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# tamil-ner-model

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5562

## 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: 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3527        | 1.0   | 938  | 0.5562          |
| 0.3543        | 2.0   | 1876 | 0.5562          |
| 0.3584        | 3.0   | 2814 | 0.5562          |
| 0.3554        | 4.0   | 3752 | 0.5562          |
| 0.363         | 5.0   | 4690 | 0.5562          |
| 0.3562        | 6.0   | 5628 | 0.5562          |
| 0.3516        | 7.0   | 6566 | 0.5562          |
| 0.3554        | 8.0   | 7504 | 0.5562          |
| 0.3594        | 9.0   | 8442 | 0.5562          |
| 0.3568        | 10.0  | 9380 | 0.5562          |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "model-index": [{"name": "tamil-ner-model", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:xtreme license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-xtreme #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

tamil-ner-model
===============


This model is a fine-tuned version of bert-base-uncased on the xtreme dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5562


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: 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: 10


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 10 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-xtreme #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 10 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3