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# categorization-finetuned-20220721-164940-distilled-20220811-074207

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: 0.1499
- Accuracy: 0.8771
- F1: 0.8763

## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 96
- 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: cosine
- lr_scheduler_warmup_steps: 1500
- num_epochs: 30.0

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
| 0.5644        | 0.56  | 2500   | 0.2739          | 0.7822   | 0.7774 |
| 0.2658        | 1.12  | 5000   | 0.2288          | 0.8159   | 0.8127 |
| 0.2307        | 1.69  | 7500   | 0.2082          | 0.8298   | 0.8273 |
| 0.2126        | 2.25  | 10000  | 0.1970          | 0.8389   | 0.8370 |
| 0.2012        | 2.81  | 12500  | 0.1888          | 0.8450   | 0.8433 |
| 0.1903        | 3.37  | 15000  | 0.1829          | 0.8496   | 0.8485 |
| 0.1846        | 3.94  | 17500  | 0.1783          | 0.8529   | 0.8511 |
| 0.1771        | 4.5   | 20000  | 0.1750          | 0.8548   | 0.8537 |
| 0.1726        | 5.06  | 22500  | 0.1727          | 0.8577   | 0.8564 |
| 0.1673        | 5.62  | 25000  | 0.1683          | 0.8602   | 0.8591 |
| 0.1648        | 6.19  | 27500  | 0.1675          | 0.8608   | 0.8597 |
| 0.1596        | 6.75  | 30000  | 0.1657          | 0.8630   | 0.8620 |
| 0.1563        | 7.31  | 32500  | 0.1635          | 0.8646   | 0.8639 |
| 0.154         | 7.87  | 35000  | 0.1613          | 0.8656   | 0.8647 |
| 0.1496        | 8.43  | 37500  | 0.1611          | 0.8666   | 0.8656 |
| 0.1496        | 9.0   | 40000  | 0.1598          | 0.8676   | 0.8669 |
| 0.1445        | 9.56  | 42500  | 0.1594          | 0.8681   | 0.8671 |
| 0.1435        | 10.12 | 45000  | 0.1588          | 0.8688   | 0.8679 |
| 0.1407        | 10.68 | 47500  | 0.1568          | 0.8703   | 0.8695 |
| 0.1382        | 11.25 | 50000  | 0.1564          | 0.8708   | 0.8700 |
| 0.1372        | 11.81 | 52500  | 0.1550          | 0.8720   | 0.8713 |
| 0.1344        | 12.37 | 55000  | 0.1559          | 0.8718   | 0.8708 |
| 0.1337        | 12.93 | 57500  | 0.1540          | 0.8735   | 0.8729 |
| 0.1303        | 13.5  | 60000  | 0.1541          | 0.8729   | 0.8721 |
| 0.1304        | 14.06 | 62500  | 0.1531          | 0.8735   | 0.8727 |
| 0.1274        | 14.62 | 65000  | 0.1535          | 0.8736   | 0.8727 |
| 0.1266        | 15.18 | 67500  | 0.1527          | 0.8750   | 0.8742 |
| 0.1251        | 15.74 | 70000  | 0.1525          | 0.8755   | 0.8748 |
| 0.1234        | 16.31 | 72500  | 0.1528          | 0.8753   | 0.8745 |
| 0.1229        | 16.87 | 75000  | 0.1516          | 0.8760   | 0.8753 |
| 0.121         | 17.43 | 77500  | 0.1523          | 0.8759   | 0.8752 |
| 0.1212        | 17.99 | 80000  | 0.1515          | 0.8760   | 0.8754 |
| 0.1185        | 18.56 | 82500  | 0.1514          | 0.8765   | 0.8757 |
| 0.1186        | 19.12 | 85000  | 0.1516          | 0.8766   | 0.8760 |
| 0.1172        | 19.68 | 87500  | 0.1506          | 0.8774   | 0.8767 |
| 0.1164        | 20.24 | 90000  | 0.1513          | 0.8770   | 0.8763 |
| 0.116         | 20.81 | 92500  | 0.1507          | 0.8774   | 0.8767 |
| 0.1145        | 21.37 | 95000  | 0.1507          | 0.8777   | 0.8770 |
| 0.1143        | 21.93 | 97500  | 0.1506          | 0.8776   | 0.8770 |
| 0.1131        | 22.49 | 100000 | 0.1507          | 0.8779   | 0.8772 |
| 0.1131        | 23.05 | 102500 | 0.1505          | 0.8779   | 0.8772 |
| 0.1123        | 23.62 | 105000 | 0.1506          | 0.8781   | 0.8774 |
| 0.1117        | 24.18 | 107500 | 0.1504          | 0.8783   | 0.8776 |
| 0.1118        | 24.74 | 110000 | 0.1503          | 0.8784   | 0.8777 |
| 0.1111        | 25.3  | 112500 | 0.1503          | 0.8783   | 0.8776 |
| 0.1111        | 25.87 | 115000 | 0.1502          | 0.8784   | 0.8777 |
| 0.1105        | 26.43 | 117500 | 0.1504          | 0.8783   | 0.8776 |
| 0.1105        | 26.99 | 120000 | 0.1502          | 0.8786   | 0.8779 |
| 0.1104        | 27.55 | 122500 | 0.1503          | 0.8786   | 0.8779 |
| 0.1096        | 28.12 | 125000 | 0.1502          | 0.8785   | 0.8779 |
| 0.1101        | 28.68 | 127500 | 0.1501          | 0.8786   | 0.8779 |
| 0.1101        | 29.24 | 130000 | 0.1502          | 0.8786   | 0.8779 |
| 0.1094        | 29.8  | 132500 | 0.1501          | 0.8786   | 0.8779 |


### 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-20220811-074207", "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-20220811-074207
==================================================================


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: 0.1499
* Accuracy: 0.8771
* F1: 0.8763


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: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 96
* 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: cosine
* lr\_scheduler\_warmup\_steps: 1500
* num\_epochs: 30.0


### 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: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 96
* 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: cosine
* lr\_scheduler\_warmup\_steps: 1500
* num\_epochs: 30.0 ### 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: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 96
* 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: cosine
* lr\_scheduler\_warmup\_steps: 1500
* num\_epochs: 30.0 ### 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: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 96
* 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: cosine
* lr\_scheduler\_warmup\_steps: 1500
* num\_epochs: 30.0### Training results### Framework versions


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