---
license: mit
base_model: prajjwal1/bert-tiny
tags:
- generated_from_trainer
model-index:
- name: mva_ner_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. -->

# mva_ner_2

This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0026
- Overall Precision: 0.9873
- Overall Recall: 0.9873
- Overall F1: 0.9873
- Overall Accuracy: 0.9987
- Year F1: 1.0
- Years Ago F1: 0.9844

## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 500

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Year F1 | Years Ago F1 |
|:-------------:|:------:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:-------:|:------------:|
| 0.0099        | 35.71  | 1000  | 0.0225          | 0.9625            | 0.9747         | 0.9686     | 0.9960           | 1.0     | 0.9612       |
| 0.0078        | 71.43  | 2000  | 0.0157          | 0.9625            | 0.9747         | 0.9686     | 0.9960           | 1.0     | 0.9612       |
| 0.0078        | 107.14 | 3000  | 0.0075          | 0.9873            | 0.9873         | 0.9873     | 0.9987           | 1.0     | 0.9844       |
| 0.0061        | 142.86 | 4000  | 0.0062          | 0.9873            | 0.9873         | 0.9873     | 0.9987           | 1.0     | 0.9844       |
| 0.0053        | 178.57 | 5000  | 0.0032          | 0.9873            | 0.9873         | 0.9873     | 0.9987           | 1.0     | 0.9844       |
| 0.0049        | 214.29 | 6000  | 0.0179          | 0.9747            | 0.9747         | 0.9747     | 0.9973           | 1.0     | 0.9688       |
| 0.0049        | 250.0  | 7000  | 0.0011          | 1.0               | 1.0            | 1.0        | 1.0              | 1.0     | 1.0          |
| 0.0034        | 285.71 | 8000  | 0.0064          | 0.9747            | 0.9747         | 0.9747     | 0.9973           | 1.0     | 0.9688       |
| 0.0037        | 321.43 | 9000  | 0.0148          | 0.9875            | 1.0            | 0.9937     | 0.9987           | 1.0     | 0.9922       |
| 0.0035        | 357.14 | 10000 | 0.0006          | 1.0               | 1.0            | 1.0        | 1.0              | 1.0     | 1.0          |
| 0.003         | 392.86 | 11000 | 0.0007          | 1.0               | 1.0            | 1.0        | 1.0              | 1.0     | 1.0          |
| 0.0028        | 428.57 | 12000 | 0.0032          | 0.9873            | 0.9873         | 0.9873     | 0.9987           | 1.0     | 0.9844       |
| 0.0025        | 464.29 | 13000 | 0.0006          | 1.0               | 1.0            | 1.0        | 1.0              | 1.0     | 1.0          |
| 0.0024        | 500.0  | 14000 | 0.0026          | 0.9873            | 0.9873         | 0.9873     | 0.9987           | 1.0     | 0.9844       |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1