You can deploy this model and start earning money today!

telmo000/
bloom-positive-reframing

Name: bloom-positive-reframing
Availability: InStock

text-generation

SALT-NLP/positive_reframing

2204.02952

1910.09700

bigscience-bloom-rail-1.0

Views

No views yet

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

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

telmo000/bloom-positive-reframing