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:
- Aspect Based Sentiment Analysis SemEval Shared Tasks (2014, 2015, 2016)
- Multi-Aspect Multi-Sentiment MAMS
Use
- 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}]
OR
- 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_predvariable
[('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')]
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)
| Test Dataset | Base Model | Fine-tuned Model | Precision | Recall | F1 Score |
|---|---|---|---|---|---|
| hotel reviews (SemEval 2015) | microsoft/deberta-v3-base | gauneg/deberta-v3-base-absa-ate-sentiment | 71.16 | 73.92 | 71.6 |
| hotel reviews (SemEval 2015) | FacebookAI/roberta-base | (this) gauneg/roberta-base-absa-ate-sentiment | 70.92 | 72.28 | 71.07 |
| hotel reviews (SemEval 2015) | microsoft/deberta-v3-large | gauneg/deberta-v3-large-absa-ate-sentiment-lora-adapter | 64.05 | 79.69 | 70.0 |
| hotel reviews (SemEval 2015) | FacebookAI/roberta-large | gauneg/roberta-large-absa-ate-sentiment-lora-adapter | 66.29 | 72.78 | 68.92 |
| ------------ | ---------- | ---------------- | --------- | ------ | -------- |
| laptop reviews (SemEval 2014) | microsoft/deberta-v3-large | gauneg/deberta-v3-large-absa-ate-sentiment-lora-adapter | 70.58 | 61.52 | 64.21 |
| laptop reviews (SemEval 2014) | FacebookAI/roberta-large | gauneg/roberta-large-absa-ate-sentiment-lora-adapter | 66.38 | 50.62 | 54.31 |
| laptop reviews (SemEval 2014) | microsoft/deberta-v3-base | gauneg/deberta-v3-base-absa-ate-sentiment | 70.82 | 48.97 | 52.08 |
| laptop reviews (SemEval 2014) | FacebookAI/roberta-base | (this) gauneg/roberta-base-absa-ate-sentiment | 73.61 | 46.38 | 49.87 |
| ------------ | ---------- | ---------------- | --------- | ------ | -------- |
| MAMS-ATE (2019) | microsoft/deberta-v3-base | gauneg/deberta-v3-base-absa-ate-sentiment | 81.07 | 79.66 | 80.35 |
| MAMS-ATE (2019) | FacebookAI/roberta-base | (this) gauneg/roberta-base-absa-ate-sentiment | 79.91 | 78.95 | 79.39 |
| MAMS-ATE (2019) | microsoft/deberta-v3-large | gauneg/deberta-v3-large-absa-ate-sentiment-lora-adapter | 74.46 | 84.5 | 78.75 |
| MAMS-ATE (2019) | FacebookAI/roberta-large | gauneg/roberta-large-absa-ate-sentiment-lora-adapter | 77.8 | 79.81 | 78.75 |
| ------------ | ---------- | ---------------- | --------- | ------ | -------- |
| restaurant reviews (SemEval 2014) | microsoft/deberta-v3-large | gauneg/deberta-v3-large-absa-ate-sentiment-lora-adapter | 88.59 | 87.0 | 87.45 |
| restaurant reviews (SemEval 2014) | FacebookAI/roberta-large | gauneg/roberta-large-absa-ate-sentiment-lora-adapter | 92.26 | 82.95 | 86.57 |
| restaurant reviews (SemEval 2014) | FacebookAI/roberta-base | (this) gauneg/roberta-base-absa-ate-sentiment | 93.07 | 81.95 | 86.32 |
| restaurant reviews (SemEval 2014) | microsoft/deberta-v3-base | gauneg/deberta-v3-base-absa-ate-sentiment | 92.94 | 81.71 | 86.01 |
| ------------ | ---------- | ---------------- | --------- | ------ | -------- |
| restaurant reviews (SemEval 2015) | microsoft/deberta-v3-base | gauneg/deberta-v3-base-absa-ate-sentiment | 72.91 | 75.4 | 72.74 |
| restaurant reviews (SemEval 2015) | FacebookAI/roberta-large | gauneg/roberta-large-absa-ate-sentiment-lora-adapter | 70.54 | 77.48 | 72.63 |
| restaurant reviews (SemEval 2015) | microsoft/deberta-v3-large | gauneg/deberta-v3-large-absa-ate-sentiment-lora-adapter | 68.32 | 79.84 | 72.28 |
| restaurant reviews (SemEval 2015) | FacebookAI/roberta-base | (this) gauneg/roberta-base-absa-ate-sentiment | 71.94 | 74.75 | 71.84 |
| ------------ | ---------- | ---------------- | --------- | ------ | -------- |
| restaurant reviews (SemEval 2016) | FacebookAI/roberta-large | gauneg/roberta-large-absa-ate-sentiment-lora-adapter | 70.22 | 75.83 | 71.84 |
| restaurant reviews (SemEval 2016) | microsoft/deberta-v3-base | gauneg/deberta-v3-base-absa-ate-sentiment | 71.54 | 73.38 | 71.2 |
| restaurant reviews (SemEval 2016) | FacebookAI/roberta-base | (this) gauneg/roberta-base-absa-ate-sentiment | 71.35 | 72.78 | 70.85 |
| restaurant reviews (SemEval 2016) | microsoft/deberta-v3-large | gauneg/deberta-v3-large-absa-ate-sentiment-lora-adapter | 66.68 | 77.97 | 70.79 |
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)
| Test Dataset | Base Model | Fine-tuned Model | Precision | Recall | F1 Score |
|---|---|---|---|---|---|
| hotel reviews (SemEval 2015) | microsoft/deberta-v3-large | gauneg/deberta-v3-large-absa-ate-sentiment-lora-adapter | 51.92 | 65.55 | 54.94 |
| hotel reviews (SemEval 2015) | FacebookAI/roberta-base | (this) gauneg/roberta-base-absa-ate-sentiment | 54.62 | 53.65 | 54.08 |
| hotel reviews (SemEval 2015) | microsoft/deberta-v3-base | gauneg/deberta-v3-base-absa-ate-sentiment | 55.43 | 56.53 | 54.03 |
| hotel reviews (SemEval 2015) | FacebookAI/roberta-large | gauneg/roberta-large-absa-ate-sentiment-lora-adapter | 52.88 | 55.19 | 53.85 |
| ------------ | ---------- | ---------------- | --------- | ------ | -------- |
| laptop reviews (SemEval 2014) | microsoft/deberta-v3-large | gauneg/deberta-v3-large-absa-ate-sentiment-lora-adapter | 44.25 | 41.55 | 42.81 |
| laptop reviews (SemEval 2014) | microsoft/deberta-v3-base | gauneg/deberta-v3-base-absa-ate-sentiment | 46.15 | 33.23 | 37.09 |
| laptop reviews (SemEval 2014) | FacebookAI/roberta-large | gauneg/roberta-large-absa-ate-sentiment-lora-adapter | 41.7 | 34.38 | 36.93 |
| laptop reviews (SemEval 2014) | FacebookAI/roberta-base | (this) gauneg/roberta-base-absa-ate-sentiment | 44.98 | 31.87 | 35.67 |
| ------------ | ---------- | ---------------- | --------- | ------ | -------- |
| MAMS-ATE (2019) | FacebookAI/roberta-base | (this) gauneg/roberta-base-absa-ate-sentiment | 72.06 | 72.98 | 72.49 |
| MAMS-ATE (2019) | microsoft/deberta-v3-base | gauneg/deberta-v3-base-absa-ate-sentiment | 72.97 | 71.63 | 72.26 |
| MAMS-ATE (2019) | FacebookAI/roberta-large | gauneg/roberta-large-absa-ate-sentiment-lora-adapter | 69.34 | 73.3 | 71.07 |
| MAMS-ATE (2019) | microsoft/deberta-v3-large | gauneg/deberta-v3-large-absa-ate-sentiment-lora-adapter | 65.74 | 75.11 | 69.77 |
| ------------ | ---------- | ---------------- | --------- | ------ | -------- |
| restaurant reviews (SemEval 2014) | FacebookAI/roberta-large | gauneg/roberta-large-absa-ate-sentiment-lora-adapter | 61.15 | 58.46 | 59.74 |
| restaurant reviews (SemEval 2014) | FacebookAI/roberta-base | (this) gauneg/roberta-base-absa-ate-sentiment | 60.13 | 56.81 | 58.13 |
| restaurant reviews (SemEval 2014) | microsoft/deberta-v3-large | gauneg/deberta-v3-large-absa-ate-sentiment-lora-adapter | 56.79 | 59.3 | 57.93 |
| restaurant reviews (SemEval 2014) | microsoft/deberta-v3-base | gauneg/deberta-v3-base-absa-ate-sentiment | 58.99 | 54.76 | 56.45 |
| ------------ | ---------- | ---------------- | --------- | ------ | -------- |
| restaurant reviews (SemEval 2015) | FacebookAI/roberta-large | gauneg/roberta-large-absa-ate-sentiment-lora-adapter | 53.89 | 55.7 | 54.11 |
| restaurant reviews (SemEval 2015) | FacebookAI/roberta-base | (this) gauneg/roberta-base-absa-ate-sentiment | 54.36 | 55.38 | 53.6 |
| restaurant reviews (SemEval 2015) | microsoft/deberta-v3-large | gauneg/deberta-v3-large-absa-ate-sentiment-lora-adapter | 51.67 | 56.58 | 53.29 |
| restaurant reviews (SemEval 2015) | microsoft/deberta-v3-base | gauneg/deberta-v3-base-absa-ate-sentiment | 54.55 | 53.68 | 53.12 |
| ------------ | ---------- | ---------------- | --------- | ------ | -------- |
| restaurant reviews (SemEval 2016) | FacebookAI/roberta-large | gauneg/roberta-large-absa-ate-sentiment-lora-adapter | 53.7 | 60.49 | 55.05 |
| restaurant reviews (SemEval 2016) | FacebookAI/roberta-base | (this) gauneg/roberta-base-absa-ate-sentiment | 52.31 | 54.58 | 52.33 |
| restaurant reviews (SemEval 2016) | microsoft/deberta-v3-base | gauneg/deberta-v3-base-absa-ate-sentiment | 52.07 | 54.58 | 52.15 |
| restaurant reviews (SemEval 2016) | microsoft/deberta-v3-large | gauneg/deberta-v3-large-absa-ate-sentiment-lora-adapter | 49.07 | 56.5 | 51.25 |
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