Instructions to use HPLT/hplt_bert_base_ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HPLT/hplt_bert_base_ar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HPLT/hplt_bert_base_ar", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_ar", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "LtgbertForMaskedLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_ltgbert.LtgbertConfig", | |
| "AutoModel": "modeling_ltgbert.LtgbertModel", | |
| "AutoModelForMaskedLM": "modeling_ltgbert.LtgbertForMaskedLM", | |
| "AutoModelForSequenceClassification": "modeling_ltgbert.LtgbertForSequenceClassification", | |
| "AutoModelForTokenClassification": "modeling_ltgbert.LtgbertForTokenClassification", | |
| "AutoModelForQuestionAnswering": "modeling_ltgbert.LtgbertForQuestionAnswering", | |
| "AutoModelForMultipleChoice": "modeling_ltgbert.LtgbertForMultipleChoice" | |
| }, | |
| "attention_probs_dropout_prob": 0.1, | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "intermediate_size": 2560, | |
| "layer_norm_eps": 1e-07, | |
| "max_position_embeddings": 512, | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "position_bucket_size": 32, | |
| "torch_dtype": "float32", | |
| "vocab_size": 32768 | |
| } |