Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Prince12f/finetuning-sentiment-model-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prince12f/finetuning-sentiment-model-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Prince12f/finetuning-sentiment-model-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Prince12f/finetuning-sentiment-model-samples") model = AutoModelForSequenceClassification.from_pretrained("Prince12f/finetuning-sentiment-model-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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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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- Loss: 0.
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- Accuracy: 0.
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- F1: 0.
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- Precision: 0.
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- Recall: 0.
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## Model description
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- Transformers 4.35.2
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- Pytorch 2.1.0+cu118
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- Tokenizers 0.15.0
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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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- Loss: 0.2691
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- Accuracy: 0.9157
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- F1: 0.9155
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- Precision: 0.9238
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- Recall: 0.9075
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## Model description
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- Transformers 4.35.2
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- Pytorch 2.1.0+cu118
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- Datasets 2.15.0
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- Tokenizers 0.15.0
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runs/Dec01_18-34-24_15b6bef61c62/events.out.tfevents.1701457271.15b6bef61c62.288.3
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version https://git-lfs.github.com/spec/v1
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oid sha256:608ace81da4c8345010c8b75bdbc01573675ee5630177a4387a524647a63e1ea
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size 560
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