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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: 0.9805194805194806
- name: Recall
type: recall
value: 0.984171322160149
- name: F1
type: f1
value: 0.9823420074349444
- name: Accuracy
type: accuracy
value: 0.9989348679713209
---
<!-- 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: 0.0053
- Precision: 0.9805
- Recall: 0.9842
- F1: 0.9823
- Accuracy: 0.9989
## 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 | 213 | 0.0572 | 0.6793 | 0.7356 | 0.7063 | 0.9820 |
| No log | 2.0 | 426 | 0.0248 | 0.8912 | 0.8887 | 0.8900 | 0.9936 |
| 0.0713 | 3.0 | 639 | 0.0118 | 0.9570 | 0.9534 | 0.9552 | 0.9973 |
| 0.0713 | 4.0 | 852 | 0.0067 | 0.9777 | 0.9800 | 0.9788 | 0.9987 |
| 0.0164 | 5.0 | 1065 | 0.0053 | 0.9805 | 0.9842 | 0.9823 | 0.9989 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|