', '15', '%', '<unk>', 'SDS', 'gel', 'with', 'a', '6', '%', '<unk>', 'gel', '<unk>', 'at', 'ambient', 'temperature', 'at', 'a', '<unk>', '100', '<unk>', 'Two', 'epithelial', 'cytokines', 'other', 'than', '<unk>', ',', '<unk>', ',', 'and', '<unk>', 'stromal', '<unk>', '(', '<unk>', ')', 'are', 'known', 'to', 'activate', '<unk>', 'in', 'the', 'lung', '[', '<unk>', ']', '.']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-LF', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-LF', 'I-LF', 'I-LF', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O']",0.9176470588235294,0.9541633921844966
-"['We', 'developed', 'a', 'variant', 'of', 'gene', 'set', 'enrichment', 'analysis', '(', '<unk>', ')', 'to', 'determine', 'whether', 'a', 'genetic', 'pathway', 'shows', 'evidence', 'for', 'age', 'regulation', '[', '23', ']', '.', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-LF', 'I-LF', 'I-LF', 'I-LF', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']",0.9411764705882353,0.9541633921844966
-"['Red', 'represents', 'samples', 'having', 'the', 'normalized', '<unk>', 'and/or', '<unk>', 'values', 'in', 'cancer', 'tissues', '<unk>', '1.1', 'folds', 'of', 'normal', 'tissues', '(', 'of', 'which', 'enhanced', '<unk>', 'and/or', '<unk>', 'level', 'may', 'be', '<unk>', 'of', 'dominant', 'survival', 'mode', 'of', '<unk>', 'signaling', ')', ';', 'blue', 'represents', 'samples', 'having', 'both', 'normalized', '<unk>', 'and', '<unk>', 'values', '<', '1.1', '(', 'of', 'which', '<unk>', 'and', '<unk>', 'levels', 'less', 'than', 'or', 'equal', 'to', 'normal', 'may', 'be', '<unk>', 'of', 'the', 'apoptosis', '-', '<unk>', 'mode', 'of', '<unk>', 'signaling', ')', '.', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-AC', 'B-O', 'B-LF', 'B-LF', 'I-LF', 'I-LF', 'I-LF', 'I-LF', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']",0.8823529411764706,0.9541633921844966
-"['(', 'D', ')', 'Lysates', 'of', '<unk>', 'cancer', 'tissues', 'before', 'and', 'after', '<unk>', '(', 'RT', ')', 'from', 'each', 'of', 'the', '<unk>', 'patients', 'were', 'subjected', 'to', 'SDS', '-', 'PAGE', 'and', 'immunoblotting', 'with', 'antibodies', 'against', '<unk>', ',', '<unk>', ',', 'and', 'C', '-', 'terminal', '(', 'C', '-', '<unk>', ')', 'of', '<unk>', '.', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-LF', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']",0.9647058823529412,0.9541633921844966
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-"['The', 'manuscript', 'by', '<unk>', 'et', 'al', '<unk>', 'the', 'relationships', 'between', 'bile', 'acid', '(', '<unk>', ')', 'levels', '/', 'synthesis', 'and', 'dementia', 'related', 'pathology', ',', 'such', 'as', 'white', 'matter', 'lesions', '(', '<unk>', ')', 'and', '<unk>', 'deposition', ',', 'as', 'well', 'as', 'vascular', 'dementia', 'risk', 'and', 'sex', 'related', 'differences', '.', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-LF', 'I-LF', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-LF', 'I-LF', 'I-LF', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'I-LF', 'I-LF', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']",0.9529411764705882,0.9541633921844966
-"['However', ',', 'current', 'literature', 'on', 'the', 'stability', 'of', 'the', '<unk>', 'in', '<unk>', 'is', 'very', 'limited', ',', '<unk>', 'exclusively', 'with', '<unk>', '[', '<unk>', ']', 'or', '<unk>', '(', '<unk>', ')', '[', '17', ']', '.', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-LF', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']",0.9529411764705882,0.9541633921844966
-"['CD', 'is', 'the', 'most', 'common', 'form', 'of', 'inflammatory', '<unk>', 'disease', ',', 'the', 'other', 'being', '<unk>', '<unk>', '(', '<unk>', ')', '.', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-LF', 'I-LF', 'B-O', 'B-AC', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']",0.9647058823529412,0.9541633921844966
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-"['<unk>', 'after', '<unk>', 'surgery', 'is', 'an', '<unk>', 'but', 'a', '<unk>', '<unk>', ']', 'The', 'reported', 'incidence', 'of', '<unk>', 'after', '<unk>', 'surgery', '<unk>', 'from', '0.05', '%', 'to', '<unk>', '%', 'with', '20', '-', 'G', '<unk>', ']', '<unk>', 'reports', 'showed', 'higher', 'rates', 'of', 'post', '-', '<unk>', 'invasive', '<unk>', 'surgery', '(', '<unk>', ')', '<unk>', ',', 'whereas', 'recent', 'reports', 'have', 'shown', 'a', '<unk>', '<unk>', ']', 'Body', 'mass', 'index', '(', 'BMI', ')', 'was', 'calculated', 'as', 'the', 'weight', 'in', '<unk>', 'divided', 'by', 'the', 'square', 'of', 'the', 'height', 'in', '<unk>', '.', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-LF', 'I-LF', 'I-LF', 'I-LF', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-LF', 'I-LF', 'I-LF', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-LF', 'I-LF', 'I-LF', 'I-LF', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>']",0.8588235294117647,0.9541633921844966
-"['<unk>', 'data', 'are', 'available', 'from', '<unk>', '.', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']",1.0,0.9541633921844966
-"['#', 'Cells', ',', 'number', 'of', 'cells', ';', '<unk>', 'CA', ',', 'average', 'cell', 'area', ';', '<unk>', ',', 'a', '<unk>', 'factor', ';', '<unk>', 'A', ',', 'leaf', 'area', ';', '<unk>', ',', '<unk>', '.', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-AC', 'B-O', 'B-LF', 'I-LF', 'I-LF', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-AC', 'B-O', 'B-LF', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'I-LF', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']",0.9411764705882353,0.9541633921844966
-"['The', 'best', 'linear', '<unk>', 'estimates', '(', '<unk>', ')', 'of', 'the', '<unk>', '<unk>', 'families', 'were', 'estimated', 'following', 'the', 'model', ':', '<unk>', '=', '<unk>', ',', 'where', '<unk>', 'is', 'the', 'phenotype', 'of', 'the', 'ith', '(', 'i', '=', '1,2', '…', ',', '<unk>', ')', 'genotype', 'in', 'the', 'jth', '(', '<unk>', '=', '<unk>', ')', '<unk>', ',', 'the', '<unk>', '(', 'm', '=', '1,2', ')', 'replicate', 'effect', 'was', '<unk>', 'in', 'each', '<unk>', '.', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-LF', 'I-LF', 'I-LF', 'I-LF', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']",0.9529411764705882,0.9541633921844966
-"['<unk>', 'is', 'the', 'overall', 'mean', ',', '<unk>', 'is', 'the', 'genotype', 'effect', ',', '<unk>', 'is', 'the', '<unk>', 'effect', ',', '<unk>', 'is', 'the', '<unk>', 'effect', ',', '<unk>', 'is', 'the', 'replicate', 'effect', ',', 'and', '<unk>', '<unk>', 'N', '(', '0', ',', '<unk>', ')', 'is', 'the', 'error', 'term', '.', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-AC', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-O', 'B-LF', 'B-O', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']",0.9764705882352941,0.9541633921844966
-"['KO', ',', 'knockout', ';', '<unk>', ',', 'postsynaptic', 'density', '.', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-AC', 'B-O', 'B-LF', 'B-O', 'B-AC', 'B-O', 'B-LF', 'I-LF', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']","['B-AC', 'B-O', 'B-LF', 'B-O', 'B-AC', 'B-O', 'B-LF', 'I-LF', 'B-O', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>']",1.0,0.9541633921844966
-- 
GitLab