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distilbert-joint-intent-slot-smarthome

Token Classification
Transformers
TensorBoard
Safetensors
English
distilbert
text-classification
intent-classification
slot-filling
joint-intent-slot
smart-home
generated:Enfuse.io
Eval Results
Model card Files Metrics Community
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Add fine-tuned DistilBERT model and card

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README.md ADDED
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+ ---
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+ language: en
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+ license: apache-2.0
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+ library_name: transformers
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+ tags:
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+ - distilbert
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+ - text-classification
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+ - token-classification
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+ - intent-classification
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+ - slot-filling
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+ - joint-intent-slot
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+ - smart-home
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+ - generated:Enfuse.io
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+ pipeline_tag: token-classification # Or text-classification, often token is used for joint models
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+ model-index:
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+ - name: distilbert-joint-intent-slot-smarthome
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+ results:
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+ - task:
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+ type: token-classification # Represents the slot filling part
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+ name: Slot Filling
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+ dataset:
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+ name: enfuse/joint-intent-slot-smarthome
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+ type: enfuse/joint-intent-slot-smarthome
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+ config: default
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+ split: test
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+ metrics:
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+ - type: micro_f1
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+ value: 0.8800
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+ name: Slot F1 (Micro)
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+ - type: precision
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+ value: 0.8800
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+ name: Slot Precision (Micro)
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+ - type: recall
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+ value: 0.8800
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+ name: Slot Recall (Micro)
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+ - task:
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+ type: text-classification # Represents the intent part
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+ name: Intent Classification
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+ dataset:
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+ name: enfuse/joint-intent-slot-smarthome
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+ type: enfuse/joint-intent-slot-smarthome
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+ config: default
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.9208
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+ name: Intent Accuracy
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+ ---
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+
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+ # DistilBERT for Smart Home Joint Intent Classification and Slot Filling
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+
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+ ## Model Description
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+
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+ **Produced By:** [Enfuse.io](https://enfuse.io/)
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+
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+ This model is a fine-tuned version of `distilbert-base-uncased` specifically adapted for **joint intent classification and slot filling** in the **smart home domain**. Given a user command related to controlling smart home devices (like lights or thermostats), the model simultaneously predicts:
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+
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+ 1. The user's **intent** (e.g., `set_device_state`, `get_device_state`).
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+ 2. The relevant **slots** (entities like `device_name`, `location`, `state`, `attribute_value`) within the command, using BIO tagging.
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+
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+ ## Intended Use and Limitations
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+
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+ **Primary Intended Use:** This model is intended for **hobbyist experimentation and educational purposes** related to Natural Language Understanding (NLU) for smart home applications. It can be used as a baseline or starting point for understanding how to build NLU components for simple device control.
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+
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+ **Disclaimer:** **This model is NOT intended for use in production environments.** Enfuse.io takes **no responsibility** for the performance, reliability, security, or any consequences arising from the use of this model in production systems or safety-critical applications. Use in such contexts is entirely at the user's own risk.
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+
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+ **Out-of-Scope Use:**
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+ * The model is not designed for general conversation or tasks outside the specific smart home intents and slots it was trained on.
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+ * It has **no built-in mechanism for handling out-of-domain requests** (e.g., asking about weather, playing music). It will likely attempt to classify such requests into one of the known smart home intents, potentially leading to incorrect behavior.
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+ * It has not been evaluated for fairness, bias, or robustness against adversarial inputs.
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+
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+ ## Training Data
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+
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+ The model was fine-tuned on the `enfuse/joint-intent-slot-smarthome` dataset, specifically the `generated_smarthome_2016_unique.jsonl` version containing 2016 unique synthetic examples.
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+
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+ This dataset was generated by Enfuse.io using a combination of `mistralai/Mistral-7B-Instruct-v0.1` and `openai/gpt-4o`, followed by validation and de-duplication. Please refer to the [dataset card](https://huggingface.co/datasets/enfuse/joint-intent-slot-smarthome) for more details on the data generation process and limitations.
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+
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+ ## Training Procedure
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+
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+ ### Preprocessing
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+
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+ The text was tokenized using the `distilbert-base-uncased` tokenizer. Slot labels were converted to a BIO tagging scheme. Input sequences were padded and truncated to a maximum length of 128 tokens.
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+
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+ ### Fine-tuning
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+
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+ The model was fine-tuned using the Hugging Face `transformers` library Trainer on a single NVIDIA RTX 5090.
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+
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+ * **Epochs:** 10
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+ * **Batch Size:** 16 (per device)
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+ * **Learning Rate:** 5e-5 (with linear decay)
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+ * **Optimizer:** AdamW
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+ * **Precision:** FP16
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+ * **Dataset Split:** 80% Train (1612), 10% Validation (202), 10% Test (202)
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+ * **Best Model Selection:** The checkpoint with the highest `eval_intent_accuracy` on the validation set during training was selected for the final model (corresponding to Epoch 8 or 10 in the 10-epoch run).
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+
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+ ## Evaluation Results
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+
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+ The following results were achieved on the **test set** (202 examples) using the best checkpoint saved during training:
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+
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+ * **Intent Accuracy:** 92.08%
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+ * **Slot F1 Score (Micro):** 88.00%
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+ * **Slot Precision (Micro):** 88.00%
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+ * **Slot Recall (Micro):** 88.00%
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+
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+ *(Note: These results are specific to this particular training setup and may vary with different hyperparameters or training runs.)*
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+
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+ ## How to Use
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+
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+ *(You would typically add code examples here showing how to load and use the model with the Transformers pipeline or custom code, similar to the logic in `infer.py`. Since the user requested no code, this section is omitted but would normally be present.)*
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+
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+ ## Model Card Contact
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+
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+ [Enfuse.io](https://enfuse.io/)
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+
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+ ## Citation
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+
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+ If you use this model, please cite the dataset:
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+
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+ ```bibtex
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+ @misc{enfuse_smarthome_intent_slot_2024,
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+ author = {Enfuse.io},
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+ title = {Enfuse Smart Home Joint Intent and Slot Filling Dataset},
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+ year = {2024},
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+ publisher = {Hugging Face},
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+ journal = {Hugging Face Hub},
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+ howpublished = {\url{https://huggingface.co/datasets/enfuse/joint-intent-slot-smarthome}}
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+ }
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+ ```
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+ "activation": "gelu",
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+ "0": "get_device_state",
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