
## General description of the model

Unlike a classical sentiment classifier, this model was built to measure the sentiment towards a particular entity on a particular pre-determined topic


```python
model = ....

text =  "I pity Facebook for their lack of commitment against global warming , I like google for its support of increased education"
# In the previous example we notice that depending on the type of entity (Google or Facebook) and depending on the type of to#pics (education or climate change) we have two types of sentiments

# Predict the sentiment towards Facebook (entity) on Climate change (topic) 
sentiment, probability = model.predict(text, topic="climate change", entity= "Facebook")
# sentiment = "negative

# Predict the sentiment towards Google (entity) on Education (topic) 
sentiment, probability = model.predict(text, topic="climate change", entity= "Facebook")
# Sentiment = "positive"

# Predict the sentiment towards Google (entity) on Climate Change (topic) 
sentiment, probability = model.predict(text, topic="climate change", entity= "Facebook")
# Sentiment = "neutral" / "not_found"

# Predict the sentiment towards Facebook (entity) on Education (topic) 
sentiment, probability = model.predict(text, topic="climate change", entity= "Facebook")
# Sentiment = "neutral" / "not_found"

```
## Training
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for sentiment classification. 
The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
3. The Training data can be downloaded from [here](https://docs.google.com/spreadsheets/d/1BVDardwVs04ZWmc5_Eg62Lyr_w_OuXysQwhne8ErkoA/edit?usp=sharing)

## Usage and Inference
For a global overview of the pipeline used for inference please refer to this [colab notebook](https://colab.research.google.com/drive/1GgEGrhQZfA1pbcB9Zl0VtV7L5wXdh6vj?usp=sharing)

## Model Performance
The performances of the model on our internal test set are:
 * Accuracy: 0.68
 * Balanced_Accuracy: 0.45
 * MCC: 0.37
 * F1: 0.49

## Potential weakness of the model

* As the model has been trained on data of short length, it is difficult to predict how the model will behave on long texts
* Although the model is robust to typos and able to deal with synonyms, the entities and topics must be as explicit as possible. 
* The model may have difficulties to detect very abstract and complex topics, a fine tuning of the model can solve this problem
* The model may have difficulty in capturing elements that are very specific to a given context

## BibTeX entry and citation info

```bibtex
author = {HasiMichael, Solofo, Bruce, Sitwala},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Sentiment Classification toward Entity and Topics},
year = {2023/04},
version = {0}
```

{"license": "apache-2.0", "tags": ["setfit", "sentence-transformers", "text-classification"], "pipeline_tag": "text-classification"}
sentence-transformers pytorch xlm-roberta setfit text-classification license:apache-2.0 region:us
TAGS
#sentence-transformers #pytorch #xlm-roberta #setfit #text-classification #license-apache-2.0 #region-us 


## General description of the model

Unlike a classical sentiment classifier, this model was built to measure the sentiment towards a particular entity on a particular pre-determined topic



## Training
This is a SetFit model that can be used for sentiment classification. 
The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
3. The Training data can be downloaded from here

## Usage and Inference
For a global overview of the pipeline used for inference please refer to this colab notebook

## Model Performance
The performances of the model on our internal test set are:
 * Accuracy: 0.68
 * Balanced_Accuracy: 0.45
 * MCC: 0.37
 * F1: 0.49

## Potential weakness of the model

* As the model has been trained on data of short length, it is difficult to predict how the model will behave on long texts
* Although the model is robust to typos and able to deal with synonyms, the entities and topics must be as explicit as possible. 
* The model may have difficulties to detect very abstract and complex topics, a fine tuning of the model can solve this problem
* The model may have difficulty in capturing elements that are very specific to a given context

## BibTeX entry and citation info



## General description of the model

Unlike a classical sentiment classifier, this model was built to measure the sentiment towards a particular entity on a particular pre-determined topic ## Training
This is a SetFit model that can be used for sentiment classification. 
The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
3. The Training data can be downloaded from here ## Usage and Inference
For a global overview of the pipeline used for inference please refer to this colab notebook ## Model Performance
The performances of the model on our internal test set are:
 * Accuracy: 0.68
 * Balanced_Accuracy: 0.45
 * MCC: 0.37
 * F1: 0.49 ## Potential weakness of the model

* As the model has been trained on data of short length, it is difficult to predict how the model will behave on long texts
* Although the model is robust to typos and able to deal with synonyms, the entities and topics must be as explicit as possible. 
* The model may have difficulties to detect very abstract and complex topics, a fine tuning of the model can solve this problem
* The model may have difficulty in capturing elements that are very specific to a given context ## BibTeX entry and citation info
TAGS
#sentence-transformers #pytorch #xlm-roberta #setfit #text-classification #license-apache-2.0 #region-us 
 ## General description of the model

Unlike a classical sentiment classifier, this model was built to measure the sentiment towards a particular entity on a particular pre-determined topic ## Training
This is a SetFit model that can be used for sentiment classification. 
The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
3. The Training data can be downloaded from here ## Usage and Inference
For a global overview of the pipeline used for inference please refer to this colab notebook ## Model Performance
The performances of the model on our internal test set are:
 * Accuracy: 0.68
 * Balanced_Accuracy: 0.45
 * MCC: 0.37
 * F1: 0.49 ## Potential weakness of the model

* As the model has been trained on data of short length, it is difficult to predict how the model will behave on long texts
* Although the model is robust to typos and able to deal with synonyms, the entities and topics must be as explicit as possible. 
* The model may have difficulties to detect very abstract and complex topics, a fine tuning of the model can solve this problem
* The model may have difficulty in capturing elements that are very specific to a given context ## BibTeX entry and citation info
TAGS
#sentence-transformers #pytorch #xlm-roberta #setfit #text-classification #license-apache-2.0 #region-us 
## General description of the model

Unlike a classical sentiment classifier, this model was built to measure the sentiment towards a particular entity on a particular pre-determined topic## Training
This is a SetFit model that can be used for sentiment classification. 
The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
3. The Training data can be downloaded from here## Usage and Inference
For a global overview of the pipeline used for inference please refer to this colab notebook## Model Performance
The performances of the model on our internal test set are:
 * Accuracy: 0.68
 * Balanced_Accuracy: 0.45
 * MCC: 0.37
 * F1: 0.49## Potential weakness of the model

* As the model has been trained on data of short length, it is difficult to predict how the model will behave on long texts
* Although the model is robust to typos and able to deal with synonyms, the entities and topics must be as explicit as possible. 
* The model may have difficulties to detect very abstract and complex topics, a fine tuning of the model can solve this problem
* The model may have difficulty in capturing elements that are very specific to a given context## BibTeX entry and citation info