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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CamemBERT fine-tuning\n",
"\n",
"Because of dependency conflicts, we will be fine-tuning the model here and then loading it and evaluating in [deepl_ner.ipynb](./deepl_ner.ipynb).\n"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
"To disable this warning, you can either:\n",
"\t- Avoid using `tokenizers` before the fork if possible\n",
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"!pip install --upgrade transformers tf-keras focal-loss"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\""
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [],
"source": [
"from app.travel_resolver.libs.nlp import data_processing as dp\n",
"\n",
"sentences, labels, vocab, unique_labels = dp.from_bio_file_to_examples(\n",
" \"./data/bio/fr.bio/10k_train_small_samples.bio\"\n",
")\n",
"\n",
"# To avoid overfitting the model on sentences that don't have any labels\n",
"lambda_sentences, lambda_labels, _, __ = dp.from_bio_file_to_examples(\n",
" \"./data/bio/fr.bio/1k_train_unlabeled_samples.bio\"\n",
")\n",
"\n",
"long_sentences, long_labels, _, __ = dp.from_bio_file_to_examples(\n",
" \"./data/bio/fr.bio/1k_train_large_samples.bio\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
"sentences = sentences + lambda_sentences + long_sentences\n",
"labels = labels + lambda_labels + long_labels"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [],
"source": [
"import app.travel_resolver.libs.nlp.data_processing as dp\n",
"\n",
"processed_sentences, processed_labels = dp.process_sentences_and_labels(\n",
" sentences, labels, return_tokens=True, stemming=False\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
" This variable will control the maximum length of the sentence \n",
" as well as the embedding size\n",
"\"\"\"\n",
"\n",
"MAX_LEN = 150"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [],
"source": [
"padded_labels = tf.keras.preprocessing.sequence.pad_sequences(\n",
" processed_labels, maxlen=MAX_LEN, padding=\"post\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [],
"source": [
"from transformers import TFAutoModelForTokenClassification, CamembertTokenizerFast\n",
"import numpy as np\n",
"\n",
"tokenizer = CamembertTokenizerFast.from_pretrained(\"cmarkea/distilcamembert-base\")"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
"tokenized_sentences = tokenizer(\n",
" processed_sentences,\n",
" is_split_into_words=True,\n",
" return_offsets_mapping=True,\n",
" truncation=True,\n",
" padding=\"max_length\",\n",
" max_length=MAX_LEN,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [],
"source": [
"def align_labels_with_tokens(encodings, labels):\n",
" \"\"\"\n",
" Aligns the labels to match the tokenized outputs.\n",
"\n",
" Args:\n",
" encodings (BatchEncoding): Tokenized outputs from the Hugging Face tokenizer (must use a fast tokenizer).\n",
" labels (List[List[int]]): Original labels for each sentence before tokenization. Each inner list corresponds to one sentence.\n",
"\n",
" Returns:\n",
" List[List[int]]: Aligned labels, where each inner list corresponds to the aligned labels for the tokenized sentence.\n",
" Special tokens and padding are assigned a value of -100.\n",
" \"\"\"\n",
" adapted_labels = []\n",
"\n",
" for i, label in enumerate(labels):\n",
" word_ids = encodings.word_ids(\n",
" batch_index=i\n",
" ) # Get word IDs for the i-th sentence\n",
" aligned_labels = []\n",
" previous_word_id = None\n",
"\n",
" for word_id in word_ids:\n",
" if word_id is None:\n",
" # Special tokens (e.g., [CLS], [SEP], or padding)\n",
" aligned_labels.append(-100)\n",
" elif word_id != previous_word_id:\n",
" # New word\n",
" aligned_labels.append(label[word_id])\n",
" else:\n",
" # Subword token (same word)\n",
" aligned_labels.append(\n",
" label[word_id]\n",
" ) # Or append -100 to ignore subwords\n",
" previous_word_id = word_id\n",
"\n",
" adapted_labels.append(aligned_labels)\n",
"\n",
" return adapted_labels"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [],
"source": [
"readapted_labels = align_labels_with_tokens(tokenized_sentences, padded_labels)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"(\n",
" train_input_ids,\n",
" test_input_ids,\n",
" train_attention_masks,\n",
" test_attention_masks,\n",
" train_labels,\n",
" test_labels,\n",
") = train_test_split(\n",
" tokenized_sentences[\"input_ids\"],\n",
" tokenized_sentences[\"attention_mask\"],\n",
" readapted_labels,\n",
" test_size=0.2,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [],
"source": [
"train_dataset = tf.data.Dataset.from_tensor_slices(\n",
" (\n",
" {\n",
" \"input_ids\": train_input_ids,\n",
" \"attention_mask\": train_attention_masks,\n",
" },\n",
" train_labels,\n",
" )\n",
")\n",
"\n",
"test_dataset = tf.data.Dataset.from_tensor_slices(\n",
" (\n",
" {\n",
" \"input_ids\": test_input_ids,\n",
" \"attention_mask\": test_attention_masks,\n",
" },\n",
" test_labels,\n",
" )\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
"def entity_accuracy(y_true, y_pred):\n",
" \"\"\"\n",
" Calculate the accuracy based on the entities. Which mean that correct `O` tags will not be taken into account.\n",
"\n",
" Parameters:\n",
" y_true (tensor): True labels.\n",
" y_pred (tensor): Predicted logits.\n",
"\n",
" Returns:\n",
" accuracy (tensor): Tag accuracy.\n",
" \"\"\"\n",
"\n",
" y_true = tf.cast(y_true, tf.float32)\n",
" # We ignore the padding and the O tag\n",
" mask = y_true > 0\n",
" mask = tf.cast(mask, tf.float32)\n",
"\n",
" y_pred_class = tf.math.argmax(y_pred, axis=-1)\n",
" y_pred_class = tf.cast(y_pred_class, tf.float32)\n",
"\n",
" matches_true_pred = tf.equal(y_true, y_pred_class)\n",
" matches_true_pred = tf.cast(matches_true_pred, tf.float32)\n",
"\n",
" matches_true_pred *= mask\n",
"\n",
" masked_acc = tf.reduce_sum(matches_true_pred) / tf.reduce_sum(mask)\n",
"\n",
" return masked_acc"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFCamembertForTokenClassification: ['roberta.embeddings.position_ids']\n",
"- This IS expected if you are initializing TFCamembertForTokenClassification from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing TFCamembertForTokenClassification from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights or buffers of the TF 2.0 model TFCamembertForTokenClassification were not initialized from the PyTorch model 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"
]
}
],
"source": [
"from focal_loss import SparseCategoricalFocalLoss\n",
"\n",
"camembert = TFAutoModelForTokenClassification.from_pretrained(\n",
" \"cmarkea/distilcamembert-base\", num_labels=len(unique_labels)\n",
")\n",
"\n",
"loss_func = SparseCategoricalFocalLoss(\n",
" gamma=2, class_weight=[1, 10, 10], from_logits=True\n",
")\n",
"\n",
"camembert.compile(\n",
" optimizer=tf.keras.optimizers.legacy.Adam(8e-4),\n",
" loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
" metrics=[entity_accuracy],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [],
"source": [
"train_dataset = train_dataset.batch(64)\n",
"test_dataset = test_dataset.batch(64)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n"
]
},
{
"ename": "TypeError",
"evalue": "in user code:\n\n File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/engine/training.py\", line 1398, in train_function *\n return step_function(self, iterator)\n File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/engine/training.py\", line 1381, in step_function **\n outputs = model.distribute_strategy.run(run_step, args=(data,))\n File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/engine/training.py\", line 1370, in run_step **\n outputs = model.train_step(data)\n File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/transformers/modeling_tf_utils.py\", line 1672, in train_step\n y_pred = self(x, training=True)\n File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/utils/traceback_utils.py\", line 70, in error_handler\n raise e.with_traceback(filtered_tb) from None\n File \"/var/folders/3h/5n6s9rcj3sx0gpncsxbq_99m0000gn/T/__autograph_generated_filepc984rni.py\", line 40, in tf__run_call_with_unpacked_inputs\n raise\n\n TypeError: Exception encountered when calling layer 'tf_camembert_for_token_classification_5' (type TFCamembertForTokenClassification).\n \n in user code:\n \n File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/transformers/modeling_tf_utils.py\", line 1393, in run_call_with_unpacked_inputs *\n return func(self, **unpacked_inputs)\n \n TypeError: outer_factory.<locals>.inner_factory.<locals>.tf__call() got an unexpected keyword argument 'offset_mapping'\n \n \n Call arguments received by layer 'tf_camembert_for_token_classification_5' (type TFCamembertForTokenClassification):\n • input_ids={'input_ids': 'tf.Tensor(shape=(None, 150), dtype=int32)', 'attention_mask': 'tf.Tensor(shape=(None, 150), dtype=int32)', 'offset_mapping': 'tf.Tensor(shape=(None, 150, 2), dtype=int32)'}\n • attention_mask=None\n • token_type_ids=None\n • position_ids=None\n • head_mask=None\n • inputs_embeds=None\n • output_attentions=None\n • output_hidden_states=None\n • return_dict=None\n • labels=None\n • training=True\n",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[103], line 7\u001b[0m\n\u001b[1;32m 1\u001b[0m early_stopping \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mEarlyStopping(\n\u001b[1;32m 2\u001b[0m monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval_loss\u001b[39m\u001b[38;5;124m\"\u001b[39m, min_delta\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.001\u001b[39m, patience\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, restore_best_weights\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 3\u001b[0m )\n\u001b[1;32m 5\u001b[0m csv_logger \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mCSVLogger(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtraining.log\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 7\u001b[0m \u001b[43mcamembert\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest_dataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mearly_stopping\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcsv_logger\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/transformers/modeling_tf_utils.py:1229\u001b[0m, in \u001b[0;36mTFPreTrainedModel.fit\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1226\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(keras\u001b[38;5;241m.\u001b[39mModel\u001b[38;5;241m.\u001b[39mfit)\n\u001b[1;32m 1227\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1228\u001b[0m args, kwargs \u001b[38;5;241m=\u001b[39m convert_batch_encoding(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m-> 1229\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/utils/traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 67\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m 68\u001b[0m \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
"File \u001b[0;32m/var/folders/3h/5n6s9rcj3sx0gpncsxbq_99m0000gn/T/__autograph_generated_filelw03dryu.py:15\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 14\u001b[0m do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m---> 15\u001b[0m retval_ \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(step_function), (ag__\u001b[38;5;241m.\u001b[39mld(\u001b[38;5;28mself\u001b[39m), ag__\u001b[38;5;241m.\u001b[39mld(iterator)), \u001b[38;5;28;01mNone\u001b[39;00m, fscope)\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m 17\u001b[0m do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
"File \u001b[0;32m~/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/transformers/modeling_tf_utils.py:1672\u001b[0m, in \u001b[0;36mTFPreTrainedModel.train_step\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 1670\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m(x, training\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, return_loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 1671\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1672\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraining\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 1673\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_using_dummy_loss:\n\u001b[1;32m 1674\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompiled_loss(y_pred\u001b[38;5;241m.\u001b[39mloss, y_pred\u001b[38;5;241m.\u001b[39mloss, sample_weight, regularization_losses\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlosses)\n",
"File \u001b[0;32m/var/folders/3h/5n6s9rcj3sx0gpncsxbq_99m0000gn/T/__autograph_generated_filepc984rni.py:37\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__run_call_with_unpacked_inputs\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 36\u001b[0m do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m---> 37\u001b[0m retval_ \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(func), (ag__\u001b[38;5;241m.\u001b[39mld(\u001b[38;5;28mself\u001b[39m),), \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mag__\u001b[38;5;241m.\u001b[39mld(unpacked_inputs)), fscope)\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m 39\u001b[0m do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
"\u001b[0;31mTypeError\u001b[0m: in user code:\n\n File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/engine/training.py\", line 1398, in train_function *\n return step_function(self, iterator)\n File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/engine/training.py\", line 1381, in step_function **\n outputs = model.distribute_strategy.run(run_step, args=(data,))\n File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/engine/training.py\", line 1370, in run_step **\n outputs = model.train_step(data)\n File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/transformers/modeling_tf_utils.py\", line 1672, in train_step\n y_pred = self(x, training=True)\n File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/utils/traceback_utils.py\", line 70, in error_handler\n raise e.with_traceback(filtered_tb) from None\n File \"/var/folders/3h/5n6s9rcj3sx0gpncsxbq_99m0000gn/T/__autograph_generated_filepc984rni.py\", line 40, in tf__run_call_with_unpacked_inputs\n raise\n\n TypeError: Exception encountered when calling layer 'tf_camembert_for_token_classification_5' (type TFCamembertForTokenClassification).\n \n in user code:\n \n File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/transformers/modeling_tf_utils.py\", line 1393, in run_call_with_unpacked_inputs *\n return func(self, **unpacked_inputs)\n \n TypeError: outer_factory.<locals>.inner_factory.<locals>.tf__call() got an unexpected keyword argument 'offset_mapping'\n \n \n Call arguments received by layer 'tf_camembert_for_token_classification_5' (type TFCamembertForTokenClassification):\n • input_ids={'input_ids': 'tf.Tensor(shape=(None, 150), dtype=int32)', 'attention_mask': 'tf.Tensor(shape=(None, 150), dtype=int32)', 'offset_mapping': 'tf.Tensor(shape=(None, 150, 2), dtype=int32)'}\n • attention_mask=None\n • token_type_ids=None\n • position_ids=None\n • head_mask=None\n • inputs_embeds=None\n • output_attentions=None\n • output_hidden_states=None\n • return_dict=None\n • labels=None\n • training=True\n"
]
}
],
"source": [
"early_stopping = tf.keras.callbacks.EarlyStopping(\n",
" monitor=\"val_loss\", min_delta=0.001, patience=0, restore_best_weights=True\n",
")\n",
"\n",
"csv_logger = tf.keras.callbacks.CSVLogger(\"training.log\")\n",
"\n",
"camembert.fit(\n",
" train_dataset,\n",
" validation_data=test_dataset,\n",
" epochs=10,\n",
" callbacks=[early_stopping, csv_logger],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(), dtype=float32, numpy=0.1186538115143776>"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from focal_loss import SparseCategoricalFocalLoss\n",
"\n",
"loss_func = SparseCategoricalFocalLoss(gamma=1)\n",
"y_true = [0, 1, 2]\n",
"y_pred = [[0.8, 0.1, 0.1], [0.2, 0.7, 0.1], [0.2, 0.2, 0.6]]\n",
"loss_func(y_true, y_pred)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"camembert.save_pretrained(\"./models/distilcamembert-base-ner-cross-entropy-11\")"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [],
"source": [
"# camembert.push_to_hub(\"CamemBERT-NER-Travel\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"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": "3.12.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|