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README.md

@@ -11,274 +11,111 @@ pipeline_tag: sentence-similarity
 library_name: sentence-transformers
 ---
 
-# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+# Resume Matcher Transformer
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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.
+A fine-tuned sentence transformer model based on `sentence-transformers/all-MiniLM-L6-v2` optimized for comparing resumes with job descriptions.
 
-## Model Details
+## Model Overview
 
-### Model Description
-- **Model Type:** Sentence Transformer
-- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
-- **Maximum Sequence Length:** 256 tokens
-- **Output Dimensionality:** 384 dimensions
-- **Similarity Function:** Cosine Similarity
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
+This model transforms resumes and job descriptions into 384-dimensional embeddings that can be compared for semantic similarity, helping to identify the best candidates for a position.
 
-### Model Sources
+### Key Specifications
+- **Base Model**: [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
+- **Output Dimensions**: 384
+- **Sequence Length**: 256 tokens maximum
+- **Similarity Function**: Cosine Similarity
+- **Pooling Strategy**: Mean pooling
 
-- **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
+## Model Architecture
 
 ```
 SentenceTransformer(
   (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: 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})
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_mean_tokens': True})
   (2): Normalize()
 )
 ```
 
 ## Usage
 
-### Direct Usage (Sentence Transformers)
-
-First install the Sentence Transformers library:
-
 ```bash
+# Install the required library
 pip install -U sentence-transformers
 ```
 
-Then you can load this model and run inference.
 ```python
 from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
 
-# Download from the 🤗 Hub
-model = SentenceTransformer("sentence_transformers_model_id")
-# Run inference
-sentences = [
-    'The weather is lovely today.',
-    "It's so sunny outside!",
-    'He drove to the stadium.',
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 384]
-
-# Get the similarity scores for the embeddings
-similarities = model.similarity(embeddings, embeddings)
-print(similarities.shape)
-# [3, 3]
-```
-
-<!--
-### 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>
+# Load the model
+model = SentenceTransformer("path/to/model")
 
-</details>
--->
-
-<!--
-### Out-of-Scope Use
+# Example job description
+job_description = "Looking for a Python backend developer with Django experience."
 
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
+# Example resumes
+resume1 = "Experienced Python developer with Flask and Django skills."
+resume2 = "Teacher with 5 years in classroom management experience."
 
-<!--
-## Bias, Risks and Limitations
+# Generate embeddings
+job_embedding = model.encode(job_description)
+resume1_embedding = model.encode(resume1)
+resume2_embedding = model.encode(resume2)
 
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
+# Calculate similarity
+similarity1 = cosine_similarity([job_embedding], [resume1_embedding])[0][0]
+similarity2 = cosine_similarity([job_embedding], [resume2_embedding])[0][0]
 
-<!--
-### Recommendations
-
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
+print(f"Similarity with Resume 1: {similarity1:.4f}")
+print(f"Similarity with Resume 2: {similarity2:.4f}")
+```
 
 ## Training Details
 
-### Training Dataset
+### Dataset Information
+- **Size**: 4 training samples
+- **Format**: Pairs of text samples with similarity labels (0.0 = no match, 1.0 = match)
+- **Loss Function**: CosineSimilarityLoss with MSELoss
 
-#### Unnamed Dataset
+### Sample Training Data
+| Resume/Profile | Job Description | Match Score |
+|:--------------|:---------------|:-----------|
+| Teacher with classroom management experience | Looking for AI/ML engineer with Python experience | 0.0 |
+| DevOps engineer with AWS, Docker, Jenkins | Hiring cloud infrastructure engineer with AWS and CI/CD tools | 1.0 |
+| Experienced Python developer with Flask and Django | Looking for backend Python developer with Django experience | 1.0 |
 
-* Size: 4 training samples
-* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
-* Approximate statistics based on the first 4 samples:
-  |         | sentence_0                                                                        | sentence_1                                                                         | label                                                         |
-  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------|
-  | type    | string                                                                            | string                                                                             | float                                                         |
-  | details | <ul><li>min: 12 tokens</li><li>mean: 14.0 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 13.25 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
-* Samples:
-  | sentence_0                                                                | sentence_1                                                                  | label            |
-  |:--------------------------------------------------------------------------|:----------------------------------------------------------------------------|:-----------------|
-  | <code>Teacher with 5 years in classroom management...</code>              | <code>Looking for AI/ML engineer with Python experience.</code>             | <code>0.0</code> |
-  | <code>DevOps engineer with AWS, Docker, Jenkins...</code>                 | <code>Hiring cloud infrastructure engineer with AWS and CI/CD tools.</code> | <code>1.0</code> |
-  | <code>Experienced Python developer with Flask and Django skills...</code> | <code>Looking for backend Python developer with Django experience.</code>   | <code>1.0</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
 
-### Training Hyperparameters
-#### Non-Default Hyperparameters
+- Training epochs: 4
+- Batch size: 2
+- Learning rate: 5e-05
+- Optimizer: AdamW
 
-- `per_device_train_batch_size`: 2
-- `per_device_eval_batch_size`: 2
-- `num_train_epochs`: 4
-- `multi_dataset_batch_sampler`: round_robin
-
-#### All Hyperparameters
-<details><summary>Click to expand</summary>
+<details><summary>View all hyperparameters</summary>
 
-- `overwrite_output_dir`: False
-- `do_predict`: False
-- `eval_strategy`: no
-- `prediction_loss_only`: True
 - `per_device_train_batch_size`: 2
 - `per_device_eval_batch_size`: 2
-- `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
+- `num_train_epochs`: 4
 - `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`: 4
-- `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}
-- `tp_size`: 0
-- `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
-- `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
-- `eval_use_gather_object`: False
-- `average_tokens_across_devices`: False
-- `prompts`: None
-- `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: round_robin
 
 </details>
 
-### Framework Versions
-- Python: 3.11.12
+## Framework Versions
 - Sentence Transformers: 4.1.0
 - Transformers: 4.51.3
 - PyTorch: 2.6.0+cu124
-- Accelerate: 1.6.0
-- Datasets: 2.14.4
-- Tokenizers: 0.21.1
+- Python: 3.11.12
 
 ## Citation
 
-### BibTeX
-
-#### Sentence Transformers
 ```bibtex
 @inproceedings{reimers-2019-sentence-bert,
     title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
@@ -289,22 +126,4 @@ You can finetune this model on your own dataset.
     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
-
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->
+```