Upload ModernBERT model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "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
+}

README.md

@@ -0,0 +1,593 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:799680
+- loss:MultipleNegativesRankingLoss
+base_model: Shuu12121/CodeModernBERT-Owl-v1
+widget:
+- source_sentence: 'Disconnects the pool.
+
+
+    Does everything that +clear+ does, except if the pool is closed
+
+    this method does nothing but +clear+ would raise PoolClosedError.
+
+
+    @since 2.1.0
+
+    @api private'
+  sentences:
+  - "def disconnect!(options = nil)\n        do_clear(options)\n      rescue Error::PoolClosedError\n\
+    \        # The \"disconnected\" state is between closed and paused.\n        #\
+    \ When we are trying to disconnect the pool, permit the pool to be\n        #\
+    \ already closed.\n      end"
+  - "func TestNamedTupleWithEscapedColumns(t *testing.T) {\n\tTestProtocols(t, func(t\
+    \ *testing.T, protocol clickhouse.Protocol) {\n\t\tconn, err := GetNativeConnection(t,\
+    \ protocol, nil, nil, nil)\n\t\tctx := context.Background()\n\t\trequire.NoError(t,\
+    \ err)\n\t\t// https://github.com/ClickHouse/ClickHouse/pull/36544\n\t\tif !CheckMinServerServerVersion(conn,\
+    \ 22, 5, 0) {\n\t\t\tt.Skip(fmt.Errorf(\"unsupported clickhouse version\"))\n\t\
+    \t\treturn\n\t\t}\n\t\tconst ddl = \"CREATE TABLE test_tuple (Col1 Tuple(`56`\
+    \ String, `a22\\\\`` Int64)) Engine MergeTree() ORDER BY tuple()\"\n\t\tdefer\
+    \ func() {\n\t\t\tconn.Exec(ctx, \"DROP TABLE IF EXISTS test_tuple\")\n\t\t}()\n\
+    \t\trequire.NoError(t, conn.Exec(ctx, ddl))\n\t\tbatch, err := conn.PrepareBatch(ctx,\
+    \ \"INSERT INTO test_tuple\")\n\t\trequire.NoError(t, err)\n\t\tvar (\n\t\t\t\
+    col1Data = map[string]any{\"56\": \"A\", \"a22`\": int64(1)}\n\t\t)\n\t\trequire.NoError(t,\
+    \ batch.Append(col1Data))\n\t\trequire.Equal(t, 1, batch.Rows())\n\t\trequire.NoError(t,\
+    \ batch.Send())\n\t\tvar col1 map[string]any\n\t\trequire.NoError(t, conn.QueryRow(ctx,\
+    \ \"SELECT * FROM test_tuple\").Scan(&col1))\n\t\tassert.Equal(t, col1Data, col1)\n\
+    \t})\n}"
+  - "def parse_region(url)\n            parts = URI.parse(url).host.split('.')\n \
+    \           parts.each_with_index do |part, index|\n              if part == 'sqs'\n\
+    \                # assume region is the part right after the 'sqs' part\n    \
+    \            return parts[index + 1]\n              end\n            end\n   \
+    \         nil # no region found\n          end"
+- source_sentence: "Cancel a running workflow by sync job ID.\n\n        This will\
+    \ search for workflows with IDs matching the pattern sync-{sync_job_id}-*\n  \
+    \      and cancel them. The workflow will catch the CancelledError and update\
+    \ the\n        sync job status to CANCELLED.\n\n        Args:\n            sync_job_id:\
+    \ The sync job ID to cancel\n\n        Returns:\n            True if a workflow\
+    \ was found and cancelled, False otherwise"
+  sentences:
+  - "async def cancel_sync_job_workflow(self, sync_job_id: str) -> bool:\n       \
+    \ \"\"\"\n        \"\"\"\n        try:\n            client = await temporal_client.get_client()\n\
+    \n            # List workflows to find the one matching our sync job\n       \
+    \     # Note: In production, you might want to store the workflow ID\n       \
+    \     # when starting it for direct lookup\n            workflows = []\n     \
+    \       async for workflow in client.list_workflows(\n                query=f'WorkflowId\
+    \ STARTS_WITH \"sync-{sync_job_id}-\"'\n            ):\n                workflows.append(workflow)\n\
+    \n            if not workflows:\n                logger.warning(f\"No running\
+    \ workflow found for sync job {sync_job_id}\")\n                return False\n\
+    \n            # Cancel the workflow(s)\n            for workflow in workflows:\n\
+    \                handle = client.get_workflow_handle(workflow.id)\n          \
+    \      await handle.cancel()\n                logger.info(\n                 \
+    \   f\"Successfully cancelled workflow {workflow.id} for sync job {sync_job_id}\"\
+    \n                )\n\n            return True\n\n        except Exception as\
+    \ e:\n            logger.error(f\"Failed to cancel workflow for sync job {sync_job_id}:\
+    \ {e}\")\n            raise"
+  - "def __init__(name, account):\n        \"\"\"\n        \n        \"\"\""
+  - "renderRows = async (\n  table,\n  viewname,\n  { columns, layout },\n  extra,\n\
+    \  rows,\n  state\n) => {\n  //console.log(columns);\n  //console.log(layout);\n\
+    \  if (!columns || !layout) return \"View not yet built\";\n\n  const fields =\
+    \ table.getFields();\n\n  const role = extra.req.user ? extra.req.user.role_id\
+    \ : 100;\n  var views = {};\n  const getView = async (name, relation) => {\n \
+    \   if (views[name]) return views[name];\n    const view_select = parse_view_select(name,\
+    \ relation);\n    const view = View.findOne({ name: view_select.viewname });\n\
+    \    if (!view) return false;\n    if (view.table_id === table.id) view.table\
+    \ = table;\n    else view.table = Table.findOne({ id: view.table_id });\n    view.view_select\
+    \ = view_select;\n    views[name] = view;\n    return view;\n  };\n  await set_load_actions_join_fieldviews({\n\
+    \    table,\n    layout,\n    fields,\n    req: extra.req,\n    res: extra.res,\n\
+    \  });\n\n  const owner_field = await table.owner_fieldname();\n  const subviewExtra\
+    \ = { ...extra };\n  if (extra.req?.generate_email) {\n    // no mjml markup for\
+    \ for nested subviews, only for the top view\n    subviewExtra.req = { ...extra.req,\
+    \ isSubView: true };\n  }\n  return await asyncMap(rows, async (row) => {\n  \
+    \  await eachView(layout, async (segment) => {\n      // do all the parsing with\
+    \ data here? make a factory\n      const view = await getView(segment.view, segment.relation);\n\
+    \      if (!view)\n        throw new InvalidConfiguration(\n          `View ${viewname}\
+    \ incorrectly configured: cannot find view ${segment.view}`\n        );\n    \
+    \  view.check_viewtemplate();\n      if (view.viewtemplateObj.renderRows && view.view_select.type\
+    \ === \"Own\") {\n        segment.contents = (\n          await view.viewtemplateObj.renderRows(\n\
+    \            view.table,\n            view.name,\n            view.configuration,\n\
+    \            subviewExtra,\n            [row],\n            state\n          )\n\
+    \        )[0];\n      } else {\n        let state1 = {};\n        const pk_name\
+    \ = table.pk_name;\n        const get_row_val = (k) => {\n          //handle expanded\
+    \ joinfields\n          if (row[k] === null) return null;\n          if (row[k]?.id\
+    \ === null) return null;\n          return row[k]?.id || row[k];\n        };\n\
+    \        const get_user_id = () => (extra.req.user ? extra.req.user.id : 0);\n\
+    \        if (view.view_select.type === \"RelationPath\" && view.table_id) {\n\
+    \          const targetTbl = Table.findOne({ id: view.table_id });\n         \
+    \ const relation = new Relation(\n            segment.relation,\n            targetTbl.name,\n\
+    \            displayType(await view.get_state_fields())\n          );\n      \
+    \    state1 = pathToState(\n            relation,\n            relation.isFixedRelation()\
+    \ ? get_user_id : get_row_val\n          );\n        } else {\n          switch\
+    \ (view.view_select.type) {\n            case \"Own\":\n              state1 =\
+    \ { [pk_name]: get_row_val(pk_name) };\n              break;\n            case\
+    \ \"Independent\":\n              state1 = {};\n              break;\n       \
+    \     case \"ChildList\":\n            case \"OneToOneShow\":\n              state1\
+    \ = {\n                [view.view_select.through\n                  ? `${view.view_select.throughTable}.${view.view_select.through}.${view.view_select.table_name}.${view.view_select.field_name}`\n\
+    \                  : view.view_select.field_name]: get_row_val(pk_name),\n   \
+    \           };\n              break;\n            case \"ParentShow\":\n     \
+    \         //todo set by pk name of parent tablr\n              state1 = {\n  \
+    \              id: get_row_val(view.view_select.field_name),\n              };\n\
+    \              break;\n          }\n        }\n        const extra_state = segment.extra_state_fml\n\
+    \          ? eval_expression(\n              segment.extra_state_fml,\n      \
+    \        {\n                ...dollarizeObject(state),\n                session_id:\
+    \ getSessionId(extra.req),\n                ...row,\n              },\n      \
+    \        extra.req.user,\n              `Extra state formula for view ${view.name}`\n\
+    \            )\n          : {};\n        const { id, ...outerState } = state;\n\
+    \        //console.log(segment);\n        if (segment.state === \"local\") {\n\
+    \          const state2 = { ...state1, ...extra_state };\n          const qs =\
+    \ stateToQueryString(state2, true);\n          if (\n            view.name ===\
+    \ viewname &&\n            JSON.stringify(state) === JSON.stringify(state2)\n\
+    \          )\n            throw new InvalidConfiguration(\n              `View\
+    \ ${view.name} embeds itself with same state; inifinite loop detected`\n     \
+    \       );\n          segment.contents = div(\n            {\n              class:\
+    \ \"d-inline\",\n              \"data-sc-embed-viewname\": view.name,\n      \
+    \        \"data-sc-local-state\": `/view/${view.name}${qs}`,\n            },\n\
+    \            await view.run(state2, subviewExtra, view.isRemoteTable())\n    \
+    \      );\n        } else {\n          const state2 = { ...outerState, ...state1,\
+    \ ...extra_state };\n          const qs = stateToQueryString(state2, true);\n\n\
+    \          if (\n            view.name === viewname &&\n            JSON.stringify(state)\
+    \ === JSON.stringify(state2)\n          )\n            throw new InvalidConfiguration(\n\
+    \              `View ${view.name} embeds itself with same state; inifinite loop\
+    \ detected`\n            );\n          segment.contents = div(\n            {\n\
+    \              class: \"d-inline\",\n              \"data-sc-embed-viewname\"\
+    : view.name,\n              \"data-sc-view-source\": `/view/${view.name}${qs}`,\n\
+    \            },\n            await view.run(state2, subviewExtra, view.isRemoteTable())\n\
+    \          );\n        }\n      }\n    });\n    const user_id = extra.req.user\
+    \ ? extra.req.user.id : null;\n\n    const is_owner =\n      table.ownership_formula\
+    \ && user_id && role > table.min_role_read\n        ? await table.is_owner(extra.req.user,\
+    \ row)\n        : owner_field && user_id && row[owner_field] === user_id;\n\n\
+    \    return render(\n      row,\n      fields,\n      layout,\n      viewname,\n\
+    \      table,\n      role,\n      extra.req,\n      is_owner,\n      state,\n\
+    \      extra\n    );\n  });\n}"
+- source_sentence: AddFlags adds flags related to NodeLifecycleController for controller
+    manager to the specified FlagSet.
+  sentences:
+  - "func (o *NodeLifecycleControllerOptions) AddFlags(fs *pflag.FlagSet) {\n\tif\
+    \ o == nil {\n\t\treturn\n\t}\n\n\tfs.DurationVar(&o.NodeStartupGracePeriod.Duration,\
+    \ \"node-startup-grace-period\", o.NodeStartupGracePeriod.Duration,\n\t\t\"Amount\
+    \ of time which we allow starting Node to be unresponsive before marking it unhealthy.\"\
+    )\n\tfs.DurationVar(&o.NodeMonitorGracePeriod.Duration, \"node-monitor-grace-period\"\
+    , o.NodeMonitorGracePeriod.Duration,\n\t\t\"Amount of time which we allow running\
+    \ Node to be unresponsive before marking it unhealthy. \"+\n\t\t\t\"Must be N\
+    \ times more than kubelet's nodeStatusUpdateFrequency, \"+\n\t\t\t\"where N means\
+    \ number of retries allowed for kubelet to post node status. \"+\n\t\t\t\"This\
+    \ value should also be greater than the sum of HTTP2_PING_TIMEOUT_SECONDS and\
+    \ HTTP2_READ_IDLE_TIMEOUT_SECONDS\")\n\tfs.Float32Var(&o.NodeEvictionRate, \"\
+    node-eviction-rate\", 0.1, \"Number of nodes per second on which pods are deleted\
+    \ in case of node failure when a zone is healthy (see --unhealthy-zone-threshold\
+    \ for definition of healthy/unhealthy). Zone refers to entire cluster in non-multizone\
+    \ clusters.\")\n\tfs.Float32Var(&o.SecondaryNodeEvictionRate, \"secondary-node-eviction-rate\"\
+    , 0.01, \"Number of nodes per second on which pods are deleted in case of node\
+    \ failure when a zone is unhealthy (see --unhealthy-zone-threshold for definition\
+    \ of healthy/unhealthy). Zone refers to entire cluster in non-multizone clusters.\
+    \ This value is implicitly overridden to 0 if the cluster size is smaller than\
+    \ --large-cluster-size-threshold.\")\n\tfs.Int32Var(&o.LargeClusterSizeThreshold,\
+    \ \"large-cluster-size-threshold\", 50, fmt.Sprintf(\"Number of nodes from which\
+    \ %s treats the cluster as large for the eviction logic purposes. --secondary-node-eviction-rate\
+    \ is implicitly overridden to 0 for clusters this size or smaller. Notice: If\
+    \ nodes reside in multiple zones, this threshold will be considered as zone node\
+    \ size threshold for each zone to determine node eviction rate independently.\"\
+    , names.NodeLifecycleController))\n\tfs.Float32Var(&o.UnhealthyZoneThreshold,\
+    \ \"unhealthy-zone-threshold\", 0.55, \"Fraction of Nodes in a zone which needs\
+    \ to be not Ready (minimum 3) for zone to be treated as unhealthy. \")\n}"
+  - "func (v Value) IsNull() bool {\n\treturn v.Val == nil || v.Typ == querypb.Type_NULL_TYPE\n\
+    }"
+  - "public function response(array $errors)\n    {\n        if ($this->ajax() ||\
+    \ $this->wantsJson()) {\n            return new JsonResponse($errors, 422);\n\
+    \        }\n\n        return $this->redirector->to($this->getRedirectUrl())\n\
+    \                                        ->withInput($this->except($this->dontFlash))\n\
+    \                                        ->withErrors($errors, $this->errorBag);\n\
+    \    }"
+- source_sentence: 'Count of all the processing errors in this task and its subtasks.
+
+
+    Generated from protobuf field <code>int32 total_processing_error_count = 21;</code>
+
+    @return int'
+  sentences:
+  - "fn add_helper(&self, msg: SignedMessage) -> Result<(), Error> {\n        let\
+    \ from = msg.from();\n        let cur_ts = self.cur_tipset.lock().clone();\n \
+    \       add_helper(\n            self.api.as_ref(),\n            self.bls_sig_cache.as_ref(),\n\
+    \            self.pending.as_ref(),\n            msg,\n            self.get_state_sequence(&from,\
+    \ &cur_ts)?,\n        )\n    }"
+  - "public function getTotalProcessingErrorCount()\n    {\n        return $this->total_processing_error_count;\n\
+    \    }"
+  - "def datetime_utc_to_local(dt):\n\t\"\"\"\n\t\n\t\"\"\"\n\tdt = dt.replace(tzinfo=dateutil.tz.tzutc())\n\
+    \tdt = dt.astimezone(dateutil.tz.tzlocal())\n\treturn dt.replace(tzinfo=None)"
+- source_sentence: "Computes the absolute value of each element retrieved from a strided\
+    \ input array `x` via a callback function and assigns each result to an element\
+    \ in a strided output array `y`.\n\n@param {NonNegativeInteger} N - number of\
+    \ indexed elements\n@param {Collection} x - input array/collection\n@param {integer}\
+    \ strideX - `x` stride length\n@param {NonNegativeInteger} offsetX - starting\
+    \ `x` index\n@param {Collection} y - destination array/collection\n@param {integer}\
+    \ strideY - `y` stride length\n@param {NonNegativeInteger} offsetY - starting\
+    \ `y` index\n@param {Callback} clbk - callback\n@param {*} [thisArg] - callback\
+    \ execution context\n@returns {Collection} `y`\n\n@example\nfunction accessor(\
+    \ v ) {\n    return v * 2.0;\n}\n\nvar x = [ 1.0, -2.0, 3.0, -4.0, 5.0 ];\nvar\
+    \ y = [ 0.0, 0.0, 0.0, 0.0, 0.0 ];\n\nabsBy( x.length, x, 1, 0, y, 1, 0, accessor\
+    \ );\n\nconsole.log( y );\n// => [ 2.0, 4.0, 6.0, 8.0, 10.0 ]"
+  sentences:
+  - "public ArrayList<Skyline> findSkyline(int start, int end) {\n        // Base\
+    \ case: only one building, return its skyline.\n        if (start == end) {\n\
+    \            ArrayList<Skyline> list = new ArrayList<>();\n            list.add(new\
+    \ Skyline(building[start].left, building[start].height));\n            list.add(new\
+    \ Skyline(building[end].right, 0)); // Add the end of the building\n         \
+    \   return list;\n        }\n\n        int mid = (start + end) / 2;\n\n      \
+    \  ArrayList<Skyline> sky1 = this.findSkyline(start, mid); // Find the skyline\
+    \ of the left half\n        ArrayList<Skyline> sky2 = this.findSkyline(mid + 1,\
+    \ end); // Find the skyline of the right half\n        return this.mergeSkyline(sky1,\
+    \ sky2); // Merge the two skylines\n    }"
+  - "def get_supported_systems_info\n          request(\n            :expects    =>\
+    \ 200,\n            :idempotent => true,\n            :method     => 'GET',\n\
+    \            :parser     => Fog::ToHashDocument.new,\n            :path      \
+    \ => 'supportedSystemsInfo'\n          )\n        end"
+  - "function absBy( N, x, strideX, offsetX, y, strideY, offsetY, clbk, thisArg )\
+    \ {\n\treturn mapBy( N, x, strideX, offsetX, y, strideY, offsetY, abs, clbk, thisArg\
+    \ ); // eslint-disable-line max-len\n}"
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on Shuu12121/CodeModernBERT-Owl-v1
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Shuu12121/CodeModernBERT-Owl-v1](https://huggingface.co/Shuu12121/CodeModernBERT-Owl-v1). 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:** [Shuu12121/CodeModernBERT-Owl-v1](https://huggingface.co/Shuu12121/CodeModernBERT-Owl-v1) <!-- at revision 33220abe62ef7d02fc36c62487e77751459d8c1a -->
+- **Maximum Sequence Length:** 1024 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/UKPLab/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': 1024, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
+  (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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Computes the absolute value of each element retrieved from a strided input array `x` via a callback function and assigns each result to an element in a strided output array `y`.\n\n@param {NonNegativeInteger} N - number of indexed elements\n@param {Collection} x - input array/collection\n@param {integer} strideX - `x` stride length\n@param {NonNegativeInteger} offsetX - starting `x` index\n@param {Collection} y - destination array/collection\n@param {integer} strideY - `y` stride length\n@param {NonNegativeInteger} offsetY - starting `y` index\n@param {Callback} clbk - callback\n@param {*} [thisArg] - callback execution context\n@returns {Collection} `y`\n\n@example\nfunction accessor( v ) {\n    return v * 2.0;\n}\n\nvar x = [ 1.0, -2.0, 3.0, -4.0, 5.0 ];\nvar y = [ 0.0, 0.0, 0.0, 0.0, 0.0 ];\n\nabsBy( x.length, x, 1, 0, y, 1, 0, accessor );\n\nconsole.log( y );\n// => [ 2.0, 4.0, 6.0, 8.0, 10.0 ]',
+    'function absBy( N, x, strideX, offsetX, y, strideY, offsetY, clbk, thisArg ) {\n\treturn mapBy( N, x, strideX, offsetX, y, strideY, offsetY, abs, clbk, thisArg ); // eslint-disable-line max-len\n}',
+    'public ArrayList<Skyline> findSkyline(int start, int end) {\n        // Base case: only one building, return its skyline.\n        if (start == end) {\n            ArrayList<Skyline> list = new ArrayList<>();\n            list.add(new Skyline(building[start].left, building[start].height));\n            list.add(new Skyline(building[end].right, 0)); // Add the end of the building\n            return list;\n        }\n\n        int mid = (start + end) / 2;\n\n        ArrayList<Skyline> sky1 = this.findSkyline(start, mid); // Find the skyline of the left half\n        ArrayList<Skyline> sky2 = this.findSkyline(mid + 1, end); // Find the skyline of the right half\n        return this.mergeSkyline(sky1, sky2); // Merge the two skylines\n    }',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[1.0000, 0.8429, 0.0136],
+#         [0.8429, 1.0000, 0.1084],
+#         [0.0136, 0.1084, 1.0000]])
+```
+
+<!--
+### 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: 799,680 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                          | sentence_1                                                                            | label                                                         |
+  |:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                                | float                                                         |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 72.08 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 165.78 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | label            |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>Set the column title<br><br>@param column - column number (first column is: 0)<br>@param title - new column title</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | <code>setHeader = function(column, newValue) {<br>    const obj = this;<br><br>    if (obj.headers[column]) {<br>        const oldValue = obj.headers[column].textContent;<br>        const onchangeheaderOldValue = (obj.options.columns && obj.options.columns[column] && obj.options.columns[column].title) || '';<br><br>        if (! newValue) {<br>            newValue = getColumnName(column);<br>        }<br><br>        obj.headers[column].textContent = newValue;<br>        // Keep the title property<br>        obj.headers[column].setAttribute('title', newValue);<br>        // Update title<br>        if (!obj.options.columns) {<br>            obj.options.columns = [];<br>        }<br>        if (!obj.options.columns[column]) {<br>            obj.options.columns[column] = {};<br>        }<br>        obj.options.columns[column].title = newValue;<br><br>        setHistory.call(obj, {<br>            action: 'setHeader',<br>            column: column,<br>            oldValue: oldValue,<br>            newValue: newValue<br>        });<br><br>        // On onchange header<br>        dispatch.c...</code> | <code>1.0</code> |
+  | <code>Elsewhere this is known as a "Weak Value Map". Whereas a std JS WeakMap<br>is weak on its keys, this map is weak on its values. It does not retain these<br>values strongly. If a given value disappears, then the entries for it<br>disappear from every weak-value-map that holds it as a value.<br><br>Just as a WeakMap only allows gc-able values as keys, a weak-value-map<br>only allows gc-able values as values.<br><br>Unlike a WeakMap, a weak-value-map unavoidably exposes the non-determinism of<br>gc to its clients. Thus, both the ability to create one, as well as each<br>created one, must be treated as dangerous capabilities that must be closely<br>held. A program with access to these can read side channels though gc that do<br>not* rely on the ability to measure duration. This is a separate, and bad,<br>timing-independent side channel.<br><br>This non-determinism also enables code to escape deterministic replay. In a<br>blockchain context, this could cause validators to differ from each other,<br>preventing consensus, and thus preventing ...</code>                | <code>makeFinalizingMap = (finalizer, opts) => {<br>  const { weakValues = false } = opts || {};<br>  if (!weakValues || !WeakRef || !FinalizationRegistry) {<br>    /** @type Map<K, V> */<br>    const keyToVal = new Map();<br>    return Far('fakeFinalizingMap', {<br>      clearWithoutFinalizing: keyToVal.clear.bind(keyToVal),<br>      get: keyToVal.get.bind(keyToVal),<br>      has: keyToVal.has.bind(keyToVal),<br>      set: (key, val) => {<br>        keyToVal.set(key, val);<br>      },<br>      delete: keyToVal.delete.bind(keyToVal),<br>      getSize: () => keyToVal.size,<br>    });<br>  }<br>  /** @type Map<K, WeakRef<any>> */<br>  const keyToRef = new Map();<br>  const registry = new FinalizationRegistry(key => {<br>    // Because this will delete the current binding of `key`, we need to<br>    // be sure that it is not called because a previous binding was collected.<br>    // We do this with the `unregister` in `set` below, assuming that<br>    // `unregister` *immediately* suppresses the finalization of the thing<br>    // it unregisters. TODO If this is...</code>                         | <code>1.0</code> |
+  | <code>Creates a function that memoizes the result of `func`. If `resolver` is<br>provided, it determines the cache key for storing the result based on the<br>arguments provided to the memoized function. By default, the first argument<br>provided to the memoized function is used as the map cache key. The `func`<br>is invoked with the `this` binding of the memoized function.<br><br>**Note:** The cache is exposed as the `cache` property on the memoized<br>function. Its creation may be customized by replacing the `_.memoize.Cache`<br>constructor with one whose instances implement the<br>[`Map`](http://ecma-international.org/ecma-262/6.0/#sec-properties-of-the-map-prototype-object)<br>method interface of `delete`, `get`, `has`, and `set`.<br><br>@static<br>@memberOf _<br>@since 0.1.0<br>@category Function<br>@param {Function} func The function to have its output memoized.<br>@param {Function} [resolver] The function to resolve the cache key.<br>@returns {Function} Returns the new memoized function.<br>@example<br><br>var object = { 'a': 1, 'b': 2 };<br>var othe...</code> | <code>function memoize(func, resolver) {<br>      if (typeof func != 'function' || (resolver && typeof resolver != 'function')) {<br>        throw new TypeError(FUNC_ERROR_TEXT);<br>      }<br>      var memoized = function() {<br>        var args = arguments,<br>            key = resolver ? resolver.apply(this, args) : args[0],<br>            cache = memoized.cache;<br><br>        if (cache.has(key)) {<br>          return cache.get(key);<br>        }<br>        var result = func.apply(this, args);<br>        memoized.cache = cache.set(key, result);<br>        return result;<br>      };<br>      memoized.cache = new (memoize.Cache || MapCache);<br>      return memoized;<br>    }</code>                                                                                                                                                                                                                                                                                                                                                                                                                                 | <code>1.0</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 120
+- `per_device_eval_batch_size`: 120
+- `fp16`: True
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 120
+- `per_device_eval_batch_size`: 120
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 3
+- `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}
+- `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`: round_robin
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0750 | 500   | 0.2167        |
+| 0.1501 | 1000  | 0.1158        |
+| 0.2251 | 1500  | 0.1081        |
+| 0.3001 | 2000  | 0.1079        |
+| 0.3752 | 2500  | 0.0994        |
+| 0.4502 | 3000  | 0.0941        |
+| 0.5252 | 3500  | 0.0873        |
+| 0.6002 | 4000  | 0.0967        |
+| 0.6753 | 4500  | 0.0863        |
+| 0.7503 | 5000  | 0.0829        |
+| 0.8253 | 5500  | 0.0821        |
+| 0.9004 | 6000  | 0.0821        |
+| 0.9754 | 6500  | 0.0794        |
+| 1.0504 | 7000  | 0.0418        |
+| 1.1255 | 7500  | 0.0237        |
+| 1.2005 | 8000  | 0.0233        |
+| 1.2755 | 8500  | 0.0231        |
+| 1.3505 | 9000  | 0.0248        |
+| 1.4256 | 9500  | 0.0245        |
+| 1.5006 | 10000 | 0.0237        |
+| 1.5756 | 10500 | 0.025         |
+| 1.6507 | 11000 | 0.0232        |
+| 1.7257 | 11500 | 0.0231        |
+| 1.8007 | 12000 | 0.0218        |
+| 1.8758 | 12500 | 0.0233        |
+| 1.9508 | 13000 | 0.0221        |
+| 2.0258 | 13500 | 0.0177        |
+| 2.1008 | 14000 | 0.0072        |
+| 2.1759 | 14500 | 0.0066        |
+| 2.2509 | 15000 | 0.0068        |
+| 2.3259 | 15500 | 0.0069        |
+| 2.4010 | 16000 | 0.0062        |
+| 2.4760 | 16500 | 0.0068        |
+| 2.5510 | 17000 | 0.0064        |
+| 2.6261 | 17500 | 0.0061        |
+| 2.7011 | 18000 | 0.0062        |
+| 2.7761 | 18500 | 0.0058        |
+| 2.8511 | 19000 | 0.0057        |
+| 2.9262 | 19500 | 0.0058        |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 5.0.0
+- Transformers: 4.53.1
+- PyTorch: 2.7.0+cu128
+- Accelerate: 1.7.0
+- 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",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

added_tokens.json

@@ -0,0 +1,7 @@
+{
+  "</s>": 50001,
+  "<mask>": 50004,
+  "<pad>": 50003,
+  "<s>": 50000,
+  "<unk>": 50002
+}

config.json

@@ -0,0 +1,48 @@
+{
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 50000,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "cls",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50001,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "hidden_activation": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "local_attention": 128,
+  "local_attention_rope_theta": 10000,
+  "local_attention_window": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 1,
+  "repad_logits_with_grad": false,
+  "rope_theta": 160000,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.1",
+  "type_vocab_size": 2,
+  "vocab_size": 50005
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.0.0",
+    "transformers": "4.53.1",
+    "pytorch": "2.7.0+cu128"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:03f7359aa09221cf3a7ec864f0a040aa737d6dd73765eccf3e48df569d5bb99a
+size 594955000

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 1024,
+    "do_lower_case": false
+}

special_tokens_map.json

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

tokenizer_config.json

@@ -0,0 +1,62 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50000": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50001": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50002": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50003": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50004": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 256,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}