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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: 0.8427
- Accuracy: 0.8790
- F1: 0.8630

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.8256        | 1.0   | 21   | 1.9309          | 0.6868   | 0.5992 |
| 1.7067        | 2.0   | 42   | 1.8220          | 0.6993   | 0.6190 |
| 1.5327        | 3.0   | 63   | 1.7250          | 0.7189   | 0.6489 |
| 1.4475        | 4.0   | 84   | 1.6374          | 0.7509   | 0.6903 |
| 1.3108        | 5.0   | 105  | 1.5627          | 0.7438   | 0.6843 |
| 1.1881        | 6.0   | 126  | 1.4905          | 0.7669   | 0.7135 |
| 1.1726        | 7.0   | 147  | 1.4287          | 0.7847   | 0.7379 |
| 1.0681        | 8.0   | 168  | 1.3705          | 0.7829   | 0.7368 |
| 0.9392        | 9.0   | 189  | 1.3214          | 0.7954   | 0.7513 |
| 0.9603        | 10.0  | 210  | 1.2741          | 0.8043   | 0.7613 |
| 0.8349        | 11.0  | 231  | 1.2415          | 0.8185   | 0.7793 |
| 0.8094        | 12.0  | 252  | 1.2028          | 0.8256   | 0.7883 |
| 0.787         | 13.0  | 273  | 1.1673          | 0.8310   | 0.7951 |
| 0.7128        | 14.0  | 294  | 1.1412          | 0.8381   | 0.8056 |
| 0.6821        | 15.0  | 315  | 1.1091          | 0.8399   | 0.8074 |
| 0.6177        | 16.0  | 336  | 1.0906          | 0.8399   | 0.8098 |
| 0.633         | 17.0  | 357  | 1.0645          | 0.8434   | 0.8170 |
| 0.5734        | 18.0  | 378  | 1.0415          | 0.8470   | 0.8199 |
| 0.5181        | 19.0  | 399  | 1.0233          | 0.8416   | 0.8153 |
| 0.4926        | 20.0  | 420  | 1.0076          | 0.8470   | 0.8209 |
| 0.4773        | 21.0  | 441  | 0.9896          | 0.8434   | 0.8184 |
| 0.4361        | 22.0  | 462  | 0.9768          | 0.8470   | 0.8216 |
| 0.4385        | 23.0  | 483  | 0.9624          | 0.8505   | 0.8261 |
| 0.3962        | 24.0  | 504  | 0.9520          | 0.8559   | 0.8309 |
| 0.392         | 25.0  | 525  | 0.9392          | 0.8577   | 0.8339 |
| 0.4095        | 26.0  | 546  | 0.9331          | 0.8577   | 0.8359 |
| 0.3389        | 27.0  | 567  | 0.9242          | 0.8577   | 0.8348 |
| 0.3296        | 28.0  | 588  | 0.9117          | 0.8577   | 0.8344 |
| 0.3527        | 29.0  | 609  | 0.9026          | 0.8665   | 0.8465 |
| 0.315         | 30.0  | 630  | 0.9008          | 0.8648   | 0.8431 |
| 0.2891        | 31.0  | 651  | 0.8923          | 0.8648   | 0.8433 |
| 0.3283        | 32.0  | 672  | 0.8818          | 0.8701   | 0.8507 |
| 0.2967        | 33.0  | 693  | 0.8799          | 0.8683   | 0.8479 |
| 0.2657        | 34.0  | 714  | 0.8750          | 0.8683   | 0.8479 |
| 0.3015        | 35.0  | 735  | 0.8727          | 0.8719   | 0.8526 |
| 0.2847        | 36.0  | 756  | 0.8656          | 0.8754   | 0.8575 |
| 0.2614        | 37.0  | 777  | 0.8630          | 0.8772   | 0.8589 |
| 0.26          | 38.0  | 798  | 0.8604          | 0.8754   | 0.8598 |
| 0.2557        | 39.0  | 819  | 0.8588          | 0.8772   | 0.8612 |
| 0.2389        | 40.0  | 840  | 0.8562          | 0.8790   | 0.8619 |
| 0.2464        | 41.0  | 861  | 0.8529          | 0.8790   | 0.8615 |
| 0.2304        | 42.0  | 882  | 0.8529          | 0.8772   | 0.8613 |
| 0.2356        | 43.0  | 903  | 0.8514          | 0.8790   | 0.8636 |
| 0.2291        | 44.0  | 924  | 0.8479          | 0.8790   | 0.8631 |
| 0.2323        | 45.0  | 945  | 0.8457          | 0.8790   | 0.8631 |
| 0.2281        | 46.0  | 966  | 0.8454          | 0.8790   | 0.8638 |
| 0.2163        | 47.0  | 987  | 0.8432          | 0.8790   | 0.8633 |
| 0.226         | 48.0  | 1008 | 0.8433          | 0.8790   | 0.8631 |
| 0.229         | 49.0  | 1029 | 0.8431          | 0.8790   | 0.8631 |
| 0.2388        | 50.0  | 1050 | 0.8427          | 0.8790   | 0.8630 |


### 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: 0.8427
* Accuracy: 0.8790
* F1: 0.8630


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