mobilebert-uncased-nli
- Base Model: google/mobilebert-uncased
- Task: Natural Language Inference (NLI)
- Framework: Hugging Face Transformers, Sentence Transformers
mobilebert-uncased-nli is a fine-tuned NLI model that classifies the relationship between pairs of sentences into three categories: entailment, neutral, and contradiction. It enhances the capabilities of google/mobilebert-uncased for improved performance on NLI tasks.
Intended Use
mobilebert-uncased-nli is ideal for applications requiring understanding of logical relationships between sentences, including:
- Semantic textual similarity
- Question answering
- Dialogue systems
- Content moderation
Performance
mobilebert-uncased-nli was trained on the sentence-transformers/all-nli dataset, achieving competitive results in sentence pair classification.
Performance on the MNLI matched validation set:
- Accuracy: 0.7645
- Precision: 0.77
- Recall: 0.76
- F1-score: 0.76
Training details
Training Details
Dataset:
Sampling:
- 100 000 training samples and 10 000 evaluation samples.
Fine-tuning Process:
- Custom Python script with adaptive precision training (bfloat16).
- Early stopping based on evaluation loss.
Hyperparameters:
- Learning Rate: 2e-5
- Batch Size: 32
- Optimizer: AdamW (weight decay: 0.01)
- Training Duration: Up to 10 epochs
Reproducibility
To ensure reproducibility:
- Fixed random seed: 42
- Environment:
- Python: 3.10.12
- PyTorch: 2.5.1
- Transformers: 4.44.2
Usage Instructions
Using Sentence Transformers
from sentence_transformers import CrossEncoder
model_name = "agentlans/mobilebert-uncased-nli"
model = CrossEncoder(model_name)
scores = model.predict(
[
("A man is eating pizza", "A man eats something"),
(
"A black race car starts up in front of a crowd of people.",
"A man is driving down a lonely road.",
),
]
)
label_mapping = ["entailment", "neutral", "contradiction"]
labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
print(labels)
Using Transformers Library
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_name = "agentlans/mobilebert-uncased-nli"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
features = tokenizer(
[
"A man is eating pizza",
"A black race car starts up in front of a crowd of people.",
],
["A man eats something", "A man is driving down a lonely road."],
padding=True,
truncation=True,
return_tensors="pt",
)
model.eval()
with torch.no_grad():
scores = model(**features).logits
label_mapping = ["entailment", "neutral", "contradiction"]
labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
print(labels)
Limitations and Ethical Considerations
mobilebert-uncased-nli may reflect biases present in the training data. Users should evaluate its performance in specific contexts to ensure fairness and accuracy.
Conclusion
mobilebert-uncased-nli offers a robust solution for NLI tasks, enhancing google/mobilebert-uncased's capabilities with straightforward integration into existing frameworks. It aids developers in building intelligent applications that require nuanced language understanding.
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