Text Classification
Transformers
PyTorch
TensorBoard
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use mariolinml/deberta-v3-base_mnli_uf_ner_1019_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mariolinml/deberta-v3-base_mnli_uf_ner_1019_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mariolinml/deberta-v3-base_mnli_uf_ner_1019_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mariolinml/deberta-v3-base_mnli_uf_ner_1019_v1") model = AutoModelForSequenceClassification.from_pretrained("mariolinml/deberta-v3-base_mnli_uf_ner_1019_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mariolinml/deberta-v3-base_mnli_uf_ner_1019_v1: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/mariolinml/deberta-v3-base_mnli_uf_ner_1019_v1/resolve/main/training_args.bin
- Command line
-
hf download hf://mariolinml/deberta-v3-base_mnli_uf_ner_1019_v1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mariolinml/deberta-v3-base_mnli_uf_ner_1019_v1/resolve/main/training_args.bin
3.44 kB
- Xet hash:
- 3bfcd2f62023624c8f535a5ba0e5ff7afb50cd0258ccd62db555d9bd69cd49ad
- Size of remote file:
- 3.44 kB
- SHA256:
- 3fa60fb41a79c6afc115a4434f233022ffd6415bbb5edff712a93335f92d836a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.