---
library_name: transformers
license: apache-2.0
base_model: dslim/distilbert-NER
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-classn-LAlg-multihead-context-width-2
  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. -->

# distilbert-classn-LAlg-multihead-context-width-2

This model is a fine-tuned version of [dslim/distilbert-NER](https://huggingface.co/dslim/distilbert-NER) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8459
- Accuracy: 0.7698
- F1: 0.7717
- Precision: 0.8049
- Recall: 0.7698

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 25
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 2.4794        | 1.3514  | 50   | 2.4632          | 0.0952   | 0.0337 | 0.0269    | 0.0952 |
| 2.4695        | 2.7027  | 100  | 2.4386          | 0.0794   | 0.0395 | 0.0332    | 0.0794 |
| 2.4253        | 4.0541  | 150  | 2.4152          | 0.0556   | 0.0351 | 0.0299    | 0.0556 |
| 2.3921        | 5.4054  | 200  | 2.3838          | 0.0794   | 0.0818 | 0.1342    | 0.0794 |
| 2.3137        | 6.7568  | 250  | 2.3342          | 0.1349   | 0.1256 | 0.1343    | 0.1349 |
| 2.2091        | 8.1081  | 300  | 2.2389          | 0.2460   | 0.2390 | 0.2692    | 0.2460 |
| 2.0017        | 9.4595  | 350  | 2.0460          | 0.3889   | 0.3931 | 0.4582    | 0.3889 |
| 1.6241        | 10.8108 | 400  | 1.7016          | 0.5476   | 0.5285 | 0.5501    | 0.5476 |
| 1.1356        | 12.1622 | 450  | 1.3357          | 0.6825   | 0.6735 | 0.7118    | 0.6825 |
| 0.7122        | 13.5135 | 500  | 1.0433          | 0.7540   | 0.7501 | 0.7818    | 0.7540 |
| 0.4063        | 14.8649 | 550  | 0.9308          | 0.7540   | 0.7541 | 0.7860    | 0.7540 |
| 0.2025        | 16.2162 | 600  | 0.8688          | 0.7857   | 0.7838 | 0.8068    | 0.7857 |
| 0.1226        | 17.5676 | 650  | 0.8284          | 0.7698   | 0.7704 | 0.8092    | 0.7698 |
| 0.0677        | 18.9189 | 700  | 0.8517          | 0.7778   | 0.7784 | 0.8180    | 0.7778 |
| 0.0386        | 20.2703 | 750  | 0.8447          | 0.7857   | 0.7859 | 0.8250    | 0.7857 |
| 0.0299        | 21.6216 | 800  | 0.8531          | 0.7698   | 0.7693 | 0.7984    | 0.7698 |
| 0.0236        | 22.9730 | 850  | 0.8446          | 0.7698   | 0.7717 | 0.8049    | 0.7698 |
| 0.0182        | 24.3243 | 900  | 0.8459          | 0.7698   | 0.7717 | 0.8049    | 0.7698 |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0