Instructions to use language-ml-lab/AzerBert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use language-ml-lab/AzerBert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="language-ml-lab/AzerBert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("language-ml-lab/AzerBert") model = AutoModelForMaskedLM.from_pretrained("language-ml-lab/AzerBert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| pipeline_tag: fill-mask | |
| widget: | |
| - text: سن نجورسن [MASK] | |
| example_title: Example 1 | |
| - text: بو [MASK] کتابی ده. | |
| example_title: Example 2 | |
| - text: دیل [MASK] اؤنملی دیر. | |
| example_title: Example 3 | |
| language: | |
| - az | |
| metrics: | |
| - perplexity | |
| # AzerBERT | |
| - Type: BERT-based language model transformer | |
| - Description: AzerBERT is a pre-trained language model specifically tailored for the Iranian Azerbaijani language. It can be used for various NLP tasks, including text classification, named entity recognition, and more. | |
| ## How to use | |
| ```python | |
| # Use a pipeline as a high-level helper | |
| from transformers import pipeline | |
| pipe = pipeline("fill-mask", model="language-ml-lab/AzerBert") | |
| ``` | |
| ```python | |
| # Load model directly | |
| from transformers import AutoTokenizer, AutoModelForMaskedLM | |
| tokenizer = AutoTokenizer.from_pretrained("language-ml-lab/AzerBert") | |
| model = AutoModelForMaskedLM.from_pretrained("language-ml-lab/AzerBert") | |
| ``` |