Instructions to use noystl/mistral_abstract_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use noystl/mistral_abstract_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="noystl/mistral_abstract_classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("noystl/mistral_abstract_classifier", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update pipeline tag
#2
by nielsr HF Staff - opened
README.md
CHANGED
|
@@ -7,7 +7,7 @@ language:
|
|
| 7 |
- en
|
| 8 |
library_name: transformers
|
| 9 |
license: cc
|
| 10 |
-
pipeline_tag:
|
| 11 |
---
|
| 12 |
|
| 13 |
This Hugging Face repository hosts a fine-tuned Mistral model designed to classify scientific abstracts based on whether they involve idea recombination, as introduced in the paper [CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature](https://huggingface.co/papers/2505.20779). The model employs a LoRA adapter on top of a Mistral base model.
|
|
|
|
| 7 |
- en
|
| 8 |
library_name: transformers
|
| 9 |
license: cc
|
| 10 |
+
pipeline_tag: text-classification
|
| 11 |
---
|
| 12 |
|
| 13 |
This Hugging Face repository hosts a fine-tuned Mistral model designed to classify scientific abstracts based on whether they involve idea recombination, as introduced in the paper [CHIMERA: A Knowledge Base of Idea Recombination in Scientific Literature](https://huggingface.co/papers/2505.20779). The model employs a LoRA adapter on top of a Mistral base model.
|