Zero-Shot Classification
PyTorch
Transformers
sentence-transformers
English
zeroshot_classifier
bert
text-classification
Instructions to use claritylab/zero-shot-vanilla-binary-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use claritylab/zero-shot-vanilla-binary-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="claritylab/zero-shot-vanilla-binary-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("claritylab/zero-shot-vanilla-binary-bert") model = AutoModelForSequenceClassification.from_pretrained("claritylab/zero-shot-vanilla-binary-bert") - sentence-transformers
How to use claritylab/zero-shot-vanilla-binary-bert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("claritylab/zero-shot-vanilla-binary-bert") 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] - Notebooks
- Google Colab
- Kaggle
add model
Browse files- README.md +47 -0
- tf_model.h5 +3 -0
README.md
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---
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tags:
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- generated_from_keras_callback
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model-index:
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- name: zero-shot-vanilla-binary-bert
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results: []
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---
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<!-- This model card has been generated automatically according to the information Keras had access to. You should
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probably proofread and complete it, then remove this comment. -->
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# zero-shot-vanilla-binary-bert
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This model was trained from scratch on an unknown dataset.
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It achieves the following results on the evaluation set:
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- optimizer: None
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- training_precision: float32
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### Training results
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### Framework versions
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- Transformers 4.16.2
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- TensorFlow 2.12.0
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- Datasets 2.12.0
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- Tokenizers 0.11.0
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:3baede029565ad74cb4616b0819ae9892e9a9ab97317494b9fe5729e4bb827f1
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size 438226200
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