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# bert-large-cased-sigir-support-refute-no-label-40

This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8371

## 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: 4e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.4511        | 1.0   | 252   | 2.0790          |
| 2.0373        | 2.0   | 504   | 1.8538          |
| 1.8052        | 3.0   | 756   | 1.6633          |
| 1.6663        | 4.0   | 1008  | 1.5591          |
| 1.5556        | 5.0   | 1260  | 1.4441          |
| 1.4505        | 6.0   | 1512  | 1.3836          |
| 1.3619        | 7.0   | 1764  | 1.3255          |
| 1.2968        | 8.0   | 2016  | 1.2505          |
| 1.2332        | 9.0   | 2268  | 1.2165          |
| 1.1788        | 10.0  | 2520  | 1.1517          |
| 1.1408        | 11.0  | 2772  | 1.1446          |
| 1.0992        | 12.0  | 3024  | 1.1512          |
| 1.0578        | 13.0  | 3276  | 1.1058          |
| 1.0277        | 14.0  | 3528  | 1.0662          |
| 1.0036        | 15.0  | 3780  | 1.0270          |
| 0.9655        | 16.0  | 4032  | 1.0207          |
| 0.9364        | 17.0  | 4284  | 1.0220          |
| 0.9085        | 18.0  | 4536  | 0.9874          |
| 0.8897        | 19.0  | 4788  | 0.9658          |
| 0.8661        | 20.0  | 5040  | 0.9603          |
| 0.8434        | 21.0  | 5292  | 0.9754          |
| 0.8248        | 22.0  | 5544  | 0.9406          |
| 0.8052        | 23.0  | 5796  | 0.9154          |
| 0.7975        | 24.0  | 6048  | 0.8760          |
| 0.7854        | 25.0  | 6300  | 0.8688          |
| 0.7673        | 26.0  | 6552  | 0.8536          |
| 0.7463        | 27.0  | 6804  | 0.8544          |
| 0.7412        | 28.0  | 7056  | 0.8514          |
| 0.7319        | 29.0  | 7308  | 0.8356          |
| 0.7143        | 30.0  | 7560  | 0.8832          |
| 0.7081        | 31.0  | 7812  | 0.8421          |
| 0.7026        | 32.0  | 8064  | 0.8295          |
| 0.687         | 33.0  | 8316  | 0.8401          |
| 0.6882        | 34.0  | 8568  | 0.8053          |
| 0.679         | 35.0  | 8820  | 0.8438          |
| 0.6672        | 36.0  | 9072  | 0.8450          |
| 0.6669        | 37.0  | 9324  | 0.8231          |
| 0.6665        | 38.0  | 9576  | 0.8410          |
| 0.6596        | 39.0  | 9828  | 0.7909          |
| 0.6556        | 40.0  | 10080 | 0.8019          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-large-cased-sigir-support-refute-no-label-40", "results": []}]}
transformers pytorch tensorboard bert fill-mask generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-large-cased-sigir-support-refute-no-label-40
=================================================


This model is a fine-tuned version of bert-large-cased on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8371


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: 4e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2