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# roberta-large-mnli-ner-2000

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: 0.2962
- Precision: 0.5550
- Recall: 0.7002
- F1: 0.6192
- Accuracy: 0.9229

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5957        | 1.0   | 47   | 0.3873          | 0.3785    | 0.5503 | 0.4485 | 0.8762   |
| 0.3783        | 2.0   | 94   | 0.3326          | 0.4809    | 0.6208 | 0.5420 | 0.8970   |
| 0.31          | 3.0   | 141  | 0.3072          | 0.4149    | 0.5996 | 0.4904 | 0.8932   |
| 0.2706        | 4.0   | 188  | 0.2973          | 0.5096    | 0.6510 | 0.5717 | 0.9096   |
| 0.2486        | 5.0   | 235  | 0.3273          | 0.4987    | 0.6454 | 0.5627 | 0.9061   |
| 0.2113        | 6.0   | 282  | 0.2658          | 0.5148    | 0.6611 | 0.5788 | 0.9146   |
| 0.1856        | 7.0   | 329  | 0.2824          | 0.5140    | 0.6767 | 0.5843 | 0.9138   |
| 0.1554        | 8.0   | 376  | 0.2944          | 0.5450    | 0.6980 | 0.6121 | 0.9181   |
| 0.1362        | 9.0   | 423  | 0.2893          | 0.5475    | 0.6969 | 0.6132 | 0.9199   |
| 0.1232        | 10.0  | 470  | 0.2962          | 0.5550    | 0.7002 | 0.6192 | 0.9229   |


### 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": "roberta-large-mnli-ner-2000", "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-2000
===========================


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: 0.2962
* Precision: 0.5550
* Recall: 0.7002
* F1: 0.6192
* Accuracy: 0.9229


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