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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Bert-NER
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ner
type: ner
config: indian_names
split: test
args: indian_names
metrics:
- name: Precision
type: precision
value: 0.9825882454474842
- name: Recall
type: recall
value: 0.9473498086204027
- name: F1
type: f1
value: 0.9646473204829485
- name: Accuracy
type: accuracy
value: 0.9779358957308153
---
<!-- 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-NER
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0525
- Precision: 0.9826
- Recall: 0.9473
- F1: 0.9646
- Accuracy: 0.9779
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0568 | 1.0 | 875 | 0.0813 | 0.9641 | 0.9244 | 0.9438 | 0.9655 |
| 0.0524 | 2.0 | 1750 | 0.0784 | 0.9619 | 0.9283 | 0.9448 | 0.9660 |
| 0.0481 | 3.0 | 2625 | 0.0719 | 0.9684 | 0.9301 | 0.9489 | 0.9685 |
| 0.0449 | 4.0 | 3500 | 0.0621 | 0.9736 | 0.9428 | 0.9579 | 0.9738 |
| 0.0384 | 5.0 | 4375 | 0.0525 | 0.9826 | 0.9473 | 0.9646 | 0.9779 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|