
# Model Card for Model ID

This model is a BLOOM-base adjusted to the sentiment transfer task, developed as part of a FourthBrain workshop on GenerativeAI.

## Model Details

### Model Description

This model is a BLOOM-base adjusted to the sentiment transfer task, where the objective is to reverse the sentiment polarity of a text without contradicting 
the original meaning. Positive reframing induces a complementary positive viewpoint (e.g. glass-half-full) escaping negative patterns. 
Based on the article [Ziems at. al (2022)](https://arxiv.org/abs/2204.02952).

Sample working space [here](https://huggingface.co/spaces/telmo000/bloom-positive-reframing).

### Input
`### Negative sentence:\n{original_text}\n\n### Reframing strategy: \n{reframing_strategy}\n\n### Reframing sentence:\n`


- **Developed by:** Telmo Correa
- **Model type:** LLM
- **Language(s) (NLP):** English
- **License:** [bigscience-bloom-rail-1.0](https://bigscience.huggingface.co/blog/the-bigscience-rail-license)
- **Finetuned from model :** [bigscience/bloom-1b7](https://huggingface.co/bigscience/bloom-1b7)

## Uses

Model trained as a proof-of-concept fine tuning of BLOOM for sentence rewriting.

### Direct Use

Model is intended to be directly used to rewrite sentences with the provided strategy.

### Out-of-Scope Use

Any uses of the model must abide by the terms of both the original BLOOM model and the Salt-NLP/positive-reframing dataset.

## Bias, Risks, and Limitations

As a fine-tuned version of BLOOM, this model carries all the biases, risks, and limitations. of its original training.

## Training Details

### Training Data

[Salt-NLP/positive-reframing](https://huggingface.co/datasets/SALT-NLP/positive_reframing)

### Training Procedure 

The baseline model [bigscience/bloom-1b7](https://huggingface.co/bigscience/bloom-1b7) was trained through 100 steps over the training split of the training data, with its prompt engineered to request explicit positive sentence reframing:

```
Below is a negative sentence, please select a reframing strategy and write the positive reframed sentence.

### Negative sentence:
NEGATIVE SENTENCE HERE

### Reframing strategy:
STRATEGY HERE

### Reframed sentence:
REFRAMED SENTENCE HERE
```


#### Training Hyperparameters

- **Training regime:** fp16 non-mixed precision, using PEFT and LoRA

## Evaluation

Evaluation not performed.

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** Coogle Colab PRO GPU
- **Hours used:** 10 min
- **Cloud Provider:** GCP
- **Compute Region:** us-west-1
- **Carbon Emitted:** 10g
{"language": ["en"], "license": "bigscience-bloom-rail-1.0", "datasets": ["SALT-NLP/positive_reframing"], "pipeline_tag": "text-generation"}
text-generation en dataset:SALT-NLP/positive_reframing arxiv:2204.02952 arxiv:1910.09700 license:bigscience-bloom-rail-1.0 has_space region:us
TAGS
#text-generation #en #dataset-SALT-NLP/positive_reframing #arxiv-2204.02952 #arxiv-1910.09700 #license-bigscience-bloom-rail-1.0 #has_space #region-us 


# Model Card for Model ID

This model is a BLOOM-base adjusted to the sentiment transfer task, developed as part of a FourthBrain workshop on GenerativeAI.

## Model Details

### Model Description

This model is a BLOOM-base adjusted to the sentiment transfer task, where the objective is to reverse the sentiment polarity of a text without contradicting 
the original meaning. Positive reframing induces a complementary positive viewpoint (e.g. glass-half-full) escaping negative patterns. 
Based on the article Ziems at. al (2022).

Sample working space here.

### Input
'### Negative sentence:\n{original_text}\n\n### Reframing strategy: \n{reframing_strategy}\n\n### Reframing sentence:\n'


- Developed by: Telmo Correa
- Model type: LLM
- Language(s) (NLP): English
- License: bigscience-bloom-rail-1.0
- Finetuned from model : bigscience/bloom-1b7

## Uses

Model trained as a proof-of-concept fine tuning of BLOOM for sentence rewriting.

### Direct Use

Model is intended to be directly used to rewrite sentences with the provided strategy.

### Out-of-Scope Use

Any uses of the model must abide by the terms of both the original BLOOM model and the Salt-NLP/positive-reframing dataset.

## Bias, Risks, and Limitations

As a fine-tuned version of BLOOM, this model carries all the biases, risks, and limitations. of its original training.

## Training Details

### Training Data

Salt-NLP/positive-reframing

### Training Procedure 

The baseline model bigscience/bloom-1b7 was trained through 100 steps over the training split of the training data, with its prompt engineered to request explicit positive sentence reframing:




#### Training Hyperparameters

- Training regime: fp16 non-mixed precision, using PEFT and LoRA

## Evaluation

Evaluation not performed.

## Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: Coogle Colab PRO GPU
- Hours used: 10 min
- Cloud Provider: GCP
- Compute Region: us-west-1
- Carbon Emitted: 10g
# Model Card for Model ID

This model is a BLOOM-base adjusted to the sentiment transfer task, developed as part of a FourthBrain workshop on GenerativeAI. ## Model Details ### Model Description

This model is a BLOOM-base adjusted to the sentiment transfer task, where the objective is to reverse the sentiment polarity of a text without contradicting 
the original meaning. Positive reframing induces a complementary positive viewpoint (e.g. glass-half-full) escaping negative patterns. 
Based on the article Ziems at. al (2022).

Sample working space here. ### Input
'### Negative sentence:\n{original_text}\n\n### Reframing strategy: \n{reframing_strategy}\n\n### Reframing sentence:\n'


- Developed by: Telmo Correa
- Model type: LLM
- Language(s) (NLP): English
- License: bigscience-bloom-rail-1.0
- Finetuned from model : bigscience/bloom-1b7 ## Uses

Model trained as a proof-of-concept fine tuning of BLOOM for sentence rewriting. ### Direct Use

Model is intended to be directly used to rewrite sentences with the provided strategy. ### Out-of-Scope Use

Any uses of the model must abide by the terms of both the original BLOOM model and the Salt-NLP/positive-reframing dataset. ## Bias, Risks, and Limitations

As a fine-tuned version of BLOOM, this model carries all the biases, risks, and limitations. of its original training. ## Training Details ### Training Data

Salt-NLP/positive-reframing ### Training Procedure 

The baseline model bigscience/bloom-1b7 was trained through 100 steps over the training split of the training data, with its prompt engineered to request explicit positive sentence reframing: #### Training Hyperparameters

- Training regime: fp16 non-mixed precision, using PEFT and LoRA ## Evaluation

Evaluation not performed. ## Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: Coogle Colab PRO GPU
- Hours used: 10 min
- Cloud Provider: GCP
- Compute Region: us-west-1
- Carbon Emitted: 10g
TAGS
#text-generation #en #dataset-SALT-NLP/positive_reframing #arxiv-2204.02952 #arxiv-1910.09700 #license-bigscience-bloom-rail-1.0 #has_space #region-us 
 # Model Card for Model ID

This model is a BLOOM-base adjusted to the sentiment transfer task, developed as part of a FourthBrain workshop on GenerativeAI. ## Model Details ### Model Description

This model is a BLOOM-base adjusted to the sentiment transfer task, where the objective is to reverse the sentiment polarity of a text without contradicting 
the original meaning. Positive reframing induces a complementary positive viewpoint (e.g. glass-half-full) escaping negative patterns. 
Based on the article Ziems at. al (2022).

Sample working space here. ### Input
'### Negative sentence:\n{original_text}\n\n### Reframing strategy: \n{reframing_strategy}\n\n### Reframing sentence:\n'


- Developed by: Telmo Correa
- Model type: LLM
- Language(s) (NLP): English
- License: bigscience-bloom-rail-1.0
- Finetuned from model : bigscience/bloom-1b7 ## Uses

Model trained as a proof-of-concept fine tuning of BLOOM for sentence rewriting. ### Direct Use

Model is intended to be directly used to rewrite sentences with the provided strategy. ### Out-of-Scope Use

Any uses of the model must abide by the terms of both the original BLOOM model and the Salt-NLP/positive-reframing dataset. ## Bias, Risks, and Limitations

As a fine-tuned version of BLOOM, this model carries all the biases, risks, and limitations. of its original training. ## Training Details ### Training Data

Salt-NLP/positive-reframing ### Training Procedure 

The baseline model bigscience/bloom-1b7 was trained through 100 steps over the training split of the training data, with its prompt engineered to request explicit positive sentence reframing: #### Training Hyperparameters

- Training regime: fp16 non-mixed precision, using PEFT and LoRA ## Evaluation

Evaluation not performed. ## Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: Coogle Colab PRO GPU
- Hours used: 10 min
- Cloud Provider: GCP
- Compute Region: us-west-1
- Carbon Emitted: 10g
TAGS
#text-generation #en #dataset-SALT-NLP/positive_reframing #arxiv-2204.02952 #arxiv-1910.09700 #license-bigscience-bloom-rail-1.0 #has_space #region-us 
# Model Card for Model ID

This model is a BLOOM-base adjusted to the sentiment transfer task, developed as part of a FourthBrain workshop on GenerativeAI.## Model Details### Model Description

This model is a BLOOM-base adjusted to the sentiment transfer task, where the objective is to reverse the sentiment polarity of a text without contradicting 
the original meaning. Positive reframing induces a complementary positive viewpoint (e.g. glass-half-full) escaping negative patterns. 
Based on the article Ziems at. al (2022).

Sample working space here.### Input
'### Negative sentence:\n{original_text}\n\n### Reframing strategy: \n{reframing_strategy}\n\n### Reframing sentence:\n'


- Developed by: Telmo Correa
- Model type: LLM
- Language(s) (NLP): English
- License: bigscience-bloom-rail-1.0
- Finetuned from model : bigscience/bloom-1b7## Uses

Model trained as a proof-of-concept fine tuning of BLOOM for sentence rewriting.### Direct Use

Model is intended to be directly used to rewrite sentences with the provided strategy.### Out-of-Scope Use

Any uses of the model must abide by the terms of both the original BLOOM model and the Salt-NLP/positive-reframing dataset.## Bias, Risks, and Limitations

As a fine-tuned version of BLOOM, this model carries all the biases, risks, and limitations. of its original training.## Training Details### Training Data

Salt-NLP/positive-reframing### Training Procedure 

The baseline model bigscience/bloom-1b7 was trained through 100 steps over the training split of the training data, with its prompt engineered to request explicit positive sentence reframing:#### Training Hyperparameters

- Training regime: fp16 non-mixed precision, using PEFT and LoRA## Evaluation

Evaluation not performed.## Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: Coogle Colab PRO GPU
- Hours used: 10 min
- Cloud Provider: GCP
- Compute Region: us-west-1
- Carbon Emitted: 10g