Text Classification
Transformers
PyTorch
Safetensors
English
bert
finance
topic-classification
text-embeddings-inference
Instructions to use hakonmh/topic-xdistil-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hakonmh/topic-xdistil-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hakonmh/topic-xdistil-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hakonmh/topic-xdistil-uncased") model = AutoModelForSequenceClassification.from_pretrained("hakonmh/topic-xdistil-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hakonmh/topic-xdistil-uncased: direct link, hf CLI and curl.
- Browser
- Download file 134 MB
-
https://huggingface.co/hakonmh/topic-xdistil-uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hakonmh/topic-xdistil-uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hakonmh/topic-xdistil-uncased/resolve/main/pytorch_model.bin
134 MB
- Xet hash:
- 4e263a35ba21980b4cd6b991a246ddae608bc726baeaef720a43401c2eaa0995
- Size of remote file:
- 134 MB
- SHA256:
- 7c0731def01df5dbc162ae9c11bb2507ebfd992d213c465ec8096bc5e6c53c50
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