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# scibert-lm-const-finetuned-20

This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_scivocab_cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0099

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.6081        | 1.0   | 118  | 2.9156          |
| 2.7954        | 2.0   | 236  | 2.5940          |
| 2.5762        | 3.0   | 354  | 2.5017          |
| 2.4384        | 4.0   | 472  | 2.3923          |
| 2.3391        | 5.0   | 590  | 2.2996          |
| 2.2417        | 6.0   | 708  | 2.3180          |
| 2.2161        | 7.0   | 826  | 2.2336          |
| 2.1918        | 8.0   | 944  | 2.2465          |
| 2.1494        | 9.0   | 1062 | 2.1871          |
| 2.1215        | 10.0  | 1180 | 2.1566          |
| 2.1015        | 11.0  | 1298 | 2.1849          |
| 2.05          | 12.0  | 1416 | 2.1092          |
| 2.0653        | 13.0  | 1534 | 2.2221          |
| 2.0261        | 14.0  | 1652 | 2.1572          |
| 2.0117        | 15.0  | 1770 | 2.1452          |
| 1.9845        | 16.0  | 1888 | 2.1433          |
| 1.9791        | 17.0  | 2006 | 2.1225          |
| 1.9979        | 18.0  | 2124 | 2.0777          |
| 1.9688        | 19.0  | 2242 | 2.1765          |
| 1.9873        | 20.0  | 2360 | 2.0099          |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model-index": [{"name": "scibert-lm-const-finetuned-20", "results": []}]}
transformers pytorch tensorboard bert fill-mask generated_from_trainer dataset:conll2003 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-conll2003 #autotrain_compatible #endpoints_compatible #region-us 

scibert-lm-const-finetuned-20
=============================


This model is a fine-tuned version of allenai/scibert\_scivocab\_cased on the conll2003 dataset.
It achieves the following results on the evaluation set:


* Loss: 2.0099


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: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-conll2003 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #dataset-conll2003 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1