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--- |
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library_name: transformers |
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license: mit |
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base_model: FacebookAI/xlm-roberta-large |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: multiclass-classifier-patents |
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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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# multiclass-classifier-patents |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0067 |
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- F1 Micro: 0.7001 |
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- Precision Micro: 0.8337 |
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- Recall Micro: 0.6034 |
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- Exact Match F1: 0.5296 |
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- Exact Match Precision: 0.5296 |
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- Exact Match Recall: 0.5296 |
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- Any Match F1: 0.9079 |
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- Any Match Precision: 0.9079 |
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- Any Match Recall: 0.9079 |
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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: 2e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Micro | Precision Micro | Recall Micro | Exact Match F1 | Exact Match Precision | Exact Match Recall | Any Match F1 | Any Match Precision | Any Match Recall | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------------:|:------------:|:--------------:|:---------------------:|:------------------:|:------------:|:-------------------:|:----------------:| |
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| 0.01 | 1.0 | 1292 | 0.0083 | 0.5977 | 0.8265 | 0.4681 | 0.4300 | 0.4300 | 0.4300 | 0.7675 | 0.7675 | 0.7675 | |
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| 0.0077 | 2.0 | 2584 | 0.0074 | 0.6595 | 0.8326 | 0.5460 | 0.4879 | 0.4879 | 0.4879 | 0.8636 | 0.8636 | 0.8636 | |
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| 0.007 | 3.0 | 3876 | 0.0071 | 0.6829 | 0.8173 | 0.5864 | 0.5035 | 0.5035 | 0.5035 | 0.8958 | 0.8958 | 0.8958 | |
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| 0.0063 | 4.0 | 5168 | 0.0069 | 0.6883 | 0.8317 | 0.5871 | 0.5140 | 0.5140 | 0.5140 | 0.8956 | 0.8956 | 0.8956 | |
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| 0.0058 | 5.0 | 6460 | 0.0068 | 0.6957 | 0.8337 | 0.5969 | 0.5182 | 0.5182 | 0.5182 | 0.9058 | 0.9058 | 0.9058 | |
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| 0.0053 | 6.0 | 7752 | 0.0069 | 0.6999 | 0.8366 | 0.6017 | 0.5271 | 0.5271 | 0.5271 | 0.9082 | 0.9082 | 0.9082 | |
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| 0.0048 | 7.0 | 9044 | 0.0069 | 0.7046 | 0.8159 | 0.6201 | 0.5225 | 0.5225 | 0.5225 | 0.9185 | 0.9185 | 0.9185 | |
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| 0.0046 | 8.0 | 10336 | 0.0069 | 0.7069 | 0.8100 | 0.6271 | 0.5241 | 0.5241 | 0.5241 | 0.9196 | 0.9196 | 0.9196 | |
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| 0.0042 | 9.0 | 11628 | 0.0070 | 0.7064 | 0.8208 | 0.6200 | 0.5282 | 0.5282 | 0.5282 | 0.9174 | 0.9174 | 0.9174 | |
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| 0.004 | 10.0 | 12920 | 0.0070 | 0.7064 | 0.8184 | 0.6214 | 0.5276 | 0.5276 | 0.5276 | 0.9177 | 0.9177 | 0.9177 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.3 |
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