
# Model Details
* The SENTIMENTAL classifier trained to predict the likelihood that a comment will be perceived as positive or negative.
* BERT based Text Classification.

# Intended Use
* Intended to be used for a wide range of use cases such as supporting human moderation and extracting polarity of review comments.
* Not intended for fully automated moderation.
* Not intended to make judgments about specific individuals.

# Factors
* Identity terms referencing frequently positive and negative emotions.

# Metrics
• Accuracy, which measures the percentage of True Positive and True Negative.

# Ethical Considerations
* TODO

# Quantitative Analyses
* TODO

# Training Data
* TODO

# Evaluation Data
* TODO

# Caveats and Recommendations
* TODO
{"license": "apache-2.0"}
transformers pytorch bert text-classification license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# Model Details
* The SENTIMENTAL classifier trained to predict the likelihood that a comment will be perceived as positive or negative.
* BERT based Text Classification.

# Intended Use
* Intended to be used for a wide range of use cases such as supporting human moderation and extracting polarity of review comments.
* Not intended for fully automated moderation.
* Not intended to make judgments about specific individuals.

# Factors
* Identity terms referencing frequently positive and negative emotions.

# Metrics
• Accuracy, which measures the percentage of True Positive and True Negative.

# Ethical Considerations
* TODO

# Quantitative Analyses
* TODO

# Training Data
* TODO

# Evaluation Data
* TODO

# Caveats and Recommendations
* TODO
# Model Details
* The SENTIMENTAL classifier trained to predict the likelihood that a comment will be perceived as positive or negative.
* BERT based Text Classification. # Intended Use
* Intended to be used for a wide range of use cases such as supporting human moderation and extracting polarity of review comments.
* Not intended for fully automated moderation.
* Not intended to make judgments about specific individuals. # Factors
* Identity terms referencing frequently positive and negative emotions. # Metrics
• Accuracy, which measures the percentage of True Positive and True Negative. # Ethical Considerations
* TODO # Quantitative Analyses
* TODO # Training Data
* TODO # Evaluation Data
* TODO # Caveats and Recommendations
* TODO
TAGS
#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Model Details
* The SENTIMENTAL classifier trained to predict the likelihood that a comment will be perceived as positive or negative.
* BERT based Text Classification. # Intended Use
* Intended to be used for a wide range of use cases such as supporting human moderation and extracting polarity of review comments.
* Not intended for fully automated moderation.
* Not intended to make judgments about specific individuals. # Factors
* Identity terms referencing frequently positive and negative emotions. # Metrics
• Accuracy, which measures the percentage of True Positive and True Negative. # Ethical Considerations
* TODO # Quantitative Analyses
* TODO # Training Data
* TODO # Evaluation Data
* TODO # Caveats and Recommendations
* TODO
passage: TAGS
#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# Model Details
* The SENTIMENTAL classifier trained to predict the likelihood that a comment will be perceived as positive or negative.
* BERT based Text Classification.# Intended Use
* Intended to be used for a wide range of use cases such as supporting human moderation and extracting polarity of review comments.
* Not intended for fully automated moderation.
* Not intended to make judgments about specific individuals.# Factors
* Identity terms referencing frequently positive and negative emotions.# Metrics
• Accuracy, which measures the percentage of True Positive and True Negative.# Ethical Considerations
* TODO# Quantitative Analyses
* TODO# Training Data
* TODO# Evaluation Data
* TODO# Caveats and Recommendations
* TODO