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README.md

@@ -1,371 +1,41 @@
 ---
+language: en
 tags:
 - sentence-transformers
-- sentence-similarity
+- intent-classification
 - feature-extraction
-- dense
-- generated_from_trainer
-- dataset_size:139128
-- loss:CosineSimilarityLoss
-base_model: sentence-transformers/paraphrase-MiniLM-L3-v2
-widget:
-- source_sentence: launch library
-  sentences:
-  - take me to the library
-  - decrease volume
-  - what is happening in bali
-- source_sentence: show news 4
-  sentences:
-  - boot protocol space
-  - end protocol space
-  - volume on
-- source_sentence: navigate to bandung
-  sentences:
-  - enable map outlines
-  - stop protocol space
-  - adlas
-- source_sentence: take me to video 4
-  sentences:
-  - go back
-  - unmute video
-  - fullscreen
-- source_sentence: news in london
-  sentences:
-  - rewind
-  - news in jakarta
-  - tap london
-pipeline_tag: sentence-similarity
-library_name: sentence-transformers
+license: mit
+datasets:
+- custom
+metrics:
+- cosine-similarity
 ---
 
-# SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L3-v2
+# Intent Classification Model
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
-
-## Model Details
-
-### Model Description
-- **Model Type:** Sentence Transformer
-- **Base model:** [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2) <!-- at revision 4ca70771034acceecb2e72475f72050fcdde4ddc -->
-- **Maximum Sequence Length:** 128 tokens
-- **Output Dimensionality:** 384 dimensions
-- **Similarity Function:** Cosine Similarity
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
-
-### Model Sources
-
-- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
-
-### Full Model Architecture
-
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False, 'architecture': 'BertModel'})
-  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-)
-```
+This is a fine-tuned SentenceTransformer model for intent classification. It was trained on custom intent data including navigation, media controls, library management, and protocol activation commands.
 
 ## Usage
 
-### Direct Usage (Sentence Transformers)
-
-First install the Sentence Transformers library:
-
-```bash
-pip install -U sentence-transformers
-```
-
-Then you can load this model and run inference.
 ```python
 from sentence_transformers import SentenceTransformer
-
-# Download from the 🤗 Hub
-model = SentenceTransformer("drithh/intent-classifier")
-# Run inference
-sentences = [
-    'news in london',
-    'news in jakarta',
-    'tap london',
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 384]
-
-# Get the similarity scores for the embeddings
-similarities = model.similarity(embeddings, embeddings)
-print(similarities)
-# tensor([[ 1.0000,  0.9868, -0.0169],
-#         [ 0.9868,  1.0000, -0.0386],
-#         [-0.0169, -0.0386,  1.0000]])
+model = SentenceTransformer('drithh/intent-classifier')
+embeddings = model.encode("go to London")
 ```
 
-<!--
-### Direct Usage (Transformers)
-
-<details><summary>Click to see the direct usage in Transformers</summary>
-
-</details>
--->
-
-<!--
-### Downstream Usage (Sentence Transformers)
-
-You can finetune this model on your own dataset.
-
-<details><summary>Click to expand</summary>
-
-</details>
--->
-
-<!--
-### Out-of-Scope Use
-
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
-
-<!--
-## Bias, Risks and Limitations
+## Supported Intents
 
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
+- **Navigation**: go to LOCATION, navigate to LOCATION
+- **Atlas**: open atlas, launch atlas
+- **Map Controls**: select LOCATION, show boundaries, hide boundaries
+- **Library**: open library, close library, go to video NUMBER
+- **Media Controls**: play video, pause video, rewind, forward
+- **News**: show news LOCATION, hide news
+- **Protocols**: activate PROTOCOL, deactivate PROTOCOL
 
-<!--
-### Recommendations
-
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
-
-## Training Details
-
-### Training Dataset
-
-#### Unnamed Dataset
-
-* Size: 139,128 training samples
-* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence_0                                                                      | sentence_1                                                                      | label                                                          |
-  |:--------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------|
-  | type    | string                                                                          | string                                                                          | float                                                          |
-  | details | <ul><li>min: 3 tokens</li><li>mean: 4.71 tokens</li><li>max: 8 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.84 tokens</li><li>max: 8 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.04</li><li>max: 1.0</li></ul> |
-* Samples:
-  | sentence_0                          | sentence_1                        | label            |
-  |:------------------------------------|:----------------------------------|:-----------------|
-  | <code>clear boundaries</code>       | <code>cancel protocol cove</code> | <code>0.0</code> |
-  | <code>take me to the library</code> | <code>show news bali</code>       | <code>0.0</code> |
-  | <code>play video 3</code>           | <code>fullscreen</code>           | <code>0.0</code> |
-* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
-  ```json
-  {
-      "loss_fct": "torch.nn.modules.loss.MSELoss"
-  }
-  ```
-
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-
-- `per_device_train_batch_size`: 32
-- `per_device_eval_batch_size`: 32
-- `multi_dataset_batch_sampler`: round_robin
-
-#### All Hyperparameters
-<details><summary>Click to expand</summary>
-
-- `overwrite_output_dir`: False
-- `do_predict`: False
-- `eval_strategy`: no
-- `prediction_loss_only`: True
-- `per_device_train_batch_size`: 32
-- `per_device_eval_batch_size`: 32
-- `per_gpu_train_batch_size`: None
-- `per_gpu_eval_batch_size`: None
-- `gradient_accumulation_steps`: 1
-- `eval_accumulation_steps`: None
-- `torch_empty_cache_steps`: None
-- `learning_rate`: 5e-05
-- `weight_decay`: 0.0
-- `adam_beta1`: 0.9
-- `adam_beta2`: 0.999
-- `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1
-- `num_train_epochs`: 3
-- `max_steps`: -1
-- `lr_scheduler_type`: linear
-- `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.0
-- `warmup_steps`: 0
-- `log_level`: passive
-- `log_level_replica`: warning
-- `log_on_each_node`: True
-- `logging_nan_inf_filter`: True
-- `save_safetensors`: True
-- `save_on_each_node`: False
-- `save_only_model`: False
-- `restore_callback_states_from_checkpoint`: False
-- `no_cuda`: False
-- `use_cpu`: False
-- `use_mps_device`: False
-- `seed`: 42
-- `data_seed`: None
-- `jit_mode_eval`: False
-- `use_ipex`: False
-- `bf16`: False
-- `fp16`: False
-- `fp16_opt_level`: O1
-- `half_precision_backend`: auto
-- `bf16_full_eval`: False
-- `fp16_full_eval`: False
-- `tf32`: None
-- `local_rank`: 0
-- `ddp_backend`: None
-- `tpu_num_cores`: None
-- `tpu_metrics_debug`: False
-- `debug`: []
-- `dataloader_drop_last`: False
-- `dataloader_num_workers`: 0
-- `dataloader_prefetch_factor`: None
-- `past_index`: -1
-- `disable_tqdm`: False
-- `remove_unused_columns`: True
-- `label_names`: None
-- `load_best_model_at_end`: False
-- `ignore_data_skip`: False
-- `fsdp`: []
-- `fsdp_min_num_params`: 0
-- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
-- `fsdp_transformer_layer_cls_to_wrap`: None
-- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
-- `deepspeed`: None
-- `label_smoothing_factor`: 0.0
-- `optim`: adamw_torch
-- `optim_args`: None
-- `adafactor`: False
-- `group_by_length`: False
-- `length_column_name`: length
-- `ddp_find_unused_parameters`: None
-- `ddp_bucket_cap_mb`: None
-- `ddp_broadcast_buffers`: False
-- `dataloader_pin_memory`: True
-- `dataloader_persistent_workers`: False
-- `skip_memory_metrics`: True
-- `use_legacy_prediction_loop`: False
-- `push_to_hub`: False
-- `resume_from_checkpoint`: None
-- `hub_model_id`: None
-- `hub_strategy`: every_save
-- `hub_private_repo`: None
-- `hub_always_push`: False
-- `hub_revision`: None
-- `gradient_checkpointing`: False
-- `gradient_checkpointing_kwargs`: None
-- `include_inputs_for_metrics`: False
-- `include_for_metrics`: []
-- `eval_do_concat_batches`: True
-- `fp16_backend`: auto
-- `push_to_hub_model_id`: None
-- `push_to_hub_organization`: None
-- `mp_parameters`: 
-- `auto_find_batch_size`: False
-- `full_determinism`: False
-- `torchdynamo`: None
-- `ray_scope`: last
-- `ddp_timeout`: 1800
-- `torch_compile`: False
-- `torch_compile_backend`: None
-- `torch_compile_mode`: None
-- `include_tokens_per_second`: False
-- `include_num_input_tokens_seen`: False
-- `neftune_noise_alpha`: None
-- `optim_target_modules`: None
-- `batch_eval_metrics`: False
-- `eval_on_start`: False
-- `use_liger_kernel`: False
-- `liger_kernel_config`: None
-- `eval_use_gather_object`: False
-- `average_tokens_across_devices`: False
-- `prompts`: None
-- `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: round_robin
-- `router_mapping`: {}
-- `learning_rate_mapping`: {}
-
-</details>
-
-### Training Logs
-| Epoch  | Step  | Training Loss |
-|:------:|:-----:|:-------------:|
-| 0.1150 | 500   | 0.0288        |
-| 0.2300 | 1000  | 0.0206        |
-| 0.3450 | 1500  | 0.0161        |
-| 0.4600 | 2000  | 0.0125        |
-| 0.5750 | 2500  | 0.0095        |
-| 0.6900 | 3000  | 0.0067        |
-| 0.8050 | 3500  | 0.0047        |
-| 0.9200 | 4000  | 0.0037        |
-| 1.0350 | 4500  | 0.0032        |
-| 1.1500 | 5000  | 0.0027        |
-| 1.2649 | 5500  | 0.0024        |
-| 1.3799 | 6000  | 0.0022        |
-| 1.4949 | 6500  | 0.002         |
-| 1.6099 | 7000  | 0.0018        |
-| 1.7249 | 7500  | 0.0017        |
-| 1.8399 | 8000  | 0.0016        |
-| 1.9549 | 8500  | 0.0015        |
-| 2.0699 | 9000  | 0.0014        |
-| 2.1849 | 9500  | 0.0013        |
-| 2.2999 | 10000 | 0.0013        |
-| 2.4149 | 10500 | 0.0012        |
-| 2.5299 | 11000 | 0.0012        |
-| 2.6449 | 11500 | 0.0012        |
-| 2.7599 | 12000 | 0.0012        |
-| 2.8749 | 12500 | 0.0011        |
-| 2.9899 | 13000 | 0.0011        |
-
-
-### Framework Versions
-- Python: 3.13.1
-- Sentence Transformers: 5.0.0
-- Transformers: 4.53.2
-- PyTorch: 2.7.1
-- Accelerate: 1.9.0
-- Datasets: 4.0.0
-- Tokenizers: 0.21.2
-
-## Citation
-
-### BibTeX
-
-#### Sentence Transformers
-```bibtex
-@inproceedings{reimers-2019-sentence-bert,
-    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
-    author = "Reimers, Nils and Gurevych, Iryna",
-    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
-    month = "11",
-    year = "2019",
-    publisher = "Association for Computational Linguistics",
-    url = "https://arxiv.org/abs/1908.10084",
-}
-```
-
-<!--
-## Glossary
-
-*Clearly define terms in order to be accessible across audiences.*
--->
-
-<!--
-## Model Card Authors
-
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
-
-<!--
-## Model Card Contact
+## Model Details
 
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->
+- **Base Model**: sentence-transformers/paraphrase-MiniLM-L3-v2
+- **Fine-tuning**: Cosine similarity loss
+- **Embedding Dimensions**: 384
+- **Training Data**: 139,128 training pairs