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# distilbert-base-uncased-finetuned-osdg

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8193
- F1 Score: 0.7962
- Accuracy: 0.8434

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.3769        | 1.0   | 1017  | 0.8258          | 0.7729   | 0.8257   |
| 0.2759        | 2.0   | 2034  | 0.8364          | 0.7773   | 0.8262   |
| 0.1412        | 3.0   | 3051  | 1.0203          | 0.7833   | 0.8379   |
| 0.1423        | 4.0   | 4068  | 1.1603          | 0.7683   | 0.8224   |
| 0.0939        | 5.0   | 5085  | 1.3029          | 0.7843   | 0.8329   |
| 0.0757        | 6.0   | 6102  | 1.3562          | 0.7931   | 0.8379   |
| 0.0801        | 7.0   | 7119  | 1.2925          | 0.7840   | 0.8395   |
| 0.0311        | 8.0   | 8136  | 1.4632          | 0.7750   | 0.8318   |
| 0.0263        | 9.0   | 9153  | 1.5760          | 0.7843   | 0.8312   |
| 0.0196        | 10.0  | 10170 | 1.5689          | 0.7890   | 0.8417   |
| 0.0313        | 11.0  | 11187 | 1.6034          | 0.7909   | 0.8417   |
| 0.0007        | 12.0  | 12204 | 1.6725          | 0.7889   | 0.8406   |
| 0.0081        | 13.0  | 13221 | 1.6463          | 0.7911   | 0.8395   |
| 0.0061        | 14.0  | 14238 | 1.7730          | 0.7861   | 0.8345   |
| 0.003         | 15.0  | 15255 | 1.8001          | 0.7847   | 0.8379   |
| 0.0002        | 16.0  | 16272 | 1.7328          | 0.7912   | 0.8434   |
| 0.0           | 17.0  | 17289 | 1.7914          | 0.8011   | 0.8489   |
| 0.0009        | 18.0  | 18306 | 1.7772          | 0.7958   | 0.8456   |
| 0.0           | 19.0  | 19323 | 1.8028          | 0.7958   | 0.8434   |
| 0.0           | 20.0  | 20340 | 1.8193          | 0.7962   | 0.8434   |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-osdg", "results": []}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-finetuned-osdg
======================================


This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.8193
* F1 Score: 0.7962
* Accuracy: 0.8434


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: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #distilbert #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: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #distilbert #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: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1