Instructions to use BEE-spoke-data/tiny-random-MPNetForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BEE-spoke-data/tiny-random-MPNetForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BEE-spoke-data/tiny-random-MPNetForMaskedLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BEE-spoke-data/tiny-random-MPNetForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("BEE-spoke-data/tiny-random-MPNetForMaskedLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from BEE-spoke-data/tiny-random-MPNetForMaskedLM: direct link, hf CLI and curl.
- Browser
- Download file 551 Bytes
-
https://huggingface.co/BEE-spoke-data/tiny-random-MPNetForMaskedLM/resolve/main/config.json
- Command line
-
hf download hf://BEE-spoke-data/tiny-random-MPNetForMaskedLM/config.json
-
curl -L -o config.json https://huggingface.co/BEE-spoke-data/tiny-random-MPNetForMaskedLM/resolve/main/config.json
551 Bytes
| { | |
| "architectures": [ | |
| "MPNetForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 64, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 64, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "mpnet", | |
| "num_attention_heads": 4, | |
| "num_hidden_layers": 5, | |
| "pad_token_id": 1, | |
| "relative_attention_num_buckets": 32, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.52.4", | |
| "vocab_size": 1125 | |
| } | |