Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use marieke93/MiniLM-evidence-types with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marieke93/MiniLM-evidence-types with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marieke93/MiniLM-evidence-types")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("marieke93/MiniLM-evidence-types") model = AutoModelForSequenceClassification.from_pretrained("marieke93/MiniLM-evidence-types") - Inference
- Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#2
by librarian-bot - opened
This is a pull request to add microsoft/MiniLM-L12-H384-uncased as a base_model field to the metadata for your model (defined in the YAML block of your model's README.md).
This information was found in the model card by doing a regular expression match on your model's README.md file.
Adding this information to your models metadata will allow users to find your model when searching for models based on a specific model and make it easier to discover the links between different models on the hub.
This PR was made by Librarian Bot. Feel free to get in touch with @davanstrien with feedback or questions.