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datalawyer
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segmenter-distill-v0.1

Token Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
Eval Results
Inference Endpoints
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+ ---
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+ library_name: transformers
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - adalbertojunior/segmentacao
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: test_v7
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+ results:
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: adalbertojunior/segmentacao
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+ type: adalbertojunior/segmentacao
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+ config: segmentacao
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+ split: validation
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+ args: segmentacao
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.6657754010695187
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+ - name: Recall
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+ type: recall
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+ value: 0.6859504132231405
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+ - name: F1
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+ type: f1
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+ value: 0.6757123473541385
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9990518084066471
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # test_v7
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+
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+ This model is a fine-tuned version of [./models/distill-bge-retromae-step](https://huggingface.co/./models/distill-bge-retromae-step) on the adalbertojunior/segmentacao dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0045
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+ - Precision: 0.6658
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+ - Recall: 0.6860
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+ - F1: 0.6757
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+ - Accuracy: 0.9991
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 1
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+ - seed: 42
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+ - gradient_accumulation_steps: 8
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+ - total_train_batch_size: 32
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 3.0
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 0.0637 | 100 | 0.0048 | 0.5339 | 0.5647 | 0.5489 | 0.9984 |
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+ | No log | 0.1274 | 200 | 0.0048 | 0.5567 | 0.6226 | 0.5878 | 0.9987 |
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+ | No log | 0.1911 | 300 | 0.0048 | 0.5745 | 0.5950 | 0.5846 | 0.9988 |
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+ | No log | 0.2548 | 400 | 0.0048 | 0.5622 | 0.5978 | 0.5794 | 0.9988 |
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+ | 0.0061 | 0.3185 | 500 | 0.0069 | 0.48 | 0.5950 | 0.5314 | 0.9983 |
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+ | 0.0061 | 0.3822 | 600 | 0.0061 | 0.5692 | 0.6116 | 0.5896 | 0.9987 |
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+ | 0.0061 | 0.4459 | 700 | 0.0052 | 0.5736 | 0.6226 | 0.5971 | 0.9988 |
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+ | 0.0061 | 0.5096 | 800 | 0.0055 | 0.5921 | 0.6198 | 0.6057 | 0.9988 |
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+ | 0.0061 | 0.5733 | 900 | 0.0057 | 0.6126 | 0.6446 | 0.6282 | 0.9989 |
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+ | 0.0008 | 0.6370 | 1000 | 0.0065 | 0.5635 | 0.6116 | 0.5865 | 0.9987 |
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+ | 0.0008 | 0.7007 | 1100 | 0.0060 | 0.5725 | 0.6529 | 0.6100 | 0.9987 |
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+ | 0.0008 | 0.7645 | 1200 | 0.0061 | 0.5704 | 0.6474 | 0.6065 | 0.9988 |
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+ | 0.0008 | 0.8282 | 1300 | 0.0053 | 0.5813 | 0.6501 | 0.6138 | 0.9988 |
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+ | 0.0008 | 0.8919 | 1400 | 0.0045 | 0.6658 | 0.6860 | 0.6757 | 0.9991 |
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+ | 0.0004 | 0.9556 | 1500 | 0.0049 | 0.6497 | 0.6694 | 0.6594 | 0.9990 |
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+ | 0.0004 | 1.0193 | 1600 | 0.0054 | 0.5707 | 0.6446 | 0.6054 | 0.9988 |
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+ | 0.0004 | 1.0830 | 1700 | 0.0047 | 0.6376 | 0.6639 | 0.6505 | 0.9990 |
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+ | 0.0004 | 1.1467 | 1800 | 0.0048 | 0.5922 | 0.6722 | 0.6297 | 0.9989 |
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+ | 0.0004 | 1.2104 | 1900 | 0.0041 | 0.6455 | 0.6722 | 0.6586 | 0.9990 |
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+ | 0.0002 | 1.2741 | 2000 | 0.0053 | 0.5686 | 0.6391 | 0.6018 | 0.9987 |
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+ | 0.0002 | 1.3378 | 2100 | 0.0046 | 0.6495 | 0.6942 | 0.6711 | 0.9990 |
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+ | 0.0002 | 1.4015 | 2200 | 0.0049 | 0.5947 | 0.6749 | 0.6323 | 0.9988 |
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+ | 0.0002 | 1.4652 | 2300 | 0.0045 | 0.6125 | 0.6749 | 0.6422 | 0.9989 |
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+ | 0.0002 | 1.5289 | 2400 | 0.0045 | 0.5701 | 0.6722 | 0.6169 | 0.9988 |
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+ | 0.0002 | 1.5926 | 2500 | 0.0058 | 0.5321 | 0.6391 | 0.5807 | 0.9986 |
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+ | 0.0002 | 1.6563 | 2600 | 0.0056 | 0.5110 | 0.6419 | 0.5690 | 0.9985 |
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+ | 0.0002 | 1.7200 | 2700 | 0.0052 | 0.5792 | 0.6446 | 0.6102 | 0.9988 |
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+ | 0.0002 | 1.7837 | 2800 | 0.0047 | 0.5941 | 0.6612 | 0.6258 | 0.9989 |
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+ | 0.0002 | 1.8474 | 2900 | 0.0051 | 0.5655 | 0.6419 | 0.6013 | 0.9988 |
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+ | 0.0001 | 1.9111 | 3000 | 0.0044 | 0.5866 | 0.6529 | 0.6180 | 0.9989 |
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+ | 0.0001 | 1.9748 | 3100 | 0.0042 | 0.5792 | 0.6446 | 0.6102 | 0.9988 |
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+ | 0.0001 | 2.0385 | 3200 | 0.0045 | 0.6015 | 0.6694 | 0.6336 | 0.9989 |
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+ | 0.0001 | 2.1022 | 3300 | 0.0063 | 0.5409 | 0.6556 | 0.5928 | 0.9987 |
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+ | 0.0001 | 2.1659 | 3400 | 0.0047 | 0.5887 | 0.6584 | 0.6216 | 0.9989 |
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+ | 0.0001 | 2.2297 | 3500 | 0.0045 | 0.6131 | 0.6722 | 0.6413 | 0.9989 |
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+ | 0.0001 | 2.2934 | 3600 | 0.0047 | 0.6193 | 0.6722 | 0.6446 | 0.9989 |
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+ | 0.0001 | 2.3571 | 3700 | 0.0047 | 0.6091 | 0.6612 | 0.6341 | 0.9989 |
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+ | 0.0001 | 2.4208 | 3800 | 0.0047 | 0.6205 | 0.6667 | 0.6428 | 0.9989 |
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+ | 0.0001 | 2.4845 | 3900 | 0.0044 | 0.6070 | 0.6722 | 0.6379 | 0.9989 |
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+ | 0.0001 | 2.5482 | 4000 | 0.0052 | 0.5355 | 0.6226 | 0.5758 | 0.9987 |
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+ | 0.0001 | 2.6119 | 4100 | 0.0047 | 0.5871 | 0.6501 | 0.6170 | 0.9989 |
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+ | 0.0001 | 2.6756 | 4200 | 0.0049 | 0.5739 | 0.6419 | 0.6060 | 0.9988 |
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+ | 0.0001 | 2.7393 | 4300 | 0.0049 | 0.5634 | 0.6364 | 0.5977 | 0.9988 |
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+ | 0.0001 | 2.8030 | 4400 | 0.0052 | 0.5634 | 0.6364 | 0.5977 | 0.9988 |
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+ | 0.0 | 2.8667 | 4500 | 0.0049 | 0.5739 | 0.6419 | 0.6060 | 0.9988 |
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+ | 0.0 | 2.9304 | 4600 | 0.0044 | 0.5796 | 0.6419 | 0.6092 | 0.9988 |
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+ | 0.0 | 2.9941 | 4700 | 0.0047 | 0.5796 | 0.6419 | 0.6092 | 0.9988 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.45.2
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 3.0.1
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+ - Tokenizers 0.20.0