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# my_awesome_model

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the mic dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0634
- Accuracy: 0.6855
- F1: 0.6010
- Precision: 0.6053
- Recall: 0.5974

## 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: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7221        | 1.0   | 5689  | 0.7082          | 0.6943   | 0.5470 | 0.6322    | 0.5336 |
| 0.6334        | 2.0   | 11378 | 0.7100          | 0.7048   | 0.5810 | 0.6452    | 0.5573 |
| 0.5185        | 3.0   | 17067 | 0.7709          | 0.6968   | 0.6057 | 0.6162    | 0.6001 |
| 0.3962        | 4.0   | 22756 | 0.8961          | 0.6881   | 0.6050 | 0.6091    | 0.6014 |
| 0.2962        | 5.0   | 28445 | 1.0634          | 0.6855   | 0.6010 | 0.6053    | 0.5974 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.10.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mic"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "my_awesome_model", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "mic", "type": "mic", "config": "MIC", "split": "test", "args": "MIC"}, "metrics": [{"type": "accuracy", "value": 0.685478199718706, "name": "Accuracy"}, {"type": "f1", "value": 0.6010172314630763, "name": "F1"}, {"type": "precision", "value": 0.6053034619227594, "name": "Precision"}, {"type": "recall", "value": 0.5973741698626668, "name": "Recall"}]}]}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:mic license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-mic #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

my\_awesome\_model
==================


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


* Loss: 1.0634
* Accuracy: 0.6855
* F1: 0.6010
* Precision: 0.6053
* Recall: 0.5974


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: 5


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* Datasets 2.10.0
* Tokenizers 0.12.1



### 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: 5 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* Datasets 2.10.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-mic #license-apache-2.0 #model-index #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: 5 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* Datasets 2.10.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-mic #license-apache-2.0 #model-index #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: 5### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* Datasets 2.10.0
* Tokenizers 0.12.1