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---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-clinical-plncmm-large-22
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. -->
# bert-finetuned-ner-clinical-plncmm-large-22
This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2380
- Precision: 0.7554
- Recall: 0.8271
- F1: 0.7896
- Accuracy: 0.9320
## 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: 3e-05
- train_batch_size: 24
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5983 | 1.0 | 572 | 0.2405 | 0.7044 | 0.7964 | 0.7476 | 0.9227 |
| 0.1979 | 2.0 | 1144 | 0.2421 | 0.7296 | 0.8189 | 0.7717 | 0.9275 |
| 0.1406 | 3.0 | 1716 | 0.2380 | 0.7554 | 0.8271 | 0.7896 | 0.9320 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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