Text Classification
Transformers
Safetensors
English
roberta
Text Classification
TDAMM
Multi-label Classification
NASA
Astrophysics
Science Document Entity
text-embeddings-inference
Instructions to use ksmu/my-tdamm-fork with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ksmu/my-tdamm-fork with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ksmu/my-tdamm-fork")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ksmu/my-tdamm-fork") model = AutoModelForSequenceClassification.from_pretrained("ksmu/my-tdamm-fork", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f7b94b5ce4b80cbef5073e343ed87c52b7e1baef07c2cf95d698c3cbaff821e3
- Size of remote file:
- 5.37 kB
- SHA256:
- 6114b4f9cfea5104f1ed40db96050a20e0bd2a096fbfda31b9ce23a54afc0dad
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