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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- indian_names
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: indian_names
type: indian_names
config: indian_names
split: train
args: indian_names
metrics:
- name: Precision
type: precision
value: 0.9939821779886587
- name: Recall
type: recall
value: 0.9958260869565217
- name: F1
type: f1
value: 0.9949032781188464
- name: Accuracy
type: accuracy
value: 0.999003984063745
---
<!-- 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 indian_names dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0050
- Precision: 0.9940
- Recall: 0.9958
- F1: 0.9949
- Accuracy: 0.9990
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 66 | 0.0440 | 0.9579 | 0.9650 | 0.9614 | 0.9906 |
| No log | 2.0 | 132 | 0.0191 | 0.9870 | 0.9821 | 0.9845 | 0.9959 |
| No log | 3.0 | 198 | 0.0098 | 0.9919 | 0.9899 | 0.9909 | 0.9980 |
| No log | 4.0 | 264 | 0.0061 | 0.9927 | 0.9935 | 0.9931 | 0.9987 |
| No log | 5.0 | 330 | 0.0050 | 0.9940 | 0.9958 | 0.9949 | 0.9990 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|