Instructions to use evm-alpha/semantic-evm-mlm-chkp1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use evm-alpha/semantic-evm-mlm-chkp1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="evm-alpha/semantic-evm-mlm-chkp1000")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("evm-alpha/semantic-evm-mlm-chkp1000") model = AutoModelForMaskedLM.from_pretrained("evm-alpha/semantic-evm-mlm-chkp1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "vocab_size": 32768, | |
| "pad_token": "[PAD]", | |
| "unk_token": "[UNK]", | |
| "cls_token": "[CLS]", | |
| "sep_token": "[SEP]", | |
| "mask_token": "[MASK]", | |
| "model_max_length": 512, | |
| "clean_up_tokenization_spaces": false | |
| } |