End of training

README.md

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+---
+tags:
+- generated_from_trainer
+model-index:
+- name: roberta-base
+  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. -->
+
+# roberta-base
+
+This model was trained from scratch on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2676
+- Law Precision: 0.8739
+- Law Recall: 0.9065
+- Law F1: 0.8899
+- Law Number: 107
+- Violated by Precision: 0.8254
+- Violated by Recall: 0.7324
+- Violated by F1: 0.7761
+- Violated by Number: 71
+- Violated on Precision: 0.5077
+- Violated on Recall: 0.5156
+- Violated on F1: 0.5116
+- Violated on Number: 64
+- Violation Precision: 0.6460
+- Violation Recall: 0.6979
+- Violation F1: 0.6710
+- Violation Number: 374
+- Overall Precision: 0.6890
+- Overall Recall: 0.7192
+- Overall F1: 0.7037
+- Overall Accuracy: 0.9504
+
+## 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: 2e-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
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Law Precision | Law Recall | Law F1 | Law Number | Violated by Precision | Violated by Recall | Violated by F1 | Violated by Number | Violated on Precision | Violated on Recall | Violated on F1 | Violated on Number | Violation Precision | Violation Recall | Violation F1 | Violation Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------:|:----------:|:------:|:----------:|:---------------------:|:------------------:|:--------------:|:------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| No log        | 1.0   | 85   | 0.7040          | 0.0           | 0.0        | 0.0    | 107        | 0.0                   | 0.0                | 0.0            | 71                 | 0.0                   | 0.0                | 0.0            | 64                 | 0.0                 | 0.0              | 0.0          | 374              | 0.0               | 0.0            | 0.0        | 0.7707           |
+| No log        | 2.0   | 170  | 0.3668          | 0.0           | 0.0        | 0.0    | 107        | 0.0                   | 0.0                | 0.0            | 71                 | 0.0                   | 0.0                | 0.0            | 64                 | 0.2416              | 0.2888           | 0.2631       | 374              | 0.2416            | 0.1753         | 0.2032     | 0.8896           |
+| No log        | 3.0   | 255  | 0.2618          | 0.3077        | 0.1869     | 0.2326 | 107        | 0.0                   | 0.0                | 0.0            | 71                 | 0.0                   | 0.0                | 0.0            | 64                 | 0.4626              | 0.5455           | 0.5006       | 374              | 0.4427            | 0.3636         | 0.3993     | 0.9171           |
+| No log        | 4.0   | 340  | 0.2232          | 0.7091        | 0.7290     | 0.7189 | 107        | 0.5316                | 0.5915             | 0.56           | 71                 | 0.3523                | 0.4844             | 0.4079         | 64                 | 0.5011              | 0.6016           | 0.5468       | 374              | 0.5179            | 0.6104         | 0.5604     | 0.9328           |
+| No log        | 5.0   | 425  | 0.1929          | 0.7778        | 0.8505     | 0.8125 | 107        | 0.84                  | 0.5915             | 0.6942         | 71                 | 0.44                  | 0.5156             | 0.4748         | 64                 | 0.5043              | 0.6257           | 0.5585       | 374              | 0.5666            | 0.6494         | 0.6051     | 0.9440           |
+| 0.489         | 6.0   | 510  | 0.2214          | 0.7227        | 0.8037     | 0.7611 | 107        | 0.7538                | 0.6901             | 0.7206         | 71                 | 0.4203                | 0.4531             | 0.4361         | 64                 | 0.5683              | 0.6337           | 0.5992       | 374              | 0.5985            | 0.6510         | 0.6236     | 0.9447           |
+| 0.489         | 7.0   | 595  | 0.2452          | 0.8598        | 0.8598     | 0.8598 | 107        | 0.7759                | 0.6338             | 0.6977         | 71                 | 0.4853                | 0.5156             | 0.5            | 64                 | 0.6460              | 0.6684           | 0.6570       | 374              | 0.6774            | 0.6818         | 0.6796     | 0.9469           |
+| 0.489         | 8.0   | 680  | 0.2409          | 0.9245        | 0.9159     | 0.9202 | 107        | 0.7625                | 0.8592             | 0.8079         | 71                 | 0.4321                | 0.5469             | 0.4828         | 64                 | 0.6614              | 0.6738           | 0.6675       | 374              | 0.6883            | 0.7240         | 0.7057     | 0.9485           |
+| 0.489         | 9.0   | 765  | 0.2760          | 0.8739        | 0.9065     | 0.8899 | 107        | 0.8529                | 0.8169             | 0.8345         | 71                 | 0.5                   | 0.5312             | 0.5152         | 64                 | 0.6014              | 0.6898           | 0.6426       | 374              | 0.6612            | 0.7256         | 0.6920     | 0.9473           |
+| 0.489         | 10.0  | 850  | 0.2676          | 0.8739        | 0.9065     | 0.8899 | 107        | 0.8254                | 0.7324             | 0.7761         | 71                 | 0.5077                | 0.5156             | 0.5116         | 64                 | 0.6460              | 0.6979           | 0.6710       | 374              | 0.6890            | 0.7192         | 0.7037     | 0.9504           |
+
+
+### Framework versions
+
+- Transformers 4.44.0
+- Pytorch 2.4.0
+- Datasets 2.21.0
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "FacebookAI/roberta-base",
+  "architectures": [
+    "RobertaForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "B-VIOLATION",
+    "1": "I-VIOLATION",
+    "2": "B-LAW",
+    "3": "B-VIOLATED ON",
+    "4": "I-VIOLATED ON",
+    "5": "I-VIOLATED BY",
+    "6": "O",
+    "7": "I-LAW",
+    "8": "B-VIOLATED BY"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-LAW": 2,
+    "B-VIOLATED BY": 8,
+    "B-VIOLATED ON": 3,
+    "B-VIOLATION": 0,
+    "I-LAW": 7,
+    "I-VIOLATED BY": 5,
+    "I-VIOLATED ON": 4,
+    "I-VIOLATION": 1,
+    "O": 6
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

model.safetensors

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training_args.bin

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