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--- |
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license: mit |
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base_model: PORTULAN/albertina-100m-portuguese-ptpt-encoder |
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tags: |
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- generated_from_trainer |
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datasets: |
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- harem |
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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: NER_harem_albertina-100m-portuguese-ptpt-encoder |
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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: harem |
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type: harem |
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config: default |
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split: test |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.67216673903604 |
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- name: Recall |
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type: recall |
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value: 0.725398313027179 |
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- name: F1 |
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type: f1 |
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value: 0.6977687626774848 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9532056132627089 |
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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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# NER_harem_albertina-100m-portuguese-ptpt-encoder |
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This model is a fine-tuned version of [PORTULAN/albertina-100m-portuguese-ptpt-encoder](https://huggingface.co/PORTULAN/albertina-100m-portuguese-ptpt-encoder) on the harem dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2583 |
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- Precision: 0.6722 |
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- Recall: 0.7254 |
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- F1: 0.6978 |
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- Accuracy: 0.9532 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 300 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 16 | 0.5322 | 0.0212 | 0.0117 | 0.0151 | 0.8615 | |
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| No log | 2.0 | 32 | 0.3238 | 0.4230 | 0.4981 | 0.4575 | 0.9110 | |
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| No log | 3.0 | 48 | 0.2460 | 0.5006 | 0.6007 | 0.5461 | 0.9369 | |
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| No log | 4.0 | 64 | 0.2240 | 0.5526 | 0.6396 | 0.5930 | 0.9414 | |
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| No log | 5.0 | 80 | 0.2088 | 0.5498 | 0.6340 | 0.5889 | 0.9492 | |
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| No log | 6.0 | 96 | 0.2068 | 0.5884 | 0.6645 | 0.6241 | 0.9496 | |
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| No log | 7.0 | 112 | 0.2253 | 0.5906 | 0.6720 | 0.6287 | 0.9481 | |
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| No log | 8.0 | 128 | 0.2115 | 0.6245 | 0.6874 | 0.6545 | 0.9516 | |
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| No log | 9.0 | 144 | 0.2187 | 0.6546 | 0.7062 | 0.6794 | 0.9533 | |
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| No log | 10.0 | 160 | 0.2398 | 0.6432 | 0.7020 | 0.6713 | 0.9495 | |
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| No log | 11.0 | 176 | 0.2554 | 0.6653 | 0.7043 | 0.6843 | 0.9526 | |
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| No log | 12.0 | 192 | 0.2397 | 0.6777 | 0.7212 | 0.6988 | 0.9529 | |
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| No log | 13.0 | 208 | 0.2565 | 0.6778 | 0.7207 | 0.6986 | 0.9531 | |
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| No log | 14.0 | 224 | 0.2700 | 0.6586 | 0.7142 | 0.6853 | 0.9506 | |
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| No log | 15.0 | 240 | 0.2700 | 0.7009 | 0.7259 | 0.7132 | 0.9544 | |
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| No log | 16.0 | 256 | 0.2688 | 0.6761 | 0.7240 | 0.6993 | 0.9532 | |
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| No log | 17.0 | 272 | 0.2741 | 0.7132 | 0.7343 | 0.7236 | 0.9558 | |
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| No log | 18.0 | 288 | 0.2732 | 0.6740 | 0.7132 | 0.6931 | 0.9530 | |
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| No log | 19.0 | 304 | 0.2745 | 0.7094 | 0.7310 | 0.7201 | 0.9550 | |
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| No log | 20.0 | 320 | 0.2583 | 0.6722 | 0.7254 | 0.6978 | 0.9532 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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