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Shuu12121
/
Owl-ph2-len2048

Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:800640
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Model card Files
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Shuu12121 commited on
Commit
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Upload SentenceTransformer model

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Files changed (10) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +477 -0
  3. config.json +47 -0
  4. config_sentence_transformers.json +14 -0
  5. model.safetensors +3 -0
  6. modules.json +14 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +402 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +3326 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": true,
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+ "pooling_mode_mean_tokens": false,
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+ "pooling_mode_max_tokens": false,
6
+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
10
+ }
README.md ADDED
@@ -0,0 +1,477 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - sentence-transformers
4
+ - sentence-similarity
5
+ - feature-extraction
6
+ - dense
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+ - generated_from_trainer
8
+ - dataset_size:800640
9
+ - loss:MultipleNegativesRankingLoss
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+ base_model: Shuu12121/CodeModernBERT-Owl-v3-len2048
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+ widget:
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+ - source_sentence: '@phpstan-param LoadClassMetadataEventArgs<ClassMetadata<object>,
13
+ ObjectManager> $args'
14
+ sentences:
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+ - "def cal_angle(vec1, vec2):\n \"\"\" \n \"\"\"\n if isinstance(vec1,\
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+ \ torch.Tensor) and isinstance(vec1, torch.Tensor):\n return torch.dot(vec1,\
17
+ \ vec2)/(vec1.norm()*vec2.norm()).item()\n elif isinstance(vec1, np.ndarray)\
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+ \ and isinstance(vec2, np.ndarray):\n return np.ndarray.dot(vec1, vec2)/(np.linalg.norm(vec1)*np.linalg.norm(vec2))"
19
+ - "public function loadClassMetadata(LoadClassMetadataEventArgs $args): void\n \
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+ \ {\n $ea = $this->getEventAdapter($args);\n // this will check\
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+ \ for our metadata\n $this->loadMetadataForObjectClass(\n $ea->getObjectManager(),\n\
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+ \ $args->getClassMetadata()\n );\n }"
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+ - "@Override\n byte[] getChannelId() throws SSLException {\n synchronized\
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+ \ (ssl) {\n if (getUseClientMode()) {\n throw new IllegalStateException(\"\
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+ Not allowed in client mode\");\n }\n\n if (isHandshakeStarted())\
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+ \ {\n throw new IllegalStateException(\n \
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+ \ \"Channel ID is only available after handshake completes\");\n }\n\
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+ \ return ssl.getTlsChannelId();\n }\n }"
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+ - source_sentence: 'Determines if the first child in the data grid will be selected
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+
31
+ by default when refreshTree() gets called.'
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+ sentences:
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+ - "constructor() {\n super();\n this.#selectedEvents = null;\n this.element.classList.add('timeline-tree-view');\n\
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+ \ this.registerRequiredCSS(timelineTreeViewStyles);\n\n this.searchResults\
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+ \ = [];\n }"
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+ - "public function getDatabase()\n {\n $platform = $this->getDatabasePlatform();\n\
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+ \ $query = $platform->getDummySelectSQL($platform->getCurrentDatabaseExpression());\n\
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+ \ $database = $this->fetchOne($query);\n\n assert(is_string($database)\
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+ \ || $database === null);\n\n return $database;\n }"
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+ - "def regulate_len(\n durations,\n enc_out,\n pace=1.0,\n mel_max_len=None,\n\
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+ \ group_size=1,\n dur_lens: torch.tensor = None,\n):\n \"\"\"\n \"\
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+ \"\"\n\n dtype = enc_out.dtype\n reps = durations.float() / pace\n reps\
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+ \ = (reps + 0.5).floor().long()\n dec_lens = reps.sum(dim=1)\n if group_size\
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+ \ > 1:\n to_pad = group_size * (torch.div(dec_lens + 1, group_size, rounding_mode='floor'))\
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+ \ - dec_lens\n reps.index_put_(\n indices=[torch.arange(dur_lens.shape[0],\
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+ \ dtype=torch.long), dur_lens - 1], values=to_pad, accumulate=True\n )\n\
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+ \ dec_lens = reps.sum(dim=1)\n\n max_len = dec_lens.max()\n reps_cumsum\
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+ \ = torch.cumsum(torch.nn.functional.pad(reps, (1, 0, 0, 0), value=0.0), dim=1)[:,\
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+ \ None, :]\n reps_cumsum = reps_cumsum.to(dtype=dtype, device=enc_out.device)\n\
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+ \n range_ = torch.arange(max_len).to(enc_out.device)[None, :, None]\n mult\
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+ \ = (reps_cumsum[:, :, :-1] <= range_) & (reps_cumsum[:, :, 1:] > range_)\n \
52
+ \ mult = mult.to(dtype)\n enc_rep = torch.matmul(mult, enc_out)\n\n if\
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+ \ mel_max_len is not None:\n enc_rep = enc_rep[:, :mel_max_len]\n \
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+ \ dec_lens = torch.clamp_max(dec_lens, mel_max_len)\n\n return enc_rep, dec_lens"
55
+ - source_sentence: "Load anns with the specified ids.\n\n :param ids (int array)\
56
+ \ : integer ids specifying anns\n :return: anns (object array) :\
57
+ \ loaded ann objects"
58
+ sentences:
59
+ - "def loadAnns(self, ids=[]):\n \"\"\"\n \"\"\"\n if _isArrayLike(ids):\n\
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+ \ return [self.anns[id] for id in ids]\n elif type(ids) == int:\n\
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+ \ return [self.anns[ids]]"
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+ - "public function getByName($name)\n {\n return $this->providers[$name]\
63
+ \ ?? null;\n }"
64
+ - "def test_multipleRangeRequestWithRangeOverlappingEnd(self):\n \"\"\"\n\
65
+ \ \n \"\"\"\n startEnds = [(0, 2), (40, len(self.payload)\
66
+ \ + 10)]\n rangeHeaderValue = b','.join([networkString(\"%s-%s\" % (s,e))\
67
+ \ for (s, e) in startEnds])\n self.request.requestHeaders.addRawHeader(b'range',\n\
68
+ \ b'bytes=' + rangeHeaderValue)\n\
69
+ \ self.resource.render(self.request)\n self.assertEqual(self.request.responseCode,\
70
+ \ http.PARTIAL_CONTENT)\n boundary = re.match(\n b'^multipart/byteranges;\
71
+ \ boundary=\"(.*)\"$',\n self.request.responseHeaders.getRawHeaders(b'content-type')[0]).group(1)\n\
72
+ \ parts = self.parseMultipartBody(b''.join(self.request.written), boundary)\n\
73
+ \ self.assertEqual(len(startEnds), len(parts))\n for part, (s, e)\
74
+ \ in zip(parts, startEnds):\n self.assertEqual(networkString(self.resource.type),\n\
75
+ \ part[b'contentType'])\n start, end, size\
76
+ \ = part[b'contentRange']\n self.assertEqual(int(start), s)\n \
77
+ \ self.assertEqual(int(end), min(e, self.resource.getFileSize()-1))\n \
78
+ \ self.assertEqual(int(size), self.resource.getFileSize())\n \
79
+ \ self.assertEqual(self.payload[s:e+1], part[b'body'])"
80
+ - source_sentence: Tests {@link ObjectUtils#clone(Object)} with an object array.
81
+ sentences:
82
+ - "def error_script(nonce, message)\n %(<script nonce=\"#{nonce}\">console.warn(\"\
83
+ #{message}\")</script>)\n end"
84
+ - "public ClientAuthentication getClientAuthentication() throws\n ClientAuthentication.UnsupportedAuthenticationMethod\
85
+ \ {\n if (getClientSecret() == null) {\n /* Without client credentials,\
86
+ \ or unspecified 'token_endpoint_auth_method',\n * we can never authenticate\
87
+ \ */\n return NoClientAuthentication.INSTANCE;\n } else if (mLastRegistrationResponse.tokenEndpointAuthMethod\
88
+ \ == null) {\n /* 'token_endpoint_auth_method': \"If omitted, the default\
89
+ \ is client_secret_basic\",\n * \"OpenID Connect Dynamic Client Registration\
90
+ \ 1.0\", Section 2 */\n return new ClientSecretBasic(getClientSecret());\n\
91
+ \ }\n\n switch (mLastRegistrationResponse.tokenEndpointAuthMethod)\
92
+ \ {\n case ClientSecretBasic.NAME:\n return new ClientSecretBasic(getClientSecret());\n\
93
+ \ case ClientSecretPost.NAME:\n return new ClientSecretPost(getClientSecret());\n\
94
+ \ case \"none\":\n return NoClientAuthentication.INSTANCE;\n\
95
+ \ default:\n throw new ClientAuthentication.UnsupportedAuthenticationMethod(\n\
96
+ \ mLastRegistrationResponse.tokenEndpointAuthMethod);\n\
97
+ \n }\n }"
98
+ - "@Test\n void testCloneOfStringArray() {\n assertTrue(Arrays.deepEquals(\n\
99
+ \ new String[]{\"string\"}, ObjectUtils.clone(new String[]{\"string\"\
100
+ })));\n }"
101
+ - source_sentence: 'Ensures that if the aasm_state column is nil and the record is
102
+ new
103
+
104
+ then the initial state gets populated after initialization
105
+
106
+
107
+ foo = Foo.new
108
+
109
+ foo.aasm_state # => "open" (where :open is the initial state)
110
+
111
+
112
+
113
+ foo = Foo.find(:first)
114
+
115
+ foo.aasm_state # => 1
116
+
117
+ foo.aasm_state = nil
118
+
119
+ foo.valid?
120
+
121
+ foo.aasm_state # => nil'
122
+ sentences:
123
+ - "def restore_plan_binding_path project:, location:, restore_channel:, restore_plan_binding:\n\
124
+ \ raise ::ArgumentError, \"project cannot contain /\" if project.to_s.include?\
125
+ \ \"/\"\n raise ::ArgumentError, \"location cannot contain /\" if\
126
+ \ location.to_s.include? \"/\"\n raise ::ArgumentError, \"restore_channel\
127
+ \ cannot contain /\" if restore_channel.to_s.include? \"/\"\n\n \"\
128
+ projects/#{project}/locations/#{location}/restoreChannels/#{restore_channel}/restorePlanBindings/#{restore_plan_binding}\"\
129
+ \n end"
130
+ - "submitPickers() {\n this._updateValueAndFireEvents(this.tempValue, true,\
131
+ \ [\"change\", \"value-changed\"]);\n this.closePicker();\n }"
132
+ - "def aasm_ensure_initial_state\n AASM::StateMachineStore.fetch(self.class,\
133
+ \ true).machine_names.each do |state_machine_name|\n # checking via\
134
+ \ respond_to? does not work in Rails <= 3\n # if respond_to?(self.class.aasm(state_machine_name).attribute_name)\
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+ \ && send(self.class.aasm(state_machine_name).attribute_name).blank? # Rails 4\n\
136
+ \ if aasm_column_is_blank?(state_machine_name)\n aasm(state_machine_name).enter_initial_state\n\
137
+ \ end\n end\n end"
138
+ pipeline_tag: sentence-similarity
139
+ library_name: sentence-transformers
140
+ ---
141
+
142
+ # SentenceTransformer based on Shuu12121/CodeModernBERT-Owl-v3-len2048
143
+
144
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Shuu12121/CodeModernBERT-Owl-v3-len2048](https://huggingface.co/Shuu12121/CodeModernBERT-Owl-v3-len2048). 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.
145
+
146
+ ## Model Details
147
+
148
+ ### Model Description
149
+ - **Model Type:** Sentence Transformer
150
+ - **Base model:** [Shuu12121/CodeModernBERT-Owl-v3-len2048](https://huggingface.co/Shuu12121/CodeModernBERT-Owl-v3-len2048) <!-- at revision 0a4061eef81283255c31176e92a60f32f90715d6 -->
151
+ - **Maximum Sequence Length:** 1024 tokens
152
+ - **Output Dimensionality:** 768 dimensions
153
+ - **Similarity Function:** Cosine Similarity
154
+ <!-- - **Training Dataset:** Unknown -->
155
+ <!-- - **Language:** Unknown -->
156
+ <!-- - **License:** Unknown -->
157
+
158
+ ### Model Sources
159
+
160
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
161
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
162
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
163
+
164
+ ### Full Model Architecture
165
+
166
+ ```
167
+ SentenceTransformer(
168
+ (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
169
+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
170
+ )
171
+ ```
172
+
173
+ ## Usage
174
+
175
+ ### Direct Usage (Sentence Transformers)
176
+
177
+ First install the Sentence Transformers library:
178
+
179
+ ```bash
180
+ pip install -U sentence-transformers
181
+ ```
182
+
183
+ Then you can load this model and run inference.
184
+ ```python
185
+ from sentence_transformers import SentenceTransformer
186
+
187
+ # Download from the 🤗 Hub
188
+ model = SentenceTransformer("sentence_transformers_model_id")
189
+ # Run inference
190
+ sentences = [
191
+ 'Ensures that if the aasm_state column is nil and the record is new\nthen the initial state gets populated after initialization\n\nfoo = Foo.new\nfoo.aasm_state # => "open" (where :open is the initial state)\n\n\nfoo = Foo.find(:first)\nfoo.aasm_state # => 1\nfoo.aasm_state = nil\nfoo.valid?\nfoo.aasm_state # => nil',
192
+ 'def aasm_ensure_initial_state\n AASM::StateMachineStore.fetch(self.class, true).machine_names.each do |state_machine_name|\n # checking via respond_to? does not work in Rails <= 3\n # if respond_to?(self.class.aasm(state_machine_name).attribute_name) && send(self.class.aasm(state_machine_name).attribute_name).blank? # Rails 4\n if aasm_column_is_blank?(state_machine_name)\n aasm(state_machine_name).enter_initial_state\n end\n end\n end',
193
+ 'submitPickers() {\n this._updateValueAndFireEvents(this.tempValue, true, ["change", "value-changed"]);\n this.closePicker();\n }',
194
+ ]
195
+ embeddings = model.encode(sentences)
196
+ print(embeddings.shape)
197
+ # [3, 768]
198
+
199
+ # Get the similarity scores for the embeddings
200
+ similarities = model.similarity(embeddings, embeddings)
201
+ print(similarities)
202
+ # tensor([[1.0000, 0.7016, 0.0313],
203
+ # [0.7016, 1.0000, 0.0769],
204
+ # [0.0313, 0.0769, 1.0000]])
205
+ ```
206
+
207
+ <!--
208
+ ### Direct Usage (Transformers)
209
+
210
+ <details><summary>Click to see the direct usage in Transformers</summary>
211
+
212
+ </details>
213
+ -->
214
+
215
+ <!--
216
+ ### Downstream Usage (Sentence Transformers)
217
+
218
+ You can finetune this model on your own dataset.
219
+
220
+ <details><summary>Click to expand</summary>
221
+
222
+ </details>
223
+ -->
224
+
225
+ <!--
226
+ ### Out-of-Scope Use
227
+
228
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
229
+ -->
230
+
231
+ <!--
232
+ ## Bias, Risks and Limitations
233
+
234
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
235
+ -->
236
+
237
+ <!--
238
+ ### Recommendations
239
+
240
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
241
+ -->
242
+
243
+ ## Training Details
244
+
245
+ ### Training Dataset
246
+
247
+ #### Unnamed Dataset
248
+
249
+ * Size: 800,640 training samples
250
+ * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
251
+ * Approximate statistics based on the first 1000 samples:
252
+ | | sentence_0 | sentence_1 | label |
253
+ |:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------|
254
+ | type | string | string | float |
255
+ | details | <ul><li>min: 8 tokens</li><li>mean: 70.54 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 163.47 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> |
256
+ * Samples:
257
+ | sentence_0 | sentence_1 | label |
258
+ |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
259
+ | <code>Generated from example definition: https://github.com/Azure/azure-rest-api-specs/blob/ee1eec42dcc710ff88db2d1bf574b2f9afe3d654/specification/eventgrid/resource-manager/Microsoft.EventGrid/stable/2025-02-15/examples/DomainTopicEventSubscriptions_Delete.json</code> | <code>func ExampleDomainTopicEventSubscriptionsClient_BeginDelete() {<br> cred, err := azidentity.NewDefaultAzureCredential(nil)<br> if err != nil {<br> log.Fatalf("failed to obtain a credential: %v", err)<br> }<br> ctx := context.Background()<br> clientFactory, err := armeventgrid.NewClientFactory("<subscription-id>", cred, nil)<br> if err != nil {<br> log.Fatalf("failed to create client: %v", err)<br> }<br> poller, err := clientFactory.NewDomainTopicEventSubscriptionsClient().BeginDelete(ctx, "examplerg", "exampleDomain1", "exampleDomainTopic1", "examplesubscription1", nil)<br> if err != nil {<br> log.Fatalf("failed to finish the request: %v", err)<br> }<br> _, err = poller.PollUntilDone(ctx, nil)<br> if err != nil {<br> log.Fatalf("failed to pull the result: %v", err)<br> }<br>}</code> | <code>1.0</code> |
260
+ | <code>newUpgradeContext creates a Context, avoid nil Context member.<br>only used for testing now.</code> | <code>func newUpgradeContext() Context {<br> return Context{<br> Context: context.Background(),<br> SubTaskConfigs: make(map[string]map[string]config.SubTaskConfig),<br> }<br>}</code> | <code>1.0</code> |
261
+ | <code>ToPostRequestInformation create new navigation property to rules for policies</code> | <code>func (m *RoleManagementPoliciesItemRulesRequestBuilder) ToPostRequestInformation(ctx context.Context, body iadcd81124412c61e647227ecfc4449d8bba17de0380ddda76f641a29edf2b242.UnifiedRoleManagementPolicyRuleable, requestConfiguration *RoleManagementPoliciesItemRulesRequestBuilderPostRequestConfiguration)(*i2ae4187f7daee263371cb1c977df639813ab50ffa529013b7437480d1ec0158f.RequestInformation, error) {<br> requestInfo := i2ae4187f7daee263371cb1c977df639813ab50ffa529013b7437480d1ec0158f.NewRequestInformation()<br> requestInfo.UrlTemplate = m.urlTemplate<br> requestInfo.PathParameters = m.pathParameters<br> requestInfo.Method = i2ae4187f7daee263371cb1c977df639813ab50ffa529013b7437480d1ec0158f.POST<br> requestInfo.Headers.Add("Accept", "application/json")<br> err := requestInfo.SetContentFromParsable(ctx, m.requestAdapter, "application/json", body)<br> if err != nil {<br> return nil, err<br> }<br> if requestConfiguration != nil {<br> requestInfo.Headers.AddAll(requestConfiguration.Header...</code> | <code>1.0</code> |
262
+ * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
263
+ ```json
264
+ {
265
+ "scale": 20.0,
266
+ "similarity_fct": "cos_sim",
267
+ "gather_across_devices": false
268
+ }
269
+ ```
270
+
271
+ ### Training Hyperparameters
272
+ #### Non-Default Hyperparameters
273
+
274
+ - `per_device_train_batch_size`: 120
275
+ - `per_device_eval_batch_size`: 120
276
+ - `num_train_epochs`: 1
277
+ - `fp16`: True
278
+ - `multi_dataset_batch_sampler`: round_robin
279
+
280
+ #### All Hyperparameters
281
+ <details><summary>Click to expand</summary>
282
+
283
+ - `overwrite_output_dir`: False
284
+ - `do_predict`: False
285
+ - `eval_strategy`: no
286
+ - `prediction_loss_only`: True
287
+ - `per_device_train_batch_size`: 120
288
+ - `per_device_eval_batch_size`: 120
289
+ - `per_gpu_train_batch_size`: None
290
+ - `per_gpu_eval_batch_size`: None
291
+ - `gradient_accumulation_steps`: 1
292
+ - `eval_accumulation_steps`: None
293
+ - `torch_empty_cache_steps`: None
294
+ - `learning_rate`: 5e-05
295
+ - `weight_decay`: 0.0
296
+ - `adam_beta1`: 0.9
297
+ - `adam_beta2`: 0.999
298
+ - `adam_epsilon`: 1e-08
299
+ - `max_grad_norm`: 1
300
+ - `num_train_epochs`: 1
301
+ - `max_steps`: -1
302
+ - `lr_scheduler_type`: linear
303
+ - `lr_scheduler_kwargs`: {}
304
+ - `warmup_ratio`: 0.0
305
+ - `warmup_steps`: 0
306
+ - `log_level`: passive
307
+ - `log_level_replica`: warning
308
+ - `log_on_each_node`: True
309
+ - `logging_nan_inf_filter`: True
310
+ - `save_safetensors`: True
311
+ - `save_on_each_node`: False
312
+ - `save_only_model`: False
313
+ - `restore_callback_states_from_checkpoint`: False
314
+ - `no_cuda`: False
315
+ - `use_cpu`: False
316
+ - `use_mps_device`: False
317
+ - `seed`: 42
318
+ - `data_seed`: None
319
+ - `jit_mode_eval`: False
320
+ - `bf16`: False
321
+ - `fp16`: True
322
+ - `fp16_opt_level`: O1
323
+ - `half_precision_backend`: auto
324
+ - `bf16_full_eval`: False
325
+ - `fp16_full_eval`: False
326
+ - `tf32`: None
327
+ - `local_rank`: 0
328
+ - `ddp_backend`: None
329
+ - `tpu_num_cores`: None
330
+ - `tpu_metrics_debug`: False
331
+ - `debug`: []
332
+ - `dataloader_drop_last`: False
333
+ - `dataloader_num_workers`: 0
334
+ - `dataloader_prefetch_factor`: None
335
+ - `past_index`: -1
336
+ - `disable_tqdm`: False
337
+ - `remove_unused_columns`: True
338
+ - `label_names`: None
339
+ - `load_best_model_at_end`: False
340
+ - `ignore_data_skip`: False
341
+ - `fsdp`: []
342
+ - `fsdp_min_num_params`: 0
343
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
344
+ - `fsdp_transformer_layer_cls_to_wrap`: None
345
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
346
+ - `parallelism_config`: None
347
+ - `deepspeed`: None
348
+ - `label_smoothing_factor`: 0.0
349
+ - `optim`: adamw_torch_fused
350
+ - `optim_args`: None
351
+ - `adafactor`: False
352
+ - `group_by_length`: False
353
+ - `length_column_name`: length
354
+ - `project`: huggingface
355
+ - `trackio_space_id`: trackio
356
+ - `ddp_find_unused_parameters`: None
357
+ - `ddp_bucket_cap_mb`: None
358
+ - `ddp_broadcast_buffers`: False
359
+ - `dataloader_pin_memory`: True
360
+ - `dataloader_persistent_workers`: False
361
+ - `skip_memory_metrics`: True
362
+ - `use_legacy_prediction_loop`: False
363
+ - `push_to_hub`: False
364
+ - `resume_from_checkpoint`: None
365
+ - `hub_model_id`: None
366
+ - `hub_strategy`: every_save
367
+ - `hub_private_repo`: None
368
+ - `hub_always_push`: False
369
+ - `hub_revision`: None
370
+ - `gradient_checkpointing`: False
371
+ - `gradient_checkpointing_kwargs`: None
372
+ - `include_inputs_for_metrics`: False
373
+ - `include_for_metrics`: []
374
+ - `eval_do_concat_batches`: True
375
+ - `fp16_backend`: auto
376
+ - `push_to_hub_model_id`: None
377
+ - `push_to_hub_organization`: None
378
+ - `mp_parameters`:
379
+ - `auto_find_batch_size`: False
380
+ - `full_determinism`: False
381
+ - `torchdynamo`: None
382
+ - `ray_scope`: last
383
+ - `ddp_timeout`: 1800
384
+ - `torch_compile`: False
385
+ - `torch_compile_backend`: None
386
+ - `torch_compile_mode`: None
387
+ - `include_tokens_per_second`: False
388
+ - `include_num_input_tokens_seen`: no
389
+ - `neftune_noise_alpha`: None
390
+ - `optim_target_modules`: None
391
+ - `batch_eval_metrics`: False
392
+ - `eval_on_start`: False
393
+ - `use_liger_kernel`: False
394
+ - `liger_kernel_config`: None
395
+ - `eval_use_gather_object`: False
396
+ - `average_tokens_across_devices`: True
397
+ - `prompts`: None
398
+ - `batch_sampler`: batch_sampler
399
+ - `multi_dataset_batch_sampler`: round_robin
400
+ - `router_mapping`: {}
401
+ - `learning_rate_mapping`: {}
402
+
403
+ </details>
404
+
405
+ ### Training Logs
406
+ | Epoch | Step | Training Loss |
407
+ |:------:|:----:|:-------------:|
408
+ | 0.0749 | 500 | 0.8671 |
409
+ | 0.1499 | 1000 | 0.0977 |
410
+ | 0.2248 | 1500 | 0.0889 |
411
+ | 0.2998 | 2000 | 0.0844 |
412
+ | 0.3747 | 2500 | 0.0773 |
413
+ | 0.4496 | 3000 | 0.0804 |
414
+ | 0.5246 | 3500 | 0.0754 |
415
+ | 0.5995 | 4000 | 0.0733 |
416
+ | 0.6745 | 4500 | 0.0683 |
417
+ | 0.7494 | 5000 | 0.0674 |
418
+ | 0.8243 | 5500 | 0.0655 |
419
+ | 0.8993 | 6000 | 0.0665 |
420
+ | 0.9742 | 6500 | 0.0637 |
421
+
422
+
423
+ ### Framework Versions
424
+ - Python: 3.10.12
425
+ - Sentence Transformers: 5.2.0
426
+ - Transformers: 4.57.3
427
+ - PyTorch: 2.8.0+cu128
428
+ - Accelerate: 1.12.0
429
+ - Datasets: 3.6.0
430
+ - Tokenizers: 0.22.1
431
+
432
+ ## Citation
433
+
434
+ ### BibTeX
435
+
436
+ #### Sentence Transformers
437
+ ```bibtex
438
+ @inproceedings{reimers-2019-sentence-bert,
439
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
440
+ author = "Reimers, Nils and Gurevych, Iryna",
441
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
442
+ month = "11",
443
+ year = "2019",
444
+ publisher = "Association for Computational Linguistics",
445
+ url = "https://arxiv.org/abs/1908.10084",
446
+ }
447
+ ```
448
+
449
+ #### MultipleNegativesRankingLoss
450
+ ```bibtex
451
+ @misc{henderson2017efficient,
452
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
453
+ 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},
454
+ year={2017},
455
+ eprint={1705.00652},
456
+ archivePrefix={arXiv},
457
+ primaryClass={cs.CL}
458
+ }
459
+ ```
460
+
461
+ <!--
462
+ ## Glossary
463
+
464
+ *Clearly define terms in order to be accessible across audiences.*
465
+ -->
466
+
467
+ <!--
468
+ ## Model Card Authors
469
+
470
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
471
+ -->
472
+
473
+ <!--
474
+ ## Model Card Contact
475
+
476
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
477
+ -->
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+ }
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