Upload ModernBERT model

1_SpladePooling/config.json

@@ -0,0 +1,5 @@
+{
+    "pooling_strategy": "max",
+    "activation_function": "relu",
+    "word_embedding_dimension": 30005
+}

README.md

@@ -0,0 +1,527 @@
+---
+tags:
+- sentence-transformers
+- sparse-encoder
+- sparse
+- splade
+- generated_from_trainer
+- dataset_size:1441500
+- loss:SpladeLoss
+- loss:SparseMultipleNegativesRankingLoss
+- loss:FlopsLoss
+base_model: Shuu12121/CodeModernBERT-Finch
+widget:
+- text: "public static TaggableReadPreference secondary(final TagSet tagSet,\n   \
+    \                                                final long maxStaleness, final\
+    \ TimeUnit timeUnit) {\n        return new SecondaryReadPreference(singletonList(tagSet),\
+    \ maxStaleness, timeUnit);\n    }"
+- text: '// DoTimeout performs the given request and waits for response during
+
+    // the given timeout duration.
+
+    //
+
+    // Request must contain at least non-zero RequestURI with full url (including
+
+    // scheme and host) or non-zero Host header + RequestURI.
+
+    //
+
+    // Client determines the server to be requested in the following order:
+
+    //
+
+    //   - from RequestURI if it contains full url with scheme and host;
+
+    //   - from Host header otherwise.
+
+    //
+
+    // The function doesn''t follow redirects. Use Get* for following redirects.
+
+    //
+
+    // Response is ignored if resp is nil.
+
+    //
+
+    // ErrTimeout is returned if the response wasn''t returned during
+
+    // the given timeout.
+
+    //
+
+    // ErrNoFreeConns is returned if all Client.MaxConnsPerHost connections
+
+    // to the requested host are busy.
+
+    //
+
+    // It is recommended obtaining req and resp via AcquireRequest
+
+    // and AcquireResponse in performance-critical code.
+
+    //
+
+    // Warning: DoTimeout does not terminate the request itself. The request will
+
+    // continue in the background and the response will be discarded.
+
+    // If requests take too long and the connection pool gets filled up please
+
+    // try setting a ReadTimeout.'
+- text: "func (c *Compressor) selectEncoder(h http.Header, w io.Writer) (io.Writer,\
+    \ string, func()) {\n\theader := h.Get(\"Accept-Encoding\")\n\n\t// Parse the\
+    \ names of all accepted algorithms from the header.\n\taccepted := strings.Split(strings.ToLower(header),\
+    \ \",\")\n\n\t// Find supported encoder by accepted list by precedence\n\tfor\
+    \ _, name := range c.encodingPrecedence {\n\t\tif matchAcceptEncoding(accepted,\
+    \ name) {\n\t\t\tif pool, ok := c.pooledEncoders[name]; ok {\n\t\t\t\tencoder\
+    \ := pool.Get().(ioResetterWriter)\n\t\t\t\tcleanup := func() {\n\t\t\t\t\tpool.Put(encoder)\n\
+    \t\t\t\t}\n\t\t\t\tencoder.Reset(w)\n\t\t\t\treturn encoder, name, cleanup\n\n\
+    \t\t\t}\n\t\t\tif fn, ok := c.encoders[name]; ok {\n\t\t\t\treturn fn(w, c.level),\
+    \ name, func() {}\n\t\t\t}\n\t\t}\n\n\t}\n\n\t// No encoder found to match the\
+    \ accepted encoding\n\treturn nil, \"\", func() {}\n}"
+- text: 'Parse the template file and return it as string
+
+
+    @param array $arrAttributes An optional attributes array
+
+
+    @return string The template markup'
+- text: "function seed_mix() {\n      a ^= b <<  11; d = add(d, a); b = add(b, c);\n\
+    \      b ^= c >>>  2; e = add(e, b); c = add(c, d);\n      c ^= d <<   8; f =\
+    \ add(f, c); d = add(d, e);\n      d ^= e >>> 16; g = add(g, d); e = add(e, f);\n\
+    \      e ^= f <<  10; h = add(h, e); f = add(f, g);\n      f ^= g >>>  4; a =\
+    \ add(a, f); g = add(g, h);\n      g ^= h <<   8; b = add(b, g); h = add(h, a);\n\
+    \      h ^= a >>>  9; c = add(c, h); a = add(a, b);\n    }"
+pipeline_tag: feature-extraction
+library_name: sentence-transformers
+---
+
+# SPLADE Sparse Encoder
+
+This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [Shuu12121/CodeModernBERT-Finch](https://huggingface.co/Shuu12121/CodeModernBERT-Finch) using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30005-dimensional sparse vector space   and can be used for semantic search and sparse retrieval.
+## Model Details
+
+### Model Description
+- **Model Type:** SPLADE Sparse Encoder
+- **Base model:** [Shuu12121/CodeModernBERT-Finch](https://huggingface.co/Shuu12121/CodeModernBERT-Finch) <!-- at revision 8159a3905097a6cea798bb86d548caed9c1ad37d -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 30005 dimensions
+- **Similarity Function:** Dot Product
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
+
+### Full Model Architecture
+
+```
+SparseEncoder(
+  (0): MLMTransformer({'max_seq_length': 8192, 'do_lower_case': False, 'architecture': 'ModernBertForMaskedLM'})
+  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30005})
+)
+```
+
+## 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 SparseEncoder
+
+# Download from the 🤗 Hub
+model = SparseEncoder("sparse_encoder_model_id")
+# Run inference
+sentences = [
+    'Will detect inputs that begin with @MyNamespace/... and replace the namespace with the corresponding path.\n\n@see \\Assetic\\Factory\\AssetFactory::parseInput()',
+    'protected function parseInput($input, array $options = array())\n    {\n        $matches = null;\n        // search for @MyNamespace/path/to/asset\n        if (preg_match("|^\\@([a-z_][_a-z0-9]*)/|i", $input, $matches)) {\n            $ns = $matches[1];\n            if (!array_key_exists($ns, $this->namespaces)) {\n                throw new \\RuntimeException("$ns : unknown namespace !");\n            }\n            $input = $this->namespaces[$ns] . substr($input, strlen($ns) + 1);\n        }\n        return parent::parseInput($input, $options);\n    }',
+    'function seed_mix() {\n      a ^= b <<  11; d = add(d, a); b = add(b, c);\n      b ^= c >>>  2; e = add(e, b); c = add(c, d);\n      c ^= d <<   8; f = add(f, c); d = add(d, e);\n      d ^= e >>> 16; g = add(g, d); e = add(e, f);\n      e ^= f <<  10; h = add(h, e); f = add(f, g);\n      f ^= g >>>  4; a = add(a, f); g = add(g, h);\n      g ^= h <<   8; b = add(b, g); h = add(h, a);\n      h ^= a >>>  9; c = add(c, h); a = add(a, b);\n    }',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 30005]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[26.3028, 23.1010,  3.4799],
+#         [23.1010, 42.4588,  6.9869],
+#         [ 3.4799,  6.9869, 59.2962]])
+```
+
+<!--
+### 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
+
+*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: 1,441,500 training samples
+* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | text1                                                                               | text2                                                                                 | label                                                         |
+  |:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                                | float                                                         |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 49.63 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 180.64 tokens</li><li>max: 6082 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
+* Samples:
+  | text1                                                                                                                             | text2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | label            |
+  |:----------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>// makeWin32File makes a new win32File from an existing file handle</code>                                                  | <code>func makeWin32File(h syscall.Handle) (*win32File, error) {<br>	f := &win32File{handle: h}<br>	ioInitOnce.Do(initIo)<br>	_, err := createIoCompletionPort(h, ioCompletionPort, 0, 0xffffffff)<br>	if err != nil {<br>		return nil, err<br>	}<br>	err = setFileCompletionNotificationModes(h, cFILE_SKIP_COMPLETION_PORT_ON_SUCCESS|cFILE_SKIP_SET_EVENT_ON_HANDLE)<br>	if err != nil {<br>		return nil, err<br>	}<br>	f.readDeadline.channel = make(timeoutChan)<br>	f.writeDeadline.channel = make(timeoutChan)<br>	return f, nil<br>}</code> | <code>1.0</code> |
+  | <code>// Convert_v1_FlexPersistentVolumeSource_To_core_FlexPersistentVolumeSource is an autogenerated conversion function.</code> | <code>func Convert_v1_FlexPersistentVolumeSource_To_core_FlexPersistentVolumeSource(in *v1.FlexPersistentVolumeSource, out *core.FlexPersistentVolumeSource, s conversion.Scope) error {<br>	return autoConvert_v1_FlexPersistentVolumeSource_To_core_FlexPersistentVolumeSource(in, out, s)<br>}</code>                                                                                                                                                                                                                                            | <code>1.0</code> |
+  | <code>// AddRunCmd is defined on the RunCmdsConfig interface.</code>                                                              | <code>func (cfg *cloudConfig) AddRunCmd(args ...string) {<br>	cfg.attrs["runcmd"] = append(cfg.RunCmds(), strings.Join(args, " "))<br>}</code>                                                                                                                                                                                                                                                                                                                                                                                                      | <code>1.0</code> |
+* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
+  ```json
+  {
+      "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
+      "document_regularizer_weight": 3e-05,
+      "query_regularizer_weight": 5e-05
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 6,000 evaluation samples
+* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | text1                                                                              | text2                                                                                 | label                                                         |
+  |:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                                | float                                                         |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 45.53 tokens</li><li>max: 495 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 183.92 tokens</li><li>max: 7677 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
+* Samples:
+  | text1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | text2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | label            |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>// establish data storage, format and dimensions of a     renderbuffer object's image</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                               | <code>func RenderbufferStorage(target uint32, internalformat uint32, width int32, height int32) {<br>	syscall.Syscall6(gpRenderbufferStorage, 4, uintptr(target), uintptr(internalformat), uintptr(width), uintptr(height), 0, 0)<br>}</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | <code>1.0</code> |
+  | <code>// GetObject is a wrapper around gtk_builder_get_object(). The returned result<br>// is an IObject, so it will need to be type-asserted to the appropriate type before<br>// being used. For example, to get an object and type assert it as a window:<br>//<br>//   obj, err := builder.GetObject("window")<br>//   if err != nil {<br>//       // object not found<br>//       return<br>//   }<br>//   if w, ok := obj.(*gtk.Window); ok {<br>//       // do stuff with w here<br>//   } else {<br>//       // not a *gtk.Window<br>//   }<br>//</code> | <code>func (b *Builder) GetObject(name string) (glib.IObject, error) {<br>	cstr := C.CString(name)<br>	defer C.free(unsafe.Pointer(cstr))<br>	c := C.gtk_builder_get_object(b.native(), (*C.gchar)(cstr))<br>	if c == nil {<br>		return nil, errors.New("object '" + name + "' not found")<br>	}<br>	obj, err := cast(c)<br>	if err != nil {<br>		return nil, err<br>	}<br>	return obj, nil<br>}</code>                                                                                                                                                                                                                                                                                                                                                                                  | <code>1.0</code> |
+  | <code>// augmentGoroutine processes source files to improve call to be more<br>// descriptive.<br>//<br>// It modifies the routine.</code>                                                                                                                                                                                                                                                                                                                                                                                                                       | <code>func (c *cache) augmentGoroutine(goroutine *Goroutine) {<br>	if c.files == nil {<br>		c.files = map[string][]byte{}<br>	}<br>	if c.parsed == nil {<br>		c.parsed = map[string]*parsedFile{}<br>	}<br>	// For each call site, look at the next call and populate it. Then we can<br>	// walk back and reformat things.<br>	for i := range goroutine.Stack.Calls {<br>		c.load(goroutine.Stack.Calls[i].LocalSrcPath)<br>	}<br><br>	// Once all loaded, we can look at the next call when available.<br>	for i := 0; i < len(goroutine.Stack.Calls)-1; i++ {<br>		// Get the AST from the previous call and process the call line with it.<br>		if f := c.getFuncAST(&goroutine.Stack.Calls[i]); f != nil {<br>			processCall(&goroutine.Stack.Calls[i], f)<br>		}<br>	}<br>}</code> | <code>1.0</code> |
+* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
+  ```json
+  {
+      "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
+      "document_regularizer_weight": 3e-05,
+      "query_regularizer_weight": 5e-05
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 2
+- `gradient_accumulation_steps`: 25
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `fp16`: True
+
+#### 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`: 2
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 25
+- `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`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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}
+- `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`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0173 | 500   | 252.5855      |
+| 0.0347 | 1000  | 0.4281        |
+| 0.0520 | 1500  | 0.071         |
+| 0.0694 | 2000  | 0.0579        |
+| 0.0867 | 2500  | 0.04          |
+| 0.1041 | 3000  | 0.0422        |
+| 0.1214 | 3500  | 0.041         |
+| 0.1387 | 4000  | 0.0347        |
+| 0.1561 | 4500  | 0.0341        |
+| 0.1734 | 5000  | 0.0288        |
+| 0.1908 | 5500  | 0.0243        |
+| 0.2081 | 6000  | 0.0249        |
+| 0.2255 | 6500  | 0.0242        |
+| 0.2428 | 7000  | 0.0204        |
+| 0.2601 | 7500  | 0.0206        |
+| 0.2775 | 8000  | 0.0198        |
+| 0.2948 | 8500  | 0.0205        |
+| 0.3122 | 9000  | 0.0176        |
+| 0.3295 | 9500  | 0.0207        |
+| 0.3469 | 10000 | 0.0196        |
+| 0.3642 | 10500 | 0.0132        |
+| 0.3815 | 11000 | 0.016         |
+| 0.3989 | 11500 | 0.0151        |
+| 0.4162 | 12000 | 0.0168        |
+| 0.4336 | 12500 | 0.0161        |
+| 0.4509 | 13000 | 0.0156        |
+| 0.4683 | 13500 | 0.0134        |
+| 0.4856 | 14000 | 0.0156        |
+| 0.5029 | 14500 | 0.0138        |
+| 0.5203 | 15000 | 0.0134        |
+| 0.5376 | 15500 | 0.0146        |
+| 0.5550 | 16000 | 0.0153        |
+| 0.5723 | 16500 | 0.0135        |
+| 0.5897 | 17000 | 0.0136        |
+| 0.6070 | 17500 | 0.0122        |
+| 0.6243 | 18000 | 0.0115        |
+| 0.6417 | 18500 | 0.0132        |
+| 0.6590 | 19000 | 0.0101        |
+| 0.6764 | 19500 | 0.0092        |
+| 0.6937 | 20000 | 0.0117        |
+| 0.7111 | 20500 | 0.0098        |
+| 0.7284 | 21000 | 0.0122        |
+| 0.7458 | 21500 | 0.0102        |
+| 0.7631 | 22000 | 0.0088        |
+| 0.7804 | 22500 | 0.0093        |
+| 0.7978 | 23000 | 0.0101        |
+| 0.8151 | 23500 | 0.0083        |
+| 0.8325 | 24000 | 0.0095        |
+| 0.8498 | 24500 | 0.0081        |
+| 0.8672 | 25000 | 0.0095        |
+| 0.8845 | 25500 | 0.009         |
+| 0.9018 | 26000 | 0.0081        |
+| 0.9192 | 26500 | 0.0065        |
+| 0.9365 | 27000 | 0.009         |
+| 0.9539 | 27500 | 0.0075        |
+| 0.9712 | 28000 | 0.0078        |
+| 0.9886 | 28500 | 0.0094        |
+
+
+### Framework Versions
+- Python: 3.11.13
+- Sentence Transformers: 5.0.0
+- Transformers: 4.53.1
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.8.1
+- Datasets: 3.6.0
+- 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",
+}
+```
+
+#### SpladeLoss
+```bibtex
+@misc{formal2022distillationhardnegativesampling,
+      title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
+      author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
+      year={2022},
+      eprint={2205.04733},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2205.04733},
+}
+```
+
+#### SparseMultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+#### FlopsLoss
+```bibtex
+@article{paria2020minimizing,
+    title={Minimizing flops to learn efficient sparse representations},
+    author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
+    journal={arXiv preprint arXiv:2004.05665},
+    year={2020}
+}
+```
+
+<!--
+## 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.*
+--> 

added_tokens.json

@@ -0,0 +1,7 @@
+{
+  "</s>": 30001,
+  "<mask>": 30004,
+  "<pad>": 30003,
+  "<s>": 30000,
+  "<unk>": 30002
+}

config.json

@@ -0,0 +1,48 @@
+{
+  "architectures": [
+    "ModernBertForMaskedLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 30000,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "cls",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 30001,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "hidden_activation": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 512,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "local_attention": 128,
+  "local_attention_rope_theta": 10000,
+  "local_attention_window": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 8,
+  "num_hidden_layers": 6,
+  "pad_token_id": 1,
+  "repad_logits_with_grad": false,
+  "rope_theta": 160000,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.1",
+  "type_vocab_size": 2,
+  "vocab_size": 30005
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SparseEncoder",
+  "__version__": {
+    "sentence_transformers": "5.0.0",
+    "transformers": "4.53.1",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "dot"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f05d60a8a6b606ce3d2eb8586679b3da4cb75fafc81fd7faeff0c1e662937482
+size 163314956

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.sparse_encoder.models.MLMTransformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_SpladePooling",
+    "type": "sentence_transformers.sparse_encoder.models.SpladePooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 8192,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,62 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "30000": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30001": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30002": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30003": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30004": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 256,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}