Update README.md

README.md

@@ -2,8 +2,6 @@
 tags:
 - sentence-transformers
 - sentence-similarity
-- feature-extraction
-- generated_from_trainer
 - dataset_size:901028
 - loss:CosineSimilarityLoss
 base_model: Shuu12121/CodeModernBERT-Owl
@@ -11,7 +9,8 @@ pipeline_tag: sentence-similarity
 library_name: sentence-transformers
 metrics:
 - pearson_cosine
-- spearman_cosine
+- accuracy
+- f1
 model-index:
 - name: SentenceTransformer based on Shuu12121/CodeModernBERT-Owl
   results:
@@ -24,386 +23,141 @@ model-index:
     metrics:
     - type: pearson_cosine
       value: 0.9481467499740959
-      name: Pearson Cosine
-    - type: spearman_cosine
-      value: 0.5635084463158045
-      name: Spearman Cosine
+      name: Training Pearson Cosine
+    - type: accuracy
+      value: 0.9900051996071408
+      name: Test Accuracy
+    - type: f1
+      value: 0.963323498754483
+      name: Test F1 Score
 license: apache-2.0
+datasets:
+- google/code_x_glue_cc_clone_detection_big_clone_bench
 ---
 
-# SentenceTransformer based on Shuu12121/CodeModernBERT-Owl
+#　SentenceTransformer based on `Shuu12121/CodeModernBERT-Owl🦉`
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Shuu12121/CodeModernBERT-Owl](https://huggingface.co/Shuu12121/CodeModernBERT-Owl). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+This model is a SentenceTransformer fine-tuned from [`Shuu12121/CodeModernBERT-Owl🦉`](https://huggingface.co/Shuu12121/CodeModernBERT-Owl) on the [BigCloneBench](https://huggingface.co/datasets/google/code_x_glue_cc_clone_detection_big_clone_bench) dataset for **code clone detection**. It maps code snippets into a 768-dimensional dense vector space for semantic similarity tasks.
 
-## Model Details
+---
+
+## 📌 Model Overview
+
+- **Architecture**: Sentence-BERT (SBERT)
+- **Base Model**: `Shuu12121/CodeModernBERT-Owl`
+- **Output Dimension**: 768
+- **Max Sequence Length**: 2048 tokens
+- **Pooling Method**: CLS token pooling
+- **Similarity Function**: Cosine Similarity
+
+---
+
+## 🏋️‍♂️ Training Configuration
 
-### Model Description
-- **Model Type:** Sentence Transformer
-- **Base model:** [Shuu12121/CodeModernBERT-Owl](https://huggingface.co/Shuu12121/CodeModernBERT-Owl) <!-- at revision c6b9f919885bc8b27718df3af11dc0fbed7e2b63 -->
-- **Maximum Sequence Length:** 2048 tokens
-- **Output Dimensionality:** 768 dimensions
-- **Similarity Function:** Cosine Similarity
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
+- **Loss Function**: `CosineSimilarityLoss`
+- **Epochs**: 1
+- **Batch Size**: 32
+- **Warmup Steps**: 3% of training steps
+- **Evaluator**: `EmbeddingSimilarityEvaluator` (on validation)
 
-### Model Sources
+---
+
+## 📊 Evaluation Metrics
+
+| Metric                    | Score              |
+|---------------------------|--------------------|
+| Pearson Cosine (Train)    | `0.9481`           |
+| Accuracy (Test)           | `0.9900`           |
+| F1 Score (Test)           | `0.9633`           |
+
+---
 
-- **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)
+## 📚 Dataset
 
-### Full Model Architecture
+- [Google BigCloneBench](https://huggingface.co/datasets/google/code_x_glue_cc_clone_detection_big_clone_bench)
+
+---
+
+## 🧪 How to Use
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("your-model-id")
+sentences = [
+    "def add(a, b): return a + b",
+    "def sum(x, y): return x + y"
+]
+embeddings = model.encode(sentences)
 
+from torch.nn.functional import cosine_similarity
+import torch
+
+score = cosine_similarity(torch.tensor([embeddings[0]]), torch.tensor([embeddings[1]]))
+print(f"Cosine similarity: {score.item():.4f}")
 ```
+
+---
+
+## 🛠️ Model Architecture
+
+```python
 SentenceTransformer(
-  (0): Transformer({'max_seq_length': 2048, 'do_lower_case': False}) with Transformer model: ModernBertModel 
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (0): Transformer({'max_seq_length': 2048}) with model 'ModernBertModel'
+  (1): Pooling({
+        'word_embedding_dimension': 768,
+        'pooling_mode_cls_token': True,
+        ...
+  })
 )
 ```
 
-## Usage
+---
+
+## 📦 Dependencies
 
-### Direct Usage (Sentence Transformers)
+- Python: `3.11.11`
+- sentence-transformers: `4.0.1`
+- transformers: `4.50.3`
+- torch: `2.6.0+cu124`
+- datasets: `3.5.0`
+- tokenizers: `0.21.1`
+- flash-attn: ✅ Installed
 
-First install the Sentence Transformers library:
+### Install Required Libraries
 
 ```bash
-pip install -U sentence-transformers
+pip install -U sentence-transformers transformers>=4.48.0 flash-attn datasets
 ```
 
-Then you can load this model and run inference.
-```python
-from sentence_transformers import SentenceTransformer
+---
 
-# Download from the 🤗 Hub
-model = SentenceTransformer("sentence_transformers_model_id")
-# Run inference
-sentences = [
-    '    public static boolean insert(final Cargo cargo) {\n        int result = 0;\n        final Connection c = DBConnection.getConnection();\n        PreparedStatement pst = null;\n        if (c == null) {\n            return false;\n        }\n        try {\n            c.setAutoCommit(false);\n            final String sql = "insert into cargo (nome) values (?)";\n            pst = c.prepareStatement(sql);\n            pst.setString(1, cargo.getNome());\n            result = pst.executeUpdate();\n            c.commit();\n        } catch (final SQLException e) {\n            try {\n                c.rollback();\n            } catch (final SQLException e1) {\n                e1.printStackTrace();\n            }\n            System.out.println("[CargoDAO.insert] Erro ao inserir -> " + e.getMessage());\n        } finally {\n            DBConnection.closePreparedStatement(pst);\n            DBConnection.closeConnection(c);\n        }\n        if (result > 0) {\n            return true;\n        } else {\n            return false;\n        }\n    }\n',
-    '    protected void runTest(URL pBaseURL, String pName, String pHref) throws Exception {\n        URL url = new URL(pBaseURL, pHref);\n        XSParser parser = new XSParser();\n        parser.setValidating(false);\n        InputSource isource = new InputSource(url.openStream());\n        isource.setSystemId(url.toString());\n        String result;\n        try {\n            parser.parse(isource);\n            ++numOk;\n            result = "Ok";\n        } catch (Exception e) {\n            ++numFailed;\n            result = e.getMessage();\n        }\n        log("Running test " + pName + " with URL " + url + ": " + result);\n    }\n',
-    '    public String generateMappackMD5(File mapPackFile) throws IOException, NoSuchAlgorithmException {\n        ZipFile zip = new ZipFile(mapPackFile);\n        try {\n            Enumeration<? extends ZipEntry> entries = zip.entries();\n            MessageDigest md5Total = MessageDigest.getInstance("MD5");\n            MessageDigest md5 = MessageDigest.getInstance("MD5");\n            while (entries.hasMoreElements()) {\n                ZipEntry entry = entries.nextElement();\n                if (entry.isDirectory()) continue;\n                String name = entry.getName();\n                if (name.toUpperCase().startsWith("META-INF")) continue;\n                md5.reset();\n                InputStream in = zip.getInputStream(entry);\n                byte[] data = Utilities.getInputBytes(in);\n                in.close();\n                byte[] digest = md5.digest(data);\n                log.trace("Hashsum " + Hex.encodeHexString(digest) + " includes \\"" + name + "\\"");\n                md5Total.update(digest);\n                md5Total.update(name.getBytes());\n            }\n            String md5sum = Hex.encodeHexString(md5Total.digest());\n            log.trace("md5sum of " + mapPackFile.getName() + ": " + md5sum);\n            return md5sum;\n        } finally {\n            zip.close();\n        }\n    }\n',
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 768]
+## 🔐 Optional: Authentication
+
+```python
+from huggingface_hub import login
+login("your_huggingface_token")
 
-# Get the similarity scores for the embeddings
-similarities = model.similarity(embeddings, embeddings)
-print(similarities.shape)
-# [3, 3]
+import wandb
+wandb.login(key="your_wandb_token")
 ```
 
-<!--
-### Direct Usage (Transformers)
+---
 
-<details><summary>Click to see the direct usage in Transformers</summary>
+## 🧾 Citation
 
-</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.*
--->
-
-## Evaluation
-
-### Metrics
-
-#### Semantic Similarity
-
-* Dataset: `val`
-* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
-
-| Metric              | Value      |
-|:--------------------|:-----------|
-| pearson_cosine      | 0.9481     |
-| **spearman_cosine** | **0.5635** |
-
-<!--
-## Bias, Risks and Limitations
-
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
-
-<!--
-### Recommendations
-
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
-
-## Training Details
-
-### Training Dataset
-
-#### Unnamed Dataset
-
-* Size: 901,028 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: 52 tokens</li><li>mean: 332.69 tokens</li><li>max: 2048 tokens</li></ul> | <ul><li>min: 54 tokens</li><li>mean: 353.29 tokens</li><li>max: 2048 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> |
-* Samples:
-  | sentence_0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | label            |
-  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
-  | <code>    public static Image load(final InputStream input, String format, Point dimension) throws CoreException {<br>        MultiStatus status = new MultiStatus(GraphVizActivator.ID, 0, "Errors occurred while running Graphviz", null);<br>        File dotInput = null, dotOutput = null;<br>        ByteArrayOutputStream dotContents = new ByteArrayOutputStream();<br>        try {<br>            dotInput = File.createTempFile(TMP_FILE_PREFIX, DOT_EXTENSION);<br>            dotOutput = File.createTempFile(TMP_FILE_PREFIX, "." + format);<br>            dotOutput.delete();<br>            FileOutputStream tmpDotOutputStream = null;<br>            try {<br>                IOUtils.copy(input, dotContents);<br>                tmpDotOutputStream = new FileOutputStream(dotInput);<br>                IOUtils.copy(new ByteArrayInputStream(dotContents.toByteArray()), tmpDotOutputStream);<br>            } finally {<br>                IOUtils.closeQuietly(tmpDotOutputStream);<br>            }<br>            IStatus result = runDot(format, dimension, dotInp...</code> | <code>    public final Matrix3D<E> read(final URL url) throws IOException {<br>        if (url == null) {<br>            throw new IllegalArgumentException("url must not be null");<br>        }<br>        InputStream inputStream = null;<br>        try {<br>            inputStream = url.openStream();<br>            return read(inputStream);<br>        } catch (IOException e) {<br>            throw e;<br>        } finally {<br>            MatrixIOUtils.closeQuietly(inputStream);<br>        }<br>    }<br></code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | <code>0.0</code> |
-  | <code>    public List<PathObject> fetchPath(BoardObject board) throws NetworkException {<br>        if (boardPathMap.containsKey(board.getId())) {<br>            return boardPathMap.get(board.getId()).getChildren();<br>        }<br>        HttpClient client = HttpConfig.newInstance();<br>        HttpGet get = new HttpGet(HttpConfig.bbsURL() + HttpConfig.BBS_0AN_BOARD + board.getId());<br>        try {<br>            HttpResponse response = client.execute(get);<br>            HttpEntity entity = response.getEntity();<br>            Document doc = XmlOperator.readDocument(entity.getContent());<br>            PathObject parent = new PathObject();<br>            BBSBodyParseHelper.parsePathList(doc, parent);<br>            parent = searchAndCreatePathFromRoot(parent);<br>            boardPathMap.put(board.getId(), parent);<br>            return parent.getChildren();<br>        } catch (Exception e) {<br>            e.printStackTrace();<br>            throw new NetworkException(e);<br>        }<br>    }<br></code>                                         | <code>    public static void readDefault() {<br>        ClassLoader l = Skeleton.class.getClassLoader();<br>        URL url;<br>        if (l != null) {<br>            url = l.getResource(DEFAULT_LOC);<br>        } else {<br>            url = ClassLoader.getSystemResource(DEFAULT_LOC);<br>        }<br>        if (url == null) {<br>            Out.error(ErrorMessages.SKEL_IO_ERROR_DEFAULT);<br>            throw new GeneratorException();<br>        }<br>        try {<br>            InputStreamReader reader = new InputStreamReader(url.openStream());<br>            readSkel(new BufferedReader(reader));<br>        } catch (IOException e) {<br>            Out.error(ErrorMessages.SKEL_IO_ERROR_DEFAULT);<br>            throw new GeneratorException();<br>        }<br>    }<br></code>                                                                                                                                                                                                                                                                                    | <code>0.0</code> |
-  | <code>    public boolean copyFile(File source, File dest) {<br>        try {<br>            FileReader in = new FileReader(source);<br>            FileWriter out = new FileWriter(dest);<br>            int c;<br>            while ((c = in.read()) != -1) out.write(c);<br>            in.close();<br>            out.close();<br>            return true;<br>        } catch (Exception e) {<br>            return false;<br>        }<br>    }<br></code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | <code>    public static boolean encodeFileToFile(String infile, String outfile) {<br>        boolean success = false;<br>        java.io.InputStream in = null;<br>        java.io.OutputStream out = null;<br>        try {<br>            in = new Base64.InputStream(new java.io.BufferedInputStream(new java.io.FileInputStream(infile)), Base64.ENCODE);<br>            out = new java.io.BufferedOutputStream(new java.io.FileOutputStream(outfile));<br>            byte[] buffer = new byte[65536];<br>            int read = -1;<br>            while ((read = in.read(buffer)) >= 0) {<br>                out.write(buffer, 0, read);<br>            }<br>            success = true;<br>        } catch (java.io.IOException exc) {<br>            exc.printStackTrace();<br>        } finally {<br>            try {<br>                in.close();<br>            } catch (Exception exc) {<br>            }<br>            try {<br>                out.close();<br>            } catch (Exception exc) {<br>            }<br>        }<br>        return success;<br>    }<br></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
-#### Non-Default Hyperparameters
-
-- `eval_strategy`: steps
-- `per_device_train_batch_size`: 32
-- `per_device_eval_batch_size`: 32
-- `num_train_epochs`: 1
-- `fp16`: True
-- `multi_dataset_batch_sampler`: round_robin
-
-#### All Hyperparameters
-<details><summary>Click to expand</summary>
-
-- `overwrite_output_dir`: False
-- `do_predict`: False
-- `eval_strategy`: steps
-- `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`: 1
-- `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`: True
-- `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
-- `dispatch_batches`: None
-- `split_batches`: 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>
-
-### Training Logs
-| Epoch  | Step  | Training Loss | val_spearman_cosine |
-|:------:|:-----:|:-------------:|:-------------------:|
-| 0.0178 | 500   | 0.1622        | -                   |
-| 0.0355 | 1000  | 0.0124        | 0.5702              |
-| 0.0533 | 1500  | 0.0087        | -                   |
-| 0.0710 | 2000  | 0.0064        | 0.5686              |
-| 0.0888 | 2500  | 0.0048        | -                   |
-| 0.1065 | 3000  | 0.0046        | 0.5753              |
-| 0.1243 | 3500  | 0.0036        | -                   |
-| 0.1421 | 4000  | 0.0039        | 0.5745              |
-| 0.1598 | 4500  | 0.0036        | -                   |
-| 0.1776 | 5000  | 0.0035        | 0.5637              |
-| 0.1953 | 5500  | 0.0036        | -                   |
-| 0.2131 | 6000  | 0.0027        | 0.5615              |
-| 0.2308 | 6500  | 0.002         | -                   |
-| 0.2486 | 7000  | 0.0019        | 0.5660              |
-| 0.2664 | 7500  | 0.0017        | -                   |
-| 0.2841 | 8000  | 0.0017        | 0.5622              |
-| 0.3019 | 8500  | 0.0019        | -                   |
-| 0.3196 | 9000  | 0.0017        | 0.5583              |
-| 0.3374 | 9500  | 0.0012        | -                   |
-| 0.3551 | 10000 | 0.0013        | 0.5547              |
-| 0.3729 | 10500 | 0.0015        | -                   |
-| 0.3907 | 11000 | 0.0011        | 0.5631              |
-| 0.4084 | 11500 | 0.0012        | -                   |
-| 0.4262 | 12000 | 0.0013        | 0.5630              |
-| 0.4439 | 12500 | 0.001         | -                   |
-| 0.4617 | 13000 | 0.0009        | 0.5607              |
-| 0.4794 | 13500 | 0.0007        | -                   |
-| 0.4972 | 14000 | 0.001         | 0.5590              |
-| 0.5150 | 14500 | 0.001         | -                   |
-| 0.5327 | 15000 | 0.0009        | 0.5572              |
-| 0.5505 | 15500 | 0.0007        | -                   |
-| 0.5682 | 16000 | 0.0006        | 0.5607              |
-| 0.5860 | 16500 | 0.0007        | -                   |
-| 0.6037 | 17000 | 0.0006        | 0.5675              |
-| 0.6215 | 17500 | 0.0007        | -                   |
-| 0.6392 | 18000 | 0.0009        | 0.5610              |
-| 0.6570 | 18500 | 0.0008        | -                   |
-| 0.6748 | 19000 | 0.0007        | 0.5583              |
-| 0.6925 | 19500 | 0.0006        | -                   |
-| 0.7103 | 20000 | 0.0006        | 0.5662              |
-| 0.7280 | 20500 | 0.0007        | -                   |
-| 0.7458 | 21000 | 0.0005        | 0.5659              |
-| 0.7635 | 21500 | 0.0004        | -                   |
-| 0.7813 | 22000 | 0.0006        | 0.5667              |
-| 0.7991 | 22500 | 0.0006        | -                   |
-| 0.8168 | 23000 | 0.0006        | 0.5644              |
-| 0.8346 | 23500 | 0.0005        | -                   |
-| 0.8523 | 24000 | 0.0003        | 0.5629              |
-| 0.8701 | 24500 | 0.0005        | -                   |
-| 0.8878 | 25000 | 0.0005        | 0.5642              |
-| 0.9056 | 25500 | 0.0006        | -                   |
-| 0.9234 | 26000 | 0.0006        | 0.5640              |
-| 0.9411 | 26500 | 0.0004        | -                   |
-| 0.9589 | 27000 | 0.0007        | 0.5634              |
-| 0.9766 | 27500 | 0.0004        | -                   |
-| 0.9944 | 28000 | 0.0005        | 0.5635              |
-| 1.0    | 28158 | -             | 0.5635              |
-
-
-### Framework Versions
-- Python: 3.11.11
-- Sentence Transformers: 4.0.1
-- Transformers: 4.50.3
-- PyTorch: 2.6.0+cu124
-- Accelerate: 1.5.2
-- Datasets: 3.5.0
-- Tokenizers: 0.21.1
-
-## 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",
+    booktitle = "EMNLP 2019",
+    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.*
--->
+## 🔓 License
 
-<!--
-## Model Card Contact
+Apache License 2.0
 
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->