Instructions to use Apizhai/Albert-IT-JobRecommendation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Apizhai/Albert-IT-JobRecommendation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Apizhai/Albert-IT-JobRecommendation")# Load model directly from transformers import AutoTokenizer, AlbertForMultiLabelSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Apizhai/Albert-IT-JobRecommendation") model = AlbertForMultiLabelSequenceClassification.from_pretrained("Apizhai/Albert-IT-JobRecommendation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f0ee8a4b6d5d4b58a105abbac00376635eb60048fc5651586406b7e2869e414
- Size of remote file:
- 3.12 kB
- SHA256:
- a3598d214e3835af23711a4850dcb2f75c8105ba7caae93cc38a72d6c5c15a49
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.