From ebb158889ddf41f1159ee8aa66db108accf098a1 Mon Sep 17 00:00:00 2001
From: "Hafiz, Aqib (PG/T - Comp Sci & Elec Eng)" <ah02821@surrey.ac.uk>
Date: Thu, 23 May 2024 17:19:30 +0000
Subject: [PATCH] Delete train_model.ipynb

---
 train_model.ipynb | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 train_model.ipynb

diff --git a/train_model.ipynb b/train_model.ipynb
deleted file mode 100644
index cb0d4a0..0000000
--- a/train_model.ipynb
+++ /dev/null
@@ -1 +0,0 @@
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charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (3.7)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (2024.2.2)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch!=1.12.0,>=1.10->transformers[torch]) (2.1.5)\n","Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch!=1.12.0,>=1.10->transformers[torch]) (1.3.0)\n","Installing collected packages: accelerate\n","Successfully installed accelerate-0.30.1\n","Collecting seqeval\n","  Downloading seqeval-1.2.2.tar.gz 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(setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16161 sha256=90c67a492f6fbc433b4055ee8d8cf5e886f90e805e3a16a3b59767c53565e9fb\n","  Stored in directory: /root/.cache/pip/wheels/1a/67/4a/ad4082dd7dfc30f2abfe4d80a2ed5926a506eb8a972b4767fa\n","Successfully built seqeval\n","Installing collected packages: seqeval\n","Successfully installed seqeval-1.2.2\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-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","/usr/local/lib/python3.10/dist-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","/usr/local/lib/python3.10/dist-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","/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/8.37k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"11ae00fb9fb04a1cab0b93453a330cfc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/188k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1f8b3a34c095473ea3c164d7925c19f0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/28.4k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ac5e28ccfb8149968a5a9f65ae7239b4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/28.7k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bf8f673243524b8583b67285b7b16224"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/1072 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cbb93d89dc0a4d63ae5734e561e51a52"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split:   0%|          | 0/126 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"afc782b7def24f69a7314af00f42341c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/153 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c09f809764eb46db8a87d357c75121ad"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d402a02e9e614019b842ebb15ffb79bc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2ba29e13bd5643378cf6c10de4e3acc3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d77eabfce092422bbeea12ba1da286fe"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"038e83b14cdf4d5ea460f1f933905d06"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"512916a5ce27426c91f8dec743c2349c"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of DistilBertForTokenClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\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"]},{"output_type":"stream","name":"stdout","text":["['[CLS]', 'for', 'this', 'purpose', 'the', 'gothenburg', 'young', 'persons', 'empowerment', 'scale', '(', 'g', '##ype', '##s', ')', 'was', 'developed', '.', '[SEP]']\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-1-9bb2cddd7216>:101: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n","  metric = load_metric(\"seqeval\")\n","/usr/local/lib/python3.10/dist-packages/datasets/load.py:759: 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/2.19.1/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"]},{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/2.47k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"236f9395f7014e4c995a9733faba63be"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:446: 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","You're using a DistilBertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='1608' max='1608' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1608/1608 1:03:19, Epoch 6/6]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: [0, 0, 0, 0, 2, 3, 3, 3, 3, 0, 1, 0, 0, 0, 0] seems not to be NE tag.\n","  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n","/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: [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] seems not to be NE tag.\n","  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n","/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: [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] seems not to be NE tag.\n","  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n","/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: [1, 0, 2, 3, 3, 0] seems not to be NE tag.\n","  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n"]},{"output_type":"execute_result","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': 0.7003484320557491,\n","  'recall': 0.7528089887640449,\n","  'f1': 0.7256317689530686,\n","  'number': 267},\n"," '0, 0, 0, 0, 2, 3, 3, 3, 3, 0, 1, 0, 0, 0, 0]': {'precision': 0.6463414634146342,\n","  'recall': 0.6921641791044776,\n","  'f1': 0.6684684684684684,\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': 0.6855345911949685,\n","  'recall': 0.7315436241610739,\n","  'f1': 0.7077922077922079,\n","  'number': 149},\n"," '1, 0, 2, 3, 3, 0]': {'precision': 0.636986301369863,\n","  'recall': 0.7209302325581395,\n","  'f1': 0.6763636363636363,\n","  'number': 129},\n"," 'overall_precision': 0.6638078902229846,\n"," 'overall_recall': 0.7160037002775208,\n"," 'overall_f1': 0.6889185580774365,\n"," 'overall_accuracy': 0.9257254548188243}"]},"metadata":{},"execution_count":1}],"source":["%pip install datasets\n","%pip install transformers\n","%pip install spacy\n","%pip install torch\n","%pip install spacy-transformers\n","%pip install transformers[torch]\n","%pip install seqeval\n","from datasets import load_dataset, load_metric\n","from transformers import AutoTokenizer, AutoModelForTokenClassification, TrainingArguments, Trainer, EarlyStoppingCallback\n","from transformers import DataCollatorForTokenClassification\n","import numpy as np\n","from seqeval.metrics import classification_report, f1_score, accuracy_score\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import torch\n","from transformers import TrainingArguments, Trainer, EarlyStoppingCallback\n","\n","import numpy as np\n","\n","# Function to load the dataset with a fallback option\n","def load_dataset_with_fallback(dataset_name, fallback_name):\n","    try:\n","        dataset = load_dataset(dataset_name, download_mode=\"force_redownload\")\n","    except Exception as e:\n","        print(f\"Error loading dataset '{dataset_name}': {e}. Using '{fallback_name}' as a fallback.\")\n","        dataset = load_dataset(fallback_name, download_mode=\"force_redownload\")\n","    return dataset\n","\n","# Load the dataset with a fallback\n","dataset = load_dataset_with_fallback(\"surrey-nlp/PLOD-CW\", \"conll2003\")\n","\n","# Load the DistilBERT tokenizer and model\n","tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n","model = AutoModelForTokenClassification.from_pretrained(\"distilbert-base-uncased\", num_labels=4)\n","\n","# Extract the train, validation, and test datasets\n","short_dataset = dataset[\"train\"]\n","val_dataset = dataset[\"validation\"]\n","test_dataset = dataset[\"test\"]\n","\n","# Tokenize the input data\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","\n","# Define the label encoding\n","label_encoding = {\"B-O\": 0, \"B-AC\": 1, \"B-LF\": 2, \"I-LF\": 3}\n","\n","# Create the label lists for train, validation, and test datasets\n","label_list = [[label_encoding.get(tag, 0) for tag in sample] for sample in short_dataset[\"ner_tags\"]]\n","val_label_list = [[label_encoding.get(tag, 0) for tag in sample] for sample in val_dataset[\"ner_tags\"]]\n","test_label_list = [[label_encoding.get(tag, 0) for tag in sample] for sample in test_dataset[\"ner_tags\"]]\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)\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","            if word_idx is None:\n","                label_ids.append(-100)\n","            elif word_idx != previous_word_idx:\n","                label_ids.append(label[word_idx])\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","\n","# Tokenize and align labels for train, validation, and test datasets\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","\n","# Convert dictionary of lists into a list of dictionaries for training\n","def turn_dict_to_list_of_dict(d):\n","    new_list = []\n","    for labels, inputs in zip(d[\"labels\"], d[\"input_ids\"]):\n","        entry = {\"input_ids\": inputs, \"labels\": labels}\n","        new_list.append(entry)\n","    return new_list\n","\n","# Convert tokenized datasets\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)\n","\n","# Load the DataCollator\n","data_collator = DataCollatorForTokenClassification(tokenizer)\n","\n","# Load the metric\n","metric = load_metric(\"seqeval\")\n","\n","def compute_metrics(p):\n","    predictions, labels = p\n","    predictions = np.argmax(predictions, axis=2)\n","\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","\n","# Define training arguments\n","model_name = \"distilbert-base-uncased\"\n","epochs = 6\n","batch_size = 4\n","learning_rate = 2e-5\n","\n","args = TrainingArguments(\n","    f\"DistilALBERT-finetuned-NER\",\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",")\n","\n","# Create the Trainer\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",")\n","\n","# Train the model\n","trainer.train()\n","\n","# Prepare the test data for evaluation\n","predictions, labels, _ = trainer.predict(tokenised_test)\n","predictions = np.argmax(predictions, axis=2)\n","\n","# Remove the predictions for the 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","# Compute the metrics on the test results\n","results = metric.compute(predictions=true_predictions, references=true_labels)\n","results\n"]},{"cell_type":"code","source":["import shutil\n","import os\n","\n","# Save the trained model and tokenizer\n","model.save_pretrained(\"distilbert-finetuned-ner\")\n","tokenizer.save_pretrained(\"distilbert-finetuned-ner\")\n","\n","# Save the training arguments\n","with open(\"distilbert-finetuned-ner/training_args.bin\", \"wb\") as f:\n","    torch.save(args, f)\n","\n","# Create a zip file of the saved model directory\n","shutil.make_archive(\"distilbert-finetuned-ner\", 'zip', \"distilbert-finetuned-ner\")\n","\n","# Function to download the zip file in a Jupyter notebook environment\n","def download_file(file_path):\n","    from google.colab import files\n","    files.download(file_path)\n","\n","# Start the download of the zip file\n","download_file(\"distilbert-finetuned-ner.zip\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":17},"id":"xS0HTK6YPKaP","executionInfo":{"status":"ok","timestamp":1716347336938,"user_tz":-60,"elapsed":25941,"user":{"displayName":"Aqib Guleria","userId":"04172181167136804177"}},"outputId":"95a058c0-343f-4052-b790-ac2a6bc261e5"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["\n","    async function download(id, filename, size) {\n","      if (!google.colab.kernel.accessAllowed) {\n","        return;\n","      }\n","      const div = document.createElement('div');\n","      const label = document.createElement('label');\n","      label.textContent = `Downloading \"${filename}\": `;\n","      div.appendChild(label);\n","      const progress = document.createElement('progress');\n","      progress.max = size;\n","      div.appendChild(progress);\n","      document.body.appendChild(div);\n","\n","      const buffers = [];\n","      let downloaded = 0;\n","\n","      const channel = await google.colab.kernel.comms.open(id);\n","      // Send a message to notify the kernel that we're ready.\n","      channel.send({})\n","\n","      for await (const message of channel.messages) {\n","        // Send a message to notify the kernel that we're ready.\n","        channel.send({})\n","        if (message.buffers) {\n","          for (const buffer of message.buffers) {\n","            buffers.push(buffer);\n","            downloaded += buffer.byteLength;\n","            progress.value = downloaded;\n","          }\n","        }\n","      }\n","      const blob = new Blob(buffers, {type: 'application/binary'});\n","      const a = document.createElement('a');\n","      a.href = window.URL.createObjectURL(blob);\n","      a.download = filename;\n","      div.appendChild(a);\n","      a.click();\n","      div.remove();\n","    }\n","  "]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["download(\"download_2a16b79a-8803-4132-9a67-4aa56e3ad88c\", \"distilbert-finetuned-ner.zip\", 245125384)"]},"metadata":{}}]},{"cell_type":"code","source":["import torch\n","from transformers import AutoTokenizer, AutoModelForTokenClassification\n","import numpy as np\n","\n","# Load the saved model and tokenizer\n","model = AutoModelForTokenClassification.from_pretrained(\"distilbert-finetuned-ner\")\n","tokenizer = AutoTokenizer.from_pretrained(\"distilbert-finetuned-ner\")\n","\n","# Define the label encoding (same as used during training)\n","label_encoding = {\"B-O\": 0, \"B-AC\": 1, \"B-LF\": 2, \"I-LF\": 3}\n","label_decoding = {v: k for k, v in label_encoding.items()}\n","\n","# Function to predict labels for user input tokens\n","def predict_ner(tokens):\n","    # Tokenize the input tokens\n","    tokenized_input = tokenizer(tokens, is_split_into_words=True, return_tensors=\"pt\", truncation=True)\n","\n","    # Make predictions\n","    with torch.no_grad():\n","        output = model(**tokenized_input)\n","\n","    predictions = np.argmax(output.logits.numpy(), axis=2)\n","\n","    # Convert predictions to labels\n","    predicted_labels = [label_decoding[label] for label in predictions[0]]\n","\n","    # Combine tokens and labels\n","    token_label_pairs = list(zip(tokens, predicted_labels))\n","\n","    return token_label_pairs\n","\n","# Test the model with user input tokens\n","user_input_tokens = [\"For\", \"this\", \"purpose\", \"the\", \"Gothenburg\", \"Young\", \"Persons\", \"Empowerment\", \"Scale\", \"(\", \"GYPES\", \")\", \"was\", \"developed\", \".\"]\n","predictions = predict_ner(user_input_tokens)\n","\n","# Print the predictions\n","for token, label in predictions:\n","    print(f\"{token}: {label}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"egb984hVQGni","executionInfo":{"status":"ok","timestamp":1716347873334,"user_tz":-60,"elapsed":627,"user":{"displayName":"Aqib Guleria","userId":"04172181167136804177"}},"outputId":"f0bdc1b1-4ec7-40c1-c6d5-8a6a389c6e45"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["For: B-O\n","this: B-O\n","purpose: B-O\n","the: B-O\n","Gothenburg: B-O\n","Young: B-LF\n","Persons: I-LF\n","Empowerment: I-LF\n","Scale: I-LF\n","(: I-LF\n","GYPES: B-O\n","): B-AC\n","was: B-AC\n","developed: B-AC\n",".: B-O\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"IaPjrWjTTJTm","executionInfo":{"status":"error","timestamp":1716350024469,"user_tz":-60,"elapsed":198,"user":{"displayName":"Aqib Guleria","userId":"04172181167136804177"}},"outputId":"1708562a-4ce1-43a5-b70c-41a7161767fe","colab":{"base_uri":"https://localhost:8080/","height":332}},"execution_count":null,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"[Errno 2] No such file or directory: 'distilbert-finetuned-ner.zip'","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-9761fcd023c7>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Extract the zip file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mzipfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZipFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mzip_ref\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mzip_ref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextractall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextract_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.10/zipfile.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, file, mode, compression, allowZip64, compresslevel, strict_timestamps)\u001b[0m\n\u001b[1;32m   1249\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1250\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1251\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilemode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1252\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1253\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mfilemode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodeDict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'distilbert-finetuned-ner.zip'"]}]}]}
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