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---
license: apache-2.0
base_model: distilbert-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: train
args: indian_names
metrics:
- name: Precision
type: precision
value: 0.9896954662296407
- name: Recall
type: recall
value: 0.9704150478224023
- name: F1
type: f1
value: 0.9799604321344418
- name: Accuracy
type: accuracy
value: 0.9894401834309103
---
<!-- 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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0320
- Precision: 0.9897
- Recall: 0.9704
- F1: 0.9800
- Accuracy: 0.9894
## 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: 0.0001
- 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0503 | 0.58 | 500 | 0.0506 | 0.9744 | 0.9656 | 0.9700 | 0.9846 |
| 0.0461 | 1.17 | 1000 | 0.0450 | 0.9781 | 0.9657 | 0.9719 | 0.9856 |
| 0.0428 | 1.75 | 1500 | 0.0424 | 0.9804 | 0.9677 | 0.9740 | 0.9864 |
| 0.0379 | 2.33 | 2000 | 0.0375 | 0.9839 | 0.9704 | 0.9771 | 0.9880 |
| 0.0352 | 2.91 | 2500 | 0.0320 | 0.9897 | 0.9704 | 0.9800 | 0.9894 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|