|
|
--- |
|
|
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.* |
|
|
--> |
