Shuu12121 commited on
Commit
7264e29
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1 Parent(s): 4fbe685

Upload ModernBERT model

Browse files
1_Pooling/config.json ADDED
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+ {
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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,
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+ "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
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+ }
README.md ADDED
@@ -0,0 +1,625 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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+ tags:
3
+ - sentence-transformers
4
+ - sentence-similarity
5
+ - feature-extraction
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+ - dense
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+ - generated_from_trainer
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+ - dataset_size:2392064
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+ - loss:CachedMultipleNegativesSymmetricRankingLoss
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+ base_model: Shuu12121/CodeModernBERT-Crow-v1.1
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+ widget:
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+ - source_sentence: 'Encapsulates the work with test rules.
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+
14
+ @param {array} aRules The test rules
15
+
16
+ @constructor
17
+
18
+ @private'
19
+ sentences:
20
+ - "createImageResizer = (width, height) => (source) => {\n const resized = new\
21
+ \ PNG({ width, height, fill: true });\n PNG.bitblt(source, resized, 0, 0, source.width,\
22
+ \ source.height, 0, 0);\n return resized;\n}"
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+ - "TestRules = function (aRules) {\n\t\t\tthis._aRules = aRules;\n\t\t}"
24
+ - "function addEventTypeNameToConfig(_ref, isInteractive) {\n var topEvent = _ref[0],\n\
25
+ \ event = _ref[1];\n\n var capitalizedEvent = event[0].toUpperCase() + event.slice(1);\n\
26
+ \ var onEvent = 'on' + capitalizedEvent;\n\n var type = {\n phasedRegistrationNames:\
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+ \ {\n bubbled: onEvent,\n captured: onEvent + 'Capture'\n },\n \
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+ \ dependencies: [topEvent],\n isInteractive: isInteractive\n };\n eventTypes$4[event]\
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+ \ = type;\n topLevelEventsToDispatchConfig[topEvent] = type;\n}"
30
+ - source_sentence: 'Check if a value has one or more properties and that value is
31
+ not undefined.
32
+
33
+ @param {any} obj The value to check.
34
+
35
+ @returns {boolean} `true` if `obj` has one or more properties that value is not
36
+ undefined.'
37
+ sentences:
38
+ - "calci = function(hashbuf, sig, pubkey) {\n for (var i = 0; i < 4; i++) {\n \
39
+ \ var Qprime;\n try {\n Qprime = getPublicKey(hashbuf, sig, i);\n \
40
+ \ } catch (e) {\n console.error(e);\n continue;\n }\n\n if (Qprime.point.eq(pubkey.point))\
41
+ \ {\n sig.i = i;\n sig.compressed = pubkey.compressed;\n return\
42
+ \ sig;\n }\n }\n\n throw new Error('Unable to find valid recovery factor');\n\
43
+ }"
44
+ - "function hasDefinedProperty(obj) {\n\tif (typeof obj === \"object\" && obj !==\
45
+ \ null) {\n\t\tfor (const key in obj) {\n\t\t\tif (typeof obj[key] !== \"undefined\"\
46
+ ) {\n\t\t\t\treturn true;\n\t\t\t}\n\t\t}\n\t}\n\treturn false;\n}"
47
+ - "function joinSequenceDiffsByShifting(sequence1, sequence2, sequenceDiffs) {\n\
48
+ \ if (sequenceDiffs.length === 0) {\n return sequenceDiffs;\n }\n\
49
+ \ const result = [];\n result.push(sequenceDiffs[0]);\n // First move\
50
+ \ them all to the left as much as possible and join them if possible\n for\
51
+ \ (let i = 1; i < sequenceDiffs.length; i++) {\n const prevResult = result[result.length\
52
+ \ - 1];\n let cur = sequenceDiffs[i];\n if (cur.seq1Range.isEmpty\
53
+ \ || cur.seq2Range.isEmpty) {\n const length = cur.seq1Range.start\
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+ \ - prevResult.seq1Range.endExclusive;\n let d;\n for (d\
55
+ \ = 1; d <= length; d++) {\n if (sequence1.getElement(cur.seq1Range.start\
56
+ \ - d) !== sequence1.getElement(cur.seq1Range.endExclusive - d) ||\n \
57
+ \ sequence2.getElement(cur.seq2Range.start - d) !== sequence2.getElement(cur.seq2Range.endExclusive\
58
+ \ - d)) {\n break;\n }\n }\n \
59
+ \ d--;\n if (d === length) {\n // Merge previous\
60
+ \ and current diff\n result[result.length - 1] = new SequenceDiff(new\
61
+ \ OffsetRange(prevResult.seq1Range.start, cur.seq1Range.endExclusive - length),\
62
+ \ new OffsetRange(prevResult.seq2Range.start, cur.seq2Range.endExclusive - length));\n\
63
+ \ continue;\n }\n cur = cur.delta(-d);\n\
64
+ \ }\n result.push(cur);\n }\n const result2 = [];\n //\
65
+ \ Then move them all to the right and join them again if possible\n for (let\
66
+ \ i = 0; i < result.length - 1; i++) {\n const nextResult = result[i +\
67
+ \ 1];\n let cur = result[i];\n if (cur.seq1Range.isEmpty || cur.seq2Range.isEmpty)\
68
+ \ {\n const length = nextResult.seq1Range.start - cur.seq1Range.endExclusive;\n\
69
+ \ let d;\n for (d = 0; d < length; d++) {\n \
70
+ \ if (!sequence1.isStronglyEqual(cur.seq1Range.start + d, cur.seq1Range.endExclusive\
71
+ \ + d) ||\n !sequence2.isStronglyEqual(cur.seq2Range.start\
72
+ \ + d, cur.seq2Range.endExclusive + d)) {\n break;\n \
73
+ \ }\n }\n if (d === length) {\n \
74
+ \ // Merge previous and current diff, write to result!\n result[i\
75
+ \ + 1] = new SequenceDiff(new OffsetRange(cur.seq1Range.start + length, nextResult.seq1Range.endExclusive),\
76
+ \ new OffsetRange(cur.seq2Range.start + length, nextResult.seq2Range.endExclusive));\n\
77
+ \ continue;\n }\n if (d > 0) {\n \
78
+ \ cur = cur.delta(d);\n }\n }\n result2.push(cur);\n\
79
+ \ }\n if (result.length > 0) {\n result2.push(result[result.length\
80
+ \ - 1]);\n }\n return result2;\n}"
81
+ - source_sentence: 'Adds two vec2''s after scaling the second operand by a scalar
82
+ value
83
+
84
+
85
+ @param {vec2} out the receiving vector
86
+
87
+ @param {ReadonlyVec2} a the first operand
88
+
89
+ @param {ReadonlyVec2} b the second operand
90
+
91
+ @param {Number} scale the amount to scale b by before adding
92
+
93
+ @returns {vec2} out'
94
+ sentences:
95
+ - "async forceStripeSubscriptionToProduct(data, options) {\n if (!this._stripeAPIService.configured)\
96
+ \ {\n throw new DataImportError({\n message: tpl(messages.noStripeConnection,\
97
+ \ {action: 'force subscription to product'})\n });\n }\n\n \
98
+ \ // Retrieve customer's existing subscription information\n const\
99
+ \ stripeCustomer = await this._stripeAPIService.getCustomer(data.customer_id);\n\
100
+ \n // Subscription can only be forced if the customer exists\n if\
101
+ \ (!stripeCustomer) {\n throw new DataImportError({message: tpl(messages.forceNoCustomer)});\n\
102
+ \ }\n\n // Subscription can only be forced if the customer has an\
103
+ \ existing subscription\n if (stripeCustomer.subscriptions.data.length\
104
+ \ === 0) {\n throw new DataImportError({message: tpl(messages.forceNoExistingSubscription)});\n\
105
+ \ }\n\n // Subscription can only be forced if the customer does\
106
+ \ not have multiple subscriptions\n if (stripeCustomer.subscriptions.data.length\
107
+ \ > 1) {\n throw new DataImportError({message: tpl(messages.forceTooManySubscriptions)});\n\
108
+ \ }\n\n const stripeSubscription = stripeCustomer.subscriptions.data[0];\n\
109
+ \n // Subscription can only be forced if the existing subscription does\
110
+ \ not have multiple items\n if (stripeSubscription.items.data.length >\
111
+ \ 1) {\n throw new DataImportError({message: tpl(messages.forceTooManySubscriptionItems)});\n\
112
+ \ }\n\n const stripeSubscriptionItem = stripeSubscription.items.data[0];\n\
113
+ \ const stripeSubscriptionItemPrice = stripeSubscriptionItem.price;\n \
114
+ \ const stripeSubscriptionItemPriceCurrency = stripeSubscriptionItemPrice.currency;\n\
115
+ \ const stripeSubscriptionItemPriceAmount = stripeSubscriptionItemPrice.unit_amount;\n\
116
+ \ const stripeSubscriptionItemPriceType = stripeSubscriptionItemPrice.type;\n\
117
+ \ const stripeSubscriptionItemPriceInterval = stripeSubscriptionItemPrice.recurring?.interval\
118
+ \ || null;\n\n // Subscription can only be forced if the existing subscription\
119
+ \ has a recurring interval\n if (!stripeSubscriptionItemPriceInterval)\
120
+ \ {\n throw new DataImportError({message: tpl(messages.forceExistingSubscriptionNotRecurring)});\n\
121
+ \ }\n\n // Retrieve Ghost product\n let ghostProduct = await\
122
+ \ this._productRepository.get(\n {id: data.product_id},\n \
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+ \ {...options, withRelated: ['stripePrices', 'stripeProducts']}\n );\n\
124
+ \n if (!ghostProduct) {\n throw new DataImportError({message:\
125
+ \ tpl(messages.productNotFound, {id: data.product_id})});\n }\n\n \
126
+ \ // If there is not a Stripe product associated with the Ghost product, ensure\
127
+ \ one is created before continuing\n if (!ghostProduct.related('stripeProducts').first())\
128
+ \ {\n // Even though we are not updating any information on the product,\
129
+ \ calling `ProductRepository.update`\n // will ensure that the product\
130
+ \ gets created in Stripe\n ghostProduct = await this._productRepository.update({\n\
131
+ \ id: data.product_id,\n name: ghostProduct.get('name'),\n\
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+ \ // Providing the pricing details will ensure the relevant prices\
133
+ \ for the Ghost product are created\n // on the Stripe product\n\
134
+ \ monthly_price: {\n amount: ghostProduct.get('monthly_price'),\n\
135
+ \ currency: ghostProduct.get('currency')\n },\n\
136
+ \ yearly_price: {\n amount: ghostProduct.get('yearly_price'),\n\
137
+ \ currency: ghostProduct.get('currency')\n }\n\
138
+ \ }, options);\n }\n\n // Find price on Ghost product\
139
+ \ matching stripe subscription item price details\n const ghostProductPrice\
140
+ \ = ghostProduct.related('stripePrices').find((price) => {\n return\
141
+ \ price.get('currency') === stripeSubscriptionItemPriceCurrency &&\n \
142
+ \ price.get('amount') === stripeSubscriptionItemPriceAmount &&\n \
143
+ \ price.get('type') === stripeSubscriptionItemPriceType &&\n \
144
+ \ price.get('interval') === stripeSubscriptionItemPriceInterval;\n \
145
+ \ });\n\n let stripePriceId;\n let isNewStripePrice = false;\n\
146
+ \n if (!ghostProductPrice) {\n // If there is not a matching\
147
+ \ price, create one on the associated Stripe product using the existing\n \
148
+ \ // subscription item price details and update the stripe subscription\
149
+ \ to use it\n const stripeProduct = ghostProduct.related('stripeProducts').first();\n\
150
+ \n const newStripePrice = await this._stripeAPIService.createPrice({\n\
151
+ \ product: stripeProduct.get('stripe_product_id'),\n \
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+ \ active: true,\n nickname: stripeSubscriptionItemPriceInterval\
153
+ \ === 'month' ? 'Monthly' : 'Yearly',\n currency: stripeSubscriptionItemPriceCurrency,\n\
154
+ \ amount: stripeSubscriptionItemPriceAmount,\n type:\
155
+ \ stripeSubscriptionItemPriceType,\n interval: stripeSubscriptionItemPriceInterval\n\
156
+ \ });\n\n await this._stripeAPIService.updateSubscriptionItemPrice(\n\
157
+ \ stripeSubscription.id,\n stripeSubscriptionItem.id,\n\
158
+ \ newStripePrice.id,\n {prorationBehavior: 'none'}\n\
159
+ \ );\n\n stripePriceId = newStripePrice.id;\n \
160
+ \ isNewStripePrice = true;\n } else {\n // If there is a matching\
161
+ \ price, and the subscription is not already using it,\n // update\
162
+ \ the subscription to use it\n stripePriceId = ghostProductPrice.get('stripe_price_id');\n\
163
+ \n if (stripeSubscriptionItem.price.id !== stripePriceId) {\n \
164
+ \ await this._stripeAPIService.updateSubscriptionItemPrice(\n \
165
+ \ stripeSubscription.id,\n stripeSubscriptionItem.id,\n\
166
+ \ stripePriceId,\n {prorationBehavior: 'none'}\n\
167
+ \ );\n }\n }\n\n // If there is a matching\
168
+ \ price, and the subscription is already using it, nothing else needs to be done\n\
169
+ \n return {\n stripePriceId,\n isNewStripePrice\n\
170
+ \ };\n }"
171
+ - "getPrefetchedVariantTrack() {\n if (!this.prefetchedVariant_) {\n return\
172
+ \ null;\n }\n return shaka.util.StreamUtils.variantToTrack(this.prefetchedVariant_);\n\
173
+ \ }"
174
+ - "function scaleAndAdd(out, a, b, scale) {\n out[0] = a[0] + b[0] * scale;\n\
175
+ \ out[1] = a[1] + b[1] * scale;\n return out;\n }"
176
+ - source_sentence: '@returns Has this player been spotted by any others?'
177
+ sentences:
178
+ - "function includes7d( x, value ) {\n\tvar xbuf;\n\tvar dx0;\n\tvar dx1;\n\tvar\
179
+ \ dx2;\n\tvar dx3;\n\tvar dx4;\n\tvar dx5;\n\tvar dx6;\n\tvar sh;\n\tvar S0;\n\
180
+ \tvar S1;\n\tvar S2;\n\tvar S3;\n\tvar S4;\n\tvar S5;\n\tvar S6;\n\tvar sx;\n\t\
181
+ var ix;\n\tvar i0;\n\tvar i1;\n\tvar i2;\n\tvar i3;\n\tvar i4;\n\tvar i5;\n\t\
182
+ var i6;\n\n\t// Note on variable naming convention: S#, dx#, dy#, i# where # corresponds\
183
+ \ to the loop number, with `0` being the innermost loop...\n\n\t// Extract loop\
184
+ \ variables for purposes of loop interchange: dimensions and loop offset (pointer)\
185
+ \ increments...\n\tsh = x.shape;\n\tsx = x.strides;\n\tif ( strides2order( sx\
186
+ \ ) === 1 ) {\n\t\t// For row-major ndarrays, the last dimensions have the fastest\
187
+ \ changing indices...\n\t\tS0 = sh[ 6 ];\n\t\tS1 = sh[ 5 ];\n\t\tS2 = sh[ 4 ];\n\
188
+ \t\tS3 = sh[ 3 ];\n\t\tS4 = sh[ 2 ];\n\t\tS5 = sh[ 1 ];\n\t\tS6 = sh[ 0 ];\n\t\
189
+ \tdx0 = sx[ 6 ]; // offset increment for innermost loop\n\t\tdx1\
190
+ \ = sx[ 5 ] - ( S0*sx[6] );\n\t\tdx2 = sx[ 4 ] - ( S1*sx[5] );\n\t\tdx3 = sx[\
191
+ \ 3 ] - ( S2*sx[4] );\n\t\tdx4 = sx[ 2 ] - ( S3*sx[3] );\n\t\tdx5 = sx[ 1 ] -\
192
+ \ ( S4*sx[2] );\n\t\tdx6 = sx[ 0 ] - ( S5*sx[1] ); // offset increment for outermost\
193
+ \ loop\n\t} else { // order === 'column-major'\n\t\t// For column-major ndarrays,\
194
+ \ the first dimensions have the fastest changing indices...\n\t\tS0 = sh[ 0 ];\n\
195
+ \t\tS1 = sh[ 1 ];\n\t\tS2 = sh[ 2 ];\n\t\tS3 = sh[ 3 ];\n\t\tS4 = sh[ 4 ];\n\t\
196
+ \tS5 = sh[ 5 ];\n\t\tS6 = sh[ 6 ];\n\t\tdx0 = sx[ 0 ]; // offset\
197
+ \ increment for innermost loop\n\t\tdx1 = sx[ 1 ] - ( S0*sx[0] );\n\t\tdx2 = sx[\
198
+ \ 2 ] - ( S1*sx[1] );\n\t\tdx3 = sx[ 3 ] - ( S2*sx[2] );\n\t\tdx4 = sx[ 4 ] -\
199
+ \ ( S3*sx[3] );\n\t\tdx5 = sx[ 5 ] - ( S4*sx[4] );\n\t\tdx6 = sx[ 6 ] - ( S5*sx[5]\
200
+ \ ); // offset increment for outermost loop\n\t}\n\t// Set a pointer to the first\
201
+ \ indexed element:\n\tix = x.offset;\n\n\t// Cache a reference to the input ndarray\
202
+ \ buffer:\n\txbuf = x.data;\n\n\t// Iterate over the ndarray dimensions...\n\t\
203
+ for ( i6 = 0; i6 < S6; i6++ ) {\n\t\tfor ( i5 = 0; i5 < S5; i5++ ) {\n\t\t\tfor\
204
+ \ ( i4 = 0; i4 < S4; i4++ ) {\n\t\t\t\tfor ( i3 = 0; i3 < S3; i3++ ) {\n\t\t\t\
205
+ \t\tfor ( i2 = 0; i2 < S2; i2++ ) {\n\t\t\t\t\t\tfor ( i1 = 0; i1 < S1; i1++ )\
206
+ \ {\n\t\t\t\t\t\t\tfor ( i0 = 0; i0 < S0; i0++ ) {\n\t\t\t\t\t\t\t\tif ( xbuf[\
207
+ \ ix ] === value ) {\n\t\t\t\t\t\t\t\t\treturn true;\n\t\t\t\t\t\t\t\t}\n\t\t\t\
208
+ \t\t\t\t\tix += dx0;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\tix += dx1;\n\t\t\t\t\t\t\
209
+ }\n\t\t\t\t\t\tix += dx2;\n\t\t\t\t\t}\n\t\t\t\t\tix += dx3;\n\t\t\t\t}\n\t\t\t\
210
+ \tix += dx4;\n\t\t\t}\n\t\t\tix += dx5;\n\t\t}\n\t\tix += dx6;\n\t}\n\treturn\
211
+ \ false;\n}"
212
+ - "_generateIntegrityFile(lockfile, patterns, flags, workspaceLayout, artifacts)\
213
+ \ {\n var _this3 = this;\n\n return (0, (_asyncToGenerator2 || _load_asyncToGenerator()).default)(function*\
214
+ \ () {\n const result = (0, (_extends2 || _load_extends()).default)({}, INTEGRITY_FILE_DEFAULTS(),\
215
+ \ {\n artifacts\n });\n\n result.topLevelPatterns = patterns;\n\
216
+ \n // If using workspaces, we also need to add the workspaces patterns to\
217
+ \ the top-level, so that we'll know if a\n // dependency is added or removed\
218
+ \ into one of them. We must take care not to read the aggregator (if !loc).\n\
219
+ \ //\n // Also note that we can't use of workspaceLayout.workspaces[].manifest._reference.patterns,\
220
+ \ because when\n // doing a \"yarn check\", the _reference property hasn't\
221
+ \ yet been properly initialized.\n\n if (workspaceLayout) {\n result.topLevelPatterns\
222
+ \ = result.topLevelPatterns.filter(function (p) {\n // $FlowFixMe\n \
223
+ \ return !workspaceLayout.getManifestByPattern(p);\n });\n\n \
224
+ \ for (var _iterator4 = Object.keys(workspaceLayout.workspaces), _isArray4\
225
+ \ = Array.isArray(_iterator4), _i4 = 0, _iterator4 = _isArray4 ? _iterator4 :\
226
+ \ _iterator4[Symbol.iterator]();;) {\n var _ref5;\n\n if (_isArray4)\
227
+ \ {\n if (_i4 >= _iterator4.length) break;\n _ref5 = _iterator4[_i4++];\n\
228
+ \ } else {\n _i4 = _iterator4.next();\n if (_i4.done)\
229
+ \ break;\n _ref5 = _i4.value;\n }\n\n const name\
230
+ \ = _ref5;\n\n if (!workspaceLayout.workspaces[name].loc) {\n \
231
+ \ continue;\n }\n\n const manifest = workspaceLayout.workspaces[name].manifest;\n\
232
+ \n if (manifest) {\n for (var _iterator5 = (_constants ||\
233
+ \ _load_constants()).DEPENDENCY_TYPES, _isArray5 = Array.isArray(_iterator5),\
234
+ \ _i5 = 0, _iterator5 = _isArray5 ? _iterator5 : _iterator5[Symbol.iterator]();;)\
235
+ \ {\n var _ref6;\n\n if (_isArray5) {\n \
236
+ \ if (_i5 >= _iterator5.length) break;\n _ref6 = _iterator5[_i5++];\n\
237
+ \ } else {\n _i5 = _iterator5.next();\n \
238
+ \ if (_i5.done) break;\n _ref6 = _i5.value;\n \
239
+ \ }\n\n const dependencyType = _ref6;\n\n const dependencies\
240
+ \ = manifest[dependencyType];\n\n if (!dependencies) {\n \
241
+ \ continue;\n }\n\n for (var _iterator6 = Object.keys(dependencies),\
242
+ \ _isArray6 = Array.isArray(_iterator6), _i6 = 0, _iterator6 = _isArray6 ? _iterator6\
243
+ \ : _iterator6[Symbol.iterator]();;) {\n var _ref7;\n\n \
244
+ \ if (_isArray6) {\n if (_i6 >= _iterator6.length) break;\n\
245
+ \ _ref7 = _iterator6[_i6++];\n } else {\n \
246
+ \ _i6 = _iterator6.next();\n if (_i6.done) break;\n\
247
+ \ _ref7 = _i6.value;\n }\n\n const\
248
+ \ dep = _ref7;\n\n result.topLevelPatterns.push(`${dep}@${dependencies[dep]}`);\n\
249
+ \ }\n }\n }\n }\n }\n\n result.topLevelPatterns.sort((_misc\
250
+ \ || _load_misc()).sortAlpha);\n\n if (flags.checkFiles) {\n result.flags.push('checkFiles');\n\
251
+ \ }\n\n if (flags.flat) {\n result.flags.push('flat');\n \
252
+ \ }\n\n if (_this3.config.ignoreScripts) {\n result.flags.push('ignoreScripts');\n\
253
+ \ }\n if (_this3.config.focus) {\n result.flags.push('focus:\
254
+ \ ' + _this3.config.focusedWorkspaceName);\n }\n\n if (_this3.config.production)\
255
+ \ {\n result.flags.push('production');\n }\n\n if (_this3.config.plugnplayEnabled)\
256
+ \ {\n result.flags.push('plugnplay');\n }\n\n const linkedModules\
257
+ \ = _this3.config.linkedModules;\n\n if (linkedModules.length) {\n \
258
+ \ result.linkedModules = linkedModules.sort((_misc || _load_misc()).sortAlpha);\n\
259
+ \ }\n\n for (var _iterator7 = Object.keys(lockfile), _isArray7 = Array.isArray(_iterator7),\
260
+ \ _i7 = 0, _iterator7 = _isArray7 ? _iterator7 : _iterator7[Symbol.iterator]();;)\
261
+ \ {\n var _ref8;\n\n if (_isArray7) {\n if (_i7 >= _iterator7.length)\
262
+ \ break;\n _ref8 = _iterator7[_i7++];\n } else {\n _i7\
263
+ \ = _iterator7.next();\n if (_i7.done) break;\n _ref8 = _i7.value;\n\
264
+ \ }\n\n const key = _ref8;\n\n result.lockfileEntries[key]\
265
+ \ = lockfile[key].resolved || '';\n }\n\n for (var _iterator8 = _this3._getModulesFolders({\
266
+ \ workspaceLayout }), _isArray8 = Array.isArray(_iterator8), _i8 = 0, _iterator8\
267
+ \ = _isArray8 ? _iterator8 : _iterator8[Symbol.iterator]();;) {\n var _ref9;\n\
268
+ \n if (_isArray8) {\n if (_i8 >= _iterator8.length) break;\n \
269
+ \ _ref9 = _iterator8[_i8++];\n } else {\n _i8 = _iterator8.next();\n\
270
+ \ if (_i8.done) break;\n _ref9 = _i8.value;\n }\n\n \
271
+ \ const modulesFolder = _ref9;\n\n if (yield (_fs || _load_fs()).exists(modulesFolder))\
272
+ \ {\n result.modulesFolders.push(path.relative(_this3.config.lockfileFolder,\
273
+ \ modulesFolder));\n }\n }\n\n if (flags.checkFiles) {\n \
274
+ \ const modulesRoot = _this3._getModulesRootFolder();\n\n result.files\
275
+ \ = (yield _this3._getIntegrityListing({ workspaceLayout })).map(function (entry)\
276
+ \ {\n return path.relative(modulesRoot, entry);\n }).sort((_misc\
277
+ \ || _load_misc()).sortAlpha);\n }\n\n return result;\n })();\n \
278
+ \ }"
279
+ - "get isSpotted() {\n return this.getProp(\"DT_BaseEntity\", \"m_bSpotted\"\
280
+ );\n }"
281
+ - source_sentence: The toggle content, if left empty it will render the default toggle
282
+ (seen above).
283
+ sentences:
284
+ - "update = () => {\n\n\t const timerId = window.requestAnimationFrame(\
285
+ \ update );\n\t const elapsed = performance.now() - timestamp;\n\t\
286
+ \ const progress = elapsed / duration;\n\t const opacity\
287
+ \ = 1.0 - progress > 0 ? 1.0 - progress : 0;\n\t const radius = progress\
288
+ \ * canvasWidth * 0.5 / dpr;\n\n\t context.clearRect( 0, 0, canvasWidth,\
289
+ \ canvasHeight );\n\t context.beginPath();\n\t context.arc(\
290
+ \ x, y, radius, 0, Math.PI * 2 );\n\t context.fillStyle = `rgba(${color.r\
291
+ \ * 255}, ${color.g * 255}, ${color.b * 255}, ${opacity})`;\n\t context.fill();\n\
292
+ \t context.closePath();\n\n\t if ( progress >= 1.0 ) {\n\
293
+ \n\t window.cancelAnimationFrame( timerId );\n\t \
294
+ \ this.updateCanvasArcByProgress( 0 );\n\n\t /**\n\t \
295
+ \ * Reticle ripple end event\n\t * @type {object}\n\t \
296
+ \ * @event Reticle#reticle-ripple-end\n\t */\n\t\
297
+ \ this.dispatchEvent( { type: 'reticle-ripple-end' } );\n\n\t \
298
+ \ }\n\n\t material.map.needsUpdate = true;\n\n\t }"
299
+ - "static _headersDictToHeadersArray(headersDict) {\n const result = [];\n \
300
+ \ for (const name of Object.keys(headersDict)) {\n const values = headersDict[name].split('\\\
301
+ n');\n for (let i = 0; i < values.length; ++i) {\n result.push({name:\
302
+ \ name, value: values[i]});\n }\n }\n return result;\n }"
303
+ - "function NavbarToggle() {\n\t (0, _classCallCheck3['default'])(this, NavbarToggle);\n\
304
+ \t return (0, _possibleConstructorReturn3['default'])(this, _React$Component.apply(this,\
305
+ \ arguments));\n\t }"
306
+ pipeline_tag: sentence-similarity
307
+ library_name: sentence-transformers
308
+ ---
309
+
310
+ # SentenceTransformer based on Shuu12121/CodeModernBERT-Crow-v1.1
311
+
312
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Shuu12121/CodeModernBERT-Crow-v1.1](https://huggingface.co/Shuu12121/CodeModernBERT-Crow-v1.1). 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.
313
+
314
+ ## Model Details
315
+
316
+ ### Model Description
317
+ - **Model Type:** Sentence Transformer
318
+ - **Base model:** [Shuu12121/CodeModernBERT-Crow-v1.1](https://huggingface.co/Shuu12121/CodeModernBERT-Crow-v1.1) <!-- at revision d7baa192c09e1e1da5c39fe9652ea9a4663084f6 -->
319
+ - **Maximum Sequence Length:** 1024 tokens
320
+ - **Output Dimensionality:** 768 dimensions
321
+ - **Similarity Function:** Cosine Similarity
322
+ <!-- - **Training Dataset:** Unknown -->
323
+ <!-- - **Language:** Unknown -->
324
+ <!-- - **License:** Unknown -->
325
+
326
+ ### Model Sources
327
+
328
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
329
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
330
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
331
+
332
+ ### Full Model Architecture
333
+
334
+ ```
335
+ SentenceTransformer(
336
+ (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
337
+ (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})
338
+ )
339
+ ```
340
+
341
+ ## Usage
342
+
343
+ ### Direct Usage (Sentence Transformers)
344
+
345
+ First install the Sentence Transformers library:
346
+
347
+ ```bash
348
+ pip install -U sentence-transformers
349
+ ```
350
+
351
+ Then you can load this model and run inference.
352
+ ```python
353
+ from sentence_transformers import SentenceTransformer
354
+
355
+ # Download from the 🤗 Hub
356
+ model = SentenceTransformer("sentence_transformers_model_id")
357
+ # Run inference
358
+ sentences = [
359
+ 'The toggle content, if left empty it will render the default toggle (seen above).',
360
+ "function NavbarToggle() {\n\t (0, _classCallCheck3['default'])(this, NavbarToggle);\n\t return (0, _possibleConstructorReturn3['default'])(this, _React$Component.apply(this, arguments));\n\t }",
361
+ "update = () => {\n\n\t const timerId = window.requestAnimationFrame( update );\n\t const elapsed = performance.now() - timestamp;\n\t const progress = elapsed / duration;\n\t const opacity = 1.0 - progress > 0 ? 1.0 - progress : 0;\n\t const radius = progress * canvasWidth * 0.5 / dpr;\n\n\t context.clearRect( 0, 0, canvasWidth, canvasHeight );\n\t context.beginPath();\n\t context.arc( x, y, radius, 0, Math.PI * 2 );\n\t context.fillStyle = `rgba(${color.r * 255}, ${color.g * 255}, ${color.b * 255}, ${opacity})`;\n\t context.fill();\n\t context.closePath();\n\n\t if ( progress >= 1.0 ) {\n\n\t window.cancelAnimationFrame( timerId );\n\t this.updateCanvasArcByProgress( 0 );\n\n\t /**\n\t * Reticle ripple end event\n\t * @type {object}\n\t * @event Reticle#reticle-ripple-end\n\t */\n\t this.dispatchEvent( { type: 'reticle-ripple-end' } );\n\n\t }\n\n\t material.map.needsUpdate = true;\n\n\t }",
362
+ ]
363
+ embeddings = model.encode(sentences)
364
+ print(embeddings.shape)
365
+ # [3, 768]
366
+
367
+ # Get the similarity scores for the embeddings
368
+ similarities = model.similarity(embeddings, embeddings)
369
+ print(similarities)
370
+ # tensor([[ 1.0000, 0.6778, -0.0447],
371
+ # [ 0.6778, 1.0000, 0.0303],
372
+ # [-0.0447, 0.0303, 1.0000]])
373
+ ```
374
+
375
+ <!--
376
+ ### Direct Usage (Transformers)
377
+
378
+ <details><summary>Click to see the direct usage in Transformers</summary>
379
+
380
+ </details>
381
+ -->
382
+
383
+ <!--
384
+ ### Downstream Usage (Sentence Transformers)
385
+
386
+ You can finetune this model on your own dataset.
387
+
388
+ <details><summary>Click to expand</summary>
389
+
390
+ </details>
391
+ -->
392
+
393
+ <!--
394
+ ### Out-of-Scope Use
395
+
396
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
397
+ -->
398
+
399
+ <!--
400
+ ## Bias, Risks and Limitations
401
+
402
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
403
+ -->
404
+
405
+ <!--
406
+ ### Recommendations
407
+
408
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
409
+ -->
410
+
411
+ ## Training Details
412
+
413
+ ### Training Dataset
414
+
415
+ #### Unnamed Dataset
416
+
417
+ * Size: 2,392,064 training samples
418
+ * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
419
+ * Approximate statistics based on the first 1000 samples:
420
+ | | sentence_0 | sentence_1 | label |
421
+ |:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------|
422
+ | type | string | string | float |
423
+ | details | <ul><li>min: 8 tokens</li><li>mean: 74.35 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 182.37 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> |
424
+ * Samples:
425
+ | sentence_0 | sentence_1 | label |
426
+ |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
427
+ | <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> |
428
+ | <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> |
429
+ | <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> |
430
+ * Loss: [<code>CachedMultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativessymmetricrankingloss) with these parameters:
431
+ ```json
432
+ {
433
+ "scale": 20.0,
434
+ "similarity_fct": "cos_sim",
435
+ "mini_batch_size": 128,
436
+ "gather_across_devices": false
437
+ }
438
+ ```
439
+
440
+ ### Training Hyperparameters
441
+ #### Non-Default Hyperparameters
442
+
443
+ - `per_device_train_batch_size`: 2048
444
+ - `per_device_eval_batch_size`: 2048
445
+ - `fp16`: True
446
+ - `multi_dataset_batch_sampler`: round_robin
447
+
448
+ #### All Hyperparameters
449
+ <details><summary>Click to expand</summary>
450
+
451
+ - `overwrite_output_dir`: False
452
+ - `do_predict`: False
453
+ - `eval_strategy`: no
454
+ - `prediction_loss_only`: True
455
+ - `per_device_train_batch_size`: 2048
456
+ - `per_device_eval_batch_size`: 2048
457
+ - `per_gpu_train_batch_size`: None
458
+ - `per_gpu_eval_batch_size`: None
459
+ - `gradient_accumulation_steps`: 1
460
+ - `eval_accumulation_steps`: None
461
+ - `torch_empty_cache_steps`: None
462
+ - `learning_rate`: 5e-05
463
+ - `weight_decay`: 0.0
464
+ - `adam_beta1`: 0.9
465
+ - `adam_beta2`: 0.999
466
+ - `adam_epsilon`: 1e-08
467
+ - `max_grad_norm`: 1
468
+ - `num_train_epochs`: 3
469
+ - `max_steps`: -1
470
+ - `lr_scheduler_type`: linear
471
+ - `lr_scheduler_kwargs`: {}
472
+ - `warmup_ratio`: 0.0
473
+ - `warmup_steps`: 0
474
+ - `log_level`: passive
475
+ - `log_level_replica`: warning
476
+ - `log_on_each_node`: True
477
+ - `logging_nan_inf_filter`: True
478
+ - `save_safetensors`: True
479
+ - `save_on_each_node`: False
480
+ - `save_only_model`: False
481
+ - `restore_callback_states_from_checkpoint`: False
482
+ - `no_cuda`: False
483
+ - `use_cpu`: False
484
+ - `use_mps_device`: False
485
+ - `seed`: 42
486
+ - `data_seed`: None
487
+ - `jit_mode_eval`: False
488
+ - `use_ipex`: False
489
+ - `bf16`: False
490
+ - `fp16`: True
491
+ - `fp16_opt_level`: O1
492
+ - `half_precision_backend`: auto
493
+ - `bf16_full_eval`: False
494
+ - `fp16_full_eval`: False
495
+ - `tf32`: None
496
+ - `local_rank`: 0
497
+ - `ddp_backend`: None
498
+ - `tpu_num_cores`: None
499
+ - `tpu_metrics_debug`: False
500
+ - `debug`: []
501
+ - `dataloader_drop_last`: False
502
+ - `dataloader_num_workers`: 0
503
+ - `dataloader_prefetch_factor`: None
504
+ - `past_index`: -1
505
+ - `disable_tqdm`: False
506
+ - `remove_unused_columns`: True
507
+ - `label_names`: None
508
+ - `load_best_model_at_end`: False
509
+ - `ignore_data_skip`: False
510
+ - `fsdp`: []
511
+ - `fsdp_min_num_params`: 0
512
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
513
+ - `fsdp_transformer_layer_cls_to_wrap`: None
514
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
515
+ - `deepspeed`: None
516
+ - `label_smoothing_factor`: 0.0
517
+ - `optim`: adamw_torch
518
+ - `optim_args`: None
519
+ - `adafactor`: False
520
+ - `group_by_length`: False
521
+ - `length_column_name`: length
522
+ - `ddp_find_unused_parameters`: None
523
+ - `ddp_bucket_cap_mb`: None
524
+ - `ddp_broadcast_buffers`: False
525
+ - `dataloader_pin_memory`: True
526
+ - `dataloader_persistent_workers`: False
527
+ - `skip_memory_metrics`: True
528
+ - `use_legacy_prediction_loop`: False
529
+ - `push_to_hub`: False
530
+ - `resume_from_checkpoint`: None
531
+ - `hub_model_id`: None
532
+ - `hub_strategy`: every_save
533
+ - `hub_private_repo`: None
534
+ - `hub_always_push`: False
535
+ - `hub_revision`: None
536
+ - `gradient_checkpointing`: False
537
+ - `gradient_checkpointing_kwargs`: None
538
+ - `include_inputs_for_metrics`: False
539
+ - `include_for_metrics`: []
540
+ - `eval_do_concat_batches`: True
541
+ - `fp16_backend`: auto
542
+ - `push_to_hub_model_id`: None
543
+ - `push_to_hub_organization`: None
544
+ - `mp_parameters`:
545
+ - `auto_find_batch_size`: False
546
+ - `full_determinism`: False
547
+ - `torchdynamo`: None
548
+ - `ray_scope`: last
549
+ - `ddp_timeout`: 1800
550
+ - `torch_compile`: False
551
+ - `torch_compile_backend`: None
552
+ - `torch_compile_mode`: None
553
+ - `include_tokens_per_second`: False
554
+ - `include_num_input_tokens_seen`: False
555
+ - `neftune_noise_alpha`: None
556
+ - `optim_target_modules`: None
557
+ - `batch_eval_metrics`: False
558
+ - `eval_on_start`: False
559
+ - `use_liger_kernel`: False
560
+ - `liger_kernel_config`: None
561
+ - `eval_use_gather_object`: False
562
+ - `average_tokens_across_devices`: False
563
+ - `prompts`: None
564
+ - `batch_sampler`: batch_sampler
565
+ - `multi_dataset_batch_sampler`: round_robin
566
+ - `router_mapping`: {}
567
+ - `learning_rate_mapping`: {}
568
+
569
+ </details>
570
+
571
+ ### Training Logs
572
+ | Epoch | Step | Training Loss |
573
+ |:------:|:----:|:-------------:|
574
+ | 0.4281 | 500 | 0.3784 |
575
+ | 0.8562 | 1000 | 0.1367 |
576
+ | 1.2842 | 1500 | 0.0707 |
577
+ | 1.7123 | 2000 | 0.0456 |
578
+ | 2.1404 | 2500 | 0.0344 |
579
+ | 2.5685 | 3000 | 0.0143 |
580
+ | 2.9966 | 3500 | 0.0136 |
581
+
582
+
583
+ ### Framework Versions
584
+ - Python: 3.10.12
585
+ - Sentence Transformers: 5.1.0
586
+ - Transformers: 4.55.3
587
+ - PyTorch: 2.7.0+cu128
588
+ - Accelerate: 1.7.0
589
+ - Datasets: 3.6.0
590
+ - Tokenizers: 0.21.4
591
+
592
+ ## Citation
593
+
594
+ ### BibTeX
595
+
596
+ #### Sentence Transformers
597
+ ```bibtex
598
+ @inproceedings{reimers-2019-sentence-bert,
599
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
600
+ author = "Reimers, Nils and Gurevych, Iryna",
601
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
602
+ month = "11",
603
+ year = "2019",
604
+ publisher = "Association for Computational Linguistics",
605
+ url = "https://arxiv.org/abs/1908.10084",
606
+ }
607
+ ```
608
+
609
+ <!--
610
+ ## Glossary
611
+
612
+ *Clearly define terms in order to be accessible across audiences.*
613
+ -->
614
+
615
+ <!--
616
+ ## Model Card Authors
617
+
618
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