Instructions to use kaanakdeniz/bert_base_uncased_content_textsim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaanakdeniz/bert_base_uncased_content_textsim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kaanakdeniz/bert_base_uncased_content_textsim")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kaanakdeniz/bert_base_uncased_content_textsim") model = AutoModelForSequenceClassification.from_pretrained("kaanakdeniz/bert_base_uncased_content_textsim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8465be5cf33a57ec63074ca407dfd5d1cada146cc1c6ffe35dfce87a2ff03975
- Size of remote file:
- 3.31 kB
- SHA256:
- f94a95d0c771fdbb48a0a4a92f408bf2572d408e6ad882151fa45f70bad4f17c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.