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# distilroberta-tcfd-disclosure

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8681

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 80
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 5    | 2.3837          |
| 2.3918        | 2.0   | 10   | 2.3787          |
| 2.3918        | 3.0   | 15   | 2.3704          |
| 2.3754        | 4.0   | 20   | 2.3623          |
| 2.3754        | 5.0   | 25   | 2.3396          |
| 2.2976        | 6.0   | 30   | 2.2599          |
| 2.2976        | 7.0   | 35   | 2.1095          |
| 2.0439        | 8.0   | 40   | 2.0184          |
| 2.0439        | 9.0   | 45   | 1.9059          |
| 1.6799        | 10.0  | 50   | 1.8469          |
| 1.6799        | 11.0  | 55   | 1.8089          |
| 1.2948        | 12.0  | 60   | 1.7263          |
| 1.2948        | 13.0  | 65   | 1.7250          |
| 0.9621        | 14.0  | 70   | 1.8106          |
| 0.9621        | 15.0  | 75   | 1.8073          |
| 0.7356        | 16.0  | 80   | 1.8681          |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
{"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["rexarski/TCFD_disclosure"], "base_model": "distilroberta-base", "model-index": [{"name": "distilroberta-tcfd-disclosure", "results": []}]}
transformers pytorch roberta text-classification generated_from_trainer en dataset:rexarski/TCFD_disclosure base_model:distilroberta-base license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #en #dataset-rexarski/TCFD_disclosure #base_model-distilroberta-base #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

distilroberta-tcfd-disclosure
=============================


This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.8681


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: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 5
* total\_train\_batch\_size: 80
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 5
* total\_train\_batch\_size: 80
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #en #dataset-rexarski/TCFD_disclosure #base_model-distilroberta-base #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 5
* total\_train\_batch\_size: 80
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #en #dataset-rexarski/TCFD_disclosure #base_model-distilroberta-base #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 5
* total\_train\_batch\_size: 80
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 20
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3