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Delete campaign-training

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  1. campaign-training/campaign-bert-model/config.json +0 -39
  2. campaign-training/campaign-bert-model/model.safetensors +0 -3
  3. campaign-training/campaign-bert-model/special_tokens_map.json +0 -7
  4. campaign-training/campaign-bert-model/tokenizer.json +0 -0
  5. campaign-training/campaign-bert-model/tokenizer_config.json +0 -58
  6. campaign-training/campaign-bert-model/vocab.txt +0 -0
  7. campaign-training/campaign_data.csv +0 -10
  8. campaign-training/label_encoder.pkl +0 -3
  9. campaign-training/results/checkpoint-1/config.json +0 -39
  10. campaign-training/results/checkpoint-1/model.safetensors +0 -3
  11. campaign-training/results/checkpoint-1/optimizer.pt +0 -3
  12. campaign-training/results/checkpoint-1/rng_state.pth +0 -3
  13. campaign-training/results/checkpoint-1/scheduler.pt +0 -3
  14. campaign-training/results/checkpoint-1/special_tokens_map.json +0 -7
  15. campaign-training/results/checkpoint-1/tokenizer.json +0 -0
  16. campaign-training/results/checkpoint-1/tokenizer_config.json +0 -58
  17. campaign-training/results/checkpoint-1/trainer_state.json +0 -33
  18. campaign-training/results/checkpoint-1/training_args.bin +0 -3
  19. campaign-training/results/checkpoint-1/vocab.txt +0 -0
  20. campaign-training/results/checkpoint-2/config.json +0 -39
  21. campaign-training/results/checkpoint-2/model.safetensors +0 -3
  22. campaign-training/results/checkpoint-2/optimizer.pt +0 -3
  23. campaign-training/results/checkpoint-2/rng_state.pth +0 -3
  24. campaign-training/results/checkpoint-2/scheduler.pt +0 -3
  25. campaign-training/results/checkpoint-2/special_tokens_map.json +0 -7
  26. campaign-training/results/checkpoint-2/tokenizer.json +0 -0
  27. campaign-training/results/checkpoint-2/tokenizer_config.json +0 -58
  28. campaign-training/results/checkpoint-2/trainer_state.json +0 -33
  29. campaign-training/results/checkpoint-2/training_args.bin +0 -3
  30. campaign-training/results/checkpoint-2/vocab.txt +0 -0
  31. campaign-training/results/checkpoint-3/config.json +0 -39
  32. campaign-training/results/checkpoint-3/model.safetensors +0 -3
  33. campaign-training/results/checkpoint-3/optimizer.pt +0 -3
  34. campaign-training/results/checkpoint-3/rng_state.pth +0 -3
  35. campaign-training/results/checkpoint-3/scheduler.pt +0 -3
  36. campaign-training/results/checkpoint-3/special_tokens_map.json +0 -7
  37. campaign-training/results/checkpoint-3/tokenizer.json +0 -0
  38. campaign-training/results/checkpoint-3/tokenizer_config.json +0 -58
  39. campaign-training/results/checkpoint-3/trainer_state.json +0 -33
  40. campaign-training/results/checkpoint-3/training_args.bin +0 -3
  41. campaign-training/results/checkpoint-3/vocab.txt +0 -0
  42. campaign-training/train_model.py +0 -58
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1
- import pandas as pd
2
- from datasets import Dataset
3
- from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
4
- from sklearn.preprocessing import LabelEncoder
5
- import joblib
6
-
7
- # Load dataset
8
- df = pd.read_csv("campaign_data.csv")
9
- df["text"] = df["client_interest"] + " Sentiment: " + df["sentiment"]
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-
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- # Encode labels
12
- le = LabelEncoder()
13
- df["label"] = le.fit_transform(df["template_id"])
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-
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- # Convert to Hugging Face dataset
16
- dataset = Dataset.from_pandas(df[["text", "label"]])
17
- dataset = dataset.train_test_split(test_size=0.2)
18
-
19
- # Tokenization
20
- tokenizer = AutoTokenizer.from_pretrained("prajjwal1/bert-tiny")
21
-
22
- def tokenize_function(batch):
23
- return tokenizer(batch["text"], padding=True, truncation=True)
24
-
25
- tokenized_dataset = dataset.map(tokenize_function, batched=True)
26
-
27
- # Load tiny model
28
- model = AutoModelForSequenceClassification.from_pretrained(
29
- "prajjwal1/bert-tiny",
30
- num_labels=len(le.classes_)
31
- )
32
-
33
- # Define training arguments
34
- training_args = TrainingArguments(
35
- output_dir="./results",
36
- per_device_train_batch_size=8,
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- per_device_eval_batch_size=8,
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- num_train_epochs=3,
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- save_strategy="epoch",
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- logging_dir="./logs"
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- )
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-
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- # Trainer setup
44
- trainer = Trainer(
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- model=model,
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- args=training_args,
47
- train_dataset=tokenized_dataset["train"],
48
- eval_dataset=tokenized_dataset["test"],
49
- tokenizer=tokenizer
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- )
51
-
52
- # Train and save
53
- trainer.train()
54
- model.save_pretrained("campaign-bert-model")
55
- tokenizer.save_pretrained("campaign-bert-model")
56
- joblib.dump(le, "label_encoder.pkl")
57
-
58
- print("✅ Model training complete and saved successfully.")