Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use fernandals/sentiment_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fernandals/sentiment_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fernandals/sentiment_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fernandals/sentiment_v2") model = AutoModelForSequenceClassification.from_pretrained("fernandals/sentiment_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from fernandals/sentiment_v2: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/fernandals/sentiment_v2/resolve/main/training_args.bin
- Command line
-
hf download hf://fernandals/sentiment_v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/fernandals/sentiment_v2/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- a617d64da46d9f025a3f7545b818bb27b623d19ef0a49ca0656a1e423399b3fd
- Size of remote file:
- 3.96 kB
- SHA256:
- 8aad62818b3ffc38027a7d68cbe252541df84a4e77bef9607f4205ce12ef38b5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.