Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use selimsametoglu/selims with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selimsametoglu/selims with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="selimsametoglu/selims")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("selimsametoglu/selims") model = AutoModelForSequenceClassification.from_pretrained("selimsametoglu/selims") - Notebooks
- Google Colab
- Kaggle
File size: 570 Bytes
f60deb2 | 1 | {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "max_len": 512, "special_tokens_map_file": "C:\\Users\\selim/.cache\\huggingface\\transformers\\ed85e7bfaa7dfcf9924004400478a6426fcab28d3e427960549371a1729115d1.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", "name_or_path": "nlptown/bert-base-multilingual-uncased-sentiment", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"} |