Add model card

README.md

@@ -1,5 +1,76 @@
 # My Dummy Model
 
-This is a dummy model for testing purposes.
+---
+language: fr
+license: apache-2.0
+tags:
+  - masked-lm
+  - camembert
+  - transformers
+  - tf
+  - french
+  - fill-mask
+---
 
-Last updated: 2025-05-30 22:21:44
+# CamemBERT MLM - Fine-tuned Model
+
+This is a TensorFlow-based masked language model (MLM) based on the [camembert-base](https://huggingface.co/camembert-base) checkpoint, a RoBERTa-like model trained on French text.
+
+## Model description
+
+This model uses the CamemBERT architecture, which is a RoBERTa-based transformer trained on large-scale French corpora (e.g., OSCAR, CCNet). It's designed to perform Masked Language Modeling (MLM) tasks.
+
+It was loaded and saved using the `transformers` library in TensorFlow (`TFAutoModelForMaskedLM`). It can be used for fill-in-the-blank tasks in French.
+
+## Intended uses & limitations
+
+### Intended uses
+- Fill-mask predictions in French
+- Feature extraction for NLP tasks
+- Fine-tuning on downstream tasks like text classification, NER, etc.
+
+### Limitations
+- Works best with French text
+- May not generalize well to other languages
+- Cannot be used for generative tasks (e.g., translation, text generation)
+
+## How to use
+
+```python
+from transformers import TFAutoModelForMaskedLM, AutoTokenizer
+import tensorflow as tf
+
+model = TFAutoModelForMaskedLM.from_pretrained("Mhammad2023/my-dummy-model")
+tokenizer = AutoTokenizer.from_pretrained("Mhammad2023/my-dummy-model")
+
+inputs = tokenizer("J'aime le [MASK] rouge.", return_tensors="tf")
+outputs = model(**inputs)
+logits = outputs.logits
+
+masked_index = tf.argmax(inputs.input_ids == tokenizer.mask_token_id, axis=1)[0]
+predicted_token_id = tf.argmax(logits[0, masked_index])
+predicted_token = tokenizer.decode([predicted_token_id])
+
+print(f"Predicted word: {predicted_token}")
+
+## Limitations and bias
+This model inherits the limitations and biases from the camembert-base checkpoint, including:
+
+Potential biases from the training data (e.g., internet corpora)
+
+## Inappropriate predictions for sensitive topics
+
+Use with caution in production or sensitive applications.
+
+## Training data
+The model was not further fine-tuned; it is based directly on camembert-base, which was trained on:
+
+OSCAR (Open Super-large Crawled ALMAnaCH coRpus)
+
+CCNet (Common Crawl News)
+
+## Training procedure
+No additional training was applied for this version. You can load and fine-tune it on your task using Trainer or Keras API.
+
+## Evaluation results
+This version has not been evaluated on downstream tasks. For evaluation metrics and benchmarks, refer to the original camembert-base model card.