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---
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
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