thx for posting vaccinations are impera... 1.0 \n",
"1485 S17ZU0LC This Baby Is Exactly Why Everyone Needs To Vac... 1.0 \n",
"4175 IIN5D33V Meeting tonight, 8:30pm in room 322 of the stu... 1.0 \n",
"\n",
" agreement \n",
"6571 1.000000 \n",
"1754 1.000000 \n",
"3325 1.000000 \n",
"1485 0.666667 \n",
"4175 1.000000 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eval.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "USbmZj8rM5Go",
"outputId": "6e33e034-dd90-4251-9f3e-e0e567314dd9"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1., -1., 0.])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eval.label.unique()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XFuOwIrCM5Gp",
"outputId": "b1dc3d50-3206-48ae-8be7-d959f427d102"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"new dataframe shapes: train is (7999, 4), eval is (2000, 4)\n"
]
}
],
"source": [
"print(f\"new dataframe shapes: train is {train.shape}, eval is {eval.shape}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ojkf70aOi23w"
},
"source": [
"## Creating a pytorch dataset"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "J_FpiKwMM5Gr",
"outputId": "de0ffa27-c7b9-41a3-bfd5-cc578695198a"
},
"outputs": [
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['tweet_id', 'safe_text', 'label', 'agreement'],\n",
" num_rows: 7999\n",
" })\n",
" eval: Dataset({\n",
" features: ['tweet_id', 'safe_text', 'label', 'agreement'],\n",
" num_rows: 2000\n",
" })\n",
"})"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from datasets import DatasetDict, Dataset\n",
"train_dataset = Dataset.from_pandas(train[['tweet_id', 'safe_text', 'label', 'agreement']])\n",
"eval_dataset = Dataset.from_pandas(eval[['tweet_id', 'safe_text', 'label', 'agreement']])\n",
"\n",
"dataset = DatasetDict({'train': train_dataset, 'eval': eval_dataset})\n",
"dataset = dataset.remove_columns('__index_level_0__')\n",
"dataset"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4pZNK3Q1i23y"
},
"source": [
"## Preprocessing our data"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"id": "-6vIJTopM5Gs"
},
"outputs": [],
"source": [
"# Preprocess text (username and link placeholders)\n",
"def preprocess(text):\n",
" new_text = []\n",
" for t in text.split(\" \"):\n",
" t = '@user' if t.startswith('@') and len(t) > 1 else t\n",
" t = 'http' if t.startswith('http') else t\n",
" new_text.append(t)\n",
" return \" \".join(new_text)\n",
"\n",
"checkpoint = \"cardiffnlp/twitter-xlm-roberta-base-sentiment\"\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
"# config = AutoConfig.from_pretrained(MODEL)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34,
"referenced_widgets": [
"de9c896a37a94335baddac22e2bfd290",
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]
},
"id": "a2kaE8v_M5Gt",
"outputId": "cb01de91-9893-4b98-ab9a-6edbf479dff1"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "de9c896a37a94335baddac22e2bfd290",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map: 0%| | 0/7999 [00:00, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no padding.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a128a3b4cb3743c894178736b99e36b1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map: 0%| | 0/2000 [00:00, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a919f348ea21475498826a7307372449",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map: 0%| | 0/7999 [00:00, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7e4e322c9087469f95570f694f983698",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map: 0%| | 0/2000 [00:00, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def transform_labels(label):\n",
"\n",
" label = label['label']\n",
" num = 0\n",
" if label == -1: #'Negative'\n",
" num = 0\n",
" elif label == 0: #'Neutral'\n",
" num = 1\n",
" elif label == 1: #'Positive'\n",
" num = 2\n",
"\n",
" return {'labels': num}\n",
"\n",
"def tokenize_data(example):\n",
" return tokenizer(example['safe_text'], padding='max_length')\n",
"\n",
"# Change the tweets to tokens that the models can exploit\n",
"dataset = dataset.map(tokenize_data, batched=True)\n",
"\n",
"# Transform\tlabels and remove the useless columns\n",
"remove_columns = ['tweet_id', 'label', 'safe_text', 'agreement']\n",
"dataset = dataset.map(transform_labels, remove_columns=remove_columns)\n",
"data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "r6qG7GhVM5Gy",
"outputId": "32ef68e3-0628-4d93-8273-c64fd3a57b12"
},
"outputs": [
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['input_ids', 'attention_mask', 'labels'],\n",
" num_rows: 7999\n",
" })\n",
" eval: Dataset({\n",
" features: ['input_ids', 'attention_mask', 'labels'],\n",
" num_rows: 2000\n",
" })\n",
"})"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DYXA4zDKM5G1"
},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "eV_vmBAGM5G1",
"outputId": "a632ea3e-b1c8-4c9f-c117-31fed7f29152"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n"
]
}
],
"source": [
"# Configure the trianing parameters like `num_train_epochs`: \n",
"# the number of time the model will repeat the training loop over the dataset\n",
"training_args = TrainingArguments(\"test_trainer\", \n",
" num_train_epochs=10, \n",
" load_best_model_at_end=True, \n",
" save_strategy='epoch',\n",
" evaluation_strategy='epoch',\n",
" logging_strategy='epoch',\n",
" logging_steps=100,\n",
" per_device_train_batch_size=8,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"id": "a643fStBM5G3"
},
"outputs": [],
"source": [
"# Loading a pretrain model while specifying the number of labels in our dataset for fine-tuning\n",
"model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=3)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"id": "2cUkWOFpM5G4"
},
"outputs": [],
"source": [
"train_dataset = dataset['train'].shuffle(seed=10) \n",
"eval_dataset = dataset['eval'].shuffle(seed=10) "
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"id": "cixKFZeOM5G5"
},
"outputs": [],
"source": [
"def compute_metrics(eval_pred):\n",
" logits, labels = eval_pred\n",
" predictions = np.argmax(logits, axis=-1)\n",
" return {\"rmse\": mean_squared_error(labels, predictions, squared=False)}"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"id": "qoNNWPz_M5G6"
},
"outputs": [],
"source": [
"trainer = Trainer(\n",
" model,\n",
" training_args, \n",
" train_dataset=train_dataset, \n",
" eval_dataset=eval_dataset,\n",
" data_collator=data_collator,\n",
" tokenizer=tokenizer,\n",
" compute_metrics=compute_metrics,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 868
},
"id": "xtsQqZJGM5G7",
"outputId": "23fcc226-e6e3-488f-d193-2161f08411df"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:407: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" warnings.warn(\n",
"You're using a XLMRobertaTokenizerFast 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"
]
},
{
"data": {
"text/html": [
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" \n",
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\n",
" [10000/10000 28:57, Epoch 10/10]\n",
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\n",
" \n",
" \n",
" \n",
" Epoch | \n",
" Training Loss | \n",
" Validation Loss | \n",
" Rmse | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
" 0.728900 | \n",
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" 1.030852 | \n",
" 0.629682 | \n",
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\n",
" \n",
" 6 | \n",
" 0.187300 | \n",
" 1.398479 | \n",
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\n",
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" 7 | \n",
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" 0.080300 | \n",
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},
"metadata": {},
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{
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\n",
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\n",
" \n",
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" \n",
" Epoch | \n",
" Training Loss | \n",
" Validation Loss | \n",
" Rmse | \n",
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\n",
" \n",
" \n",
" \n",
" 1 | \n",
" 0.728900 | \n",
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"TrainOutput(global_step=10000, training_loss=0.2861065677642822, metrics={'train_runtime': 1771.8633, 'train_samples_per_second': 45.145, 'train_steps_per_second': 5.644, 'total_flos': 1705660480665036.0, 'train_loss': 0.2861065677642822, 'epoch': 10.0})"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trainer.train()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cjVR-ilCM5G8"
},
"source": [
"Don't worry the above issue, it is a `KeyboardInterrupt` that means I stopped the training to avoid taking a long time to finish."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 141
},
"id": "vZ8w2aNBM5G9",
"outputId": "75692846-c52c-445c-a9f4-cbcf04f2d9bf"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
"
\n",
" [250/250 00:05]\n",
"
\n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"{'eval_loss': 0.6727872490882874,\n",
" 'eval_rmse': 0.650384501660364,\n",
" 'eval_runtime': 5.5238,\n",
" 'eval_samples_per_second': 362.072,\n",
" 'eval_steps_per_second': 45.259,\n",
" 'epoch': 10.0}"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Launch the final evaluation \n",
"trainer.evaluate()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yHY9rVqfM5G-"
},
"source": [
"## Pushing to HuggingFace\n",
"Some checkpoints of the model are automatically saved locally in `test_trainer/` during the training."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YnGHAwdLM5HA"
},
"source": [
"You may also upload the model on the Hugging Face Platform... [Read more](https://huggingface.co/docs/hub/models-uploading)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 212,
"referenced_widgets": [
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