Instructions to use monsoon-nlp/es-seq2seq-gender-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use monsoon-nlp/es-seq2seq-gender-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="monsoon-nlp/es-seq2seq-gender-encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/es-seq2seq-gender-encoder") model = AutoModel.from_pretrained("monsoon-nlp/es-seq2seq-gender-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from monsoon-nlp/es-seq2seq-gender-encoder: direct link, hf CLI and curl.
- Browser
- Download file 439 MB
-
https://huggingface.co/monsoon-nlp/es-seq2seq-gender-encoder/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://monsoon-nlp/es-seq2seq-gender-encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/monsoon-nlp/es-seq2seq-gender-encoder/resolve/main/pytorch_model.bin
439 MB
- Xet hash:
- b1208fb370bde281f061ec779a83b8943dc1dc8493240a835c39e47d327b897d
- Size of remote file:
- 439 MB
- SHA256:
- e205508db7c5062377ce1744efe7e909289d523c268c953b867d9002c2a42115
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