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# categorization-finetuned-20220721-164940-distilled-20220810-123313

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: 0.0787
- Accuracy: 0.8416
- F1: 0.8396

## 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: 64
- eval_batch_size: 64
- seed: 314
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 15.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.2976        | 0.56  | 2500  | 0.1441          | 0.7219   | 0.7071 |
| 0.1417        | 1.12  | 5000  | 0.1180          | 0.7719   | 0.7653 |
| 0.1236        | 1.69  | 7500  | 0.1076          | 0.7901   | 0.7854 |
| 0.1148        | 2.25  | 10000 | 0.1014          | 0.8015   | 0.7977 |
| 0.1092        | 2.81  | 12500 | 0.0972          | 0.8089   | 0.8052 |
| 0.1043        | 3.37  | 15000 | 0.0942          | 0.8135   | 0.8102 |
| 0.1013        | 3.94  | 17500 | 0.0916          | 0.8181   | 0.8147 |
| 0.0985        | 4.5   | 20000 | 0.0897          | 0.8219   | 0.8190 |
| 0.0962        | 5.06  | 22500 | 0.0881          | 0.8241   | 0.8215 |
| 0.0945        | 5.62  | 25000 | 0.0866          | 0.8270   | 0.8246 |
| 0.0928        | 6.19  | 27500 | 0.0857          | 0.8286   | 0.8262 |
| 0.0912        | 6.75  | 30000 | 0.0843          | 0.8310   | 0.8286 |
| 0.0901        | 7.31  | 32500 | 0.0836          | 0.8321   | 0.8299 |
| 0.0887        | 7.87  | 35000 | 0.0827          | 0.8339   | 0.8315 |
| 0.0879        | 8.43  | 37500 | 0.0821          | 0.8350   | 0.8329 |
| 0.0875        | 9.0   | 40000 | 0.0814          | 0.8362   | 0.8342 |
| 0.0865        | 9.56  | 42500 | 0.0811          | 0.8370   | 0.8348 |
| 0.0855        | 10.12 | 45000 | 0.0806          | 0.8375   | 0.8355 |
| 0.0853        | 10.68 | 47500 | 0.0798          | 0.8386   | 0.8367 |
| 0.0845        | 11.25 | 50000 | 0.0799          | 0.8392   | 0.8372 |
| 0.0844        | 11.81 | 52500 | 0.0793          | 0.8401   | 0.8383 |
| 0.0838        | 12.37 | 55000 | 0.0793          | 0.8402   | 0.8381 |
| 0.0834        | 12.93 | 57500 | 0.0790          | 0.8410   | 0.8390 |
| 0.0832        | 13.5  | 60000 | 0.0788          | 0.8414   | 0.8394 |
| 0.083         | 14.06 | 62500 | 0.0787          | 0.8415   | 0.8395 |
| 0.0828        | 14.62 | 65000 | 0.0787          | 0.8416   | 0.8396 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "categorization-finetuned-20220721-164940-distilled-20220810-123313", "results": []}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

categorization-finetuned-20220721-164940-distilled-20220810-123313
==================================================================


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: 0.0787
* Accuracy: 0.8416
* F1: 0.8396


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: 64
* eval\_batch\_size: 64
* seed: 314
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1500
* num\_epochs: 15.0


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7e-06
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 314
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1500
* num\_epochs: 15.0 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #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: 64
* eval\_batch\_size: 64
* seed: 314
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1500
* num\_epochs: 15.0 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #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: 64
* eval\_batch\_size: 64
* seed: 314
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1500
* num\_epochs: 15.0### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.11.6