End of training

README.md

@@ -1,119 +1,65 @@
 ---
-language: en
-datasets:
-  - jigsaw-toxic-comment-classification-challenge
+library_name: transformers
 tags:
-  - text-classification
-  - multi-label-classification
-  - toxicity-detection
-  - bert
-  - transformers
-  - pytorch
-license: apache-2.0
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
 model-index:
-  - name: BERT Multi-label Toxic Comment Classifier
-    results:
-      - task:
-          name: Multi-label Text Classification
-          type: multi-label-classification
-        dataset:
-          name: Jigsaw Toxic Comment Classification Challenge
-          type: jigsaw-toxic-comment-classification-challenge
-        metrics:
-          - name: F1 Score (Macro)
-            type: f1
-            value: 0.XX  # Replace with your actual score
-          - name: Accuracy
-            type: accuracy
-            value: 0.XX  # Replace with your actual score
+- name: bert-multilabel-jigsaw-toxic-classifier
+  results: []
 ---
 
-# BERT Multi-label Toxic Comment Classifier
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-This model is a fine-tuned [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) transformer for **multi-label classification** on the [Jigsaw Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge) dataset.
+# bert-multilabel-jigsaw-toxic-classifier
 
-It predicts multiple toxicity-related labels per comment, including:
-- toxicity
-- severe toxicity
-- obscene
-- threat
-- insult
-- identity attack
-- sexual explicit
+This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.6768
+- Accuracy: 0.9187
+- F1: 0.0
+- Precision: 0.0
+- Recall: 0.0
 
-## Model Details
+## Model description
 
-- **Base Model**: `bert-base-uncased`
-- **Task**: Multi-label text classification
-- **Dataset**: Jigsaw Toxic Comment Classification Challenge (processed version)
-- **Labels**: 7 toxicity-related categories
-- **Training Epochs**: 2
-- **Batch Size**: 16 (train), 64 (eval)
-- **Metrics**: Accuracy, Macro F1, Precision, Recall
+More information needed
 
-## Usage
+## Intended uses & limitations
 
-```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+More information needed
 
-tokenizer = AutoTokenizer.from_pretrained("Koushim/bert-multilabel-jigsaw-toxic-classifier")
-model = AutoModelForSequenceClassification.from_pretrained("Koushim/bert-multilabel-jigsaw-toxic-classifier")
+## Training and evaluation data
 
-text = "You are a wonderful person!"
-inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
-outputs = model(**inputs)
+More information needed
 
-# Sigmoid to get probabilities for each label
-import torch
-probs = torch.sigmoid(outputs.logits)
-print(probs)
-````
+## Training procedure
 
-## Labels
+### Training hyperparameters
 
-| Index | Label            |
-| ----- | ---------------- |
-| 0     | toxicity         |
-| 1     | severe_toxicity |
-| 2     | obscene          |
-| 3     | threat           |
-| 4     | insult           |
-| 5     | identity_attack |
-| 6     | sexual_explicit |
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 1
 
-## Training Details
+### Training results
 
-* Training Set: Full dataset (160k+ samples)
-* Loss Function: Binary Cross Entropy (via `BertForSequenceClassification` with `problem_type="multi_label_classification"`)
-* Optimizer: AdamW
-* Learning Rate: 2e-5
-* Evaluation Strategy: Epoch-based evaluation with early stopping on F1 score
-* Model Framework: PyTorch with Hugging Face Transformers
+| Training Loss | Epoch | Step   | Validation Loss | Accuracy | F1  | Precision | Recall |
+|:-------------:|:-----:|:------:|:---------------:|:--------:|:---:|:---------:|:------:|
+| 1.3585        | 1.0   | 112805 | 1.6768          | 0.9187   | 0.0 | 0.0       | 0.0    |
 
-## Repository Contents
 
-* `pytorch_model.bin` - trained model weights
-* `config.json` - model configuration
-* `tokenizer.json`, `vocab.txt` - tokenizer files
-* `README.md` - this file
+### Framework versions
 
-## How to Fine-tune or Train
-
-You can fine-tune this model using the Hugging Face `Trainer` API with your own dataset or the original Jigsaw dataset.
-
-## Citation
-
-If you use this model in your research or project, please cite:
-
-```
-@article{devlin2019bert,
-  title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
-  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
-  journal={arXiv preprint arXiv:1810.04805},
-  year={2019}
-}
-```
-
-## License
-
-Apache 2.0 License
+- Transformers 4.51.3
+- Pytorch 2.6.0+cu124
+- Datasets 3.6.0
+- Tokenizers 0.21.1

config.json

@@ -0,0 +1,44 @@
+{
+  "architectures": [
+    "CustomBertForMultiLabel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "multi_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+oid sha256:83a8952e52eb1695db7036cf5ae93257e9bb71fb7b808db91e5ebaaed5f0ca9b
+size 437974136

training_args.bin

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