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# roberta-large-financial-phrasebank-allagree1

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the financial_phrasebank dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1417
- Accuracy: 0.9735
- F1: 0.9736

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.503         | 1.0   | 227  | 0.2774          | 0.9513   | 0.9517 |
| 0.177         | 2.0   | 454  | 0.1518          | 0.9779   | 0.9778 |
| 0.0789        | 3.0   | 681  | 0.1364          | 0.9823   | 0.9822 |
| 0.0512        | 4.0   | 908  | 0.1131          | 0.9779   | 0.9778 |
| 0.03          | 5.0   | 1135 | 0.1417          | 0.9735   | 0.9736 |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["financial_phrasebank"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "roberta-large-financial-phrasebank-allagree1", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "financial_phrasebank", "type": "financial_phrasebank", "config": "sentences_allagree", "split": "train", "args": "sentences_allagree"}, "metrics": [{"type": "accuracy", "value": 0.9734513274336283, "name": "Accuracy"}, {"type": "f1", "value": 0.9736033872259027, "name": "F1"}]}]}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer dataset:financial_phrasebank license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-financial_phrasebank #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

roberta-large-financial-phrasebank-allagree1
============================================


This model is a fine-tuned version of roberta-large on the financial\_phrasebank dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1417
* Accuracy: 0.9735
* F1: 0.9736


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-financial_phrasebank #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-financial_phrasebank #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5### Training results### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1