gimmeursocks
/

ara-e5-small

@@ -1,199 +1,166 @@
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+Here is the `README.md` file based on the dataset "Omartificial-Intelligence-Space/Arabic-NLi-Triplet" and the provided code and training details:
+---
+# Arabic NLI Triplet - Sentence Transformer Model
+This repository contains a fine-tuned Sentence Transformer model trained on the "Omartificial-Intelligence-Space/Arabic-NLi-Triplet" dataset. The model is trained to generate 384-dimensional embeddings for semantic similarity tasks like paraphrase mining, sentence similarity, and clustering in Arabic.
+## Model Overview
+- **Model Type:** Sentence Transformer
+- **Base Model:** `intfloat/multilingual-e5-small`
+- **Training Dataset:** [Omartificial-Intelligence-Space/Arabic-NLi-Triplet](https://huggingface.co/datasets/Omartificial-Intelligence-Space/Arabic-NLi-Triplet)
+- **Similarity Function:** Cosine Similarity
+- **Embedding Dimensionality:** 384 dimensions
+- **Maximum Sequence Length:** 128 tokens
+- **Performance Improvement:** The model achieved around 10% improvement when tested on the test set of the provided dataset, compared to the base model's performance.
+## Dataset
+### Arabic NLI Triplet Dataset
+The dataset contains triplets of sentences in Arabic: an anchor sentence, a positive sentence (semantically similar to the anchor), and a negative sentence (semantically dissimilar to the anchor). The dataset is designed for learning sentence representations through triplet margin loss.
+Dataset Link: [Omartificial-Intelligence-Space/Arabic-NLi-Triplet](https://huggingface.co/datasets/Omartificial-Intelligence-Space/Arabic-NLi-Triplet)
+## Training Process
+### Loss Function: Triplet Margin Loss
+We used the Triplet Margin Loss with a margin of `1.0`. The model is trained to minimize the distance between anchor and positive embeddings, while maximizing the distance between anchor and negative embeddings.
+### Training Loss Progress:
+Below is the training loss recorded at various steps during the training process:
+| Step  | Training Loss |
+|-------|---------------|
+| 500   | 0.136500      |
+| 1000  | 0.126500      |
+| 1500  | 0.127300      |
+| 2000  | 0.114500      |
+| 2500  | 0.110600      |
+| 3000  | 0.102300      |
+| 3500  | 0.101300      |
+| 4000  | 0.106900      |
+| 4500  | 0.097200      |
+| 5000  | 0.091700      |
+| 5500  | 0.092400      |
+| 6000  | 0.095500      |
+## Model Training Code
+The model was trained using the following code (without resuming from checkpoints):
+```python
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModel, TrainingArguments, Trainer
+from torch.nn import TripletMarginLoss
+# Load dataset
+dataset = load_dataset("Omartificial-Intelligence-Space/Arabic-NLi-Triplet")
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("intfloat/multilingual-e5-small")
+# Tokenize function
+def tokenize_function(examples):
+    anchor_encodings = tokenizer(examples['anchor'], truncation=True, padding='max_length', max_length=128)
+    positive_encodings = tokenizer(examples['positive'], truncation=True, padding='max_length', max_length=128)
+    negative_encodings = tokenizer(examples['negative'], truncation=True, padding='max_length', max_length=128)
+    return {
+        'anchor_input_ids': anchor_encodings['input_ids'],
+        'anchor_attention_mask': anchor_encodings['attention_mask'],
+        'positive_input_ids': positive_encodings['input_ids'],
+        'positive_attention_mask': positive_encodings['attention_mask'],
+        'negative_input_ids': negative_encodings['input_ids'],
+        'negative_attention_mask': negative_encodings['attention_mask'],
+    }
+tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset["train"].column_names)
+# Define triplet loss
+triplet_loss = TripletMarginLoss(margin=1.0)
+def compute_loss(anchor_embedding, positive_embedding, negative_embedding):
+    return triplet_loss(anchor_embedding, positive_embedding, negative_embedding)
+# Load model
+model = AutoModel.from_pretrained("intfloat/multilingual-e5-small")
+class TripletTrainer(Trainer):
+    def compute_loss(self, model, inputs, return_outputs=False):
+        anchor_input_ids = inputs['anchor_input_ids'].to(self.args.device)
+        anchor_attention_mask = inputs['anchor_attention_mask'].to(self.args.device)
+        positive_input_ids = inputs['positive_input_ids'].to(self.args.device)
+        positive_attention_mask = inputs['positive_attention_mask'].to(self.args.device)
+        negative_input_ids = inputs['negative_input_ids'].to(self.args.device)
+        negative_attention_mask = inputs['negative_attention_mask'].to(self.args.device)
+        anchor_embeds = model(input_ids=anchor_input_ids, attention_mask=anchor_attention_mask).last_hidden_state.mean(dim=1)
+        positive_embeds = model(input_ids=positive_input_ids, attention_mask=positive_attention_mask).last_hidden_state.mean(dim=1)
+        negative_embeds = model(input_ids=negative_input_ids, attention_mask=negative_attention_mask).last_hidden_state.mean(dim=1)
+        return compute_loss(anchor_embeds, positive_embeds, negative_embeds)
+# Training arguments
+training_args = TrainingArguments(
+    output_dir="/content/drive/MyDrive/results",
+    learning_rate=2e-5,
+    per_device_train_batch_size=16,
+    num_train_epochs=3,
+    weight_decay=0.01,
+    logging_dir='/content/drive/MyDrive/logs',
+    remove_unused_columns=False,
+    fp16=True,
+    save_total_limit=3,
+)
+# Initialize trainer
+trainer = TripletTrainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets['train'],
+)
+# Start training
+trainer.train()
+# Save model and evaluate
+trainer.save_model("/content/drive/MyDrive/fine-tuned-multilingual-e5")
+results = trainer.evaluate()
+print(results)
+```
+## Framework Versions
+- Python: 3.10.11
+- Sentence Transformers: 3.0.1
+- Transformers: 4.44.2
+- PyTorch: 2.4.0
+- Datasets: 2.21.0
+## How to Use
+To use the model, install the required libraries and load the model with the following code:
+```bash
+pip install -U sentence-transformers
+```
+```python
+from sentence_transformers import SentenceTransformer
+# Load the fine-tuned model
+model = SentenceTransformer("gimmeursocks/ara-e5-small")
+# Run inference
+sentences = ['أنا سعيد', 'الجو جميل اليوم', 'هذا كلب كبير']
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+```
+## Citation
+If you use this model or dataset, please cite the corresponding paper or dataset source.