---
base_model: microsoft/mdeberta-v3-base
datasets:
- eriktks/conll2003
language:
- en
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
pipeline_tag: token-classification
tags:
- generated_from_trainer
model-index:
- name: mdeberta
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: eriktks/conll2003
      type: eriktks/conll2003
      config: conll2003
      split: validation
      args: conll2003
    metrics:
    - type: precision
      value: 0.9566232899566233
      name: Precision
    - type: recall
      value: 0.9649949511948839
      name: Recall
    - type: f1
      value: 0.9607908847184986
      name: F1
    - type: accuracy
      value: 0.9929130485572991
      name: Accuracy
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# mdeberta-v3-base-conll2003-en

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the [eriktks/conll2003](https://huggingface.co/datasets/eriktks/conll2003) dataset (English split of the CONLL 2003).
It achieves the following results on the evaluation set:
- Loss: 0.0342
- Precision: 0.9566
- Recall: 0.9650
- F1: 0.9608
- Accuracy: 0.9929

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 439  | 0.0509          | 0.9303    | 0.9456 | 0.9379 | 0.9890   |
| 0.1482        | 2.0   | 878  | 0.0359          | 0.9501    | 0.9583 | 0.9542 | 0.9918   |
| 0.0335        | 3.0   | 1317 | 0.0338          | 0.9530    | 0.9615 | 0.9572 | 0.9924   |
| 0.0191        | 4.0   | 1756 | 0.0346          | 0.9538    | 0.9635 | 0.9586 | 0.9926   |
| 0.0137        | 5.0   | 2195 | 0.0342          | 0.9566    | 0.9650 | 0.9608 | 0.9929   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1