!pip install transformers datasets evaluate
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: transformers in /usr/local/lib/python3.9/dist-packages (4.27.4) Requirement already satisfied: datasets in /usr/local/lib/python3.9/dist-packages (2.11.0) Requirement already satisfied: evaluate in /usr/local/lib/python3.9/dist-packages (0.4.0) Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers) (4.65.0) Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers) (3.10.7) Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (0.13.4) Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (0.13.3) Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (23.0) Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.22.4) Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers) (2.27.1) Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.10.31) Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (6.0) Requirement already satisfied: xxhash in /usr/local/lib/python3.9/dist-packages (from datasets) (3.2.0) Requirement already satisfied: aiohttp in /usr/local/lib/python3.9/dist-packages (from datasets) (3.8.4) Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.9/dist-packages (from datasets) (9.0.0) Requirement already satisfied: multiprocess in /usr/local/lib/python3.9/dist-packages (from datasets) (0.70.14) Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.9/dist-packages (from datasets) (2023.3.0) Requirement already satisfied: dill<0.3.7,>=0.3.0 in /usr/local/lib/python3.9/dist-packages (from datasets) (0.3.6) Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from datasets) (1.4.4) Requirement already satisfied: responses<0.19 in /usr/local/lib/python3.9/dist-packages (from datasets) (0.18.0) Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (6.0.4) Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (22.2.0) Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (1.3.1) Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (1.3.3) Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (1.8.2) Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (2.0.12) Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (4.0.2) Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0) Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (3.4) Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (1.26.15) Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2022.12.7) Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2.8.2) Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2022.7.1) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)
# here we are setting up the dataset
from datasets import load_dataset
dataset = load_dataset("yelp_review_full")
print(dataset)
WARNING:datasets.builder:Found cached dataset yelp_review_full (/root/.cache/huggingface/datasets/yelp_review_full/yelp_review_full/1.0.0/e8e18e19d7be9e75642fc66b198abadb116f73599ec89a69ba5dd8d1e57ba0bf)
0%| | 0/2 [00:00<?, ?it/s]
DatasetDict({
train: Dataset({
features: ['label', 'text'],
num_rows: 650000
})
test: Dataset({
features: ['label', 'text'],
num_rows: 50000
})
})
# here we are printing out the dataset
print(dataset['train'])
print(dataset['train'].features)
print(dataset['train'].num_rows)
print(dataset['train'][0])
Dataset({
features: ['label', 'text'],
num_rows: 650000
})
{'label': ClassLabel(names=['1 star', '2 star', '3 stars', '4 stars', '5 stars'], id=None), 'text': Value(dtype='string', id=None)}
650000
{'label': 4, 'text': "dr. goldberg offers everything i look for in a general practitioner. he's nice and easy to talk to without being patronizing; he's always on time in seeing his patients; he's affiliated with a top-notch hospital (nyu) which my parents have explained to me is very important in case something happens and you need surgery; and you can get referrals to see specialists without having to see him first. really, what more do you need? i'm sitting here trying to think of any complaints i have about him, but i'm really drawing a blank."}
# here we are setting up the model
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5).to("cpu")
Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.bias'] - This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
# here we are printing out the model
test_output1 = tokenizer("hello world", return_tensors="pt").to("cpu")
test_output2 = model(**test_output1)
print(test_output1)
print(test_output2)
{'input_ids': tensor([[ 101, 19082, 1362, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1]])}
SequenceClassifierOutput(loss=None, logits=tensor([[ 0.3448, -0.1447, 0.0858, 0.1302, -0.1413]],
grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)
# making the train/test dataset to length
train_dataset = dataset["train"].select(range(1000))
test_dataset = dataset["test"].select(range(100))
# making the train/test dataset tokenized
def tokenize_function(example):
return tokenizer(example["text"], padding="max_length", truncation=True)
train_dataset = train_dataset.map(tokenize_function, batched=True)
test_dataset = test_dataset.map(tokenize_function, batched=True)
WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/yelp_review_full/yelp_review_full/1.0.0/e8e18e19d7be9e75642fc66b198abadb116f73599ec89a69ba5dd8d1e57ba0bf/cache-e6d642dddd74df5f.arrow WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/yelp_review_full/yelp_review_full/1.0.0/e8e18e19d7be9e75642fc66b198abadb116f73599ec89a69ba5dd8d1e57ba0bf/cache-244a330e7ff0fc6e.arrow
# let's see what output of tokenize_function is
print(dataset["train"][0])
print(dataset["train"][0].keys())
print("\n")
print(tokenize_function(dataset["train"][0]))
print(tokenize_function(dataset["train"][0]).keys())
{'label': 4, 'text': "dr. goldberg offers everything i look for in a general practitioner. he's nice and easy to talk to without being patronizing; he's always on time in seeing his patients; he's affiliated with a top-notch hospital (nyu) which my parents have explained to me is very important in case something happens and you need surgery; and you can get referrals to see specialists without having to see him first. really, what more do you need? i'm sitting here trying to think of any complaints i have about him, but i'm really drawing a blank."}
dict_keys(['label', 'text'])
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dict_keys(['input_ids', 'token_type_ids', 'attention_mask'])
# let's see what output of tokenize_function after map
print(train_dataset[0])
print(train_dataset[0].keys())
{'label': 4, 'text': "dr. goldberg offers everything i look for in a general practitioner. he's nice and easy to talk to without being patronizing; he's always on time in seeing his patients; he's affiliated with a top-notch hospital (nyu) which my parents have explained to me is very important in case something happens and you need surgery; and you can get referrals to see specialists without having to see him first. really, what more do you need? i'm sitting here trying to think of any complaints i have about him, but i'm really drawing a blank.", 'input_ids': [101, 173, 1197, 119, 2284, 2953, 3272, 1917, 178, 1440, 1111, 1107, 170, 1704, 22351, 119, 1119, 112, 188, 3505, 1105, 3123, 1106, 2037, 1106, 1443, 1217, 10063, 4404, 132, 1119, 112, 188, 1579, 1113, 1159, 1107, 3195, 1117, 4420, 132, 1119, 112, 188, 6559, 1114, 170, 1499, 118, 23555, 2704, 113, 183, 9379, 114, 1134, 1139, 2153, 1138, 3716, 1106, 1143, 1110, 1304, 1696, 1107, 1692, 1380, 5940, 1105, 1128, 1444, 6059, 132, 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0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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dict_keys(['label', 'text', 'input_ids', 'token_type_ids', 'attention_mask'])
# preparing the metrics for evaluation
import numpy as np
import evaluate
metric = evaluate.load("accuracy")
def compute_metrics(eval_pred):
logits, labels = eval_pred
predictions = np.argmax(logits, axis=-1)
return metric.compute(predictions=predictions, references=labels)
# preparing the trainer for training
from transformers import TrainingArguments
from transformers import Trainer
training_args = TrainingArguments(
output_dir="./some_local_dir",
overwrite_output_dir=True,
per_device_train_batch_size=4,
dataloader_num_workers=2,
max_steps=100,
logging_steps=1,
evaluation_strategy="steps",
eval_steps=5
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=test_dataset,
compute_metrics=compute_metrics,
)
# what is the accuracy BEFORE training
trainer.evaluate()
{'eval_loss': 1.591485619544983,
'eval_accuracy': 0.24,
'eval_runtime': 5.0309,
'eval_samples_per_second': 19.877,
'eval_steps_per_second': 2.584}
trainer.train()
/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: 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 warnings.warn(
| Step | Training Loss | Validation Loss | Accuracy |
|---|---|---|---|
| 5 | 1.521500 | 1.875694 | 0.160000 |
| 10 | 1.677400 | 1.662221 | 0.210000 |
| 15 | 1.673900 | 1.577609 | 0.240000 |
| 20 | 1.594100 | 1.568710 | 0.290000 |
| 25 | 1.436200 | 1.583501 | 0.200000 |
| 30 | 1.706300 | 1.684151 | 0.140000 |
| 35 | 1.379200 | 1.617138 | 0.170000 |
| 40 | 1.577600 | 1.575732 | 0.330000 |
| 45 | 1.854000 | 1.531822 | 0.330000 |
| 50 | 1.534100 | 1.464615 | 0.360000 |
| 55 | 1.533500 | 1.511700 | 0.350000 |
| 60 | 1.759300 | 1.627622 | 0.380000 |
| 65 | 1.501600 | 1.453165 | 0.450000 |
| 70 | 1.407000 | 1.401512 | 0.510000 |
| 75 | 1.658300 | 1.394413 | 0.460000 |
| 80 | 1.498400 | 1.375948 | 0.440000 |
| 85 | 1.550100 | 1.415350 | 0.420000 |
| 90 | 1.233700 | 1.434097 | 0.400000 |
| 95 | 1.358500 | 1.415705 | 0.400000 |
| 100 | 1.667600 | 1.403976 | 0.400000 |
TrainOutput(global_step=100, training_loss=1.5707407319545745, metrics={'train_runtime': 116.5447, 'train_samples_per_second': 3.432, 'train_steps_per_second': 0.858, 'total_flos': 105247256985600.0, 'train_loss': 1.5707407319545745, 'epoch': 0.4})
# what is the accuracy AFTER training
trainer.evaluate()
{'eval_loss': 1.4039757251739502,
'eval_accuracy': 0.4,
'eval_runtime': 3.6833,
'eval_samples_per_second': 27.15,
'eval_steps_per_second': 3.529,
'epoch': 0.4}