Text Classification
Transformers
TensorFlow
English
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use nikoslefkos/relex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nikoslefkos/relex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nikoslefkos/relex")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nikoslefkos/relex") model = AutoModelForSequenceClassification.from_pretrained("nikoslefkos/relex", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -4,7 +4,7 @@ base_model: distilbert-base-cased
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tags:
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- generated_from_keras_callback
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model-index:
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- name: nikoslefkos/
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results: []
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datasets:
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- relbert/t_rex
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# nikoslefkos/rebert_trex
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on relbert/
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It achieves the following results on the evaluation set:
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- Train Loss: 0.8598
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- Train Accuracy: 0.7326
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tags:
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- generated_from_keras_callback
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model-index:
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- name: nikoslefkos/relex
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results: []
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datasets:
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- relbert/t_rex
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# nikoslefkos/rebert_trex
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on relbert/t_rex.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.8598
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- Train Accuracy: 0.7326
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