Training

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.

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

Use

• Making end-to-end inference with a pipeline

from transformers import pipeline

ate_sent_pipeline = pipeline(task='ner', 
                  aggregation_strategy='simple',
                  model="gauneg/deberta-v3-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)

Expected output

[{'entity_group': 'pos', #sentiment polarity
  'score': 0.87505656,
  'word': 'food', # aspect term
  'start': 25,
  'end': 30},
 {'entity_group': 'neg',# sentiment polarity
  'score': 0.4558051,
  'word': 'service', #aspect term
  'start': 55,
  'end': 63}]

OR

• Making token level inferences with Auto classes

from transformers import AutoTokenizer, AutoModelForTokenClassification
model_id = "gauneg/deberta-v3-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("../models/deberta-v3-base-bio-w-pol/", 
                                                        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


# selecting the most favoured labels for each token from the logits
y_pred_fin = y_pred.logits.argmax(dim=-1)[0]


# since first and the last tokens are excluded ([CLS] and [SEP]) they have to be removed before decoding
decoded_pred = [id2lab[logx.item()] for logx in y_pred_fin[1:-1]]

## displaying the input tokens with predictions and skipping [CLS] and [SEP] 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))

Expected output

[('▁Been', 'O'),
 ('▁here', 'O'),
 ('▁a', 'O'),
 ('▁few', 'O'),
 ('▁times', 'O'),
 ('▁and', 'O'),
 ('▁food', 'B-pos'),
 ('▁has', 'O'),
 ('▁always', 'O'),
 ('▁been', 'O'),
 ('▁good', 'O'),
 ('▁but', 'O'),
 ('▁service', 'B-neg'),
 ('▁really', 'O'),
 ('▁suffers', 'O'),
 ('▁when', 'O'),
 ('▁it', 'O'),
 ('▁gets', 'O'),
 ('▁crowded', 'O'),
 ('.', 'O')]

Evaluation on Benchmark Test Datasets

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)

ATE (Aspect Term Extraction Only)

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)