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fineweb-edu-classifier

Text Classification
Transformers
Safetensors
English
bert
Inference Endpoints
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  1. optimizer.pt +0 -3
  2. rng_state.pth +0 -3
  3. scheduler.pt +0 -3
  4. src/README.md +20 -0
  5. src/run_edu_bert.py +52 -0
  6. src/run_edu_bert.slurm +29 -0
  7. src/train_edu_bert.py +118 -0
  8. src/train_edu_bert.slurm +22 -0
  9. trainer_state.json +0 -2235
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src/README.md ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Educational value classifier
2
+
3
+ ### 1. Finetune a model for educational value regression
4
+
5
+ * edit `train_edu_bert.slurm`
6
+ ```bash
7
+ --base_model_name="Snowflake/snowflake-arctic-embed-m" \ # BERT-like base model
8
+ --dataset_name="HuggingFaceTB/LLM_juries_fineweb_430k_annotations" \ # Llama3-annotated eduational value dataset
9
+ --target_column="score"
10
+ ```
11
+ * run the training script on a SLURM cluster:
12
+ ```bash
13
+ sbatch train_edu_bert.slurm
14
+ ```
15
+
16
+ ### 2. Annotate a dataset with the educational scores predicted by the model
17
+
18
+ ```bash
19
+ sbatch run_edu_bert.slurm
20
+ ```
src/run_edu_bert.py ADDED
@@ -0,0 +1,52 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import argparse
3
+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
4
+ from datasets import load_dataset
5
+
6
+
7
+ def main(args):
8
+ tokenizer = AutoTokenizer.from_pretrained(args.model_name)
9
+ model = AutoModelForSequenceClassification.from_pretrained(args.model_name, torch_dtype=torch.bfloat16)
10
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
11
+ model.to(device)
12
+
13
+ dataset = load_dataset(args.dataset_name, args.dataset_config,
14
+ split="train", cache_dir="/scratch/cosmo/cache/", num_proc=12)
15
+ dataset = dataset.filter(lambda x, i: i % args.num_shards == args.shard, with_indices=True, num_proc=12)
16
+
17
+ def compute_scores(batch):
18
+ inputs = tokenizer(batch[args.text_column], return_tensors="pt", padding="longest", truncation=True).to(device)
19
+ with torch.no_grad():
20
+ outputs = model(**inputs)
21
+ logits = outputs.logits.squeeze(-1).float().cpu().numpy()
22
+
23
+ batch["score"] = logits.tolist()
24
+ batch["int_score"] = [int(round(max(0, min(score, 5)))) for score in logits]
25
+ return batch
26
+
27
+ dataset = dataset.map(compute_scores, batched=True, batch_size=512)
28
+
29
+ while True:
30
+ try:
31
+ config_name = f"{args.output_dataset_config}_{args.shard}"
32
+ dataset.push_to_hub(args.output_dataset_name, config_name=config_name, private=True, max_shard_size="4096MB")
33
+ break
34
+ except Exception as e:
35
+ print(e)
36
+ continue
37
+
38
+
39
+ if __name__ == "__main__":
40
+ parser = argparse.ArgumentParser()
41
+
42
+ parser.add_argument("--model_name", type=str, default="HHuggingFaceFW/fineweb-edu-classifier")
43
+ parser.add_argument("--dataset_name", type=str, default="HuggingFaceFW/fineweb")
44
+ parser.add_argument("--dataset_config", type=str, default="default")
45
+ parser.add_argument("--output_dataset_name", type=str, default="HuggingFaceFW/fineweb-edu")
46
+ parser.add_argument("--output_dataset_config", type=str, default="default")
47
+ parser.add_argument("--text_column", type=str, default="text")
48
+ parser.add_argument("--shard", type=int, required=True)
49
+ parser.add_argument("--num_shards", type=int, required=True)
50
+
51
+ args = parser.parse_args()
52
+ main(args)
src/run_edu_bert.slurm ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH --job-name=run_edu_bert
3
+ #SBATCH --partition hopper-prod
4
+ #SBATCH --qos=normal
5
+ #SBATCH --requeue
6
+ #SBATCH --nodes=1
7
+ #SBATCH --ntasks-per-node=1
8
+ #SBATCH --cpus-per-task=12
9
+ #SBATCH --mem-per-cpu=20G
10
+ #SBATCH --gpus=1
11
+ #SBATCH -o %x_%j.out
12
+ #SBATCH -e %x_%j.err
13
+ #SBATCH --time=7-00:00:00
14
+ #SBATCH --array=0,1,2,3,4,6,7,8,26,29,31%32
15
+
16
+ set -x -e
17
+ source ~/.bashrc
18
+ source "/admin/home/anton/miniforge3/etc/profile.d/conda.sh"
19
+ source activate lighteval
20
+
21
+ python run_edu_bert.py \
22
+ --model_name="HuggingFaceFW/fineweb-edu-classifier" \
23
+ --dataset_name="HuggingFaceFW/fineweb" \
24
+ --dataset_config="CC-MAIN-2019-04" \
25
+ --output_dataset_name="HuggingFaceFW/fineweb-edu-annotations" \
26
+ --output_dataset_config="CC-MAIN-2019-04" \
27
+ --text_column="text" \
28
+ --shard ${SLURM_ARRAY_TASK_ID} \
29
+ --num_shards 32
src/train_edu_bert.py ADDED
@@ -0,0 +1,118 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from transformers import (
2
+ AutoTokenizer,
3
+ DataCollatorWithPadding,
4
+ TrainingArguments,
5
+ Trainer,
6
+ AutoModelForSequenceClassification,
7
+ )
8
+ from datasets import load_dataset, ClassLabel
9
+ import numpy as np
10
+ import evaluate
11
+ import argparse
12
+ import os
13
+ from sklearn.metrics import classification_report, confusion_matrix
14
+
15
+
16
+ def compute_metrics(eval_pred):
17
+ precision_metric = evaluate.load("precision")
18
+ recall_metric = evaluate.load("recall")
19
+ f1_metric = evaluate.load("f1")
20
+ accuracy_metric = evaluate.load("accuracy")
21
+
22
+ logits, labels = eval_pred
23
+ preds = np.round(logits.squeeze()).clip(0, 5).astype(int)
24
+ labels = np.round(labels.squeeze()).astype(int)
25
+ precision = precision_metric.compute(
26
+ predictions=preds, references=labels, average="macro"
27
+ )["precision"]
28
+ recall = recall_metric.compute(
29
+ predictions=preds, references=labels, average="macro"
30
+ )["recall"]
31
+ f1 = f1_metric.compute(predictions=preds, references=labels, average="macro")["f1"]
32
+ accuracy = accuracy_metric.compute(predictions=preds, references=labels)["accuracy"]
33
+
34
+ report = classification_report(labels, preds)
35
+ cm = confusion_matrix(labels, preds)
36
+ print("Validation Report:\n" + report)
37
+ print("Confusion Matrix:\n" + str(cm))
38
+
39
+ return {
40
+ "precision": precision,
41
+ "recall": recall,
42
+ "f1_macro": f1,
43
+ "accuracy": accuracy,
44
+ }
45
+
46
+
47
+ def main(args):
48
+ dataset = load_dataset(
49
+ args.dataset_name, split="train", cache_dir="/scratch/cosmo/cache/", num_proc=8
50
+ )
51
+ dataset = dataset.map(
52
+ lambda x: {args.target_column: np.clip(int(x[args.target_column]), 0, 5)}, num_proc=8
53
+ )
54
+
55
+ dataset = dataset.cast_column(
56
+ args.target_column, ClassLabel(names=[str(i) for i in range(6)])
57
+ )
58
+ dataset = dataset.train_test_split(
59
+ train_size=0.9, seed=42, stratify_by_column=args.target_column
60
+ )
61
+
62
+ tokenizer = AutoTokenizer.from_pretrained(args.base_model_name)
63
+
64
+ def preprocess(examples):
65
+ batch = tokenizer(examples["text"], truncation=True)
66
+ batch["labels"] = np.float32(examples[args.target_column])
67
+ return batch
68
+
69
+ dataset = dataset.map(preprocess, batched=True)
70
+ data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
71
+ model = AutoModelForSequenceClassification.from_pretrained(args.base_model_name, num_labels=1, classifier_dropout=0.0, hidden_dropout_prob=0.0)
72
+
73
+ for param in model.bert.embeddings.parameters():
74
+ param.requires_grad = False
75
+ for param in model.bert.encoder.parameters():
76
+ param.requires_grad = False
77
+
78
+ training_args = TrainingArguments(
79
+ output_dir=args.checkpoint_dir,
80
+ evaluation_strategy="steps",
81
+ save_strategy="steps",
82
+ eval_steps=1000,
83
+ save_steps=1000,
84
+ logging_steps=100,
85
+ learning_rate=3e-4,
86
+ num_train_epochs=20,
87
+ seed=0,
88
+ per_device_train_batch_size=256,
89
+ per_device_eval_batch_size=128,
90
+ load_best_model_at_end=True,
91
+ metric_for_best_model="f1_macro",
92
+ greater_is_better=True,
93
+ bf16=True,
94
+ )
95
+
96
+ trainer = Trainer(
97
+ model=model,
98
+ args=training_args,
99
+ train_dataset=dataset["train"],
100
+ eval_dataset=dataset["test"],
101
+ tokenizer=tokenizer,
102
+ data_collator=data_collator,
103
+ compute_metrics=compute_metrics,
104
+ )
105
+
106
+ trainer.train()
107
+ trainer.save_model(os.path.join(args.checkpoint_dir, "final"))
108
+
109
+
110
+ if __name__ == "__main__":
111
+ parser = argparse.ArgumentParser()
112
+ parser.add_argument("--base_model_name", type=str, default="Snowflake/snowflake-arctic-embed-m")
113
+ parser.add_argument("--dataset_name", type=str, default="HuggingFaceTB/llama3_edu_500k_binary_labels")
114
+ parser.add_argument("--target_column", type=str, default="score")
115
+ parser.add_argument("--checkpoint_dir", type=str, default="/fsx/anton/cosmopedia/edu_score/bert_snowflake_regression")
116
+ args = parser.parse_args()
117
+
118
+ main(args)
src/train_edu_bert.slurm ADDED
@@ -0,0 +1,22 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH --job-name=train_edu_bert
3
+ #SBATCH --partition hopper-prod
4
+ #SBATCH --nodes=1
5
+ #SBATCH --ntasks-per-node=1
6
+ #SBATCH --cpus-per-task=16
7
+ #SBATCH --mem-per-cpu=20G
8
+ #SBATCH --gpus=1
9
+ #SBATCH -o %x_%j.out
10
+ #SBATCH -e %x_%j.err
11
+ #SBATCH --time=1-00:00:00
12
+
13
+ set -x -e
14
+ source ~/.bashrc
15
+ source "/admin/home/anton/miniforge3/etc/profile.d/conda.sh"
16
+ source activate lighteval
17
+
18
+ python train_edu_bert.py \
19
+ --base_model_name="Snowflake/snowflake-arctic-embed-m" \
20
+ --dataset_name="HuggingFaceTB/LLM_juries_fineweb_430k_annotations" \
21
+ --target_column="median_score"\
22
+ --checkpoint_dir="/fsx/anton/cosmopedia/edu_score/snowflake_regression_median_jury"
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