Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Timothy1337/finetuning-sentiment-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Timothy1337/finetuning-sentiment-imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Timothy1337/finetuning-sentiment-imdb")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Timothy1337/finetuning-sentiment-imdb") model = AutoModelForSequenceClassification.from_pretrained("Timothy1337/finetuning-sentiment-imdb") - Notebooks
- Google Colab
- Kaggle
Tanakrit commited on
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Parent(s): 40b6070
update model card README.md
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README.md
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tags:
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datasets:
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metrics:
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value: 0.
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# finetuning-sentiment-imdb
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This model is a fine-tuned version of [
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It achieves the following results on the evaluation set:
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## Model description
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license: apache-2.0
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tags:
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datasets:
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.82
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- name: F1
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type: f1
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value: 0.8235294117647058
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# finetuning-sentiment-imdb
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This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the imdb dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4138
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- Accuracy: 0.82
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- F1: 0.8235
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## Model description
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