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# SPECTER-finetuned-DAGPap22

This model is a fine-tuned version of [allenai/specter](https://huggingface.co/allenai/specter) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0023
- Accuracy: 0.9993
- F1: 0.9995

## 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: 6e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.3422        | 1.0   | 669   | 0.4135          | 0.8914   | 0.9140 |
| 0.1074        | 2.0   | 1338  | 0.1216          | 0.9746   | 0.9811 |
| 0.0329        | 3.0   | 2007  | 0.0064          | 0.9989   | 0.9992 |
| 0.0097        | 4.0   | 2676  | 0.0132          | 0.9972   | 0.9980 |
| 0.0123        | 5.0   | 3345  | 0.0231          | 0.9961   | 0.9971 |
| 0.0114        | 6.0   | 4014  | 0.0080          | 0.9985   | 0.9989 |
| 0.0029        | 7.0   | 4683  | 0.2207          | 0.9727   | 0.9797 |
| 0.0075        | 8.0   | 5352  | 0.0145          | 0.9974   | 0.9981 |
| 0.0098        | 9.0   | 6021  | 0.0047          | 0.9994   | 0.9996 |
| 0.0025        | 10.0  | 6690  | 0.0000          | 1.0      | 1.0    |
| 0.0044        | 11.0  | 7359  | 0.0035          | 0.9993   | 0.9995 |
| 0.0           | 12.0  | 8028  | 0.0027          | 0.9996   | 0.9997 |
| 0.0027        | 13.0  | 8697  | 0.0036          | 0.9993   | 0.9995 |
| 0.0055        | 14.0  | 9366  | 0.0017          | 0.9998   | 0.9999 |
| 0.0           | 15.0  | 10035 | 0.0000          | 1.0      | 1.0    |
| 0.0           | 16.0  | 10704 | 0.0000          | 1.0      | 1.0    |
| 0.0022        | 17.0  | 11373 | 0.0111          | 0.9981   | 0.9986 |
| 0.0004        | 18.0  | 12042 | 0.0011          | 0.9994   | 0.9996 |
| 0.0           | 19.0  | 12711 | 0.0020          | 0.9994   | 0.9996 |
| 0.0           | 20.0  | 13380 | 0.0023          | 0.9993   | 0.9995 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["text-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "SPECTER-finetuned-DAGPap22", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

SPECTER-finetuned-DAGPap22
==========================


This model is a fine-tuned version of allenai/specter on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0023
* Accuracy: 0.9993
* F1: 0.9995


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: 6e-06
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-06
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-06
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6e-06
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 20
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1