---
base_model: manucos/finetuned__roberta-base-bne__augmented-ultrasounds
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test-finetuned__roberta-base-bne__augmented-ultrasounds-ner
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# test-finetuned__roberta-base-bne__augmented-ultrasounds-ner

This model is a fine-tuned version of [manucos/finetuned__roberta-base-bne__augmented-ultrasounds](https://huggingface.co/manucos/finetuned__roberta-base-bne__augmented-ultrasounds) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3332
- Precision: 0.7926
- Recall: 0.8856
- F1: 0.8365
- Accuracy: 0.9236

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 206  | 0.2753          | 0.7460    | 0.8411 | 0.7907 | 0.9106   |
| No log        | 2.0   | 412  | 0.2692          | 0.7770    | 0.8603 | 0.8165 | 0.9238   |
| 0.2993        | 3.0   | 618  | 0.3276          | 0.7493    | 0.8472 | 0.7952 | 0.9087   |
| 0.2993        | 4.0   | 824  | 0.2983          | 0.7847    | 0.8704 | 0.8253 | 0.9180   |
| 0.054         | 5.0   | 1030 | 0.3066          | 0.7852    | 0.8806 | 0.8302 | 0.9221   |
| 0.054         | 6.0   | 1236 | 0.3211          | 0.7652    | 0.8806 | 0.8188 | 0.9211   |
| 0.054         | 7.0   | 1442 | 0.3314          | 0.7883    | 0.8704 | 0.8273 | 0.9189   |
| 0.0205        | 8.0   | 1648 | 0.3245          | 0.7827    | 0.8785 | 0.8278 | 0.9224   |
| 0.0205        | 9.0   | 1854 | 0.3306          | 0.7825    | 0.8846 | 0.8304 | 0.9235   |
| 0.0128        | 10.0  | 2060 | 0.3332          | 0.7926    | 0.8856 | 0.8365 | 0.9236   |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1