Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use adriansanz/intent_analysis_xml_5ep_v1_es_ca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adriansanz/intent_analysis_xml_5ep_v1_es_ca with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="adriansanz/intent_analysis_xml_5ep_v1_es_ca")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("adriansanz/intent_analysis_xml_5ep_v1_es_ca") model = AutoModelForSequenceClassification.from_pretrained("adriansanz/intent_analysis_xml_5ep_v1_es_ca", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b4d93e7c6ce3cc035b9fd0919a858c2ac04cfb90b244c1a825bd14a87807fa38
- Size of remote file:
- 5.3 kB
- SHA256:
- e5ca2ea3698169004702f92965046152395f779d7477c7c3318057f2532e610b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.