---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-downstream-indian-ner
  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. -->

# roberta-base-downstream-indian-ner

This model is a fine-tuned version of [roberta-base](https://huggingface.co./roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2666
- Precision: 0.5248
- Recall: 0.7557
- F1: 0.6195
- Accuracy: 0.9547

## 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: 3e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 86   | 0.3551          | 0.0892    | 0.4171 | 0.1469 | 0.7997   |
| No log        | 2.0   | 172  | 0.2383          | 0.1328    | 0.4684 | 0.2070 | 0.8327   |
| No log        | 3.0   | 258  | 0.2159          | 0.2075    | 0.5253 | 0.2975 | 0.8922   |
| No log        | 4.0   | 344  | 0.2013          | 0.2338    | 0.5344 | 0.3253 | 0.9025   |
| No log        | 5.0   | 430  | 0.1926          | 0.2732    | 0.5476 | 0.3646 | 0.9131   |
| 0.396         | 6.0   | 516  | 0.2002          | 0.2821    | 0.5717 | 0.3778 | 0.9134   |
| 0.396         | 7.0   | 602  | 0.2103          | 0.3407    | 0.6220 | 0.4403 | 0.9267   |
| 0.396         | 8.0   | 688  | 0.1944          | 0.3388    | 0.6265 | 0.4398 | 0.9256   |
| 0.396         | 9.0   | 774  | 0.2118          | 0.3477    | 0.6349 | 0.4494 | 0.9291   |
| 0.396         | 10.0  | 860  | 0.2274          | 0.4096    | 0.6729 | 0.5092 | 0.9396   |
| 0.396         | 11.0  | 946  | 0.2318          | 0.4527    | 0.7047 | 0.5513 | 0.9450   |
| 0.0715        | 12.0  | 1032 | 0.2439          | 0.4436    | 0.6946 | 0.5414 | 0.9443   |
| 0.0715        | 13.0  | 1118 | 0.2385          | 0.4781    | 0.7379 | 0.5802 | 0.9460   |
| 0.0715        | 14.0  | 1204 | 0.2420          | 0.4584    | 0.7065 | 0.5560 | 0.9460   |
| 0.0715        | 15.0  | 1290 | 0.2455          | 0.4992    | 0.7344 | 0.5944 | 0.9502   |
| 0.0715        | 16.0  | 1376 | 0.2513          | 0.5377    | 0.7644 | 0.6313 | 0.9572   |
| 0.0715        | 17.0  | 1462 | 0.2670          | 0.5354    | 0.7627 | 0.6291 | 0.9558   |
| 0.0344        | 18.0  | 1548 | 0.2687          | 0.5020    | 0.7351 | 0.5966 | 0.9505   |
| 0.0344        | 19.0  | 1634 | 0.2666          | 0.5248    | 0.7557 | 0.6195 | 0.9547   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1