Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use kevinwlip/ProsusAI-finbert-1500-samples-fine-tune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kevinwlip/ProsusAI-finbert-1500-samples-fine-tune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kevinwlip/ProsusAI-finbert-1500-samples-fine-tune")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kevinwlip/ProsusAI-finbert-1500-samples-fine-tune") model = AutoModelForSequenceClassification.from_pretrained("kevinwlip/ProsusAI-finbert-1500-samples-fine-tune") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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- Accuracy: 0.
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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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### Framework versions
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- Transformers 4.
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- Pytorch 2.3.
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.4229
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- Accuracy: 0.73
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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: 10
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### Training results
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### Framework versions
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- Transformers 4.43.3
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- Pytorch 2.3.1+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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runs/Jul29_02-04-19_3b5b415a013a/events.out.tfevents.1722218662.3b5b415a013a.5656.4
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