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# categorization-finetuned-20220721-164940-pruned-20220803-184651

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: 0.4673
- Accuracy: 0.8760
- F1: 0.8751

## 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: 1000
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.3404        | 0.51  | 2000  | 0.4329          | 0.8872   | 0.8865 |
| 0.3433        | 1.01  | 4000  | 0.4280          | 0.8883   | 0.8876 |
| 0.3281        | 1.52  | 6000  | 0.4302          | 0.8890   | 0.8883 |
| 0.331         | 2.02  | 8000  | 0.4265          | 0.8891   | 0.8885 |
| 0.3224        | 2.53  | 10000 | 0.4300          | 0.8881   | 0.8874 |
| 0.3361        | 3.04  | 12000 | 0.4291          | 0.8889   | 0.8882 |
| 0.3323        | 3.54  | 14000 | 0.4337          | 0.8878   | 0.8871 |
| 0.3556        | 4.05  | 16000 | 0.4345          | 0.8857   | 0.8851 |
| 0.3663        | 4.56  | 18000 | 0.4417          | 0.8836   | 0.8828 |
| 0.3902        | 5.06  | 20000 | 0.4555          | 0.8789   | 0.8781 |
| 0.4036        | 5.57  | 22000 | 0.4556          | 0.8788   | 0.8779 |
| 0.4305        | 6.07  | 24000 | 0.4697          | 0.8751   | 0.8742 |
| 0.4501        | 6.58  | 26000 | 0.4763          | 0.8738   | 0.8725 |
| 0.4733        | 7.09  | 28000 | 0.4857          | 0.8710   | 0.8700 |
| 0.4851        | 7.59  | 30000 | 0.4863          | 0.8705   | 0.8695 |
| 0.4846        | 8.1   | 32000 | 0.4849          | 0.8708   | 0.8698 |
| 0.4856        | 8.61  | 34000 | 0.4835          | 0.8707   | 0.8695 |
| 0.4774        | 9.11  | 36000 | 0.4797          | 0.8719   | 0.8708 |
| 0.4635        | 9.62  | 38000 | 0.4776          | 0.8728   | 0.8717 |
| 0.4561        | 10.12 | 40000 | 0.4746          | 0.8739   | 0.8729 |
| 0.4475        | 10.63 | 42000 | 0.4705          | 0.8749   | 0.8740 |
| 0.4413        | 11.14 | 44000 | 0.4691          | 0.8754   | 0.8744 |
| 0.4389        | 11.64 | 46000 | 0.4679          | 0.8760   | 0.8750 |
| 0.4361        | 12.15 | 48000 | 0.4677          | 0.8759   | 0.8749 |
| 0.4362        | 12.65 | 50000 | 0.4672          | 0.8763   | 0.8753 |
| 0.4309        | 13.16 | 52000 | 0.4671          | 0.8761   | 0.8751 |
| 0.4316        | 13.67 | 54000 | 0.4670          | 0.8764   | 0.8754 |
| 0.4321        | 14.17 | 56000 | 0.4668          | 0.8764   | 0.8755 |
| 0.4311        | 14.68 | 58000 | 0.4668          | 0.8764   | 0.8754 |


### 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-20220803-184651", "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-20220803-184651
===============================================================


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: 0.4673
* Accuracy: 0.8760
* F1: 0.8751


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: 1000
* 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: 1000
* 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: 1000
* 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: 1000
* 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