---
language:
- en
license: mit
base_model: prajjwal1/bert-tiny
tags:
- pytorch
- BertForTokenClassification
- named-entity-recognition
- roberta-base
- generated_from_trainer
metrics:
- recall
- precision
- f1
- accuracy
model-index:
- name: bert-tiny-ontonotes
  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. -->

# bert-tiny-ontonotes

This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the tner/ontonotes5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1917
- Recall: 0.7193
- Precision: 0.6817
- F1: 0.7000
- Accuracy: 0.9476

## 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: 32
- eval_batch_size: 160
- seed: 75241309
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 6000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Recall | Precision | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 0.4283        | 0.31  | 600  | 0.3864          | 0.4561 | 0.4260    | 0.4405 | 0.9058   |
| 0.3214        | 0.63  | 1200 | 0.2865          | 0.5865 | 0.5485    | 0.5669 | 0.9265   |
| 0.2886        | 0.94  | 1800 | 0.2439          | 0.6432 | 0.6165    | 0.6295 | 0.9354   |
| 0.2511        | 1.25  | 2400 | 0.2233          | 0.6765 | 0.6250    | 0.6497 | 0.9389   |
| 0.2224        | 1.56  | 3000 | 0.2088          | 0.6878 | 0.6642    | 0.6758 | 0.9433   |
| 0.2181        | 1.88  | 3600 | 0.2001          | 0.7105 | 0.6684    | 0.6888 | 0.9451   |
| 0.215         | 2.19  | 4200 | 0.1954          | 0.7140 | 0.6795    | 0.6963 | 0.9469   |
| 0.1907        | 2.5   | 4800 | 0.1934          | 0.7169 | 0.6776    | 0.6967 | 0.9470   |
| 0.209         | 2.82  | 5400 | 0.1918          | 0.7185 | 0.6812    | 0.6994 | 0.9475   |
| 0.2073        | 3.13  | 6000 | 0.1917          | 0.7193 | 0.6817    | 0.7000 | 0.9476   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0