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# distilbert-base-uncased-finetuned-sprint-meds

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8121
- Accuracy: 0.8843
- F1: 0.8655

## 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: 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4894        | 1.0   | 21   | 0.9107          | 0.8612   | 0.8354 |
| 0.4471        | 2.0   | 42   | 0.8964          | 0.8630   | 0.8363 |
| 0.4086        | 3.0   | 63   | 0.8796          | 0.8612   | 0.8348 |
| 0.3651        | 4.0   | 84   | 0.8581          | 0.8665   | 0.8415 |
| 0.3365        | 5.0   | 105  | 0.8546          | 0.8683   | 0.8429 |
| 0.3241        | 6.0   | 126  | 0.8448          | 0.8701   | 0.8467 |
| 0.299         | 7.0   | 147  | 0.8372          | 0.8683   | 0.8461 |
| 0.2498        | 8.0   | 168  | 0.8340          | 0.8737   | 0.8500 |
| 0.2579        | 9.0   | 189  | 0.8199          | 0.8737   | 0.8498 |
| 0.2526        | 10.0  | 210  | 0.8191          | 0.8772   | 0.8549 |
| 0.2243        | 11.0  | 231  | 0.8227          | 0.8719   | 0.8476 |
| 0.1888        | 12.0  | 252  | 0.8254          | 0.8719   | 0.8489 |
| 0.2159        | 13.0  | 273  | 0.8163          | 0.8772   | 0.8541 |
| 0.1845        | 14.0  | 294  | 0.8117          | 0.8754   | 0.8533 |
| 0.1774        | 15.0  | 315  | 0.8107          | 0.8772   | 0.8529 |
| 0.1503        | 16.0  | 336  | 0.8109          | 0.8790   | 0.8589 |
| 0.1565        | 17.0  | 357  | 0.8141          | 0.8772   | 0.8533 |
| 0.1539        | 18.0  | 378  | 0.8174          | 0.8772   | 0.8556 |
| 0.1393        | 19.0  | 399  | 0.8132          | 0.8790   | 0.8587 |
| 0.1279        | 20.0  | 420  | 0.8171          | 0.8826   | 0.8602 |
| 0.1231        | 21.0  | 441  | 0.8134          | 0.8808   | 0.8603 |
| 0.119         | 22.0  | 462  | 0.8132          | 0.8843   | 0.8628 |
| 0.1058        | 23.0  | 483  | 0.8043          | 0.8826   | 0.8631 |
| 0.1106        | 24.0  | 504  | 0.8159          | 0.8808   | 0.8596 |
| 0.1036        | 25.0  | 525  | 0.8090          | 0.8826   | 0.8612 |
| 0.0895        | 26.0  | 546  | 0.8093          | 0.8879   | 0.8666 |
| 0.1001        | 27.0  | 567  | 0.8121          | 0.8843   | 0.8636 |
| 0.0956        | 28.0  | 588  | 0.8113          | 0.8808   | 0.8609 |
| 0.0954        | 29.0  | 609  | 0.8099          | 0.8790   | 0.8581 |
| 0.0856        | 30.0  | 630  | 0.8169          | 0.8826   | 0.8616 |
| 0.0819        | 31.0  | 651  | 0.8204          | 0.8790   | 0.8590 |
| 0.0888        | 32.0  | 672  | 0.8125          | 0.8826   | 0.8644 |
| 0.0806        | 33.0  | 693  | 0.8144          | 0.8826   | 0.8628 |
| 0.0836        | 34.0  | 714  | 0.8153          | 0.8790   | 0.8583 |
| 0.0832        | 35.0  | 735  | 0.8139          | 0.8843   | 0.8644 |
| 0.0719        | 36.0  | 756  | 0.8134          | 0.8826   | 0.8623 |
| 0.0843        | 37.0  | 777  | 0.8141          | 0.8826   | 0.8637 |
| 0.0768        | 38.0  | 798  | 0.8157          | 0.8826   | 0.8616 |
| 0.0765        | 39.0  | 819  | 0.8183          | 0.8808   | 0.8621 |
| 0.0685        | 40.0  | 840  | 0.8139          | 0.8808   | 0.8628 |
| 0.0696        | 41.0  | 861  | 0.8149          | 0.8808   | 0.8631 |
| 0.0747        | 42.0  | 882  | 0.8144          | 0.8843   | 0.8655 |
| 0.0709        | 43.0  | 903  | 0.8136          | 0.8843   | 0.8655 |
| 0.0666        | 44.0  | 924  | 0.8140          | 0.8843   | 0.8661 |
| 0.071         | 45.0  | 945  | 0.8123          | 0.8808   | 0.8634 |
| 0.0682        | 46.0  | 966  | 0.8137          | 0.8843   | 0.8661 |
| 0.0743        | 47.0  | 987  | 0.8119          | 0.8843   | 0.8661 |
| 0.069         | 48.0  | 1008 | 0.8113          | 0.8843   | 0.8661 |
| 0.0624        | 49.0  | 1029 | 0.8119          | 0.8843   | 0.8655 |
| 0.0713        | 50.0  | 1050 | 0.8121          | 0.8843   | 0.8655 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sprint-meds", "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-sprint-meds
=============================================


This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8121
* Accuracy: 0.8843
* F1: 0.8655


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: 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: 50


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 50 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
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: 2e-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: 50 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
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: 2e-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: 50### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3