Text Classification
Transformers
PyTorch
ONNX
English
albert
text-classfication
int8
Intel® Neural Compressor
neural-compressor
PostTrainingStatic
Instructions to use INC4AI/albert-base-v2-sst2-int8-static-inc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use INC4AI/albert-base-v2-sst2-int8-static-inc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="INC4AI/albert-base-v2-sst2-int8-static-inc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("INC4AI/albert-base-v2-sst2-int8-static-inc") model = AutoModelForSequenceClassification.from_pretrained("INC4AI/albert-base-v2-sst2-int8-static-inc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 483 Bytes
0764f41 | 1 | {"do_lower_case": true, "remove_space": true, "keep_accents": false, "bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "<pad>", "cls_token": "[CLS]", "mask_token": {"content": "[MASK]", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "Alireza1044/albert-base-v2-sst2", "tokenizer_class": "AlbertTokenizer"} |