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# roberta-sent-generali

This model was fine-tuned on Roberta Large using a private dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4885
- F1: 0.9104

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.355         | 1.0   | 262  | 0.3005          | 0.8829 |
| 0.2201        | 2.0   | 524  | 0.3566          | 0.8930 |
| 0.1293        | 3.0   | 786  | 0.3644          | 0.9193 |
| 0.0662        | 4.0   | 1048 | 0.4202          | 0.9145 |
| 0.026         | 5.0   | 1310 | 0.4885          | 0.9104 |


### Framework versions

- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1

{"language": "en", "tags": ["generated_from_trainer"], "metrics": ["f1"], "widget": [{"text": "Moody\u2019s decision to upgrade the credit rating of Air Liquide is all the more remarkable as it is taking place in a more difficult macroeconomic and geopolitical environment. It underlines the Group\u2019s capacity to maintain a high level of cash flow despite the fluctuations of the economy. Following Standard & Poor\u2019s decision to upgrade Air Liquide\u2019s credit rating, this decision recognizes the Group\u2019s level of debt, which has been brought back to its pre-Airgas 2016 acquisition level in five years. It also reflects the largely demonstrated resilience of the Group\u2019s business model."}], "model-index": [{"name": "roberta-sent-generali", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer en autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #autotrain_compatible #endpoints_compatible #region-us 

roberta-sent-generali
=====================


This model was fine-tuned on Roberta Large using a private dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4885
* F1: 0.9104


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: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 5### Training results### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1