bloom-positive-reframing

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:

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 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