Text Classification
Transformers
Safetensors
English
modernbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use sfarrukhm/modernbert-setfit-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sfarrukhm/modernbert-setfit-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sfarrukhm/modernbert-setfit-nli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sfarrukhm/modernbert-setfit-nli") model = AutoModelForSequenceClassification.from_pretrained("sfarrukhm/modernbert-setfit-nli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Muhammad Farrukh Mehmood commited on
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README.md
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## Model Description
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This model is a fine-tuned version of [`answerdotai/ModernBERT-base`](https://huggingface.co/answerdotai/ModernBERT-base) trained on a subset of the [SetFit/mnli](https://huggingface.co/datasets/SetFit/mnli) dataset. It is
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## Intended Uses & Limitations
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## Model Description
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This model is a fine-tuned version of [`answerdotai/ModernBERT-base`](https://huggingface.co/answerdotai/ModernBERT-base) trained on a subset of the [SetFit/mnli](https://huggingface.co/datasets/SetFit/mnli) dataset. It is trained for natural language inference (NLI) tasks, where the goal is to determine the relationship between two text inputs (e.g., entailment, contradiction, or neutrality).
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## Intended Uses & Limitations
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