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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: my_awesome_wnut_model
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: 0.9994470908472269
- name: Recall
type: recall
value: 0.9994045846978268
- name: F1
type: f1
value: 0.9994258373205741
- name: Accuracy
type: accuracy
value: 0.9998240191819092
---
<!-- 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. -->
# my_awesome_wnut_model
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: 0.0015
- Precision: 0.9994
- Recall: 0.9994
- F1: 0.9994
- Accuracy: 0.9998
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1477 | 1.0 | 626 | 0.0548 | 0.9686 | 0.9604 | 0.9644 | 0.9887 |
| 0.0571 | 2.0 | 1252 | 0.0249 | 0.9833 | 0.9820 | 0.9827 | 0.9949 |
| 0.037 | 3.0 | 1878 | 0.0075 | 0.9962 | 0.9953 | 0.9957 | 0.9987 |
| 0.0101 | 4.0 | 2504 | 0.0027 | 0.9987 | 0.9984 | 0.9986 | 0.9996 |
| 0.004 | 5.0 | 3130 | 0.0015 | 0.9994 | 0.9994 | 0.9994 | 0.9998 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|