Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use fidsinn/distilbert-base-future with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fidsinn/distilbert-base-future with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fidsinn/distilbert-base-future")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fidsinn/distilbert-base-future") model = AutoModelForSequenceClassification.from_pretrained("fidsinn/distilbert-base-future") - Notebooks
- Google Colab
- Kaggle
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# distilbert-base-future
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.1142
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# distilbert-base-future
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## Table of Contents
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- [Model description](#model_description)
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- [Intended uses & limitations](#intended_uses_&_limitations)
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- [Training and evaluation data](#training_and_evaluation_data)
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- [Training procedure](#training_procedure)
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.1142
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