Hugging Face's logo

annazdr
/
nace-pl-v2

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:12822
loss:BatchAllTripletLoss
text-embeddings-inference
Inference Endpoints
Model card Files Community
annazdr commited on
Commit
d2bbe26
•
1 Parent(s): 8f34d32

Add new SentenceTransformer model.

Browse files
Files changed (12) hide show
  1. .gitattributes +2 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +341 -0
  4. config.json +26 -0
  5. config_sentence_transformers.json +10 -0
  6. model.safetensors +3 -0
  7. modules.json +14 -0
  8. sentence_bert_config.json +4 -0
  9. special_tokens_map.json +51 -0
  10. tokenizer.json +3 -0
  11. tokenizer_config.json +64 -0
  12. unigram.json +3 -0
.gitattributes CHANGED
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
37
+ unigram.json filter=lfs diff=lfs merge=lfs -text
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 384,
3
+ "pooling_mode_cls_token": false,
4
+ "pooling_mode_mean_tokens": true,
5
+ "pooling_mode_max_tokens": false,
6
+ "pooling_mode_mean_sqrt_len_tokens": false,
7
+ "pooling_mode_weightedmean_tokens": false,
8
+ "pooling_mode_lasttoken": false,
9
+ "include_prompt": true
10
+ }
README.md ADDED
@@ -0,0 +1,341 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language: []
3
+ library_name: sentence-transformers
4
+ tags:
5
+ - sentence-transformers
6
+ - sentence-similarity
7
+ - feature-extraction
8
+ - generated_from_trainer
9
+ - dataset_size:12822
10
+ - loss:BatchAllTripletLoss
11
+ base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
12
+ datasets: []
13
+ widget:
14
+ - source_sentence: parcel-packing and gift-wrapping
15
+ sentences:
16
+ - retail sale of cleaning products, e
17
+ - cafeterias
18
+ - ' '
19
+ - source_sentence: Sprzedaż detaliczna mięsa i wyrobów z mięsa
20
+ sentences:
21
+ - ' '
22
+ - ' revenues from sale of advertising space'
23
+ - g
24
+ - source_sentence: g
25
+ sentences:
26
+ - installation of the system and provision of training and support to users of the
27
+ system- activities of auditing and certification of computing and data processing
28
+ infrastructures and services
29
+ - ' revenues from sale of advertising space'
30
+ - 47.75 Retail sale of cosmetic and toilet articles
31
+ - source_sentence: lighterage, salvage activities
32
+ sentences:
33
+ - hairstyling
34
+ - ' this class also includes: cladding of metal pipes with plastics'
35
+ - usługi pośrednictwa w zakresie transportu pasażerskiego
36
+ - source_sentence: manufacture of glass mirrors
37
+ sentences:
38
+ - manufacture of electroplating machinery
39
+ - ' protective face shields/visors, of plastics, e'
40
+ - cow peas
41
+ pipeline_tag: sentence-similarity
42
+ ---
43
+
44
+ # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
45
+
46
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
47
+
48
+ ## Model Details
49
+
50
+ ### Model Description
51
+ - **Model Type:** Sentence Transformer
52
+ - **Base model:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb -->
53
+ - **Maximum Sequence Length:** 128 tokens
54
+ - **Output Dimensionality:** 384 tokens
55
+ - **Similarity Function:** Cosine Similarity
56
+ <!-- - **Training Dataset:** Unknown -->
57
+ <!-- - **Language:** Unknown -->
58
+ <!-- - **License:** Unknown -->
59
+
60
+ ### Model Sources
61
+
62
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
63
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
64
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
65
+
66
+ ### Full Model Architecture
67
+
68
+ ```
69
+ SentenceTransformer(
70
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
71
+ (1): Pooling({'word_embedding_dimension': 384, '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})
72
+ )
73
+ ```
74
+
75
+ ## Usage
76
+
77
+ ### Direct Usage (Sentence Transformers)
78
+
79
+ First install the Sentence Transformers library:
80
+
81
+ ```bash
82
+ pip install -U sentence-transformers
83
+ ```
84
+
85
+ Then you can load this model and run inference.
86
+ ```python
87
+ from sentence_transformers import SentenceTransformer
88
+
89
+ # Download from the 🤗 Hub
90
+ model = SentenceTransformer("annazdr/nace-pl-v2")
91
+ # Run inference
92
+ sentences = [
93
+ 'manufacture of glass mirrors',
94
+ ' protective face shields/visors, of plastics, e',
95
+ 'manufacture of electroplating machinery',
96
+ ]
97
+ embeddings = model.encode(sentences)
98
+ print(embeddings.shape)
99
+ # [3, 384]
100
+
101
+ # Get the similarity scores for the embeddings
102
+ similarities = model.similarity(embeddings, embeddings)
103
+ print(similarities.shape)
104
+ # [3, 3]
105
+ ```
106
+
107
+ <!--
108
+ ### Direct Usage (Transformers)
109
+
110
+ <details><summary>Click to see the direct usage in Transformers</summary>
111
+
112
+ </details>
113
+ -->
114
+
115
+ <!--
116
+ ### Downstream Usage (Sentence Transformers)
117
+
118
+ You can finetune this model on your own dataset.
119
+
120
+ <details><summary>Click to expand</summary>
121
+
122
+ </details>
123
+ -->
124
+
125
+ <!--
126
+ ### Out-of-Scope Use
127
+
128
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
129
+ -->
130
+
131
+ <!--
132
+ ## Bias, Risks and Limitations
133
+
134
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
135
+ -->
136
+
137
+ <!--
138
+ ### Recommendations
139
+
140
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
141
+ -->
142
+
143
+ ## Training Details
144
+
145
+ ### Training Dataset
146
+
147
+ #### Unnamed Dataset
148
+
149
+
150
+ * Size: 12,822 training samples
151
+ * Columns: <code>sentence_0</code> and <code>label</code>
152
+ * Approximate statistics based on the first 1000 samples:
153
+ | | sentence_0 | label |
154
+ |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
155
+ | type | string | int |
156
+ | details | <ul><li>min: 2 tokens</li><li>mean: 15.14 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~0.20%</li><li>1: ~0.10%</li><li>2: ~0.20%</li><li>4: ~0.30%</li><li>5: ~0.10%</li><li>6: ~0.10%</li><li>7: ~0.40%</li><li>9: ~0.10%</li><li>10: ~0.60%</li><li>11: ~0.20%</li><li>12: ~0.30%</li><li>13: ~0.30%</li><li>14: ~0.10%</li><li>15: ~0.10%</li><li>16: ~0.40%</li><li>17: ~0.10%</li><li>18: ~0.40%</li><li>20: ~0.40%</li><li>22: ~0.30%</li><li>23: ~0.30%</li><li>24: ~0.30%</li><li>25: ~0.40%</li><li>27: ~0.20%</li><li>28: ~0.10%</li><li>30: ~0.10%</li><li>32: ~0.10%</li><li>33: ~0.20%</li><li>34: ~0.10%</li><li>35: ~0.30%</li><li>37: ~0.30%</li><li>38: ~0.30%</li><li>39: ~0.30%</li><li>41: ~0.20%</li><li>42: ~0.10%</li><li>43: ~0.20%</li><li>44: ~0.50%</li><li>46: ~0.10%</li><li>48: ~0.20%</li><li>49: ~0.30%</li><li>50: ~0.30%</li><li>51: ~0.20%</li><li>52: ~0.40%</li><li>53: ~0.30%</li><li>54: ~0.20%</li><li>55: ~0.20%</li><li>56: ~0.20%</li><li>58: ~0.20%</li><li>59: ~0.10%</li><li>60: ~0.30%</li><li>61: ~0.20%</li><li>63: ~0.40%</li><li>64: ~0.30%</li><li>65: ~0.10%</li><li>66: ~0.70%</li><li>68: ~0.10%</li><li>69: ~0.20%</li><li>70: ~0.50%</li><li>71: ~0.30%</li><li>72: ~0.10%</li><li>73: ~0.40%</li><li>74: ~0.20%</li><li>75: ~0.30%</li><li>76: ~0.20%</li><li>78: ~0.10%</li><li>79: ~0.10%</li><li>80: ~0.10%</li><li>81: ~0.30%</li><li>82: ~0.30%</li><li>83: ~0.30%</li><li>84: ~0.10%</li><li>85: ~0.20%</li><li>86: ~0.20%</li><li>89: ~0.10%</li><li>90: ~0.10%</li><li>91: ~0.30%</li><li>92: ~0.20%</li><li>93: ~0.10%</li><li>94: ~0.30%</li><li>95: ~0.20%</li><li>96: ~0.20%</li><li>97: ~0.40%</li><li>98: ~0.70%</li><li>99: ~0.20%</li><li>100: ~0.50%</li><li>101: ~0.20%</li><li>102: ~0.10%</li><li>103: ~0.10%</li><li>104: ~0.20%</li><li>106: ~0.10%</li><li>108: ~0.20%</li><li>110: ~0.10%</li><li>111: ~0.10%</li><li>112: ~0.20%</li><li>115: ~0.10%</li><li>116: ~0.10%</li><li>119: ~0.30%</li><li>120: ~0.10%</li><li>121: ~0.20%</li><li>123: ~0.10%</li><li>125: ~0.20%</li><li>126: ~0.10%</li><li>127: ~0.20%</li><li>128: ~0.40%</li><li>130: ~0.20%</li><li>134: ~0.10%</li><li>135: ~0.10%</li><li>136: ~0.10%</li><li>138: ~0.10%</li><li>139: ~0.10%</li><li>140: ~0.20%</li><li>141: ~0.10%</li><li>142: ~0.10%</li><li>143: ~0.40%</li><li>144: ~0.10%</li><li>148: ~0.10%</li><li>149: ~0.10%</li><li>150: ~0.30%</li><li>151: ~0.10%</li><li>152: ~0.30%</li><li>153: ~0.40%</li><li>154: ~0.50%</li><li>156: ~0.10%</li><li>157: ~0.30%</li><li>158: ~0.20%</li><li>159: ~0.30%</li><li>160: ~0.10%</li><li>161: ~0.10%</li><li>162: ~0.10%</li><li>163: ~0.10%</li><li>165: ~0.10%</li><li>166: ~0.20%</li><li>167: ~0.20%</li><li>168: ~0.20%</li><li>170: ~0.10%</li><li>171: ~0.10%</li><li>172: ~0.10%</li><li>173: ~0.10%</li><li>174: ~0.20%</li><li>176: ~0.20%</li><li>178: ~0.10%</li><li>179: ~0.10%</li><li>181: ~0.10%</li><li>182: ~0.30%</li><li>183: ~0.30%</li><li>184: ~0.20%</li><li>185: ~0.30%</li><li>186: ~0.40%</li><li>187: ~0.20%</li><li>188: ~0.40%</li><li>189: ~0.20%</li><li>190: ~0.50%</li><li>191: ~0.30%</li><li>192: ~0.40%</li><li>193: ~0.10%</li><li>196: ~0.20%</li><li>197: ~0.20%</li><li>198: ~0.30%</li><li>199: ~0.60%</li><li>200: ~0.50%</li><li>201: ~0.10%</li><li>202: ~0.10%</li><li>203: ~0.30%</li><li>204: ~0.10%</li><li>205: ~0.30%</li><li>206: ~0.40%</li><li>208: ~0.20%</li><li>210: ~0.20%</li><li>211: ~0.40%</li><li>212: ~0.20%</li><li>214: ~0.30%</li><li>215: ~0.10%</li><li>217: ~0.30%</li><li>218: ~0.20%</li><li>220: ~0.30%</li><li>221: ~0.10%</li><li>222: ~0.20%</li><li>223: ~0.10%</li><li>225: ~0.10%</li><li>226: ~0.10%</li><li>227: ~0.20%</li><li>228: ~0.10%</li><li>230: ~0.10%</li><li>231: ~0.30%</li><li>233: ~0.10%</li><li>234: ~0.10%</li><li>235: ~0.20%</li><li>236: ~0.20%</li><li>237: ~0.20%</li><li>238: ~0.30%</li><li>239: ~0.10%</li><li>240: ~0.10%</li><li>241: ~0.20%</li><li>242: ~0.10%</li><li>243: ~0.40%</li><li>244: ~0.40%</li><li>245: ~0.20%</li><li>246: ~0.20%</li><li>247: ~0.30%</li><li>248: ~0.20%</li><li>249: ~0.20%</li><li>250: ~0.10%</li><li>253: ~0.30%</li><li>254: ~0.50%</li><li>255: ~0.30%</li><li>256: ~0.20%</li><li>257: ~0.20%</li><li>258: ~0.20%</li><li>259: ~0.10%</li><li>260: ~0.60%</li><li>261: ~0.10%</li><li>262: ~0.10%</li><li>264: ~0.30%</li><li>266: ~0.10%</li><li>267: ~0.10%</li><li>269: ~0.20%</li><li>271: ~0.10%</li><li>272: ~0.10%</li><li>273: ~0.10%</li><li>274: ~0.40%</li><li>275: ~0.10%</li><li>276: ~0.30%</li><li>277: ~0.20%</li><li>278: ~0.10%</li><li>279: ~0.20%</li><li>281: ~0.10%</li><li>283: ~0.40%</li><li>284: ~0.10%</li><li>285: ~0.20%</li><li>286: ~0.10%</li><li>287: ~0.20%</li><li>289: ~0.20%</li><li>290: ~0.20%</li><li>291: ~0.20%</li><li>292: ~0.30%</li><li>293: ~0.20%</li><li>294: ~0.20%</li><li>295: ~0.40%</li><li>296: ~0.20%</li><li>297: ~0.20%</li><li>298: ~0.10%</li><li>302: ~0.10%</li><li>303: ~0.10%</li><li>306: ~0.60%</li><li>307: ~0.50%</li><li>310: ~0.40%</li><li>311: ~0.40%</li><li>313: ~0.10%</li><li>314: ~0.40%</li><li>316: ~0.10%</li><li>319: ~0.20%</li><li>320: ~0.10%</li><li>322: ~0.50%</li><li>324: ~0.20%</li><li>325: ~0.30%</li><li>326: ~0.30%</li><li>327: ~0.10%</li><li>328: ~0.10%</li><li>329: ~0.10%</li><li>330: ~0.10%</li><li>331: ~0.10%</li><li>332: ~0.20%</li><li>334: ~0.10%</li><li>336: ~0.30%</li><li>337: ~0.50%</li><li>338: ~0.10%</li><li>341: ~0.10%</li><li>343: ~0.10%</li><li>344: ~0.20%</li><li>347: ~0.20%</li><li>348: ~0.10%</li><li>349: ~0.10%</li><li>350: ~0.50%</li><li>351: ~0.70%</li><li>352: ~0.20%</li><li>353: ~0.10%</li><li>354: ~0.20%</li><li>355: ~0.10%</li><li>356: ~0.10%</li><li>357: ~0.20%</li><li>358: ~0.30%</li><li>359: ~0.10%</li><li>360: ~0.20%</li><li>361: ~0.30%</li><li>362: ~0.10%</li><li>363: ~0.10%</li><li>364: ~0.10%</li><li>365: ~0.30%</li><li>368: ~0.30%</li><li>369: ~0.20%</li><li>372: ~0.30%</li><li>373: ~0.10%</li><li>374: ~0.30%</li><li>375: ~0.70%</li><li>376: ~0.10%</li><li>377: ~0.20%</li><li>378: ~0.20%</li><li>380: ~0.10%</li><li>381: ~0.10%</li><li>382: ~0.20%</li><li>383: ~0.10%</li><li>385: ~0.20%</li><li>393: ~0.10%</li><li>394: ~0.10%</li><li>395: ~0.20%</li><li>396: ~0.30%</li><li>398: ~0.10%</li><li>399: ~0.20%</li><li>401: ~0.20%</li><li>402: ~0.20%</li><li>404: ~0.40%</li><li>405: ~0.10%</li><li>407: ~0.20%</li><li>409: ~0.20%</li><li>410: ~0.10%</li><li>411: ~0.10%</li><li>412: ~0.10%</li><li>413: ~0.20%</li><li>414: ~0.20%</li><li>415: ~0.10%</li><li>416: ~0.10%</li><li>417: ~0.10%</li><li>418: ~0.10%</li><li>419: ~0.20%</li><li>420: ~0.10%</li><li>421: ~0.20%</li><li>423: ~0.30%</li><li>424: ~0.10%</li><li>425: ~0.10%</li><li>427: ~0.20%</li><li>428: ~0.10%</li><li>429: ~0.10%</li><li>430: ~0.10%</li><li>432: ~0.10%</li><li>434: ~0.10%</li><li>435: ~0.40%</li><li>436: ~0.20%</li><li>437: ~0.30%</li><li>438: ~0.20%</li><li>440: ~0.20%</li><li>441: ~0.30%</li><li>442: ~0.20%</li><li>443: ~0.10%</li><li>444: ~0.30%</li><li>445: ~0.20%</li><li>446: ~0.20%</li><li>448: ~0.20%</li><li>449: ~0.30%</li><li>451: ~0.20%</li><li>452: ~0.10%</li><li>454: ~0.20%</li><li>455: ~0.20%</li><li>456: ~0.10%</li><li>458: ~0.30%</li><li>459: ~0.10%</li><li>460: ~0.10%</li><li>462: ~0.10%</li><li>463: ~0.40%</li><li>464: ~0.10%</li><li>465: ~0.20%</li><li>466: ~0.10%</li><li>467: ~0.40%</li><li>468: ~0.10%</li><li>469: ~0.30%</li><li>471: ~0.10%</li><li>475: ~0.30%</li><li>476: ~0.50%</li><li>477: ~0.10%</li><li>479: ~0.40%</li><li>480: ~0.30%</li><li>482: ~0.10%</li><li>483: ~0.30%</li><li>484: ~0.10%</li><li>485: ~0.20%</li><li>486: ~0.10%</li><li>487: ~0.10%</li><li>490: ~0.30%</li><li>491: ~0.40%</li><li>492: ~0.40%</li><li>493: ~0.10%</li><li>494: ~0.10%</li><li>495: ~0.10%</li><li>498: ~0.20%</li><li>499: ~0.40%</li><li>500: ~0.30%</li><li>501: ~0.30%</li><li>502: ~0.30%</li><li>504: ~0.20%</li><li>505: ~0.20%</li><li>506: ~0.10%</li><li>507: ~0.20%</li><li>508: ~0.10%</li><li>511: ~0.10%</li><li>512: ~0.60%</li><li>513: ~0.10%</li><li>515: ~0.10%</li><li>516: ~0.30%</li><li>517: ~0.40%</li><li>519: ~0.30%</li><li>520: ~0.30%</li><li>521: ~0.10%</li><li>522: ~0.20%</li><li>523: ~0.10%</li><li>524: ~0.50%</li><li>525: ~0.60%</li><li>527: ~0.20%</li><li>528: ~0.10%</li><li>530: ~0.10%</li><li>533: ~0.40%</li><li>534: ~0.50%</li><li>535: ~0.40%</li><li>536: ~0.10%</li><li>537: ~0.20%</li><li>538: ~0.40%</li><li>539: ~0.10%</li><li>540: ~0.10%</li><li>542: ~0.30%</li><li>543: ~0.10%</li><li>544: ~0.10%</li><li>545: ~0.20%</li><li>546: ~0.20%</li><li>548: ~0.20%</li><li>549: ~0.20%</li><li>550: ~0.30%</li><li>551: ~0.30%</li><li>552: ~0.10%</li><li>554: ~0.10%</li><li>555: ~0.20%</li><li>557: ~0.20%</li><li>560: ~0.10%</li><li>561: ~0.20%</li><li>562: ~0.10%</li><li>564: ~0.40%</li><li>565: ~0.10%</li><li>566: ~0.10%</li><li>567: ~0.20%</li><li>570: ~0.10%</li><li>572: ~0.30%</li><li>573: ~0.10%</li><li>574: ~0.10%</li><li>575: ~0.10%</li><li>576: ~0.10%</li><li>577: ~0.20%</li><li>578: ~0.50%</li><li>579: ~0.40%</li><li>581: ~0.20%</li><li>585: ~0.40%</li><li>586: ~0.10%</li><li>587: ~0.20%</li><li>588: ~0.20%</li><li>590: ~0.20%</li><li>592: ~0.10%</li><li>595: ~0.10%</li><li>597: ~0.20%</li><li>600: ~0.10%</li><li>601: ~0.10%</li><li>603: ~0.10%</li><li>604: ~0.10%</li><li>608: ~0.10%</li><li>611: ~0.10%</li><li>612: ~0.20%</li><li>613: ~0.10%</li><li>619: ~0.20%</li><li>620: ~0.20%</li><li>622: ~0.10%</li><li>625: ~0.20%</li><li>629: ~0.10%</li><li>631: ~0.20%</li><li>632: ~0.10%</li><li>633: ~0.20%</li><li>634: ~0.10%</li><li>635: ~0.40%</li><li>640: ~0.10%</li><li>643: ~0.10%</li><li>645: ~0.10%</li><li>648: ~0.10%</li></ul> |
157
+ * Samples:
158
+ | sentence_0 | label |
159
+ |:----------------------------------------------------------------------------------|:-----------------|
160
+ | <code>swimming clubs</code> | <code>475</code> |
161
+ | <code> </code> | <code>581</code> |
162
+ | <code>this class includes: mining of ores valued chiefly for iron content</code> | <code>351</code> |
163
+ * Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss)
164
+
165
+ ### Training Hyperparameters
166
+ #### Non-Default Hyperparameters
167
+
168
+ - `per_device_train_batch_size`: 256
169
+ - `per_device_eval_batch_size`: 256
170
+ - `num_train_epochs`: 4
171
+ - `multi_dataset_batch_sampler`: round_robin
172
+
173
+ #### All Hyperparameters
174
+ <details><summary>Click to expand</summary>
175
+
176
+ - `overwrite_output_dir`: False
177
+ - `do_predict`: False
178
+ - `eval_strategy`: no
179
+ - `prediction_loss_only`: True
180
+ - `per_device_train_batch_size`: 256
181
+ - `per_device_eval_batch_size`: 256
182
+ - `per_gpu_train_batch_size`: None
183
+ - `per_gpu_eval_batch_size`: None
184
+ - `gradient_accumulation_steps`: 1
185
+ - `eval_accumulation_steps`: None
186
+ - `learning_rate`: 5e-05
187
+ - `weight_decay`: 0.0
188
+ - `adam_beta1`: 0.9
189
+ - `adam_beta2`: 0.999
190
+ - `adam_epsilon`: 1e-08
191
+ - `max_grad_norm`: 1
192
+ - `num_train_epochs`: 4
193
+ - `max_steps`: -1
194
+ - `lr_scheduler_type`: linear
195
+ - `lr_scheduler_kwargs`: {}
196
+ - `warmup_ratio`: 0.0
197
+ - `warmup_steps`: 0
198
+ - `log_level`: passive
199
+ - `log_level_replica`: warning
200
+ - `log_on_each_node`: True
201
+ - `logging_nan_inf_filter`: True
202
+ - `save_safetensors`: True
203
+ - `save_on_each_node`: False
204
+ - `save_only_model`: False
205
+ - `restore_callback_states_from_checkpoint`: False
206
+ - `no_cuda`: False
207
+ - `use_cpu`: False
208
+ - `use_mps_device`: False
209
+ - `seed`: 42
210
+ - `data_seed`: None
211
+ - `jit_mode_eval`: False
212
+ - `use_ipex`: False
213
+ - `bf16`: False
214
+ - `fp16`: False
215
+ - `fp16_opt_level`: O1
216
+ - `half_precision_backend`: auto
217
+ - `bf16_full_eval`: False
218
+ - `fp16_full_eval`: False
219
+ - `tf32`: None
220
+ - `local_rank`: 0
221
+ - `ddp_backend`: None
222
+ - `tpu_num_cores`: None
223
+ - `tpu_metrics_debug`: False
224
+ - `debug`: []
225
+ - `dataloader_drop_last`: False
226
+ - `dataloader_num_workers`: 0
227
+ - `dataloader_prefetch_factor`: None
228
+ - `past_index`: -1
229
+ - `disable_tqdm`: False
230
+ - `remove_unused_columns`: True
231
+ - `label_names`: None
232
+ - `load_best_model_at_end`: False
233
+ - `ignore_data_skip`: False
234
+ - `fsdp`: []
235
+ - `fsdp_min_num_params`: 0
236
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
237
+ - `fsdp_transformer_layer_cls_to_wrap`: None
238
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
239
+ - `deepspeed`: None
240
+ - `label_smoothing_factor`: 0.0
241
+ - `optim`: adamw_torch
242
+ - `optim_args`: None
243
+ - `adafactor`: False
244
+ - `group_by_length`: False
245
+ - `length_column_name`: length
246
+ - `ddp_find_unused_parameters`: None
247
+ - `ddp_bucket_cap_mb`: None
248
+ - `ddp_broadcast_buffers`: False
249
+ - `dataloader_pin_memory`: True
250
+ - `dataloader_persistent_workers`: False
251
+ - `skip_memory_metrics`: True
252
+ - `use_legacy_prediction_loop`: False
253
+ - `push_to_hub`: False
254
+ - `resume_from_checkpoint`: None
255
+ - `hub_model_id`: None
256
+ - `hub_strategy`: every_save
257
+ - `hub_private_repo`: False
258
+ - `hub_always_push`: False
259
+ - `gradient_checkpointing`: False
260
+ - `gradient_checkpointing_kwargs`: None
261
+ - `include_inputs_for_metrics`: False
262
+ - `eval_do_concat_batches`: True
263
+ - `fp16_backend`: auto
264
+ - `push_to_hub_model_id`: None
265
+ - `push_to_hub_organization`: None
266
+ - `mp_parameters`:
267
+ - `auto_find_batch_size`: False
268
+ - `full_determinism`: False
269
+ - `torchdynamo`: None
270
+ - `ray_scope`: last
271
+ - `ddp_timeout`: 1800
272
+ - `torch_compile`: False
273
+ - `torch_compile_backend`: None
274
+ - `torch_compile_mode`: None
275
+ - `dispatch_batches`: None
276
+ - `split_batches`: None
277
+ - `include_tokens_per_second`: False
278
+ - `include_num_input_tokens_seen`: False
279
+ - `neftune_noise_alpha`: None
280
+ - `optim_target_modules`: None
281
+ - `batch_eval_metrics`: False
282
+ - `batch_sampler`: batch_sampler
283
+ - `multi_dataset_batch_sampler`: round_robin
284
+
285
+ </details>
286
+
287
+ ### Framework Versions
288
+ - Python: 3.10.12
289
+ - Sentence Transformers: 3.0.1
290
+ - Transformers: 4.41.2
291
+ - PyTorch: 2.3.0+cu121
292
+ - Accelerate: 0.31.0
293
+ - Datasets: 2.20.0
294
+ - Tokenizers: 0.19.1
295
+
296
+ ## Citation
297
+
298
+ ### BibTeX
299
+
300
+ #### Sentence Transformers
301
+ ```bibtex
302
+ @inproceedings{reimers-2019-sentence-bert,
303
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
304
+ author = "Reimers, Nils and Gurevych, Iryna",
305
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
306
+ month = "11",
307
+ year = "2019",
308
+ publisher = "Association for Computational Linguistics",
309
+ url = "https://arxiv.org/abs/1908.10084",
310
+ }
311
+ ```
312
+
313
+ #### BatchAllTripletLoss
314
+ ```bibtex
315
+ @misc{hermans2017defense,
316
+ title={In Defense of the Triplet Loss for Person Re-Identification},
317
+ author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
318
+ year={2017},
319
+ eprint={1703.07737},
320
+ archivePrefix={arXiv},
321
+ primaryClass={cs.CV}
322
+ }
323
+ ```
324
+
325
+ <!--
326
+ ## Glossary
327
+
328
+ *Clearly define terms in order to be accessible across audiences.*
329
+ -->
330
+
331
+ <!--
332
+ ## Model Card Authors
333
+
334
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
335
+ -->
336
+
337
+ <!--
338
+ ## Model Card Contact
339
+
340
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
341
+ -->
config.json ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
3
+ "architectures": [
4
+ "BertModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "classifier_dropout": null,
8
+ "gradient_checkpointing": false,
9
+ "hidden_act": "gelu",
10
+ "hidden_dropout_prob": 0.1,
11
+ "hidden_size": 384,
12
+ "initializer_range": 0.02,
13
+ "intermediate_size": 1536,
14
+ "layer_norm_eps": 1e-12,
15
+ "max_position_embeddings": 512,
16
+ "model_type": "bert",
17
+ "num_attention_heads": 12,
18
+ "num_hidden_layers": 12,
19
+ "pad_token_id": 0,
20
+ "position_embedding_type": "absolute",
21
+ "torch_dtype": "float32",
22
+ "transformers_version": "4.41.2",
23
+ "type_vocab_size": 2,
24
+ "use_cache": true,
25
+ "vocab_size": 250037
26
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "3.0.1",
4
+ "transformers": "4.41.2",
5
+ "pytorch": "2.3.0+cu121"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": null
10
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:434670349084dab643f777c990d669afc316e82ddc19d8b9e419c41e47b510a7
3
+ size 470637416
modules.json ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "idx": 0,
4
+ "name": "0",
5
+ "path": "",
6
+ "type": "sentence_transformers.models.Transformer"
7
+ },
8
+ {
9
+ "idx": 1,
10
+ "name": "1",
11
+ "path": "1_Pooling",
12
+ "type": "sentence_transformers.models.Pooling"
13
+ }
14
+ ]
sentence_bert_config.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "max_seq_length": 128,
3
+ "do_lower_case": false
4
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,51 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<s>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "cls_token": {
10
+ "content": "<s>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "eos_token": {
17
+ "content": "</s>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "mask_token": {
24
+ "content": "<mask>",
25
+ "lstrip": true,
26
+ "normalized": false,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ },
30
+ "pad_token": {
31
+ "content": "<pad>",
32
+ "lstrip": false,
33
+ "normalized": false,
34
+ "rstrip": false,
35
+ "single_word": false
36
+ },
37
+ "sep_token": {
38
+ "content": "</s>",
39
+ "lstrip": false,
40
+ "normalized": false,
41
+ "rstrip": false,
42
+ "single_word": false
43
+ },
44
+ "unk_token": {
45
+ "content": "<unk>",
46
+ "lstrip": false,
47
+ "normalized": false,
48
+ "rstrip": false,
49
+ "single_word": false
50
+ }
51
+ }
tokenizer.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
3
+ size 17082987
tokenizer_config.json ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "<s>",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "1": {
12
+ "content": "<pad>",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "2": {
20
+ "content": "</s>",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "3": {
28
+ "content": "<unk>",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "250001": {
36
+ "content": "<mask>",
37
+ "lstrip": true,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "bos_token": "<s>",
45
+ "clean_up_tokenization_spaces": true,
46
+ "cls_token": "<s>",
47
+ "do_lower_case": true,
48
+ "eos_token": "</s>",
49
+ "mask_token": "<mask>",
50
+ "max_length": 128,
51
+ "model_max_length": 128,
52
+ "pad_to_multiple_of": null,
53
+ "pad_token": "<pad>",
54
+ "pad_token_type_id": 0,
55
+ "padding_side": "right",
56
+ "sep_token": "</s>",
57
+ "stride": 0,
58
+ "strip_accents": null,
59
+ "tokenize_chinese_chars": true,
60
+ "tokenizer_class": "BertTokenizer",
61
+ "truncation_side": "right",
62
+ "truncation_strategy": "longest_first",
63
+ "unk_token": "<unk>"
64
+ }
unigram.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:da145b5e7700ae40f16691ec32a0b1fdc1ee3298db22a31ea55f57a966c4a65d
3
+ size 14763260