Training complete
Browse files- README.md +66 -0
- config.json +2 -2
- pytorch_model.bin +2 -2
- tokenizer.json +2 -2
- tokenizer_config.json +8 -43
- training_args.bin +2 -2
README.md
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---
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tags:
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- generated_from_trainer
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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: xlm-roberta-base-wolof
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results: []
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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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# xlm-roberta-base-wolof
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This model was trained from scratch on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2733
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- Precision: 0.7251
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- Recall: 0.7220
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- F1: 0.7236
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- Accuracy: 0.9586
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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: 4.899923663123727e-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: 3
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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 | 226 | 0.2416 | 0.7653 | 0.6519 | 0.7041 | 0.9587 |
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| No log | 2.0 | 452 | 0.2573 | 0.6917 | 0.7300 | 0.7104 | 0.9568 |
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| 0.0212 | 3.0 | 678 | 0.2733 | 0.7251 | 0.7220 | 0.7236 | 0.9586 |
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### Framework versions
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- Transformers 4.33.0
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- Pytorch 2.0.0
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- Datasets 2.1.0
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- Tokenizers 0.13.3
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config.json
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{
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"_name_or_path": "
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"architectures": [
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"XLMRobertaForTokenClassification"
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],
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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{
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"_name_or_path": "/kaggle/input/xlm-roberta-model/xlm_roberta_base_pretrained_wolof_pytorch",
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"architectures": [
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"XLMRobertaForTokenClassification"
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],
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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pytorch_model.bin
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tokenizer.json
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tokenizer_config.json
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{
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"special": true
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": true,
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training_args.bin
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