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# results

This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3320
- Accuracy: 0.8951
- F1: 0.8964
- Precision: 0.8978
- Recall: 0.8965

## 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: 0.0003
- 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: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2783        | 1.0   | 625  | 0.3046          | 0.8949   | 0.8960 | 0.8970    | 0.8963 |
| 0.1878        | 2.0   | 1250 | 0.3139          | 0.8954   | 0.8971 | 0.8995    | 0.8965 |
| 0.1311        | 3.0   | 1875 | 0.3320          | 0.8951   | 0.8964 | 0.8978    | 0.8965 |


### Framework versions

- Transformers 4.22.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["ag_news"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "results", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "ag_news", "type": "ag_news", "config": "default", "split": "train[:40000]", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8951, "name": "Accuracy"}, {"type": "f1", "value": 0.8964447542636089, "name": "F1"}, {"type": "precision", "value": 0.8978261707981314, "name": "Precision"}, {"type": "recall", "value": 0.896474840596734, "name": "Recall"}]}]}]}
transformers pytorch bert text-classification generated_from_trainer dataset:ag_news license:mit model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-ag_news #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

results
=======


This model is a fine-tuned version of prajjwal1/bert-tiny on the ag\_news dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3320
* Accuracy: 0.8951
* F1: 0.8964
* Precision: 0.8978
* Recall: 0.8965


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: 0.0003
* 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: 3
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.22.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* 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: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.22.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-ag_news #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* 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: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.22.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-ag_news #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* 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: 3
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.22.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1