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agentlans/
mobilebert-uncased-nli

Name: mobilebert-uncased-nli
Availability: InStock

sentence-transformers

safetensors

mobilebert

text-classification

natural-language-inference

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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:
- Used sentence-transformers/all-nli.
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

python

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

python

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.