Instructions to use IIIT-L/albert-base-v2-finetuned-non-code-mixed-DS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIIT-L/albert-base-v2-finetuned-non-code-mixed-DS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IIIT-L/albert-base-v2-finetuned-non-code-mixed-DS")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIIT-L/albert-base-v2-finetuned-non-code-mixed-DS") model = AutoModelForSequenceClassification.from_pretrained("IIIT-L/albert-base-v2-finetuned-non-code-mixed-DS") - Notebooks
- Google Colab
- Kaggle
| { | |
| "bos_token": "[CLS]", | |
| "cls_token": "[CLS]", | |
| "do_lower_case": true, | |
| "eos_token": "[SEP]", | |
| "keep_accents": false, | |
| "mask_token": { | |
| "__type": "AddedToken", | |
| "content": "[MASK]", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "model_max_length": 512, | |
| "name_or_path": "albert-base-v2", | |
| "pad_token": "<pad>", | |
| "remove_space": true, | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": null, | |
| "tokenizer_class": "AlbertTokenizer", | |
| "unk_token": "<unk>" | |
| } | |