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# q05_kaggle_distilbert_inverted_weights

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: 0.0977
- Accuracy: 0.8581
- F1: 0.3892
- Recall: 0.4038
- Precision: 0.3758

## 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: 32
- eval_batch_size: 32
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.2883        | 0.86  | 50   | 0.1095          | 0.8041   | 0.3035 | 0.3072 | 0.3137    |
| 0.124         | 1.72  | 100  | 0.1317          | 0.8497   | 0.3806 | 0.3933 | 0.3687    |
| 0.1062        | 2.59  | 150  | 0.1585          | 0.8361   | 0.3900 | 0.4294 | 0.3674    |
| 0.0888        | 3.45  | 200  | 0.1000          | 0.8328   | 0.3500 | 0.3531 | 0.3505    |
| 0.0789        | 4.31  | 250  | 0.1004          | 0.8395   | 0.3555 | 0.3573 | 0.3587    |
| 0.0649        | 5.17  | 300  | 0.0977          | 0.8581   | 0.3892 | 0.4038 | 0.3758    |
| 0.0526        | 6.03  | 350  | 0.1649          | 0.8615   | 0.3985 | 0.4222 | 0.3794    |
| 0.0384        | 6.9   | 400  | 0.1455          | 0.8733   | 0.4581 | 0.4546 | 0.5005    |
| 0.0351        | 7.76  | 450  | 0.1883          | 0.8767   | 0.5082 | 0.4999 | 0.5376    |
| 0.0344        | 8.62  | 500  | 0.2364          | 0.8733   | 0.5062 | 0.5104 | 0.5179    |
| 0.024         | 9.48  | 550  | 0.1847          | 0.8767   | 0.5483 | 0.5109 | 0.6784    |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "recall", "precision"], "model-index": [{"name": "q05_kaggle_distilbert_inverted_weights", "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 

q05\_kaggle\_distilbert\_inverted\_weights
==========================================


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: 0.0977
* Accuracy: 0.8581
* F1: 0.3892
* Recall: 0.4038
* Precision: 0.3758


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: 32
* eval\_batch\_size: 32
* seed: 0
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* label\_smoothing\_factor: 0.1


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 0
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* label\_smoothing\_factor: 0.1 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* 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: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 0
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* label\_smoothing\_factor: 0.1 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3