Zero-Shot Classification
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
English
deberta-v2
text-classification
Instructions to use cross-encoder/nli-deberta-v3-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cross-encoder/nli-deberta-v3-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cross-encoder/nli-deberta-v3-base") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use cross-encoder/nli-deberta-v3-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="cross-encoder/nli-deberta-v3-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cross-encoder/nli-deberta-v3-base") model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/nli-deberta-v3-base") - Notebooks
- Google Colab
- Kaggle
Push tokenizer again
Browse filesIn this PR, I'm repushing the tokenizer with a newer version of transformers, with the goal of also generating the tokenizer.json used by the `tokenizers`-backed fast tokenizers. There should not be any changes in the performance of the tokenizer, the only difference is that Transformers and Sentence Transformers can now directly use the fast tokenizer without having to convert it from the slow tokenizer. This should make loading the model faster.
- Tom Aarsen
- added_tokens.json +3 -1
- special_tokens_map.json +15 -1
- tokenizer.json +0 -0
- tokenizer_config.json +59 -1
added_tokens.json
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{
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{
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"[MASK]": 128000
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}
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special_tokens_map.json
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@@ -1 +1,15 @@
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{
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{
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"bos_token": "[CLS]",
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"cls_token": "[CLS]",
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"eos_token": "[SEP]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
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{
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"3": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"128000": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"bos_token": "[CLS]",
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"clean_up_tokenization_spaces": false,
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"cls_token": "[CLS]",
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"do_lower_case": false,
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"eos_token": "[SEP]",
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"extra_special_tokens": {},
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"sp_model_kwargs": {},
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"split_by_punct": false,
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"tokenizer_class": "DebertaV2Tokenizer",
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"unk_token": "[UNK]",
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"vocab_type": "spm"
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}
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