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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
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
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