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import argparse |
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import gc |
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import json |
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import os |
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import datasets |
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import pandas as pd |
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import torch |
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from tqdm import tqdm |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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TOTAL_NUM_FILES_C4_TRAIN = 1024 |
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def parse_args(): |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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"--start", |
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type=int, |
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required=True, |
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help="Starting file number to download. Valid values: 0 - 1023", |
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) |
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parser.add_argument( |
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"--end", |
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type=int, |
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required=True, |
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help="Ending file number to download. Valid values: 0 - 1023", |
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) |
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parser.add_argument("--batch_size", type=int, default=16, help="Batch size") |
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parser.add_argument( |
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"--model_name", |
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type=str, |
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default="taskydata/deberta-v3-base_10xp3nirstbbflanseuni_10xc4", |
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help="Model name", |
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) |
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parser.add_argument( |
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"--local_cache_location", |
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type=str, |
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default="c4_download", |
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help="local cache location from where the dataset will be loaded", |
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) |
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parser.add_argument( |
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"--use_local_cache_location", |
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type=bool, |
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default=True, |
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help="Set True if you want to load the dataset from local cache.", |
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) |
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parser.add_argument( |
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"--clear_dataset_cache", |
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type=bool, |
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default=False, |
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help="Set True if you want to delete the dataset files from the cache after inference.", |
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) |
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parser.add_argument( |
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"--release_memory", |
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type=bool, |
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default=True, |
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help="Set True if you want to release the memory of used variables.", |
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) |
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args = parser.parse_args() |
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return args |
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def chunks(l, n): |
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for i in range(0, len(l), n): |
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yield l[i : i + n] |
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def batch_tokenize(data, batch_size): |
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batches = list(chunks(data, batch_size)) |
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tokenized_batches = [] |
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for batch in batches: |
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tensor = tokenizer( |
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batch, |
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return_tensors="pt", |
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padding="max_length", |
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truncation=True, |
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max_length=512, |
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) |
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tokenized_batches.append(tensor) |
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return tokenized_batches, batches |
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def batch_inference(data, batch_size=16): |
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preds = [] |
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tokenized_batches, batches = batch_tokenize(data, batch_size) |
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for i in tqdm(range(len(batches))): |
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with torch.no_grad(): |
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logits = model(**tokenized_batches[i].to(device)).logits.cpu() |
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preds.extend(logits) |
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return preds |
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if __name__ == "__main__": |
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args = parse_args() |
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tokenizer = AutoTokenizer.from_pretrained(args.model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(args.model_name) |
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") |
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model.to(device) |
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if args.use_local_cache_location: |
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file_name = f"c4-train.{global_id}.json.gz" |
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data_files = {"train": f"{args.local_cache_location}/{file_name}"} |
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c4 = datasets.load_dataset("json", data_files=data_files, split="train") |
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else: |
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file_name = f"en/c4-train.{global_id}.json.gz" |
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data_files = {"train": file_name} |
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c4 = datasets.load_dataset( |
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"allenai/c4", data_files=data_files, split="train" |
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) |
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df = pd.DataFrame(c4, index=None) |
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texts = df["text"].to_list() |
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preds = batch_inference(texts, batch_size=args.batch_size) |
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assert len(preds) == len(texts) |
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df['timestamp'] = df['timestamp'].astype(str) |
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with open(c4taskyprobas_path, "w") as f, open(c4tasky_path, "w") as g: |
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for i in range(len(preds)): |
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predicted_class_id = preds[i].argmax().item() |
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pred = model.config.id2label[predicted_class_id] |
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tasky_proba = torch.softmax(preds[i], dim=-1)[-1].item() |
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f.write(json.dumps({"proba": tasky_proba}) + "\n") |
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if int(predicted_class_id) == 1: |
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g.write( |
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json.dumps( |
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{ |
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"proba": tasky_proba, |
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"text": texts[i], |
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"timestamp": df["timestamp"][i], |
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"url": df["url"][i], |
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} |
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) |
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+ "\n" |
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) |
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if args.release_memory: |
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del preds |
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del texts |
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del df |
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gc.collect() |
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if args.clear_dataset_cache: |
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os.remove(f"{args.local_cache_location}/{file_name}") |
