Text Classification
Transformers
Safetensors
PyTorch
English
bert
biomedical
text-embeddings-inference
Instructions to use STRIDE-lab/scibert-study-conclusion-20251005 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use STRIDE-lab/scibert-study-conclusion-20251005 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="STRIDE-lab/scibert-study-conclusion-20251005")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("STRIDE-lab/scibert-study-conclusion-20251005") model = AutoModelForSequenceClassification.from_pretrained("STRIDE-lab/scibert-study-conclusion-20251005", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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library_name: transformers
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base_model:
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tags:
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# scibert_study_conclusion_20251005
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## Base Model
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## Task
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"threshold": 0.5,
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"is_multilabel": false
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```
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library_name: transformers
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base_model:
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- allenai/scibert_scivocab_uncased
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tags:
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- transformers
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- pytorch
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- biomedical
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- text-classification
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-classification
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---
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# scibert_study_conclusion_20251005
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## Base Model
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`allenai/scibert_scivocab_uncased`
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## Task
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"threshold": 0.5,
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"is_multilabel": false
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}
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```
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