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# relevance-model

This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3134
- Train Binary Accuracy: 0.8773
- Validation Loss: 0.3633
- Validation Binary Accuracy: 0.8541
- Epoch: 2

## 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:
- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Binary Accuracy | Validation Loss | Validation Binary Accuracy | Epoch |
|:----------:|:---------------------:|:---------------:|:--------------------------:|:-----:|
| 0.3980     | 0.8289                | 0.3739          | 0.8541                     | 0     |
| 0.3446     | 0.8606                | 0.3614          | 0.8505                     | 1     |
| 0.3134     | 0.8773                | 0.3633          | 0.8541                     | 2     |


### Framework versions

- Transformers 4.16.0
- TensorFlow 2.7.0
- Datasets 1.18.1
- Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "hfl/chinese-roberta-wwm-ext", "model-index": [{"name": "relevance-model", "results": []}]}
transformers tf bert text-classification generated_from_keras_callback base_model:hfl/chinese-roberta-wwm-ext license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-hfl/chinese-roberta-wwm-ext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

relevance-model
===============


This model is a fine-tuned version of hfl/chinese-roberta-wwm-ext on an unknown dataset.
It achieves the following results on the evaluation set:


* Train Loss: 0.3134
* Train Binary Accuracy: 0.8773
* Validation Loss: 0.3633
* Validation Binary Accuracy: 0.8541
* Epoch: 2


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:


* optimizer: {'name': 'Adam', 'learning\_rate': 5e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.16.0
* TensorFlow 2.7.0
* Datasets 1.18.1
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 5e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.16.0
* TensorFlow 2.7.0
* Datasets 1.18.1
* Tokenizers 0.11.0
TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-hfl/chinese-roberta-wwm-ext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 5e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.16.0
* TensorFlow 2.7.0
* Datasets 1.18.1
* Tokenizers 0.11.0
passage: TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #base_model-hfl/chinese-roberta-wwm-ext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 5e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.16.0
* TensorFlow 2.7.0
* Datasets 1.18.1
* Tokenizers 0.11.0