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--- |
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license: apache-2.0 |
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datasets: |
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- mteb/imdb |
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- lmqg/qg_squad |
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- commoncrawl/statistics |
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language: |
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- en |
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- es |
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- fr |
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metrics: |
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- accuracy |
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- f1 |
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- perplexity |
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- bleu |
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base_model: |
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- google-bert/bert-base-uncased |
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new_version: mradermacher/Slm-4B-Instruct-v1.0.1-GGUF |
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pipeline_tag: text-classification |
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library_name: transformers |
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tags: |
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- text-classification |
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- sentiment-analysis |
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- NLP |
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- transformer |
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--- |
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# BasePlate |
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## Model Description |
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The **BasePlate** model is a [brief description of what the model does, e.g., "a transformer-based model fine-tuned for text classification tasks"]. |
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It can be used for [list the tasks it can perform, e.g., text generation, sentiment analysis, etc.]. The model is based on [mention the underlying architecture or base model, e.g., BERT, GPT-2, etc.]. |
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### Model Features: |
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- Task: [e.g., Text Classification, Question Answering, Summarization] |
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- Languages: [List supported languages, e.g., English, French, Spanish, etc.] |
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- Dataset: [Name of the dataset(s) used to train the model, e.g., "Fine-tuned on the IMDB reviews dataset."] |
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- Performance: [Optional: Describe the model's performance metrics, e.g., "Achieved an F1 score of 92% on the test set."] |
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## Intended Use |
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This model is intended for [intended use cases, e.g., text classification tasks, content moderation, etc.]. |
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### How to Use: |
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Here’s a simple usage example in Python using the `transformers` library: |
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```python |
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from transformers import pipeline |
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# Load the pre-trained model |
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model = pipeline('text-classification', model='huggingface/BasePlate') |
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# Example usage |
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text = "This is an example sentence." |
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result = model(text) |
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print(result) |
