This model classifies the stance of tweets toward Bitcoin (BTC) and Ethereum (ETH) as Bullish, Bearish, or Neutral using a Natural Language Inference (NLI) approach.

It was fine-tuned from MoritzLaurer/DeBERTa-v3-base-mnli as part of a master's thesis on NLI-based cryptocurrency stance detection.

How it works

Instead of standard 3-class classification, this model frames stance detection as an entailment task. For each tweet, three hypotheses are constructed (one per stance), and the model scores which hypothesis is most entailed by the tweet:

Stance	Hypothesis
Bullish	"The author's perspective on {target} in this tweet reflects a bullish sentiment."
Bearish	"The author's perspective on {target} in this tweet reflects a bearish sentiment."
Neutral	"The author's perspective on {target} in this tweet reflects a neutral sentiment."

The predicted stance is the one with the highest entailment score.

Usage

python

from transformers import pipeline

classifier = pipeline("zero-shot-classification", model="syahrezapratama/crypto-stance-nli")

tweet = "Bitcoin is going to the moon! $100k is just the beginning 🚀"

result = classifier(
    tweet,
    candidate_labels=["bullish", "bearish", "neutral"],
    hypothesis_template="The author's perspective on BTC in this tweet reflects a {} sentiment.",
)

print(result["labels"][0])  # "bullish"
print(result["scores"][0])  # ~0.999

Manual inference (more control)

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "syahrezapratama/crypto-stance-nli"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()

tweet = "I'm not sure where ETH is headed, could go either way"
stances = ["bullish", "bearish", "neutral"]
template = "The author's perspective on ETH in this tweet reflects a {} sentiment."

scores = []
for stance in stances:
    hypothesis = template.format(stance)
    inputs = tokenizer(tweet, hypothesis, return_tensors="pt", truncation=True, max_length=128)
    with torch.no_grad():
        logits = model(**inputs).logits
    # Entailment is index 0 for DeBERTa-MNLI
    entailment_score = torch.softmax(logits, dim=-1)[0, 0].item()
    scores.append(entailment_score)

predicted = stances[scores.index(max(scores))]
print(f"Predicted stance: {predicted}")  # "neutral"

Performance

Evaluated on a held-out test set of 450 tweets (70/15/15 train/val/test split, seed=42).

Overall metrics

Metric	Value
Accuracy	80.67%
Macro F1	0.7606
Weighted F1	0.8009

Per-class metrics

Class	Precision	Recall	F1	Support
Bearish	0.8108	0.6383	0.7143	47
Neutral	0.8137	0.9154	0.8616	272
Bullish	0.7850	0.6412	0.7059	131

Comparison with baselines

Model	Paradigm	Accuracy	Macro F1
DeBERTa-MNLI	Zero-Shot (Baseline)	43.33%	0.4192
DeBERTa-MNLI	Zero-Shot (OPRO)	50.44%	0.4538
DeBERTa-NLI	Fine-Tuned	80.67%	0.7606
GPT-4o	Zero-Shot	76.67%	0.7275

Training details

Dataset

Source: 3,000 cryptocurrency tweets about BTC and ETH
Labels: Bullish (873), Neutral (1,814), Bearish (313)
Split: 2,100 train / 450 val / 450 test (seed=42)

NLI training approach

Each tweet is expanded into 3 NLI premise–hypothesis pairs:

Correct stance → entailment
Incorrect stances → contradiction

This results in 6,300 training pairs from 2,100 tweets.

Hyperparameters

Parameter	Value
Base model	MoritzLaurer/DeBERTa-v3-base-mnli
Learning rate	2e-5
Batch size	8 (physical) × 2 (accumulation) = 16 (effective)
Max epochs	5 (early stopping patience = 3)
Max sequence length	128
Warmup	10% linear warmup + linear decay
Weight decay	0.01
Class weights	Bearish=3.19, Neutral=0.55, Bullish=1.15
Optimizer	AdamW
Best epoch	Early stopped based on validation macro F1

Hypothesis template (OPRO-optimized)

The hypothesis template was optimized using OPRO (Optimization by PROmpting) with GPT-4o-mini:

"The author's perspective on {target} in this tweet reflects a {stance} sentiment."

Limitations

Domain-specific: Trained only on cryptocurrency tweets (BTC and ETH). May not generalize to other financial assets or domains.
Class imbalance: Bearish tweets are underrepresented (10.4% of data), leading to lower recall on bearish stance despite class weighting.
Language: English only.
Temporal: Trained on tweets from a specific time period. Cryptocurrency language and sentiment patterns evolve rapidly.
Tweet preprocessing: Best results when input text is preprocessed (URL/mention removal, emoji handling) similar to the training data.

Citation

If you use this model, please cite:

bibtex

@mastersthesis{pratama2026cryptostancenli,
  title={NLI-Based Cryptocurrency Stance Detection},
  author={Pratama, Syahreza},
  year={2026},
  school={Your University}
}

License

MIT

syahrezapratama/deberta-crypto-stance

An NLI-Based Approach to Asset-Specific Stance Detection in Cryptocurrency Tweets