Fill-Mask
Transformers
PyTorch
TensorFlow
JAX
albert
pretraining
multilingual
masked-language-modeling
sentence-order-prediction
xlmindic
nlp
indoaryan
indicnlp
iso15919
Instructions to use ibraheemmoosa/xlmindic-base-multiscript with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibraheemmoosa/xlmindic-base-multiscript with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ibraheemmoosa/xlmindic-base-multiscript")# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("ibraheemmoosa/xlmindic-base-multiscript") model = AutoModelForPreTraining.from_pretrained("ibraheemmoosa/xlmindic-base-multiscript", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": ".", | |
| "architectures": [ | |
| "AlbertForPreTraining" | |
| ], | |
| "attention_probs_dropout_prob": 0, | |
| "bos_token_id": 2, | |
| "classifier_dropout_prob": 0.1, | |
| "embedding_size": 128, | |
| "eos_token_id": 3, | |
| "hidden_act": "gelu_new", | |
| "hidden_dropout_prob": 0, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "inner_group_num": 1, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "albert", | |
| "num_attention_heads": 12, | |
| "num_hidden_groups": 1, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.15.0", | |
| "type_vocab_size": 2, | |
| "vocab_size": 50000 | |
| } | |