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---
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
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