Add toxicity calculation script

calculate_toxicity_labels.py

@@ -0,0 +1,61 @@
+from transformers import AutoTokenizer, FlaxBertForSequenceClassification
+import datasets
+import jax
+import jax.numpy as jnp
+import time
+from flax.training.common_utils import shard
+from jax import pmap
+
+
+def pred_fn(inputs):
+    outputs = model(**inputs)
+    return jax.nn.sigmoid(outputs.logits)
+
+
+def get_toxicity(batch, batch_size):
+    num_examples = len(batch["text"])
+    inputs = tokenizer(
+        batch["text"],
+        return_tensors="np",
+        truncation=True,
+        padding="max_length",
+        max_length=512,
+    )
+
+    inputs = shard(
+        {
+            k: jnp.pad(jnp.array(v), ((0, batch_size - num_examples), (0, 0)))
+            for k, v in inputs.items()
+        }
+    )
+    preds = p_pred(inputs)
+    preds = preds.reshape(-1, preds.shape[-1])[:num_examples]
+    for k, v in model.config.id2label.items():
+        batch[v] = preds[:, k].tolist()
+    return batch
+
+
+p_pred = pmap(pred_fn, "inputs")
+
+tokenizer = AutoTokenizer.from_pretrained("TurkuNLP/bert-large-finnish-cased-toxicity")
+model = FlaxBertForSequenceClassification.from_pretrained(
+    "TurkuNLP/bert-large-finnish-cased-toxicity", from_pt=True, dtype=jnp.bfloat16
+)
+
+
+dataset = datasets.load_from_disk("/researchdisk/mc4_3.1.0_fi_cleaned")
+
+BATCH_SIZE = 8192
+dataset = dataset.map(
+    get_toxicity,
+    num_proc=1,
+    batched=True,
+    batch_size=BATCH_SIZE,
+    fn_kwargs={"batch_size": BATCH_SIZE},
+)
+print(dataset)
+
+# SAVE DATASET
+dataset.save_to_disk(
+    "/researchdisk/mc4_3.1.0_fi_cleaned_dataset_toxicity_labels", num_proc=32
+)