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# categorization-finetuned-20220721-164940-pruned-20220803-123018

This model is a fine-tuned version of [carted-nlp/categorization-finetuned-20220721-164940](https://huggingface.co/carted-nlp/categorization-finetuned-20220721-164940) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5476
- Accuracy: 0.8558
- F1: 0.8539

## 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: 7e-06
- train_batch_size: 48
- eval_batch_size: 48
- seed: 314
- gradient_accumulation_steps: 6
- total_train_batch_size: 288
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.3421        | 0.51  | 2000  | 0.4324          | 0.8871   | 0.8864 |
| 0.3435        | 1.01  | 4000  | 0.4276          | 0.8885   | 0.8878 |
| 0.327         | 1.52  | 6000  | 0.4300          | 0.8891   | 0.8884 |
| 0.3299        | 2.02  | 8000  | 0.4266          | 0.8891   | 0.8885 |
| 0.3217        | 2.53  | 10000 | 0.4303          | 0.8881   | 0.8873 |
| 0.3347        | 3.04  | 12000 | 0.4291          | 0.8885   | 0.8879 |
| 0.3307        | 3.54  | 14000 | 0.4334          | 0.8873   | 0.8867 |
| 0.3537        | 4.05  | 16000 | 0.4340          | 0.8850   | 0.8844 |
| 0.3659        | 4.56  | 18000 | 0.4426          | 0.8828   | 0.8819 |
| 0.3933        | 5.06  | 20000 | 0.4485          | 0.8805   | 0.8796 |
| 0.4117        | 5.57  | 22000 | 0.4553          | 0.8779   | 0.8768 |
| 0.4501        | 6.07  | 24000 | 0.4734          | 0.8734   | 0.8725 |
| 0.4848        | 6.58  | 26000 | 0.4895          | 0.8690   | 0.8678 |
| 0.5182        | 7.09  | 28000 | 0.5137          | 0.8634   | 0.8617 |
| 0.54          | 7.59  | 30000 | 0.5165          | 0.8625   | 0.8610 |
| 0.5582        | 8.1   | 32000 | 0.5312          | 0.8591   | 0.8572 |
| 0.5728        | 8.61  | 34000 | 0.5382          | 0.8574   | 0.8556 |
| 0.5883        | 9.11  | 36000 | 0.5514          | 0.8553   | 0.8534 |
| 0.5942        | 9.62  | 38000 | 0.5563          | 0.8534   | 0.8512 |
| 0.6015        | 10.12 | 40000 | 0.5592          | 0.8536   | 0.8516 |
| 0.603         | 10.63 | 42000 | 0.5585          | 0.8533   | 0.8513 |
| 0.5972        | 11.14 | 44000 | 0.5585          | 0.8541   | 0.8520 |
| 0.5938        | 11.64 | 46000 | 0.5546          | 0.8548   | 0.8529 |
| 0.5882        | 12.15 | 48000 | 0.5515          | 0.8554   | 0.8535 |
| 0.5799        | 12.65 | 50000 | 0.5488          | 0.8561   | 0.8541 |
| 0.572         | 13.16 | 52000 | 0.5473          | 0.8566   | 0.8547 |
| 0.5718        | 13.67 | 54000 | 0.5468          | 0.8566   | 0.8547 |
| 0.5698        | 14.17 | 56000 | 0.5464          | 0.8566   | 0.8547 |
| 0.5696        | 14.68 | 58000 | 0.5464          | 0.8566   | 0.8547 |


### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.9.1+cu111
- Datasets 2.3.2
- Tokenizers 0.11.6

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "categorization-finetuned-20220721-164940-pruned-20220803-123018", "results": []}]}
transformers pytorch roberta text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

categorization-finetuned-20220721-164940-pruned-20220803-123018
===============================================================


This model is a fine-tuned version of carted-nlp/categorization-finetuned-20220721-164940 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5476
* Accuracy: 0.8558
* F1: 0.8539


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: 7e-06
* train\_batch\_size: 48
* eval\_batch\_size: 48
* seed: 314
* gradient\_accumulation\_steps: 6
* total\_train\_batch\_size: 288
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15


### Training results



### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.9.1+cu111
* Datasets 2.3.2
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7e-06
* train\_batch\_size: 48
* eval\_batch\_size: 48
* seed: 314
* gradient\_accumulation\_steps: 6
* total\_train\_batch\_size: 288
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.9.1+cu111
* Datasets 2.3.2
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7e-06
* train\_batch\_size: 48
* eval\_batch\_size: 48
* seed: 314
* gradient\_accumulation\_steps: 6
* total\_train\_batch\_size: 288
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.9.1+cu111
* Datasets 2.3.2
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7e-06
* train\_batch\_size: 48
* eval\_batch\_size: 48
* seed: 314
* gradient\_accumulation\_steps: 6
* total\_train\_batch\_size: 288
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15### Training results### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.9.1+cu111
* Datasets 2.3.2
* Tokenizers 0.11.6