Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use zboxi7/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zboxi7/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zboxi7/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zboxi7/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("zboxi7/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update model card README.md
Browse files
README.md
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license: apache-2.0
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tags:
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- generated_from_trainer
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model-index:
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- name: finetuning-sentiment-model-3000-samples
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results: []
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# finetuning-sentiment-model-3000-samples
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the
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## Model description
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs:
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### Training results
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license: apache-2.0
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tags:
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metrics:
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- accuracy
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model-index:
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- name: finetuning-sentiment-model-3000-samples
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results: []
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# finetuning-sentiment-model-3000-samples
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This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.1460
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- Accuracy: 0.75
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## Model description
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 5
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### Training results
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