Roberta Base Absa Ate Sentiment

🚀 RoBERTa-base ABSA ATE Sentiment Model

This model is designed for token classification tasks, specifically for aspect term extraction (ATE) and sentiment analysis. It can extract aspect terms from text and predict the associated sentiment polarity, providing valuable insights for sentiment analysis in various domains.

🚀 Quick Start

This model is designed for token classification tasks, enabling it to extract aspect terms and predict the sentiment polarity associated with the extracted aspect terms. The extracted aspect terms will be the span(s) from the input text on which a sentiment is being expressed.

✨ Features

• Aspect Term Extraction: Extracts aspect terms from input text.
• Sentiment Polarity Prediction: Predicts the sentiment polarity (positive, negative, neutral) associated with the extracted aspect terms.
• Trained on Multiple Datasets: Trained on a variety of datasets, including SemEval tasks and MAMS, ensuring generalization across different domains.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

• Using the pipeline directly for end-to-end inference:

from transformers import pipeline

ate_sent_pipeline = pipeline(task='ner', 
                  aggregation_strategy='simple',
                  model="gauneg/roberta-base-absa-ate-sentiment")

text_input = "Been here a few times and food has always been good but service really suffers when it gets crowded."
ate_sent_pipeline(text_input)

• pipeline output:

[{'entity_group': 'pos', #sentiment polarity
  'score': 0.8447307,
  'word': ' food', # aspect term
  'start': 26,
  'end': 30},
 {'entity_group': 'neg', #sentiment polarity
  'score': 0.81927896,
  'word': ' service', #aspect term
  'start': 56,
  'end': 63}]

Advanced Usage

• Making token level inferences with Auto classes

from transformers import AutoTokenizer, AutoModelForTokenClassification
model_id = "gauneg/roberta-base-absa-ate-sentiment"
tokenizer = AutoTokenizer.from_pretrained(model_id)


# the sequence of labels used during training
labels = {"B-neu": 1, "I-neu": 2, "O": 0, "B-neg": 3, "B-con": 4, "I-pos": 5, "B-pos": 6, "I-con": 7, "I-neg": 8, "X": -100}
id2lab = {idx: lab for lab, idx in labels.items()}
lab2id = {lab: idx for lab, idx in labels.items()}

model = AutoModelForTokenClassification.from_pretrained(model_id, 
                                                        num_labels=len(labels), id2label=id2lab, label2id=lab2id)

# making one prediction at a time (should be padded/batched and truncated for efficiency)
text_input = "Been here a few times and food has always been good but service really suffers when it gets crowded."
tok_inputs = tokenizer(text_input, return_tensors="pt")


y_pred = model(**tok_inputs) # predicting the logits

# since first and the last tokens are excluded (<s> and </s>)
# they have to be removed before decoding the labels predicted against them
y_pred_fin = y_pred.logits.argmax(dim=-1)[0][1:-1] # selecting the most favoured labels for each token from the logits


decoded_pred = [id2lab[logx.item()] for logx in y_pred_fin]


## displaying the input tokens with predictions and skipping <s> and </s> tokens at the beginning and the end respectively
decoded_toks = tok_inputs['input_ids'][0][1:-1]
tok_levl_pred = list(zip(tokenizer.convert_ids_to_tokens(decoded_toks), decoded_pred))

• results in tok_level_pred variable

[('Be', 'O'),
 ('en', 'O'),
 ('Ġhere', 'O'),
 ('Ġa', 'O'),
 ('Ġfew', 'O'),
 ('Ġtimes', 'O'),
 ('Ġand', 'O'),
 ('Ġfood', 'B-pos'),
 ('Ġhas', 'O'),
 ('Ġalways', 'O'),
 ('Ġbeen', 'O'),
 ('Ġgood', 'O'),
 ('Ġbut', 'O'),
 ('Ġservice', 'B-neg'),
 ('Ġreally', 'O'),
 ('Ġsuffers', 'O'),
 ('Ġwhen', 'O'),
 ('Ġit', 'O'),
 ('Ġgets', 'O'),
 ('Ġcrowded', 'O'),
 ('.', 'O')]

📚 Documentation

Datasets

This model has been trained on the following datasets:

1. Aspect Based Sentiment Analysis SemEval Shared Tasks (2014, 2015, 2016)
2. Multi-Aspect Multi-Sentiment MAMS

Evaluation on Benchmark Test Datasets

ATE (Aspect Term Extraction Only)

The first evaluation is for token-extraction task without considering the polarity of the extracted tokens. The tokens expected to be extracted are aspect term tokens on which the sentiments have been expressed. (scores are expressed as micro-averages of B-I-O labels)

Aspect Sentiment Evaluation

This evaluation considers token-extraction task with polarity of the extracted tokens. The tokens expected to be extracted are aspect term tokens on which the sentiments have been expressed along with the polarity of the sentiments. (scores are expressed as macro-averages)

🔧 Technical Details

No technical details are provided in the original document.

📄 License

This model is licensed under the Apache-2.0 license.