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import gradio as gr |
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from transformers import pipeline |
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from transformers import AutoTokenizer |
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from datasets import load_dataset |
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from transformers import DataCollatorWithPadding |
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raw_datasets = load_dataset("glue", "sst2") |
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raw_datasets |
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checkpoint = "bert-base-uncased" |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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def tokenize_function(example): |
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return tokenizer(example["sentence"], truncation=True) |
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tokenized_datasets = raw_datasets.map(tokenize_function, batched=True,remove_columns=['idx','sentence']) |
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tokenized_datasets |
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data_collator = DataCollatorWithPadding(tokenizer=tokenizer) |
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from transformers import TrainingArguments |
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from transformers import AutoModelForSequenceClassification |
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from datasets import load_metric |
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from transformers import Trainer |
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import numpy as np |
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training_args = TrainingArguments("test-trainer", evaluation_strategy="epoch") |
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model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2) |
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def compute_metrics(eval_preds): |
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metric = load_metric("glue", "sst2") |
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logits, labels = eval_preds |
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predictions = np.argmax(logits, axis=-1) |
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return metric.compute(predictions=predictions, references=labels) |
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trainer = Trainer( |
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model, |
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training_args, |
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train_dataset=tokenized_datasets["train"], |
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eval_dataset=tokenized_datasets["validation"], |
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data_collator=data_collator, |
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tokenizer=tokenizer, |
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compute_metrics=compute_metrics, |
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) |
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gr.Interface( |
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fn=trainer.train, |
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inputs=None, |
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outputs=None, |
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title="test", |
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).launch() |
