Instructions to use Xenova/EsperBERTo-small-pos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Xenova/EsperBERTo-small-pos with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('token-classification', 'Xenova/EsperBERTo-small-pos'); - Transformers
How to use Xenova/EsperBERTo-small-pos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Xenova/EsperBERTo-small-pos")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Xenova/EsperBERTo-small-pos") model = AutoModelForTokenClassification.from_pretrained("Xenova/EsperBERTo-small-pos", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Xenova/EsperBERTo-small-pos: direct link, hf CLI and curl.
- Browser
- Download file 331 MB
-
https://huggingface.co/Xenova/EsperBERTo-small-pos/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Xenova/EsperBERTo-small-pos/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Xenova/EsperBERTo-small-pos/resolve/main/flax_model.msgpack
331 MB
- Xet hash:
- abc24ae9d40633e9f552423a35e2465afe93d35837063e282e45a47215791689
- Size of remote file:
- 331 MB
- SHA256:
- fc59c5b16cfe0888c80bc8e4ef17b5feed02dacd32a22354abf860bf17cba85e
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