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# mongolian-roberta-large-mnli-ner

This model is a fine-tuned version of [roberta-large-mnli](https://huggingface.co/roberta-large-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1941
- Precision: 0.7734
- Recall: 0.8488
- F1: 0.8094
- Accuracy: 0.9582

## 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: 32
- 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3433        | 1.0   | 477  | 0.2252          | 0.6196    | 0.7338 | 0.6719 | 0.9288   |
| 0.2067        | 2.0   | 954  | 0.1859          | 0.6981    | 0.7908 | 0.7416 | 0.9381   |
| 0.165         | 3.0   | 1431 | 0.1776          | 0.7308    | 0.8112 | 0.7689 | 0.9455   |
| 0.1362        | 4.0   | 1908 | 0.1639          | 0.7513    | 0.8265 | 0.7871 | 0.9520   |
| 0.109         | 5.0   | 2385 | 0.1703          | 0.7524    | 0.8302 | 0.7894 | 0.9517   |
| 0.0873        | 6.0   | 2862 | 0.1690          | 0.7643    | 0.8396 | 0.8002 | 0.9552   |
| 0.0697        | 7.0   | 3339 | 0.1754          | 0.7696    | 0.8442 | 0.8052 | 0.9557   |
| 0.0552        | 8.0   | 3816 | 0.1793          | 0.7687    | 0.8468 | 0.8059 | 0.9572   |
| 0.0434        | 9.0   | 4293 | 0.1878          | 0.7842    | 0.8507 | 0.8161 | 0.9580   |
| 0.0354        | 10.0  | 4770 | 0.1941          | 0.7734    | 0.8488 | 0.8094 | 0.9582   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"language": ["mn"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "mongolian-roberta-large-mnli-ner", "results": []}]}
transformers pytorch tensorboard roberta token-classification generated_from_trainer mn license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #mn #license-mit #autotrain_compatible #endpoints_compatible #region-us 

mongolian-roberta-large-mnli-ner
================================


This model is a fine-tuned version of roberta-large-mnli on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1941
* Precision: 0.7734
* Recall: 0.8488
* F1: 0.8094
* Accuracy: 0.9582


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: 32
* 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 4.28.1
* Pytorch 2.0.0+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: 32
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #mn #license-mit #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: 32
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3