transformers pytorch tensorboard roberta token-classification generated_from_trainer dataset:pharmaconer license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- pharmaconer
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: pharmacoNER
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: pharmaconer
      type: pharmaconer
      config: PharmaCoNER
      split: validation
      args: PharmaCoNER
    metrics:
    - type: precision
      value: 0.9057634526085769
      name: Precision
    - type: recall
      value: 0.9025585193249864
      name: Recall
    - type: f1
      value: 0.9041581458759373
      name: F1
    - type: accuracy
      value: 0.9948434782608696
      name: Accuracy
---

<!-- 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. -->

# pharmacoNER

This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the pharmaconer dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0251
- Precision: 0.9058
- Recall: 0.9026
- F1: 0.9042
- Accuracy: 0.9948

## 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: 2e-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
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0272        | 1.0   | 1017 | 0.0288          | 0.8047    | 0.8503 | 0.8269 | 0.9914   |
| 0.0114        | 2.0   | 2034 | 0.0240          | 0.8950    | 0.8998 | 0.8974 | 0.9945   |
| 0.006         | 3.0   | 3051 | 0.0251          | 0.9058    | 0.9026 | 0.9042 | 0.9948   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
