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# distilbert-base-uncased-finetuned-scientific-eval

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: 0.1810
- Precision: 0.5820
- Recall: 0.6719
- F1: 0.6237
- Accuracy: 0.9483

## 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: 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 71   | 0.3580          | 0.3871    | 0.1893 | 0.2542 | 0.9068   |
| No log        | 2.0   | 142  | 0.2235          | 0.5585    | 0.4669 | 0.5086 | 0.9332   |
| No log        | 3.0   | 213  | 0.1870          | 0.6355    | 0.5994 | 0.6169 | 0.9456   |
| No log        | 4.0   | 284  | 0.1857          | 0.5915    | 0.6120 | 0.6016 | 0.9474   |
| No log        | 5.0   | 355  | 0.1810          | 0.5820    | 0.6719 | 0.6237 | 0.9483   |
| No log        | 6.0   | 426  | 0.1969          | 0.6108    | 0.6435 | 0.6267 | 0.9505   |
| No log        | 7.0   | 497  | 0.1914          | 0.5965    | 0.6530 | 0.6235 | 0.9513   |
| 0.1851        | 8.0   | 568  | 0.1964          | 0.6040    | 0.6593 | 0.6305 | 0.9517   |
| 0.1851        | 9.0   | 639  | 0.2026          | 0.6023    | 0.6593 | 0.6295 | 0.9517   |
| 0.1851        | 10.0  | 710  | 0.2027          | 0.6011    | 0.6751 | 0.6360 | 0.9508   |


### Framework versions

- Transformers 4.27.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-scientific-eval", "results": []}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-finetuned-scientific-eval
=================================================


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: 0.1810
* Precision: 0.5820
* Recall: 0.6719
* F1: 0.6237
* Accuracy: 0.9483


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: 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: 10


### Training results



### Framework versions


* Transformers 4.27.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 10 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #distilbert #token-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: 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: 10 ### Training results ### Framework versions


* Transformers 4.27.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3