| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:8118 |
| - loss:CachedMultipleNegativesRankingLoss |
| base_model: benjamintli/modernbert-cosqa |
| widget: |
| - source_sentence: python create path if doesnt exist |
| sentences: |
| - "def clean_whitespace(string, compact=False):\n \"\"\"Return string with compressed\ |
| \ whitespace.\"\"\"\n for a, b in (('\\r\\n', '\\n'), ('\\r', '\\n'), ('\\\ |
| n\\n', '\\n'),\n ('\\t', ' '), (' ', ' ')):\n string =\ |
| \ string.replace(a, b)\n if compact:\n for a, b in (('\\n', ' '), ('[\ |
| \ ', '['),\n (' ', ' '), (' ', ' '), (' ', ' ')):\n \ |
| \ string = string.replace(a, b)\n return string.strip()" |
| - "def rotateImage(img, angle):\n \"\"\"\n\n querries scipy.ndimage.rotate\ |
| \ routine\n :param img: image to be rotated\n :param angle: angle to be\ |
| \ rotated (radian)\n :return: rotated image\n \"\"\"\n imgR = scipy.ndimage.rotate(img,\ |
| \ angle, reshape=False)\n return imgR" |
| - "def check_create_folder(filename):\n \"\"\"Check if the folder exisits. If\ |
| \ not, create the folder\"\"\"\n os.makedirs(os.path.dirname(filename), exist_ok=True)" |
| - source_sentence: how decompiled python code looks like |
| sentences: |
| - "def xeval(source, optimize=True):\n \"\"\"Compiles to native Python bytecode\ |
| \ and runs program, returning the\n topmost value on the stack.\n\n Args:\n\ |
| \ optimize: Whether to optimize the code after parsing it.\n\n Returns:\n\ |
| \ None: If the stack is empty\n obj: If the stack contains a single\ |
| \ value\n [obj, obj, ...]: If the stack contains many values\n \"\"\"\ |
| \n native = xcompile(source, optimize=optimize)\n return native()" |
| - "def html(header_rows):\n \"\"\"\n Convert a list of tuples describing a\ |
| \ table into a HTML string\n \"\"\"\n name = 'table%d' % next(tablecounter)\n\ |
| \ return HtmlTable([map(str, row) for row in header_rows], name).render()" |
| - "def cint8_array_to_numpy(cptr, length):\n \"\"\"Convert a ctypes int pointer\ |
| \ array to a numpy array.\"\"\"\n if isinstance(cptr, ctypes.POINTER(ctypes.c_int8)):\n\ |
| \ return np.fromiter(cptr, dtype=np.int8, count=length)\n else:\n \ |
| \ raise RuntimeError('Expected int pointer')" |
| - source_sentence: python calling pytest from a python script |
| sentences: |
| - "def draw_image(self, ax, image):\n \"\"\"Process a matplotlib image object\ |
| \ and call renderer.draw_image\"\"\"\n self.renderer.draw_image(imdata=utils.image_to_base64(image),\n\ |
| \ extent=image.get_extent(),\n \ |
| \ coordinates=\"data\",\n style={\"\ |
| alpha\": image.get_alpha(),\n \"zorder\"\ |
| : image.get_zorder()},\n mplobj=image)" |
| - "def test(): # pragma: no cover\n \"\"\"Execute the unit tests on an installed\ |
| \ copy of unyt.\n\n Note that this function requires pytest to run. If pytest\ |
| \ is not\n installed this function will raise ImportError.\n \"\"\"\n \ |
| \ import pytest\n import os\n\n pytest.main([os.path.dirname(os.path.abspath(__file__))])" |
| - "def is_int(string):\n \"\"\"\n Checks if a string is an integer. If the\ |
| \ string value is an integer\n return True, otherwise return False. \n \n\ |
| \ Args:\n string: a string to test.\n\n Returns: \n boolean\n\ |
| \ \"\"\"\n try:\n a = float(string)\n b = int(a)\n except\ |
| \ ValueError:\n return False\n else:\n return a == b" |
| - source_sentence: python datetime get last day in a month |
| sentences: |
| - "def upgrade(directory, sql, tag, x_arg, revision):\n \"\"\"Upgrade to a later\ |
| \ version\"\"\"\n _upgrade(directory, revision, sql, tag, x_arg)" |
| - "def flat_list(lst):\n \"\"\"This function flatten given nested list.\n \ |
| \ Argument:\n nested list\n Returns:\n flat list\n \"\"\"\n\ |
| \ if isinstance(lst, list):\n for item in lst:\n for i in\ |
| \ flat_list(item):\n yield i\n else:\n yield lst" |
| - "def get_last_weekday_in_month(year, month, weekday):\n \"\"\"Get the last\ |
| \ weekday in a given month. e.g:\n\n >>> # the last monday in Jan 2013\n\ |
| \ >>> Calendar.get_last_weekday_in_month(2013, 1, MON)\n datetime.date(2013,\ |
| \ 1, 28)\n \"\"\"\n day = date(year, month, monthrange(year, month)[1])\n\ |
| \ while True:\n if day.weekday() == weekday:\n \ |
| \ break\n day = day - timedelta(days=1)\n return day" |
| - source_sentence: first duplicate element in list in python |
| sentences: |
| - "def python_mime(fn):\n \"\"\"\n Decorator, which adds correct MIME type\ |
| \ for python source to the decorated\n bottle API function.\n \"\"\"\n \ |
| \ @wraps(fn)\n def python_mime_decorator(*args, **kwargs):\n response.content_type\ |
| \ = \"text/x-python\"\n\n return fn(*args, **kwargs)\n\n return python_mime_decorator" |
| - "def purge_duplicates(list_in):\n \"\"\"Remove duplicates from list while preserving\ |
| \ order.\n\n Parameters\n ----------\n list_in: Iterable\n\n Returns\n\ |
| \ -------\n list\n List of first occurences in order\n \"\"\"\n\ |
| \ _list = []\n for item in list_in:\n if item not in _list:\n \ |
| \ _list.append(item)\n return _list" |
| - "def getRect(self):\n\t\t\"\"\"\n\t\tReturns the window bounds as a tuple of (x,y,w,h)\n\ |
| \t\t\"\"\"\n\t\treturn (self.x, self.y, self.w, self.h)" |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy@1 |
| - cosine_accuracy@3 |
| - cosine_accuracy@5 |
| - cosine_accuracy@10 |
| - cosine_precision@1 |
| - cosine_precision@3 |
| - cosine_precision@5 |
| - cosine_precision@10 |
| - cosine_recall@1 |
| - cosine_recall@3 |
| - cosine_recall@5 |
| - cosine_recall@10 |
| - cosine_ndcg@10 |
| - cosine_mrr@10 |
| - cosine_map@100 |
| model-index: |
| - name: SentenceTransformer based on benjamintli/modernbert-cosqa |
| results: |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| name: eval |
| type: eval |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.6197339246119734 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@3 |
| value: 0.88470066518847 |
| name: Cosine Accuracy@3 |
| - type: cosine_accuracy@5 |
| value: 0.9390243902439024 |
| name: Cosine Accuracy@5 |
| - type: cosine_accuracy@10 |
| value: 0.9778270509977827 |
| name: Cosine Accuracy@10 |
| - type: cosine_precision@1 |
| value: 0.6197339246119734 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.29490022172949004 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.18780487804878046 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.0977827050997783 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.6197339246119734 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.88470066518847 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.9390243902439024 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 0.9778270509977827 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.8124675617500997 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@10 |
| value: 0.7577473339668463 |
| name: Cosine Mrr@10 |
| - type: cosine_map@100 |
| value: 0.7588050805217604 |
| name: Cosine Map@100 |
| --- |
| |
| # SentenceTransformer based on benjamintli/modernbert-cosqa |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [benjamintli/modernbert-cosqa](https://huggingface.co/benjamintli/modernbert-cosqa). 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. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [benjamintli/modernbert-cosqa](https://huggingface.co/benjamintli/modernbert-cosqa) <!-- at revision c85b25617894d583fafad7eb7421b7dc0aab0ad9 --> |
| - **Maximum Sequence Length:** 512 tokens |
| - **Output Dimensionality:** 768 dimensions |
| - **Similarity Function:** Cosine Similarity |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SentenceTransformer( |
| (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'OptimizedModule'}) |
| (1): Pooling({'word_embedding_dimension': 768, '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}) |
| ) |
| ``` |
|
|
| ## Usage |
|
|
| ### Direct Usage (Sentence Transformers) |
|
|
| First install the Sentence Transformers library: |
|
|
| ```bash |
| pip install -U sentence-transformers |
| ``` |
|
|
| Then you can load this model and run inference. |
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| # Download from the 🤗 Hub |
| model = SentenceTransformer("modernbert-cosqa") |
| # Run inference |
| queries = [ |
| "first duplicate element in list in python", |
| ] |
| documents = [ |
| 'def purge_duplicates(list_in):\n """Remove duplicates from list while preserving order.\n\n Parameters\n ----------\n list_in: Iterable\n\n Returns\n -------\n list\n List of first occurences in order\n """\n _list = []\n for item in list_in:\n if item not in _list:\n _list.append(item)\n return _list', |
| 'def getRect(self):\n\t\t"""\n\t\tReturns the window bounds as a tuple of (x,y,w,h)\n\t\t"""\n\t\treturn (self.x, self.y, self.w, self.h)', |
| 'def python_mime(fn):\n """\n Decorator, which adds correct MIME type for python source to the decorated\n bottle API function.\n """\n @wraps(fn)\n def python_mime_decorator(*args, **kwargs):\n response.content_type = "text/x-python"\n\n return fn(*args, **kwargs)\n\n return python_mime_decorator', |
| ] |
| query_embeddings = model.encode_query(queries) |
| document_embeddings = model.encode_document(documents) |
| print(query_embeddings.shape, document_embeddings.shape) |
| # [1, 768] [3, 768] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(query_embeddings, document_embeddings) |
| print(similarities) |
| # tensor([[ 0.5986, -0.0006, -0.0122]]) |
| ``` |
|
|
| <!-- |
| ### 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.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Information Retrieval |
|
|
| * Dataset: `eval` |
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | cosine_accuracy@1 | 0.6197 | |
| | cosine_accuracy@3 | 0.8847 | |
| | cosine_accuracy@5 | 0.939 | |
| | cosine_accuracy@10 | 0.9778 | |
| | cosine_precision@1 | 0.6197 | |
| | cosine_precision@3 | 0.2949 | |
| | cosine_precision@5 | 0.1878 | |
| | cosine_precision@10 | 0.0978 | |
| | cosine_recall@1 | 0.6197 | |
| | cosine_recall@3 | 0.8847 | |
| | cosine_recall@5 | 0.939 | |
| | cosine_recall@10 | 0.9778 | |
| | **cosine_ndcg@10** | **0.8125** | |
| | cosine_mrr@10 | 0.7577 | |
| | cosine_map@100 | 0.7588 | |
| |
| <!-- |
| ## 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: 8,118 training samples |
| * Columns: <code>query</code> and <code>positive</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | query | positive | |
| |:--------|:--------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
| | type | string | string | |
| | details | <ul><li>min: 6 tokens</li><li>mean: 9.3 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 35 tokens</li><li>mean: 85.05 tokens</li><li>max: 512 tokens</li></ul> | |
| * Samples: |
| | query | positive | |
| |:--------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>python code for opening geojson file</code> | <code>def _loadfilepath(self, filepath, **kwargs):<br> """This loads a geojson file into a geojson python<br> dictionary using the json module.<br> <br> Note: to load with a different text encoding use the encoding argument.<br> """<br> with open(filepath, "r") as f:<br> data = json.load(f, **kwargs)<br> return data</code> | |
| | <code>python 3 none compare with int</code> | <code>def is_natural(x):<br> """A non-negative integer."""<br> try:<br> is_integer = int(x) == x<br> except (TypeError, ValueError):<br> return False<br> return is_integer and x >= 0</code> | |
| | <code>design db memory cache python</code> | <code>def refresh(self, document):<br> """ Load a new copy of a document from the database. does not<br> replace the old one """<br> try:<br> old_cache_size = self.cache_size<br> self.cache_size = 0<br> obj = self.query(type(document)).filter_by(mongo_id=document.mongo_id).one()<br> finally:<br> self.cache_size = old_cache_size<br> self.cache_write(obj)<br> return obj</code> | |
| * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "mini_batch_size": 64, |
| "gather_across_devices": false, |
| "directions": [ |
| "query_to_doc" |
| ], |
| "partition_mode": "joint", |
| "hardness_mode": null, |
| "hardness_strength": 0.0 |
| } |
| ``` |
| |
| ### Evaluation Dataset |
| |
| #### Unnamed Dataset |
| |
| * Size: 902 evaluation samples |
| * Columns: <code>query</code> and <code>positive</code> |
| * Approximate statistics based on the first 902 samples: |
| | | query | positive | |
| |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
| | type | string | string | |
| | details | <ul><li>min: 6 tokens</li><li>mean: 9.24 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 38 tokens</li><li>mean: 86.55 tokens</li><li>max: 332 tokens</li></ul> | |
| * Samples: |
| | query | positive | |
| |:--------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>how to remove masked items in python array</code> | <code>def ma(self):<br> """Represent data as a masked array.<br><br> The array is returned with column-first indexing, i.e. for a data file with<br> columns X Y1 Y2 Y3 ... the array a will be a[0] = X, a[1] = Y1, ... .<br><br> inf and nan are filtered via :func:`numpy.isfinite`.<br> """<br> a = self.array<br> return numpy.ma.MaskedArray(a, mask=numpy.logical_not(numpy.isfinite(a)))</code> | |
| | <code>python deepcopy basic type</code> | <code>def __deepcopy__(self, memo):<br> """Improve deepcopy speed."""<br> return type(self)(value=self._value, enum_ref=self.enum_ref)</code> | |
| | <code>python number of non nan rows in a row</code> | <code>def count_rows_with_nans(X):<br> """Count the number of rows in 2D arrays that contain any nan values."""<br> if X.ndim == 2:<br> return np.where(np.isnan(X).sum(axis=1) != 0, 1, 0).sum()</code> | |
| * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "mini_batch_size": 64, |
| "gather_across_devices": false, |
| "directions": [ |
| "query_to_doc" |
| ], |
| "partition_mode": "joint", |
| "hardness_mode": null, |
| "hardness_strength": 0.0 |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `per_device_train_batch_size`: 1024 |
| - `num_train_epochs`: 10 |
| - `learning_rate`: 2e-06 |
| - `warmup_steps`: 0.1 |
| - `bf16`: True |
| - `eval_strategy`: epoch |
| - `per_device_eval_batch_size`: 1024 |
| - `push_to_hub`: True |
| - `hub_model_id`: modernbert-cosqa |
| - `load_best_model_at_end`: True |
| - `dataloader_num_workers`: 4 |
| - `batch_sampler`: no_duplicates |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `per_device_train_batch_size`: 1024 |
| - `num_train_epochs`: 10 |
| - `max_steps`: -1 |
| - `learning_rate`: 2e-06 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `warmup_steps`: 0.1 |
| - `optim`: adamw_torch_fused |
| - `optim_args`: None |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `optim_target_modules`: None |
| - `gradient_accumulation_steps`: 1 |
| - `average_tokens_across_devices`: True |
| - `max_grad_norm`: 1.0 |
| - `label_smoothing_factor`: 0.0 |
| - `bf16`: True |
| - `fp16`: False |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `use_cache`: False |
| - `neftune_noise_alpha`: None |
| - `torch_empty_cache_steps`: None |
| - `auto_find_batch_size`: False |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `include_num_input_tokens_seen`: no |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `disable_tqdm`: False |
| - `project`: huggingface |
| - `trackio_space_id`: trackio |
| - `eval_strategy`: epoch |
| - `per_device_eval_batch_size`: 1024 |
| - `prediction_loss_only`: True |
| - `eval_on_start`: False |
| - `eval_do_concat_batches`: True |
| - `eval_use_gather_object`: False |
| - `eval_accumulation_steps`: None |
| - `include_for_metrics`: [] |
| - `batch_eval_metrics`: False |
| - `save_only_model`: False |
| - `save_on_each_node`: False |
| - `enable_jit_checkpoint`: False |
| - `push_to_hub`: True |
| - `hub_private_repo`: None |
| - `hub_model_id`: modernbert-cosqa |
| - `hub_strategy`: every_save |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `load_best_model_at_end`: True |
| - `ignore_data_skip`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `full_determinism`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `use_cpu`: False |
| - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| - `parallelism_config`: None |
| - `dataloader_drop_last`: False |
| - `dataloader_num_workers`: 4 |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `dataloader_prefetch_factor`: None |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `train_sampling_strategy`: random |
| - `length_column_name`: length |
| - `ddp_find_unused_parameters`: None |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `ddp_backend`: None |
| - `ddp_timeout`: 1800 |
| - `fsdp`: [] |
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| - `deepspeed`: None |
| - `debug`: [] |
| - `skip_memory_metrics`: True |
| - `do_predict`: False |
| - `resume_from_checkpoint`: None |
| - `warmup_ratio`: None |
| - `local_rank`: -1 |
| - `prompts`: None |
| - `batch_sampler`: no_duplicates |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | eval_cosine_ndcg@10 | |
| |:-------:|:------:|:-------------:|:---------------:|:-------------------:| |
| | 1.0 | 8 | - | 0.3550 | 0.8071 | |
| | 1.25 | 10 | 1.0218 | - | - | |
| | 2.0 | 16 | - | 0.3508 | 0.8110 | |
| | 2.5 | 20 | 0.9890 | - | - | |
| | 3.0 | 24 | - | 0.3466 | 0.8131 | |
| | 3.75 | 30 | 0.9778 | - | - | |
| | 4.0 | 32 | - | 0.3439 | 0.8136 | |
| | **5.0** | **40** | **0.9507** | **0.3417** | **0.8148** | |
| | 6.0 | 48 | - | 0.3404 | 0.8120 | |
| | 6.25 | 50 | 0.9429 | - | - | |
| | 7.0 | 56 | - | 0.3387 | 0.8131 | |
| | 7.5 | 60 | 0.9267 | - | - | |
| | 8.0 | 64 | - | 0.3378 | 0.8127 | |
| | 8.75 | 70 | 0.9396 | - | - | |
| | 9.0 | 72 | - | 0.3370 | 0.8106 | |
| | 10.0 | 80 | 0.9099 | 0.3366 | 0.8125 | |
| |
| * The bold row denotes the saved checkpoint. |
| |
| ### Framework Versions |
| - Python: 3.12.12 |
| - Sentence Transformers: 5.3.0 |
| - Transformers: 5.3.0 |
| - PyTorch: 2.10.0+cu128 |
| - Accelerate: 1.13.0 |
| - Datasets: 4.8.2 |
| - Tokenizers: 0.22.2 |
| |
| ## 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", |
| } |
| ``` |
| |
| #### CachedMultipleNegativesRankingLoss |
| ```bibtex |
| @misc{gao2021scaling, |
| title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, |
| author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, |
| year={2021}, |
| eprint={2101.06983}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG} |
| } |
| ``` |
| |
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