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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: named-entity-recognition-distilbert-A
results: []
---
<!-- 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. -->
# named-entity-recognition-distilbert-A
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the Multinerd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0606
- Precision: 0.8940
- Recall: 0.9027
- F1: 0.8983
- Accuracy: 0.9833
## 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: 2e-05
- train_batch_size: 32
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.032 | 1.0 | 8205 | 0.0496 | 0.8843 | 0.8928 | 0.8885 | 0.9825 |
| 0.019 | 2.0 | 16410 | 0.0540 | 0.9046 | 0.8909 | 0.8977 | 0.9835 |
| 0.0121 | 3.0 | 24615 | 0.0606 | 0.8940 | 0.9027 | 0.8983 | 0.9833 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
### Citation
### Bibtex
```
@software{Ali_Raza,
author = {Raza, Ali},
license = { BSD-2-Clause license},
title = {{Named Entity Recognition using Multinerd}},
url = {https://github.com/raza4729/NER}
}
```
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