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from transformers import AutoTokenizer, FlaxBertForSequenceClassification |
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import datasets |
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import jax |
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import jax.numpy as jnp |
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import time |
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from flax.training.common_utils import shard |
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from jax import pmap |
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def pred_fn(inputs): |
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outputs = model(**inputs) |
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return jax.nn.sigmoid(outputs.logits) |
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def get_toxicity(batch, batch_size): |
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num_examples = len(batch["text"]) |
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inputs = tokenizer( |
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batch["text"], |
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return_tensors="np", |
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truncation=True, |
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padding="max_length", |
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max_length=512, |
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) |
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inputs = shard( |
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{ |
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k: jnp.pad(jnp.array(v), ((0, batch_size - num_examples), (0, 0))) |
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for k, v in inputs.items() |
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} |
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) |
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preds = p_pred(inputs) |
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preds = preds.reshape(-1, preds.shape[-1])[:num_examples] |
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for k, v in model.config.id2label.items(): |
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batch[v] = preds[:, k].tolist() |
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return batch |
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p_pred = pmap(pred_fn, "inputs") |
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tokenizer = AutoTokenizer.from_pretrained("TurkuNLP/bert-large-finnish-cased-toxicity") |
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model = FlaxBertForSequenceClassification.from_pretrained( |
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"TurkuNLP/bert-large-finnish-cased-toxicity", from_pt=True, dtype=jnp.bfloat16 |
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) |
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dataset = datasets.load_from_disk("/researchdisk/mc4_3.1.0_fi_cleaned") |
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BATCH_SIZE = 8192 |
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dataset = dataset.map( |
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get_toxicity, |
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num_proc=1, |
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batched=True, |
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batch_size=BATCH_SIZE, |
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fn_kwargs={"batch_size": BATCH_SIZE}, |
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) |
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print(dataset) |
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dataset.save_to_disk( |
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"/researchdisk/mc4_3.1.0_fi_cleaned_dataset_toxicity_labels", num_proc=32 |
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) |
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