metadata

license: apache-2.0
datasets:
  - mteb/imdb
  - lmqg/qg_squad
  - commoncrawl/statistics
language:
  - en
  - es
  - fr
metrics:
  - accuracy
  - f1
  - perplexity
  - bleu
base_model:
  - google-bert/bert-base-uncased
new_version: mradermacher/Slm-4B-Instruct-v1.0.1-GGUF
pipeline_tag: text-classification
library_name: transformers
tags:
  - text-classification
  - sentiment-analysis
  - NLP
  - transformer

BasePlate

Model Description

The BasePlate model is a [brief description of what the model does, e.g., "a transformer-based model fine-tuned for text classification tasks"].

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.].

Model Features:

Task: [e.g., Text Classification, Question Answering, Summarization]
Languages: [List supported languages, e.g., English, French, Spanish, etc.]
Dataset: [Name of the dataset(s) used to train the model, e.g., "Fine-tuned on the IMDB reviews dataset."]
Performance: [Optional: Describe the model's performance metrics, e.g., "Achieved an F1 score of 92% on the test set."]

Intended Use

This model is intended for [intended use cases, e.g., text classification tasks, content moderation, etc.].

How to Use:

Here’s a simple usage example in Python using the transformers library:

from transformers import pipeline

# Load the pre-trained model
model = pipeline('text-classification', model='huggingface/BasePlate')

# Example usage
text = "This is an example sentence."
result = model(text)
print(result)