
### Overview
Fine tuned VinAI's BERTweet base model on the Wiki Weasel 2.0 Corpus from the [Szeged Uncertainty Corpus](https://rgai.inf.u-szeged.hu/node/160) for hedge (linguistic uncertainty) detection in social media texts. Model was trained and optimised using Ray Tune's implementation of Deep Mind's Population Based Training with the arithmetic mean of Accuracy & F1 as its evaluation metric.

### Labels
* LABEL_1 = Positive (Hedge is detected within text)
* LABEL_0 = Negative (No Hedges detected within text)

### <a name="models2"></a> Model Performance
Model | Accuracy | F1-Score | Accuracy & F1-Score
---|---|---|---
`BERTweet-Hedge` | 0.9680 | 0.8765 | 0.9222

{"language": ["en"], "license": "mit", "tags": ["uncertainty-detection", "social-media", "text-classification"], "widget": [{"text": "It seems like Bitcoin prices are heading into bearish territory.", "example_title": "Hedge Detection (Positive - Label 1)"}, {"text": "Bitcoin prices have fallen by 42% in the last 30 days.", "example_title": "Hedge Detection (Negative - Label 0)"}]}
transformers pytorch safetensors roberta text-classification uncertainty-detection social-media en license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #uncertainty-detection #social-media #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 

### Overview


Fine tuned VinAI's BERTweet base model on the Wiki Weasel 2.0 Corpus from the Szeged Uncertainty Corpus for hedge (linguistic uncertainty) detection in social media texts. Model was trained and optimised using Ray Tune's implementation of Deep Mind's Population Based Training with the arithmetic mean of Accuracy & F1 as its evaluation metric.


### Labels


* LABEL\_1 = Positive (Hedge is detected within text)
* LABEL\_0 = Negative (No Hedges detected within text)


###  Model Performance




### Overview


Fine tuned VinAI's BERTweet base model on the Wiki Weasel 2.0 Corpus from the Szeged Uncertainty Corpus for hedge (linguistic uncertainty) detection in social media texts. Model was trained and optimised using Ray Tune's implementation of Deep Mind's Population Based Training with the arithmetic mean of Accuracy & F1 as its evaluation metric. ### Labels


* LABEL\_1 = Positive (Hedge is detected within text)
* LABEL\_0 = Negative (No Hedges detected within text) ###  Model Performance
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #uncertainty-detection #social-media #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Overview


Fine tuned VinAI's BERTweet base model on the Wiki Weasel 2.0 Corpus from the Szeged Uncertainty Corpus for hedge (linguistic uncertainty) detection in social media texts. Model was trained and optimised using Ray Tune's implementation of Deep Mind's Population Based Training with the arithmetic mean of Accuracy & F1 as its evaluation metric. ### Labels


* LABEL\_1 = Positive (Hedge is detected within text)
* LABEL\_0 = Negative (No Hedges detected within text) ###  Model Performance
passage: TAGS
#transformers #pytorch #safetensors #roberta #text-classification #uncertainty-detection #social-media #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Overview


Fine tuned VinAI's BERTweet base model on the Wiki Weasel 2.0 Corpus from the Szeged Uncertainty Corpus for hedge (linguistic uncertainty) detection in social media texts. Model was trained and optimised using Ray Tune's implementation of Deep Mind's Population Based Training with the arithmetic mean of Accuracy & F1 as its evaluation metric.### Labels


* LABEL\_1 = Positive (Hedge is detected within text)
* LABEL\_0 = Negative (No Hedges detected within text)###  Model Performance