rttl-ai
/

foody-bert

Text Classification

Transformers

PyTorch

bert

Inference Endpoints

Model card Files Files and versions Community

rttl commited on Jun 18, 2023

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@@ -16,11 +16,7 @@ Foody-bert results from the second round of fine-tuning on the text classificati
 Continuation of fine-tuning of [senty-bert](https://huggingface.co/rttl-ai/senty-bert), which is fine-tuned on yelp reviews and Stanford sentiment treebank with ternary labels (neutral, positive, negative).
-- analysis. Ms., Stanford University and Facebook AI Research.
-- **Shared by [Optional]:** Hugging Face
-- **Model type:** Language model
-- **Language(s) (NLP):** More information needed
 - **License:** bigscience-bloom-rail-1.0
 - **Related Models:** More information needed
     - **Parent Model:** More information needed
@@ -40,30 +36,14 @@ Continuation of fine-tuning of [senty-bert](https://huggingface.co/rttl-ai/senty
 - We urge caution about using these models for sentiment prediction in other domains. For example, sentiment expression in medical contexts and professional evaluations can be different from sentiment expression in product/service reviews.
-## Downstream Use [Optional]
- More information needed
-## Out-of-Scope Use
- More information needed
 # Bias, Risks, and Limitations
 Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
 ## Recommendations
 - We recommend careful study of how these models behave, even when they are used in the domain on which they were trained and assessed. The models are deep learning models about which it is challenging to gain full analytic command; two examples that appear synonymous to human readers can receive very different predictions from these models, in ways that are hard to anticipate or explain, and so it is crucial to do continual qualitative and quantitative evaluation as part of any deployment.
 - We advise even more caution when using these models in new domains, as sentiment expression can shift in subtle (and not-so-subtle) ways across different domains, and this could lead specific phenomena to be mis-handled in ways that could have dramatic and pernicious consequences.
@@ -78,44 +58,6 @@ The model was trained on product/service reviews from Yelp, reviews from Amazon,
 For extensive details on these datasets are included in the [associated Paper](https://arxiv.org/abs/2012.15349).
-## Training Procedure
-### Preprocessing
-More information needed
-### Speeds, Sizes, Times
-More information needed
-# Evaluation
-## Testing Data, Factors & Metrics
-### Testing Data
-More information needed
-### Factors
-More information needed
-### Metrics
-More information needed
-## Results
-More information needed
-# Model Examination
-More information needed
 # Environmental Impact
 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).
@@ -160,20 +102,6 @@ More information needed
     year={2020}}
 ```
-# Glossary [optional]
-More information needed
-# More Information [optional]
-More information needed
-# Model Card Authors [optional]
-[Christopher Potts](http://web.stanford.edu/~cgpotts/), [Zhengxuan Wu](http://zen-wu.social), Atticus Geiger, and [Douwe Kiela](https://douwekiela.github.io). 2020. DynaSent: A dynamic benchmark for sentiment analysis. Ms., Stanford University and Facebook AI Research, in collabertation with  the Hugging Face team
-# Model Card Contact
-More information needed
 # How to Get Started with the Model

 Continuation of fine-tuning of [senty-bert](https://huggingface.co/rttl-ai/senty-bert), which is fine-tuned on yelp reviews and Stanford sentiment treebank with ternary labels (neutral, positive, negative).
+- **Language(s) (NLP):** English
 - **License:** bigscience-bloom-rail-1.0
 - **Related Models:** More information needed
     - **Parent Model:** More information needed
 - We urge caution about using these models for sentiment prediction in other domains. For example, sentiment expression in medical contexts and professional evaluations can be different from sentiment expression in product/service reviews.
 # Bias, Risks, and Limitations
 Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
 ## Recommendations
 - We recommend careful study of how these models behave, even when they are used in the domain on which they were trained and assessed. The models are deep learning models about which it is challenging to gain full analytic command; two examples that appear synonymous to human readers can receive very different predictions from these models, in ways that are hard to anticipate or explain, and so it is crucial to do continual qualitative and quantitative evaluation as part of any deployment.
 - We advise even more caution when using these models in new domains, as sentiment expression can shift in subtle (and not-so-subtle) ways across different domains, and this could lead specific phenomena to be mis-handled in ways that could have dramatic and pernicious consequences.
 For extensive details on these datasets are included in the [associated Paper](https://arxiv.org/abs/2012.15349).
 # Environmental Impact
 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).
     year={2020}}
 ```
 # How to Get Started with the Model