Text Classification
Transformers
Safetensors
English
xlm-roberta
text-generation-inference
unsloth
trl
text-embeddings-inference
Instructions to use cataluna84/LIDL1Bv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cataluna84/LIDL1Bv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cataluna84/LIDL1Bv3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cataluna84/LIDL1Bv3") model = AutoModelForSequenceClassification.from_pretrained("cataluna84/LIDL1Bv3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use cataluna84/LIDL1Bv3 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cataluna84/LIDL1Bv3 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cataluna84/LIDL1Bv3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cataluna84/LIDL1Bv3 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="cataluna84/LIDL1Bv3", max_seq_length=2048, )
Uploaded model
- Developed by: cataluna84
- License: apache-2.0
- Finetuned from model : intfloat/multilingual-e5-large
This xlm-roberta model was trained 2x faster with Unsloth
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Model tree for cataluna84/LIDL1Bv3
Base model
intfloat/multilingual-e5-large