transformers tensorboard safetensors camembert token-classification generated_from_trainer dataset:quaero base_model:Dr-BERT/DrBERT-7GB base_model:finetune:Dr-BERT/DrBERT-7GB license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: Dr-BERT/DrBERT-7GB
datasets:
- quaero
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: drbert-7gb-finedtuned-ner
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: quaero
      type: quaero
      config: medline
      split: validation
      args: medline
    metrics:
    - type: precision
      value: 0.5055292259083728
      name: Precision
    - type: recall
      value: 0.5696484201157098
      name: Recall
    - type: f1
      value: 0.5356769198577107
      name: F1
    - type: accuracy
      value: 0.8004338394793926
      name: Accuracy
---

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# drbert-7gb-finedtuned-ner

This model is a fine-tuned version of [Dr-BERT/DrBERT-7GB](https://huggingface.co/Dr-BERT/DrBERT-7GB) on the quaero dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2330
- Precision: 0.5055
- Recall: 0.5696
- F1: 0.5357
- Accuracy: 0.8004

## 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: 5e-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   | 105  | 0.7430          | 0.4129    | 0.4775 | 0.4428 | 0.7671   |
| No log        | 2.0   | 210  | 0.6968          | 0.4888    | 0.5042 | 0.4964 | 0.7888   |
| No log        | 3.0   | 315  | 0.8218          | 0.5059    | 0.5323 | 0.5188 | 0.7952   |
| No log        | 4.0   | 420  | 0.9307          | 0.4869    | 0.5563 | 0.5193 | 0.7913   |
| 0.4134        | 5.0   | 525  | 0.9970          | 0.4688    | 0.5581 | 0.5095 | 0.7870   |
| 0.4134        | 6.0   | 630  | 1.0503          | 0.4992    | 0.5541 | 0.5252 | 0.7930   |
| 0.4134        | 7.0   | 735  | 1.1364          | 0.5034    | 0.5607 | 0.5305 | 0.7994   |
| 0.4134        | 8.0   | 840  | 1.1994          | 0.4865    | 0.5701 | 0.5250 | 0.7937   |
| 0.4134        | 9.0   | 945  | 1.2287          | 0.4948    | 0.5683 | 0.5290 | 0.7982   |
| 0.028         | 10.0  | 1050 | 1.2330          | 0.5055    | 0.5696 | 0.5357 | 0.8004   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
