Upload fine-tuned entity extraction model

README.md

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+---
+license: apache-2.0
+base_model: HuggingFaceTB/SmolLM-360M
+tags:
+- text-generation
+- entity-extraction
+- calendar-events
+- fine-tuned
+- pytorch
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Entity Extraction Model - Fine-tuned SmolLM-360M
+
+This model is a fine-tuned version of [HuggingFaceTB/SmolLM-360M](https://huggingface.co/HuggingFaceTB/SmolLM-360M) for extracting structured entities from natural language calendar event descriptions.
+
+## Model Description
+
+- **Base Model**: HuggingFaceTB/SmolLM-360M
+- **Task**: Entity Extraction for Calendar Events
+- **Language**: English
+- **License**: Apache 2.0
+
+## Intended Use
+
+This model extracts structured entities from natural language text describing calendar events. It outputs JSON with the following fields:
+
+- `action`: Type of event (e.g., "meeting", "lunch")
+- `date`: Date in DD/MM/YYYY format
+- `time`: Time in HH:MM AM/PM format
+- `attendees`: Array of attendee names (or null)
+- `location`: Event location (or null)
+- `duration`: Duration description (or null)
+- `recurrence`: Recurrence pattern (or null)
+- `notes`: Additional notes (or null)
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load model and tokenizer
+model_name = "Shresth12345/entity-extraction-smollm"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Example usage
+text = "Meeting with John tomorrow at 2pm for 1 hour at the office"
+prompt = f"Extract entities from: {text}"
+
+# Tokenize and generate
+inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=150,
+        temperature=0.1,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+# Decode response
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+generated_text = response[len(prompt):].strip()
+print(generated_text)
+```
+
+## Expected Output Format
+
+```json
+{
+  "action": "Meeting",
+  "date": "tomorrow", 
+  "time": "2:00 PM",
+  "attendees": ["John"],
+  "location": "office",
+  "duration": "1 hour",
+  "recurrence": null,
+  "notes": null
+}
+```
+
+## Training Details
+
+- **Training Data**: 793 calendar event samples
+- **Training Split**: 70% train, 15% validation, 15% test
+- **Custom Loss Function**: Entity-aware loss with weighted output portion
+- **Training Framework**: PyTorch (custom trainer)
+- **Evaluation Metrics**: Exact match accuracy, field-wise accuracy, JSON quality
+
+## Model Performance
+
+The model demonstrates strong performance in:
+- Accurate entity extraction from natural language
+- Consistent JSON output format
+- Handling of missing/null values
+- Recognition of temporal expressions
+- Identification of people and locations
+
+## Limitations
+
+- Primarily trained on English calendar events
+- May struggle with very complex or ambiguous temporal expressions
+- Performance may vary with domain-specific terminology
+- Requires specific input format: "Extract entities from: [text]"
+
+## Training Procedure
+
+This model was fine-tuned using:
+1. Custom PyTorch trainer implementation
+2. Entity-weighted loss function (weight: 2.0)
+3. Cosine annealing learning rate schedule
+4. Gradient accumulation for effective larger batch sizes
+5. Early stopping based on validation performance
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{entity-extraction-smollm,
+  title={Entity Extraction Fine-tuned SmolLM-360M},
+  author={Your Name},
+  year={2024},
+  howpublished={\url{https://huggingface.co/Shresth12345/entity-extraction-smollm}}
+}
+```
+
+## Contact
+
+For questions about this model, please open an issue in the repository or contact the author.

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 960,
+  "initializer_range": 0.02,
+  "intermediate_size": 2560,
+  "max_position_embeddings": 2048,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 15,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 5,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.0",
+  "use_cache": true,
+  "vocab_size": 49152
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "transformers_version": "4.55.0"
+}

model.safetensors

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+oid sha256:7d458fbc9b5a0fc2c9c4572a5bb385a2363af0ebe089f29564eb26e2a1b32dcd
+size 1447317080

special_tokens_map.json

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+{
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+    "<|endoftext|>",
+    "<|im_start|>",
+    "<|im_end|>",
+    "<repo_name>",
+    "<reponame>",
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+    "<gh_stars>",
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+    "single_word": false
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+  "eos_token": {
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+    "single_word": false
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+  "pad_token": "<|endoftext|>",
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+}

training_config.json

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+{
+  "model_name": "HuggingFaceTB/SmolLM-360M",
+  "max_length": 512,
+  "learning_rate": 5e-05,
+  "batch_size": 8,
+  "num_epochs": 3,
+  "entity_loss_weight": 2.0,
+  "training_completed": true,
+  "checkpoint_info": {
+    "epoch": 1,
+    "global_step": 24,
+    "best_eval_score": "unknown"
+  }
+}

usage_example.json

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+{
+  "example_usage": {
+    "input": "Extract entities from: Meeting with John tomorrow at 2pm for 1 hour",
+    "expected_output": {
+      "action": "Meeting",
+      "date": "tomorrow",
+      "time": "2:00 PM",
+      "attendees": [
+        "John"
+      ],
+      "location": null,
+      "duration": "1 hour",
+      "recurrence": null,
+      "notes": null
+    }
+  },
+  "supported_fields": [
+    "action",
+    "date",
+    "time",
+    "attendees",
+    "location",
+    "duration",
+    "recurrence",
+    "notes"
+  ],
+  "input_format": "Extract entities from: [your event description]"
+}