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# bert-base-uncased-yahoo_answers_topics

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the yahoo_answers_topics dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8092
- Accuracy: 0.7499

## 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: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 86625
- training_steps: 866250

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.162         | 0.01  | 2000  | 1.7444          | 0.5681   |
| 1.3126        | 0.02  | 4000  | 1.0081          | 0.7054   |
| 0.9592        | 0.03  | 6000  | 0.9021          | 0.7234   |
| 0.8903        | 0.05  | 8000  | 0.8827          | 0.7276   |
| 0.8685        | 0.06  | 10000 | 0.8540          | 0.7341   |
| 0.8422        | 0.07  | 12000 | 0.8547          | 0.7365   |
| 0.8535        | 0.08  | 14000 | 0.8264          | 0.7372   |
| 0.8178        | 0.09  | 16000 | 0.8331          | 0.7389   |
| 0.8325        | 0.1   | 18000 | 0.8242          | 0.7411   |
| 0.8181        | 0.12  | 20000 | 0.8356          | 0.7437   |
| 0.8171        | 0.13  | 22000 | 0.8090          | 0.7451   |
| 0.8092        | 0.14  | 24000 | 0.8469          | 0.7392   |
| 0.8057        | 0.15  | 26000 | 0.8185          | 0.7478   |
| 0.8085        | 0.16  | 28000 | 0.8090          | 0.7467   |
| 0.8229        | 0.17  | 30000 | 0.8225          | 0.7417   |
| 0.8151        | 0.18  | 32000 | 0.8262          | 0.7419   |
| 0.81          | 0.2   | 34000 | 0.8149          | 0.7383   |
| 0.8073        | 0.21  | 36000 | 0.8225          | 0.7441   |
| 0.816         | 0.22  | 38000 | 0.8037          | 0.744    |
| 0.8217        | 0.23  | 40000 | 0.8409          | 0.743    |
| 0.82          | 0.24  | 42000 | 0.8286          | 0.7385   |
| 0.8101        | 0.25  | 44000 | 0.8282          | 0.7413   |
| 0.8254        | 0.27  | 46000 | 0.8170          | 0.7414   |


### Framework versions

- Transformers 4.10.2
- Pytorch 1.7.1
- Datasets 1.6.1
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer", "sibyl"], "datasets": ["yahoo_answers_topics"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-yahoo_answers_topics", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "yahoo_answers_topics", "type": "yahoo_answers_topics", "args": "yahoo_answers_topics"}, "metrics": [{"type": "accuracy", "value": 0.7499166666666667, "name": "Accuracy"}]}]}]}
transformers pytorch bert text-classification generated_from_trainer sibyl dataset:yahoo_answers_topics license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-yahoo_answers_topics #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-base-uncased-yahoo\_answers\_topics
========================================


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


* Loss: 0.8092
* Accuracy: 0.7499


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: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 86625
* training\_steps: 866250


### Training results



### Framework versions


* Transformers 4.10.2
* Pytorch 1.7.1
* Datasets 1.6.1
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 86625
* training\_steps: 866250 ### Training results ### Framework versions


* Transformers 4.10.2
* Pytorch 1.7.1
* Datasets 1.6.1
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-yahoo_answers_topics #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 86625
* training\_steps: 866250 ### Training results ### Framework versions


* Transformers 4.10.2
* Pytorch 1.7.1
* Datasets 1.6.1
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #sibyl #dataset-yahoo_answers_topics #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 86625
* training\_steps: 866250### Training results### Framework versions


* Transformers 4.10.2
* Pytorch 1.7.1
* Datasets 1.6.1
* Tokenizers 0.10.3