Updated model card with new performance metrics and versioning information

README.md

@@ -1,3 +1,55 @@
----
-license: unknown
----
+---
+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:
+```python
+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)