End of training

README.md

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+---
+license: mit
+base_model: law-ai/InLegalBERT
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+model-index:
+- name: InLegalBERT
+  results: []
+---
+
+<!-- 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. -->
+
+# InLegalBERT
+
+This model is a fine-tuned version of [law-ai/InLegalBERT](https://huggingface.co/law-ai/InLegalBERT) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.5527
+- Accuracy: 0.7591
+- Precision: 0.7598
+- Recall: 0.7591
+- Precision Macro: 0.6792
+- Recall Macro: 0.6780
+- Macro Fpr: 0.0228
+- Weighted Fpr: 0.0222
+- Weighted Specificity: 0.9703
+- Macro Specificity: 0.9820
+- Weighted Sensitivity: 0.7591
+- Macro Sensitivity: 0.6780
+- F1 Micro: 0.7591
+- F1 Macro: 0.6756
+- F1 Weighted: 0.7583
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Precision Macro | Recall Macro | Macro Fpr | Weighted Fpr | Weighted Specificity | Macro Specificity | Weighted Sensitivity | Macro Sensitivity | F1 Micro | F1 Macro | F1 Weighted |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
+| 1.9079        | 1.0   | 643  | 1.2971          | 0.5732   | 0.5257    | 0.5732 | 0.3206          | 0.3555       | 0.0535    | 0.0505       | 0.9314               | 0.9670            | 0.5732               | 0.3555            | 0.5732   | 0.3189   | 0.5343      |
+| 1.2081        | 2.0   | 1286 | 0.9146          | 0.7103   | 0.7163    | 0.7103 | 0.6091          | 0.5215       | 0.0287    | 0.0283       | 0.9651               | 0.9784            | 0.7103               | 0.5215            | 0.7103   | 0.5206   | 0.7070      |
+| 0.9303        | 3.0   | 1929 | 0.8692          | 0.7405   | 0.7472    | 0.7405 | 0.6654          | 0.5940       | 0.0248    | 0.0244       | 0.9679               | 0.9806            | 0.7405               | 0.5940            | 0.7405   | 0.5993   | 0.7362      |
+| 0.4996        | 4.0   | 2572 | 1.1656          | 0.7033   | 0.7270    | 0.7033 | 0.6366          | 0.6241       | 0.0297    | 0.0292       | 0.9651               | 0.9779            | 0.7033               | 0.6241            | 0.7033   | 0.6125   | 0.6959      |
+| 0.3592        | 5.0   | 3215 | 1.0837          | 0.7459   | 0.7535    | 0.7459 | 0.6627          | 0.6131       | 0.0241    | 0.0238       | 0.9668               | 0.9808            | 0.7459               | 0.6131            | 0.7459   | 0.6261   | 0.7447      |
+| 0.2809        | 6.0   | 3858 | 1.2175          | 0.7545   | 0.7607    | 0.7545 | 0.6758          | 0.6585       | 0.0232    | 0.0227       | 0.9695               | 0.9816            | 0.7545               | 0.6585            | 0.7545   | 0.6599   | 0.7531      |
+| 0.1664        | 7.0   | 4501 | 1.3113          | 0.7637   | 0.7645    | 0.7637 | 0.6855          | 0.6886       | 0.0221    | 0.0216       | 0.9717               | 0.9824            | 0.7637               | 0.6886            | 0.7637   | 0.6841   | 0.7631      |
+| 0.0733        | 8.0   | 5144 | 1.4751          | 0.7552   | 0.7610    | 0.7552 | 0.6835          | 0.6990       | 0.0231    | 0.0226       | 0.9697               | 0.9817            | 0.7552               | 0.6990            | 0.7552   | 0.6871   | 0.7566      |
+| 0.0716        | 9.0   | 5787 | 1.5509          | 0.7637   | 0.7605    | 0.7637 | 0.7018          | 0.7035       | 0.0224    | 0.0216       | 0.9690               | 0.9822            | 0.7637               | 0.7035            | 0.7637   | 0.7006   | 0.7609      |
+| 0.0286        | 10.0  | 6430 | 1.5527          | 0.7591   | 0.7598    | 0.7591 | 0.6792          | 0.6780       | 0.0228    | 0.0222       | 0.9703               | 0.9820            | 0.7591               | 0.6780            | 0.7591   | 0.6756   | 0.7583      |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.1.2
+- Datasets 2.1.0
+- Tokenizers 0.15.2

config.json

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+{
+  "_name_or_path": "law-ai/InLegalBERT",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_ids": 0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Issue",
+    "1": "Court Discourse",
+    "2": "Conclusion",
+    "3": "Precedent Analysis",
+    "4": "Section Analysis",
+    "5": "Argument by Petitioner",
+    "6": "Fact",
+    "7": "Argument by Respondent",
+    "8": "Ratio",
+    "9": "Appellant",
+    "10": "Respondent",
+    "11": "Argument by Appellant",
+    "12": "Petitioner",
+    "13": "Judge",
+    "14": "Argument by Defendant"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Appellant": 9,
+    "Argument by Appellant": 11,
+    "Argument by Defendant": 14,
+    "Argument by Petitioner": 5,
+    "Argument by Respondent": 7,
+    "Conclusion": 2,
+    "Court Discourse": 1,
+    "Fact": 6,
+    "Issue": 0,
+    "Judge": 13,
+    "Petitioner": 12,
+    "Precedent Analysis": 3,
+    "Ratio": 8,
+    "Respondent": 10,
+    "Section Analysis": 4
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

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runs/Apr12_18-36-28_b0fca03b936f/events.out.tfevents.1712946989.b0fca03b936f.34.4

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training_args.bin

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