Instructions to use facebook/w2v-bert-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/w2v-bert-2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="facebook/w2v-bert-2.0")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("facebook/w2v-bert-2.0") model = AutoModel.from_pretrained("facebook/w2v-bert-2.0") - Notebooks
- Google Colab
- Kaggle
Link to "SeamlessM4T v1" paper, where the w2v-BERT 2.0 was presented for the first time.
#23
by zuazo - opened
The w2v-BERT 2.0 model was initially introduced in the "SeamlessM4T v1" paper, specifically in Section 4.1, available at https://arxiv.org/abs/2308.11596.
While the "SeamlessM4T v2" paper also discusses this model, it does not delve into the same level of detail as the v1 paper.