Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Valencio/LLM_course_MLM_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Valencio/LLM_course_MLM_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Valencio/LLM_course_MLM_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Valencio/LLM_course_MLM_model") model = AutoModelForSequenceClassification.from_pretrained("Valencio/LLM_course_MLM_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 44caca5184d3192b2ffb18923d96e4f1d61755cfa16398646cfdcffa0d274097
- Size of remote file:
- 268 MB
- SHA256:
- 0624d1326a2ea1c667764592af1d9158b3e5273caa6bb7545dd74c24a330b628
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.