Push ../models/xlnet/xlnet-base-cased/biored-augmentations-only/ trained on biored-train_160_splits.pt (160 samples)
e65e0ea
verified
| language: | |
| - en | |
| license: mit | |
| base_model: xlnet-base-cased | |
| tags: | |
| - low-resource NER | |
| - token_classification | |
| - biomedicine | |
| - medical NER | |
| - generated_from_trainer | |
| datasets: | |
| - medicine | |
| metrics: | |
| - accuracy | |
| - precision | |
| - recall | |
| - f1 | |
| model-index: | |
| - name: Dagobert42/xlnet-base-cased-biored-augmented | |
| 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. --> | |
| # Dagobert42/xlnet-base-cased-biored-augmented | |
| This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the bigbio/biored dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1552 | |
| - Accuracy: 0.9545 | |
| - Precision: 0.8651 | |
| - Recall: 0.8306 | |
| - F1: 0.8454 | |
| - Weighted F1: 0.9544 | |
| ## 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: 1.8e-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_ratio: 0.004 | |
| - num_epochs: 50 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Weighted F1 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:| | |
| | No log | 0.5 | 10 | 0.2276 | 0.9252 | 0.7871 | 0.7482 | 0.7616 | 0.9233 | | |
| | No log | 1.0 | 20 | 0.2124 | 0.9318 | 0.8363 | 0.7571 | 0.7923 | 0.9298 | | |
| | No log | 1.5 | 30 | 0.2052 | 0.9342 | 0.8199 | 0.794 | 0.8057 | 0.9334 | | |
| | No log | 2.0 | 40 | 0.1958 | 0.9396 | 0.8132 | 0.8049 | 0.8038 | 0.9384 | | |
| | No log | 2.5 | 50 | 0.2043 | 0.9385 | 0.8162 | 0.8086 | 0.811 | 0.9377 | | |
| | No log | 3.0 | 60 | 0.1948 | 0.9409 | 0.8413 | 0.8109 | 0.8249 | 0.9404 | | |
| | No log | 3.5 | 70 | 0.1951 | 0.9436 | 0.8449 | 0.7963 | 0.8186 | 0.9425 | | |
| | No log | 4.0 | 80 | 0.2032 | 0.941 | 0.8169 | 0.8158 | 0.8158 | 0.9411 | | |
| | No log | 4.5 | 90 | 0.1984 | 0.944 | 0.827 | 0.8125 | 0.8194 | 0.9435 | | |
| | No log | 5.0 | 100 | 0.1982 | 0.9451 | 0.8313 | 0.8072 | 0.8184 | 0.9443 | | |
| | No log | 5.5 | 110 | 0.1968 | 0.9456 | 0.8249 | 0.8124 | 0.8178 | 0.945 | | |
| | No log | 6.0 | 120 | 0.2083 | 0.9432 | 0.8113 | 0.8173 | 0.8136 | 0.9429 | | |
| | No log | 6.5 | 130 | 0.2105 | 0.9441 | 0.8355 | 0.8132 | 0.8236 | 0.9436 | | |
| | No log | 7.0 | 140 | 0.2083 | 0.9439 | 0.8312 | 0.8207 | 0.8253 | 0.9439 | | |
| | No log | 7.5 | 150 | 0.2145 | 0.9447 | 0.8293 | 0.8051 | 0.8161 | 0.9437 | | |
| ### Framework versions | |
| - Transformers 4.35.2 | |
| - Pytorch 2.0.1+cu117 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.0 | |