---
license: mit
base_model: microsoft/xtremedistil-l6-h256-uncased
tags:
- generated_from_trainer
datasets:
- nbroad/company_names
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xtremedistil-l6-h256-company-names
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: nbroad/company_names
      type: nbroad/company_names
    metrics:
    - name: Precision
      type: precision
      value: 0.6998602375960866
    - name: Recall
      type: recall
      value: 0.7154210197339048
    - name: F1
      type: f1
      value: 0.7075550845586612
    - name: Accuracy
      type: accuracy
      value: 0.9702296390871982
---

<!-- 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. -->

# xtremedistil-l6-h256-company-names

This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on the nbroad/company_names dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0789
- Precision: 0.6999
- Recall: 0.7154
- F1: 0.7076
- Accuracy: 0.9702

## 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: 8e-05
- train_batch_size: 48
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1052        | 1.0   | 2126 | 0.0854          | 0.6824    | 0.6605 | 0.6713 | 0.9678   |
| 0.0724        | 2.0   | 4252 | 0.0814          | 0.6925    | 0.7042 | 0.6983 | 0.9696   |
| 0.0778        | 3.0   | 6378 | 0.0789          | 0.6999    | 0.7154 | 0.7076 | 0.9702   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.14.1