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# roberta-large-mnli-ner-demo

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: 0.3031
- Precision: 0.5963
- Recall: 0.6724
- F1: 0.6321
- Accuracy: 0.9073

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.8144        | 1.0   | 64   | 0.7253          | 0.0482    | 0.0131 | 0.0206 | 0.8188   |
| 0.7601        | 2.0   | 128  | 0.7279          | 0.0482    | 0.0131 | 0.0206 | 0.8188   |
| 0.7494        | 3.0   | 192  | 0.5408          | 0.0482    | 0.0131 | 0.0206 | 0.8188   |
| 0.521         | 4.0   | 256  | 0.4369          | 0.4465    | 0.5225 | 0.4816 | 0.8653   |
| 0.4497        | 5.0   | 320  | 0.3912          | 0.4791    | 0.5289 | 0.5028 | 0.8648   |
| 0.3849        | 6.0   | 384  | 0.3620          | 0.6039    | 0.6218 | 0.6127 | 0.8955   |
| 0.3326        | 7.0   | 448  | 0.3216          | 0.5830    | 0.6482 | 0.6139 | 0.8975   |
| 0.2959        | 8.0   | 512  | 0.3183          | 0.5750    | 0.6404 | 0.6059 | 0.8975   |
| 0.2617        | 9.0   | 576  | 0.3061          | 0.5785    | 0.6674 | 0.6198 | 0.9037   |
| 0.2396        | 10.0  | 640  | 0.3031          | 0.5963    | 0.6724 | 0.6321 | 0.9073   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"language": ["mn"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "roberta-large-mnli-ner-demo", "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 

roberta-large-mnli-ner-demo
===========================


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: 0.3031
* Precision: 0.5963
* Recall: 0.6724
* F1: 0.6321
* Accuracy: 0.9073


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 2.11.0
* 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 2.11.0
* 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 2.11.0
* 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 2.11.0
* Tokenizers 0.13.3