Text Classification
Transformers
PyTorch
JAX
Spanish
bert
sentiment-analysis
text-embeddings-inference
Instructions to use finiteautomata/beto-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use finiteautomata/beto-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="finiteautomata/beto-sentiment-analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("finiteautomata/beto-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("finiteautomata/beto-sentiment-analysis", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from finiteautomata/beto-sentiment-analysis: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/finiteautomata/beto-sentiment-analysis/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://finiteautomata/beto-sentiment-analysis/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/finiteautomata/beto-sentiment-analysis/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- 0f15dc237a212b0e463b970ceb17219ad280dba44141360161937b26b60b60df
- Size of remote file:
- 440 MB
- SHA256:
- 6301fc83af4c375b6b6c1d8916ec87bd853c2715250b91645da1e859ba6e000a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.