End of training

1_Dense/config.json

@@ -0,0 +1,7 @@
+{
+    "in_features": 256,
+    "out_features": 128,
+    "bias": false,
+    "activation_function": "torch.nn.modules.linear.Identity",
+    "use_residual": false
+}

1_Dense/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bbb071d07d0af640cd2849c44c5f5ad18a0ac0213b481337c547991730e39a3a
+size 131160

README.md

@@ -0,0 +1,459 @@
+---
+tags:
+- ColBERT
+- PyLate
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1188486
+- loss:Contrastive
+base_model: jhu-clsp/ettin-encoder-17m
+datasets:
+- benjamintli/code-retrieval-combined-v2-llm-negatives
+pipeline_tag: sentence-similarity
+library_name: PyLate
+---
+
+# PyLate model based on jhu-clsp/ettin-encoder-17m
+
+This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [jhu-clsp/ettin-encoder-17m](https://huggingface.co/jhu-clsp/ettin-encoder-17m) on the [code-retrieval-combined-v2-llm-negatives](https://huggingface.co/datasets/benjamintli/code-retrieval-combined-v2-llm-negatives) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
+
+## Model Details
+
+### Model Description
+- **Model Type:** PyLate model
+- **Base model:** [jhu-clsp/ettin-encoder-17m](https://huggingface.co/jhu-clsp/ettin-encoder-17m) <!-- at revision 987607455c61e7a5bbc85f7758e0512ea6d0ae4c -->
+- **Document Length:** 180 tokens
+- **Query Length:** 32 tokens
+- **Output Dimensionality:** 128 tokens
+- **Similarity Function:** MaxSim
+- **Training Dataset:**
+    - [code-retrieval-combined-v2-llm-negatives](https://huggingface.co/datasets/benjamintli/code-retrieval-combined-v2-llm-negatives)
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
+- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
+- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
+
+### Full Model Architecture
+
+```
+ColBERT(
+  (0): Transformer({'max_seq_length': 31, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
+  (1): Dense({'in_features': 256, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
+)
+```
+
+## Usage
+First install the PyLate library:
+
+```bash
+pip install -U pylate
+```
+
+### Retrieval
+
+Use this model with PyLate to index and retrieve documents. The index uses [FastPLAID](https://github.com/lightonai/fast-plaid) for efficient similarity search.
+
+#### Indexing documents
+
+Load the ColBERT model and initialize the PLAID index, then encode and index your documents:
+
+```python
+from pylate import indexes, models, retrieve
+
+# Step 1: Load the ColBERT model
+model = models.ColBERT(
+    model_name_or_path="colbert-code-17m",
+)
+
+# Step 2: Initialize the PLAID index
+index = indexes.PLAID(
+    index_folder="pylate-index",
+    index_name="index",
+    override=True,  # This overwrites the existing index if any
+)
+
+# Step 3: Encode the documents
+documents_ids = ["1", "2", "3"]
+documents = ["document 1 text", "document 2 text", "document 3 text"]
+
+documents_embeddings = model.encode(
+    documents,
+    batch_size=32,
+    is_query=False,  # Ensure that it is set to False to indicate that these are documents, not queries
+    show_progress_bar=True,
+)
+
+# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
+index.add_documents(
+    documents_ids=documents_ids,
+    documents_embeddings=documents_embeddings,
+)
+```
+
+Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
+
+```python
+# To load an index, simply instantiate it with the correct folder/name and without overriding it
+index = indexes.PLAID(
+    index_folder="pylate-index",
+    index_name="index",
+)
+```
+
+#### Retrieving top-k documents for queries
+
+Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
+To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
+
+```python
+# Step 1: Initialize the ColBERT retriever
+retriever = retrieve.ColBERT(index=index)
+
+# Step 2: Encode the queries
+queries_embeddings = model.encode(
+    ["query for document 3", "query for document 1"],
+    batch_size=32,
+    is_query=True,  #  # Ensure that it is set to False to indicate that these are queries
+    show_progress_bar=True,
+)
+
+# Step 3: Retrieve top-k documents
+scores = retriever.retrieve(
+    queries_embeddings=queries_embeddings,
+    k=10,  # Retrieve the top 10 matches for each query
+)
+```
+
+### Reranking
+If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
+
+```python
+from pylate import rank, models
+
+queries = [
+    "query A",
+    "query B",
+]
+
+documents = [
+    ["document A", "document B"],
+    ["document 1", "document C", "document B"],
+]
+
+documents_ids = [
+    [1, 2],
+    [1, 3, 2],
+]
+
+model = models.ColBERT(
+    model_name_or_path="colbert-code-17m",
+)
+
+queries_embeddings = model.encode(
+    queries,
+    is_query=True,
+)
+
+documents_embeddings = model.encode(
+    documents,
+    is_query=False,
+)
+
+reranked_documents = rank.rerank(
+    documents_ids=documents_ids,
+    queries_embeddings=queries_embeddings,
+    documents_embeddings=documents_embeddings,
+)
+```
+
+<!--
+### 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
+
+#### code-retrieval-combined-v2-llm-negatives
+
+* Dataset: [code-retrieval-combined-v2-llm-negatives](https://huggingface.co/datasets/benjamintli/code-retrieval-combined-v2-llm-negatives) at [1917069](https://huggingface.co/datasets/benjamintli/code-retrieval-combined-v2-llm-negatives/tree/19170694c88339259e15b3dcc7f99db2136bd00a)
+* Size: 1,188,486 training samples
+* Columns: <code>query</code>, <code>positive</code>, <code>source</code>, <code>hard_negatives</code>, and <code>negatives</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                             | positive                                                                           | source                                                                           | hard_negatives                     | negatives                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             | string                                                                           | list                               | string                                                                             |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 23.61 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 31.43 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.46 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>size: 1 elements</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 31.15 tokens</li><li>max: 32 tokens</li></ul> |
+* Samples:
+  | query                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | source                     | hard_negatives                                                                                                                                                                                                                                                                                                                                                                                                                                           | negatives                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>wait for AWS PCA CSR propagation before issuing certificate</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | <code>func (c *ACMPCA) WaitUntilCertificateAuthorityCSRCreated(input *GetCertificateAuthorityCsrInput) error {<br>	return c.WaitUntilCertificateAuthorityCSRCreatedWithContext(aws.BackgroundContext(), input)<br>}</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | <code>csn_syntethic</code> | <code>['func (c *ACMPCA) WaitUntilAuditReportCreated(input *DescribeCertificateAuthorityAuditReportInput) error {\n\treturn c.WaitUntilAuditReportCreatedWithContext(aws.BackgroundContext(), input)\n}']</code>                                                                                                                                                                                                                                         | <code>func (c *ACMPCA) WaitUntilAuditReportCreated(input *DescribeCertificateAuthorityAuditReportInput) error {<br>	return c.WaitUntilAuditReportCreatedWithContext(aws.BackgroundContext(), input)<br>}</code>                                                                                                                                                                                                                                                        |
+  | <code>func (f *Filter) Gather() ([]*dto.MetricFamily, error) {<br>	mfs, err := f.Gatherer.Gather()<br>	if err != nil {<br></code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | <code>		return nil, err<br>	}<br>	return f.Matcher.Match(mfs), nil<br>}</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | <code>csn_ccr</code>       | <code>['\n\tf.err = err\n\tif err != nil {\n\t\tfor _, node := range f.cells {\n\t\t\tnode.PropagateWatchError(err)\n\t\t}\n\t}\n}']</code>                                                                                                                                                                                                                                                                                                              | <code><br>	f.err = err<br>	if err != nil {<br>		for _, node := range f.cells {<br>			node.PropagateWatchError(err)<br>		}<br>	}<br>}</code>                                                                                                                                                                                                                                                                                                                            |
+  | <code>def latent_to_dist(name, x, hparams, output_channels=None):<br>  """Map latent to the mean and log-scale of a Gaussian.<br><br>  Args:<br>    name: variable scope.<br>    x: 4-D Tensor of shape (NHWC)<br>    hparams: HParams.<br>      latent_architecture - can be "single_conv", "glow_nn" or "glow_resnet",<br>                            default = single_conv<br>      latent_encoder_depth - int, depth of architecture, valid if<br>                             latent_architecture is "glow_nn" or "glow_resnet".<br>      latent_pre_output_channels - 512, valid only when latent_architecture<br>                                   is "glow_nn".<br>      latent_encoder_width - 512, maximum width of the network<br>    output_channels: int, number of output channels of the mean (and std).<br>                     if not provided, set it to be the output channels of x.<br>  Returns:<br>    dist: instance of tfp.distributions.Normal<br>  Raises:<br>    ValueError: If architecture not in ["single_conv", "glow_nn"]<br>  """<br>  architecture = hparams.get("latent_a...</code> | <code>     mid_channels=mid_channels)<br>      mean_log_scale = conv("glow_nn_zeros", mean_log_scale,<br>                            filter_size=[3, 3], stride=[1, 1],<br>                            output_channels=2*output_channels,<br>                            apply_actnorm=False, conv_init="zeros")<br>    elif architecture == "glow_resnet":<br>      h = x<br>      for layer in range(depth):<br>        h3 = conv_stack("latent_resnet_%d" % layer, h,<br>                        mid_channels=width, output_channels=x_shape[-1],<br>                        dropout=hparams.coupling_dropout)<br>        h += h3<br>      mean_log_scale = conv("glow_res_final", h, conv_init="zeros",<br>                            output_channels=2*output_channels,<br>                            apply_actnorm=False)<br>    else:<br>      raise ValueError("expected architecture to be single_conv or glow_nn "<br>                       "got %s" % architecture)<br><br>    mean = mean_log_scale[:, :, :, 0::2]<br>    log_scale = mean_log_scale[:, :, :, 1::2]<br>    return tfp.distribu...</code> | <code>csn_ccr</code>       | <code>['        first_relu=False,\n        padding="SAME",\n        strides=(2, 2),\n        force2d=True,\n        name="conv0")\n    x = common_layers.conv_block(\n        x, 64, [((1, 1), (3, 3))], padding="SAME", force2d=True, name="conv1")\n    x = xnet_resblock(x, min(128, hidden_dim), True, "block0")\n    x = xnet_resblock(x, min(256, hidden_dim), False, "block1")\n    return xnet_resblock(x, hidden_dim, False, "block2")']</code> | <code>        first_relu=False,<br>        padding="SAME",<br>        strides=(2, 2),<br>        force2d=True,<br>        name="conv0")<br>    x = common_layers.conv_block(<br>        x, 64, [((1, 1), (3, 3))], padding="SAME", force2d=True, name="conv1")<br>    x = xnet_resblock(x, min(128, hidden_dim), True, "block0")<br>    x = xnet_resblock(x, min(256, hidden_dim), False, "block1")<br>    return xnet_resblock(x, hidden_dim, False, "block2")</code> |
+* Loss: <code>pylate.losses.contrastive.Contrastive</code>
+
+### Evaluation Dataset
+
+#### code-retrieval-combined-v2-llm-negatives
+
+* Dataset: [code-retrieval-combined-v2-llm-negatives](https://huggingface.co/datasets/benjamintli/code-retrieval-combined-v2-llm-negatives) at [1917069](https://huggingface.co/datasets/benjamintli/code-retrieval-combined-v2-llm-negatives/tree/19170694c88339259e15b3dcc7f99db2136bd00a)
+* Size: 12,005 evaluation samples
+* Columns: <code>query</code>, <code>positive</code>, <code>source</code>, <code>hard_negatives</code>, and <code>negatives</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                             | positive                                                                           | source                                                                           | hard_negatives                     | negatives                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             | string                                                                           | list                               | string                                                                             |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 23.78 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 31.45 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.55 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>size: 1 elements</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 31.15 tokens</li><li>max: 32 tokens</li></ul> |
+* Samples:
+  | query                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | source                     | hard_negatives                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | negatives                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>public static function list_templates($competencyid, $onlyvisible) {<br>        global $DB;<br><br>        $sql = 'SELECT tpl.*<br>                  FROM {' . template::TABLE . '} tpl<br>                  JOIN {' . self::TABLE . '} tplcomp<br>                    ON tplcomp.templateid = tpl.id<br>                 WHERE tplcomp.competencyid = ? ';<br>        $params = array($competencyid);<br><br>        if ($onlyvisible) {<br>            $sql .= ' AND tpl.visible = ?';<br>            $params[] = 1;<br>        }<br><br>  </code> | <code>      $sql .= ' ORDER BY tpl.id ASC';<br><br>        $results = $DB->get_records_sql($sql, $params);<br><br>        $instances = array();<br>        foreach ($results as $result) {<br>            array_push($instances, new template(0, $result));<br>        }<br><br>        return $instances;<br>    }</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | <code>csn_ccr</code>       | <code>["            FROM {assign_grades} g\n                   JOIN(' . $esql . ') e ON e.id = g.userid\n                   WHERE g.assignment = :assignid';\n\n        return $DB->count_records_sql($sql, $params);\n    }"]</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | <code>            FROM {assign_grades} g<br>                   JOIN(' . $esql . ') e ON e.id = g.userid<br>                   WHERE g.assignment = :assignid';<br><br>        return $DB->count_records_sql($sql, $params);<br>    }</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+  | <code>python multiprocessing sandboxed process isolation file system network</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | <code>def start(self):<br>        '''Create a process in which the isolated code will be run.'''<br>        assert self._client is None<br><br>        logger.debug('IsolationContext[%d] starting', id(self))<br><br>        # Create the queues<br>        request_queue = multiprocessing.Queue()<br>        response_queue = multiprocessing.Queue()<br><br>        # Launch the server process<br>        server = Server(request_queue, response_queue)  # Do not keep a reference to this object!<br>        server_process = multiprocessing.Process(target=server.loop)<br>        server_process.start()<br><br>        # Create a client to talk to the server<br>        self._client = Client(server_process, request_queue, response_queue)</code>                                                                                                                                                                                                                                                                                                                                                                                     | <code>csn_syntethic</code> | <code>['def start(cls, _init_logging=True):\n        """\n        Arrange for the subprocess to be started, if it is not already running.\n\n        The parent process picks a UNIX socket path the child will use prior to\n        fork, creates a socketpair used essentially as a semaphore, then blocks\n        waiting for the child to indicate the UNIX socket is ready for use.\n\n        :param bool _init_logging:\n            For testing, if :data:`False`, don\'t initialize logging.\n        """\n        if cls.worker_sock is not None:\n            return\n\n        if faulthandler is not None:\n            faulthandler.enable()\n\n        mitogen.utils.setup_gil()\n        cls.unix_listener_path = mitogen.unix.make_socket_path()\n        cls.worker_sock, cls.child_sock = socket.socketpair()\n        atexit.register(lambda: clean_shutdown(cls.worker_sock))\n        mitogen.core.set_cloexec(cls.worker_sock.fileno())\n        mitogen.core.set_cloexec(cls.child_sock.fileno())\n\n        cls.profiling = os.environ.get(\'MITOGEN_PROFILING\') is not None\n        if cls.profiling:\n            mitogen.core.enable_profiling()\n        if _init_logging:\n            ansible_mitogen.logging.setup()\n\n        cls.original_env = dict(os.environ)\n        cls.child_pid = os.fork()\n        if cls.child_pid:\n            save_pid(\'controller\')\n            ansible_mitogen.logging.set_process_name(\'top\')\n            ansible_mitogen.affinity.policy.assign_controller()\n            cls.child_sock.close()\n            cls.child_sock = None\n            mitogen.core.io_op(cls.worker_sock.recv, 1)\n        else:\n            save_pid(\'mux\')\n            ansible_mitogen.logging.set_process_name(\'mux\')\n            ansible_mitogen.affinity.policy.assign_muxprocess()\n            cls.worker_sock.close()\n            cls.worker_sock = None\n            self = cls()\n            self.worker_main()']</code> | <code>def start(cls, _init_logging=True):<br>        """<br>        Arrange for the subprocess to be started, if it is not already running.<br><br>        The parent process picks a UNIX socket path the child will use prior to<br>        fork, creates a socketpair used essentially as a semaphore, then blocks<br>        waiting for the child to indicate the UNIX socket is ready for use.<br><br>        :param bool _init_logging:<br>            For testing, if :data:`False`, don't initialize logging.<br>        """<br>        if cls.worker_sock is not None:<br>            return<br><br>        if faulthandler is not None:<br>            faulthandler.enable()<br><br>        mitogen.utils.setup_gil()<br>        cls.unix_listener_path = mitogen.unix.make_socket_path()<br>        cls.worker_sock, cls.child_sock = socket.socketpair()<br>        atexit.register(lambda: clean_shutdown(cls.worker_sock))<br>        mitogen.core.set_cloexec(cls.worker_sock.fileno())<br>        mitogen.core.set_cloexec(cls.child_sock.fileno())<br><br>        cls.profiling = os.environ.get('MI...</code> |
+  | <code>updates the current cookies with a new set<br><br>@param array $cookies new cookies with which to update current ones<br><br>@return    boolean always return true<br>@access    private</code>                                                                                                                                                                                                                                                                                                                                                      | <code>function UpdateCookies($cookies)
  <br>    {
  <br>        if (sizeof($this->cookies) == 0) {
  <br>            // no existing cookies: take whatever is new
  <br>            if (sizeof($cookies) > 0) {
  <br>                $this->debug('Setting new cookie(s)');
  <br>                $this->cookies = $cookies;
  <br>            }
  <br>
  <br>            return TRUE;
  <br>        }
  <br>        if (sizeof($cookies) == 0) {
  <br>            // no new cookies: keep what we've got
  <br>            return TRUE;
  <br>        }
  <br>        // merge
  <br>        foreach ($cookies as $newCookie) {
  <br>            if (!is_array($newCookie)) {
  <br>                continue;
  <br>            }
  <br>            if ((!isset($newCookie['name'])) \|\| (!isset($newCookie['value']))) {
  <br>                continue;
  <br>            }
  <br>            $newName = $newCookie['name'];
  <br>
  <br>            $found = FALSE;
  <br>            for ($i = 0; $i < count($this->cookies); $i++) {
  <br>                $cookie = $this->cookies[$i];
  <br>                if (!is_array($cookie)) {
  <br>                    continue;
  <br>       ...</code> | <code>csn</code>           | <code>["public function setCookies(array $cookies) : Request\n    {\n        $query = http_build_query($cookies);\n        parse_str($query, $this->cookies);\n\n        if ($cookies) {\n            $cookie = str_replace('&', '; ', $query);\n            $this->setHeader('Cookie', $cookie);\n        } else {\n            $this->removeHeader('Cookie');\n        }\n\n        return $this;\n    }"]</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | <code>public function setCookies(array $cookies) : Request<br>    {<br>        $query = http_build_query($cookies);<br>        parse_str($query, $this->cookies);<br><br>        if ($cookies) {<br>            $cookie = str_replace('&', '; ', $query);<br>            $this->setHeader('Cookie', $cookie);<br>        } else {<br>            $this->removeHeader('Cookie');<br>        }<br><br>        return $this;<br>    }</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+* Loss: <code>pylate.losses.contrastive.Contrastive</code>
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `learning_rate`: 3e-06
+- `num_train_epochs`: 1
+- `fp16`: True
+- `hub_model_id`: colbert-code-17m
+
+#### 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`: 256
+- `per_device_eval_batch_size`: 256
+- `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`: 3e-06
+- `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`: 0.0
+- `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}
+- `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}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `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`: colbert-code-17m
+- `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>
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 5.1.1
+- PyLate: 1.4.0
+- Transformers: 4.56.2
+- PyTorch: 2.9.0+cu128
+- Accelerate: 1.13.0
+- Datasets: 4.8.4
+- 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"
+}
+```
+
+#### PyLate
+```bibtex
+@inproceedings{DBLP:conf/cikm/ChaffinS25,
+  author       = {Antoine Chaffin and
+                  Rapha{"{e}}l Sourty},
+  editor       = {Meeyoung Cha and
+                  Chanyoung Park and
+                  Noseong Park and
+                  Carl Yang and
+                  Senjuti Basu Roy and
+                  Jessie Li and
+                  Jaap Kamps and
+                  Kijung Shin and
+                  Bryan Hooi and
+                  Lifang He},
+  title        = {PyLate: Flexible Training and Retrieval for Late Interaction Models},
+  booktitle    = {Proceedings of the 34th {ACM} International Conference on Information
+                  and Knowledge Management, {CIKM} 2025, Seoul, Republic of Korea, November
+                  10-14, 2025},
+  pages        = {6334--6339},
+  publisher    = {{ACM}},
+  year         = {2025},
+  url          = {https://github.com/lightonai/pylate},
+  doi          = {10.1145/3746252.3761608},
+}
+```
+
+<!--
+## 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.*
+-->

config.json

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+{
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "causal_mask": false,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "dtype": "float32",
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 256,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 384,
+  "is_causal": false,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 160000.0,
+  "max_position_embeddings": 7999,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 4,
+  "num_hidden_layers": 7,
+  "pad_token_id": 50283,
+  "position_embedding_type": "sans_pos",
+  "repad_logits_with_grad": false,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "transformers_version": "4.56.2",
+  "vocab_size": 50370
+}

config_sentence_transformers.json

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+{
+  "model_type": "ColBERT",
+  "__version__": {
+    "sentence_transformers": "5.1.1",
+    "transformers": "4.56.2",
+    "pytorch": "2.9.0+cu128"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "MaxSim",
+  "query_prefix": "[Q] ",
+  "document_prefix": "[D] ",
+  "query_length": 32,
+  "document_length": 180,
+  "attend_to_expansion_tokens": false,
+  "skiplist_words": [
+    "!",
+    "\"",
+    "#",
+    "$",
+    "%",
+    "&",
+    "'",
+    "(",
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+    "*",
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+    "]",
+    "^",
+    "_",
+    "`",
+    "{",
+    "|",
+    "}",
+    "~"
+  ],
+  "do_query_expansion": true
+}

eval/triplet_evaluation_results.csv

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+epoch,steps,accuracy
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+0.21537798836958863,1000,0.8142440915107727
+0.43075597673917726,2000,0.8224073648452759
+0.6461339651087659,3000,0.8259891867637634
+0.8615119534783545,4000,0.8282382488250732

model.safetensors

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modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Dense",
+    "type": "pylate.models.Dense.Dense"
+  }
+]

sentence_bert_config.json

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+{
+    "max_seq_length": 31,
+    "do_lower_case": false
+}

special_tokens_map.json

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+{
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