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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:152913
- loss:BatchAllTripletLoss
base_model: Almawave/Velvet-2B
widget:
- source_sentence: La crisi non tocca il mercato del “luxury food”, che continua a
crescere
sentences:
- Avviso aggiornamento (1.55)
- Il fotoritocco sui social per fingere di essere al Mic
- Turchia, Biberovic giocherà da passaportato nella finestra FIBA
- source_sentence: 'Miller stagione finita, Pagani: «Rimpiazzo? Valutiamo il mercato
europeo»'
sentences:
- Beautiful, le trame della settimana dal 2 al 7 dicembre
- 'Corte dei conti: concorso per 8 funzionari a tempo indeterminato'
- 'Leonardo, AD: incontrato stamani numero uno Airbus su alleanza satellitare'
- source_sentence: 'Il segreto di Jalen Hurts in una foto sullo smartphone: così ha
vinto e sconfitto gli scettici'
sentences:
- Scarcerato il boss Ernesto Fazzalari, era il latitante più ricercato dopo Messina
Denaro
- 'UniVdA: ecco il nuovo master in Psicologia dello sport'
- 'Gran Turismo 7: arrivano quattro nuove auto con l’update 1.55 [VIDEO]'
- source_sentence: 'San Vito al Torre, recuperato un antico monumento funerario romano
dal fiume: la scoperta'
sentences:
- Charlotte Casiraghi in pubblico dopo le voci di divorzio
- Mr. Bezos, la sua non è imparzialità ma viltà
- 'Elisa Di Francisca a La Talpa: “Sono troppo vera per tenere segreti”'
- source_sentence: 'NBA, dopo l’addio a Schroeder i Nets promuovono Simmons in quintetto:
l''idea è correre'
sentences:
- 'Picasso a Milano: al Mudec la mostra sulle metamorfosi del maestro'
- Italia Viva e +Europa non parteciperanno alle elezioni regionali in Liguria
- 'Achille Costacurta rivela: «Sono stato rinchiuso per un anno e sette mesi in
un centro penale'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- average_precision
- f1
- precision
- recall
- threshold
model-index:
- name: SentenceTransformer based on Almawave/Velvet-2B
results:
- task:
type: paraphrase-mining
name: Paraphrase Mining
dataset:
name: Unknown
type: unknown
metrics:
- type: average_precision
value: 0.5283532795784699
name: Average Precision
- type: f1
value: 0.5502357974952371
name: F1
- type: precision
value: 0.5567564151181899
name: Precision
- type: recall
value: 0.5438661480521084
name: Recall
- type: threshold
value: 0.9310455322265625
name: Threshold
---
# SentenceTransformer based on Almawave/Velvet-2B
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Almawave/Velvet-2B](https://huggingface.co/Almawave/Velvet-2B). It maps sentences & paragraphs to a 2048-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Almawave/Velvet-2B](https://huggingface.co/Almawave/Velvet-2B) <!-- at revision 3b864694ae4d80923ac39cee130c5eeb7d8808b6 -->
- **Maximum Sequence Length:** 32768 tokens
- **Output Dimensionality:** 2048 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 32768, 'do_lower_case': False}) with Transformer model: MistralModel
(1): Pooling({'word_embedding_dimension': 2048, 'pooling_mode_cls_token': False, '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': True, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("ancc/Velvet-2B-embedding-news")
# Run inference
sentences = [
"NBA, dopo l’addio a Schroeder i Nets promuovono Simmons in quintetto: l'idea è correre",
'Italia Viva e +Europa non parteciperanno alle elezioni regionali in Liguria',
'Achille Costacurta rivela: «Sono stato rinchiuso per un anno e sette mesi in un centro penale',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 2048]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Paraphrase Mining
* Evaluated with [<code>ParaphraseMiningEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.ParaphraseMiningEvaluator)
| Metric | Value |
|:----------------------|:-----------|
| **average_precision** | **0.5284** |
| f1 | 0.5502 |
| precision | 0.5568 |
| recall | 0.5439 |
| threshold | 0.931 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 152,913 training samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence | label |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| type | string | int |
| details | <ul><li>min: 3 tokens</li><li>mean: 19.14 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>0: ~0.50%</li><li>1: ~0.50%</li><li>2: ~0.50%</li><li>3: ~0.50%</li><li>4: ~0.50%</li><li>5: ~0.20%</li><li>6: ~0.50%</li><li>7: ~0.50%</li><li>8: ~0.20%</li><li>9: ~0.50%</li><li>11: ~0.50%</li><li>12: ~0.50%</li><li>13: ~0.50%</li><li>15: ~0.50%</li><li>16: ~0.20%</li><li>17: ~0.30%</li><li>18: ~0.50%</li><li>19: ~0.40%</li><li>20: ~0.50%</li><li>21: ~0.50%</li><li>22: ~0.30%</li><li>23: ~0.50%</li><li>24: ~0.50%</li><li>25: ~0.50%</li><li>26: ~0.50%</li><li>27: ~0.50%</li><li>28: ~0.40%</li><li>29: ~0.50%</li><li>30: ~0.20%</li><li>31: ~0.30%</li><li>32: ~0.40%</li><li>33: ~0.50%</li><li>34: ~0.50%</li><li>36: ~0.50%</li><li>38: ~0.50%</li><li>39: ~0.50%</li><li>40: ~0.30%</li><li>41: ~0.20%</li><li>43: ~0.50%</li><li>44: ~0.50%</li><li>45: ~0.50%</li><li>46: ~0.40%</li><li>47: ~0.30%</li><li>48: ~0.30%</li><li>49: ~0.50%</li><li>50: ~0.30%</li><li>51: ~0.50%</li><li>52: ~0.50%</li><li>53: ~0.50%</li><li>54: ~0.50%</li><li>55: ~0.20%</li><li>56: ~0.50%</li><li>57: ~0.50%</li><li>59: ~0.50%</li><li>60: ~0.40%</li><li>62: ~0.50%</li><li>63: ~0.20%</li><li>64: ~0.50%</li><li>65: ~0.40%</li><li>66: ~0.50%</li><li>67: ~0.40%</li><li>68: ~0.50%</li><li>69: ~0.50%</li><li>70: ~0.50%</li><li>71: ~0.20%</li><li>72: ~0.50%</li><li>73: ~0.20%</li><li>74: ~0.50%</li><li>75: ~0.50%</li><li>77: ~0.50%</li><li>78: ~0.30%</li><li>79: ~0.50%</li><li>80: ~0.40%</li><li>81: ~0.30%</li><li>82: ~0.20%</li><li>83: ~0.40%</li><li>84: ~0.30%</li><li>85: ~0.50%</li><li>87: ~0.50%</li><li>88: ~0.20%</li><li>90: ~0.50%</li><li>91: ~0.20%</li><li>92: ~0.50%</li><li>93: ~0.40%</li><li>94: ~0.50%</li><li>95: ~0.50%</li><li>97: ~0.50%</li><li>98: ~0.40%</li><li>99: ~0.50%</li><li>100: ~0.30%</li><li>101: ~0.50%</li><li>103: ~0.20%</li><li>104: ~0.50%</li><li>106: ~0.40%</li><li>107: ~0.20%</li><li>108: ~0.40%</li><li>109: ~0.30%</li><li>110: ~0.50%</li><li>111: ~0.40%</li><li>112: ~0.50%</li><li>113: ~0.30%</li><li>115: ~0.30%</li><li>116: ~0.50%</li><li>117: ~0.50%</li><li>118: ~0.30%</li><li>120: ~0.30%</li><li>121: ~0.50%</li><li>122: ~0.20%</li><li>123: ~0.40%</li><li>124: ~0.30%</li><li>125: ~0.40%</li><li>126: ~0.50%</li><li>127: ~0.50%</li><li>128: ~0.50%</li><li>129: ~0.50%</li><li>130: ~0.50%</li><li>131: ~0.50%</li><li>132: ~0.40%</li><li>133: ~0.50%</li><li>134: ~0.30%</li><li>135: ~0.50%</li><li>136: ~0.30%</li><li>137: ~0.30%</li><li>138: ~0.50%</li><li>139: ~0.50%</li><li>140: ~0.20%</li><li>141: ~0.50%</li><li>142: ~0.50%</li><li>143: ~0.50%</li><li>144: ~0.30%</li><li>145: ~0.30%</li><li>146: ~0.30%</li><li>147: ~0.50%</li><li>148: ~0.30%</li><li>149: ~0.20%</li><li>150: ~0.50%</li><li>151: ~0.30%</li><li>152: ~0.20%</li><li>153: ~0.50%</li><li>154: ~0.50%</li><li>155: ~0.40%</li><li>156: ~0.20%</li><li>158: ~0.50%</li><li>159: ~0.50%</li><li>160: ~0.50%</li><li>161: ~0.40%</li><li>162: ~0.20%</li><li>163: ~0.40%</li><li>164: ~0.20%</li><li>165: ~0.50%</li><li>166: ~0.50%</li><li>167: ~0.50%</li><li>168: ~0.20%</li><li>169: ~0.50%</li><li>170: ~0.50%</li><li>171: ~0.20%</li><li>172: ~0.50%</li><li>173: ~0.50%</li><li>174: ~0.40%</li><li>175: ~0.50%</li><li>176: ~0.30%</li><li>178: ~0.50%</li><li>180: ~0.20%</li><li>182: ~0.50%</li><li>183: ~0.50%</li><li>184: ~0.30%</li><li>185: ~0.50%</li><li>187: ~0.20%</li><li>188: ~0.50%</li><li>189: ~0.50%</li><li>191: ~0.50%</li><li>192: ~0.50%</li><li>193: ~0.50%</li><li>194: ~0.20%</li><li>195: ~0.30%</li><li>196: ~0.50%</li><li>197: ~0.50%</li><li>199: ~0.50%</li><li>200: ~0.50%</li><li>201: ~0.20%</li><li>202: ~0.50%</li><li>203: ~0.50%</li><li>204: ~0.30%</li><li>205: ~0.50%</li><li>206: ~0.20%</li><li>207: ~0.40%</li><li>208: ~0.50%</li><li>209: ~0.30%</li><li>210: ~0.30%</li><li>211: ~0.50%</li><li>212: ~0.20%</li><li>213: ~0.50%</li><li>214: ~0.50%</li><li>215: ~0.40%</li><li>216: ~0.50%</li><li>217: ~0.40%</li><li>218: ~0.50%</li><li>219: ~0.50%</li><li>220: ~0.20%</li><li>221: ~0.50%</li><li>222: ~0.40%</li><li>223: ~0.50%</li><li>224: ~0.30%</li><li>225: ~0.40%</li><li>227: ~0.20%</li><li>228: ~0.30%</li><li>230: ~0.40%</li><li>231: ~0.40%</li><li>232: ~0.50%</li><li>233: ~0.50%</li><li>234: ~0.50%</li><li>235: ~0.50%</li><li>236: ~0.40%</li><li>237: ~0.50%</li><li>238: ~0.40%</li><li>239: ~0.50%</li><li>240: ~0.50%</li><li>241: ~0.50%</li><li>242: ~0.30%</li><li>243: ~0.50%</li><li>244: ~0.50%</li><li>245: ~0.50%</li><li>246: ~0.50%</li><li>247: ~0.20%</li><li>248: ~0.50%</li><li>249: ~0.50%</li><li>250: ~0.50%</li><li>251: ~0.50%</li><li>252: ~0.20%</li><li>253: ~0.50%</li><li>254: ~0.50%</li><li>255: ~0.30%</li><li>256: ~0.50%</li><li>257: ~0.50%</li><li>258: ~0.20%</li><li>259: ~0.50%</li><li>260: ~0.30%</li><li>261: ~0.50%</li><li>262: ~0.50%</li><li>263: ~0.20%</li><li>264: ~0.50%</li><li>265: ~0.20%</li></ul> |
* Samples:
| sentence | label |
|:-------------------------------------------------------------------------------------------|:---------------|
| <code>MERCATO LBA - Treviso, Giofrè: "Mercato in continua osservazione, vedremo..."</code> | <code>0</code> |
| <code>Ky Bowman: Non sono soddisfatto delle mie performance</code> | <code>0</code> |
| <code>LBA - Treviso, Giofrè: "Sabato la Reggiana, dobbiamo vincere. Punto"</code> | <code>0</code> |
* Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss)
### Evaluation Dataset
#### Unnamed Dataset
* Size: 9,310 evaluation samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| type | string | int |
| details | <ul><li>min: 4 tokens</li><li>mean: 19.04 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>0: ~0.50%</li><li>1: ~0.30%</li><li>2: ~0.50%</li><li>4: ~0.50%</li><li>5: ~0.20%</li><li>6: ~0.30%</li><li>7: ~0.50%</li><li>8: ~0.50%</li><li>9: ~0.50%</li><li>10: ~0.50%</li><li>12: ~0.40%</li><li>13: ~0.30%</li><li>14: ~0.30%</li><li>15: ~0.40%</li><li>16: ~0.20%</li><li>17: ~0.50%</li><li>18: ~0.40%</li><li>19: ~0.40%</li><li>20: ~0.50%</li><li>21: ~0.50%</li><li>22: ~0.50%</li><li>23: ~0.50%</li><li>24: ~0.20%</li><li>25: ~0.20%</li><li>26: ~0.50%</li><li>27: ~0.50%</li><li>28: ~0.50%</li><li>30: ~0.50%</li><li>31: ~0.20%</li><li>32: ~0.30%</li><li>33: ~0.50%</li><li>34: ~0.20%</li><li>35: ~0.50%</li><li>37: ~0.50%</li><li>38: ~0.20%</li><li>39: ~0.50%</li><li>40: ~0.50%</li><li>41: ~0.20%</li><li>42: ~0.30%</li><li>43: ~0.30%</li><li>44: ~0.50%</li><li>45: ~0.30%</li><li>46: ~0.50%</li><li>47: ~0.50%</li><li>48: ~0.30%</li><li>49: ~0.40%</li><li>50: ~0.30%</li><li>51: ~0.20%</li><li>52: ~0.50%</li><li>53: ~0.20%</li><li>54: ~0.30%</li><li>55: ~0.50%</li><li>56: ~0.50%</li><li>57: ~0.20%</li><li>58: ~0.50%</li><li>59: ~0.50%</li><li>60: ~0.50%</li><li>61: ~0.30%</li><li>62: ~0.30%</li><li>63: ~0.20%</li><li>65: ~0.50%</li><li>67: ~0.40%</li><li>68: ~0.50%</li><li>69: ~0.50%</li><li>70: ~0.20%</li><li>71: ~0.40%</li><li>72: ~0.50%</li><li>73: ~0.50%</li><li>74: ~0.30%</li><li>77: ~0.20%</li><li>78: ~0.50%</li><li>79: ~0.50%</li><li>80: ~0.20%</li><li>81: ~0.50%</li><li>82: ~0.20%</li><li>83: ~0.50%</li><li>84: ~0.50%</li><li>85: ~0.50%</li><li>89: ~0.20%</li><li>90: ~0.50%</li><li>91: ~0.50%</li><li>92: ~0.30%</li><li>93: ~0.40%</li><li>94: ~0.50%</li><li>95: ~0.30%</li><li>96: ~0.20%</li><li>97: ~0.50%</li><li>98: ~0.20%</li><li>99: ~0.50%</li><li>100: ~0.50%</li><li>101: ~0.50%</li><li>102: ~0.50%</li><li>103: ~0.30%</li><li>104: ~0.50%</li><li>105: ~0.50%</li><li>106: ~0.50%</li><li>107: ~0.50%</li><li>108: ~0.50%</li><li>109: ~0.50%</li><li>110: ~0.20%</li><li>111: ~0.50%</li><li>112: ~0.50%</li><li>113: ~0.20%</li><li>114: ~0.50%</li><li>115: ~0.40%</li><li>116: ~0.50%</li><li>117: ~0.20%</li><li>118: ~0.50%</li><li>120: ~0.50%</li><li>121: ~0.30%</li><li>122: ~0.50%</li><li>123: ~0.40%</li><li>124: ~0.20%</li><li>125: ~0.50%</li><li>126: ~0.20%</li><li>129: ~0.50%</li><li>130: ~0.30%</li><li>131: ~0.50%</li><li>132: ~0.40%</li><li>133: ~0.50%</li><li>134: ~0.50%</li><li>135: ~0.50%</li><li>136: ~0.20%</li><li>137: ~0.30%</li><li>138: ~0.50%</li><li>140: ~0.30%</li><li>141: ~0.30%</li><li>142: ~0.50%</li><li>143: ~0.40%</li><li>144: ~0.50%</li><li>145: ~0.50%</li><li>146: ~0.30%</li><li>147: ~0.20%</li><li>148: ~0.50%</li><li>149: ~0.40%</li><li>150: ~0.50%</li><li>151: ~0.20%</li><li>152: ~0.50%</li><li>154: ~0.20%</li><li>155: ~0.20%</li><li>156: ~0.50%</li><li>157: ~0.50%</li><li>158: ~0.50%</li><li>159: ~0.50%</li><li>160: ~0.20%</li><li>162: ~0.50%</li><li>163: ~0.20%</li><li>164: ~0.20%</li><li>165: ~0.20%</li><li>166: ~0.20%</li><li>167: ~0.30%</li><li>168: ~0.50%</li><li>169: ~0.50%</li><li>170: ~0.50%</li><li>171: ~0.40%</li><li>172: ~0.30%</li><li>173: ~0.50%</li><li>174: ~0.50%</li><li>175: ~0.30%</li><li>176: ~0.50%</li><li>178: ~0.50%</li><li>179: ~0.20%</li><li>180: ~0.50%</li><li>181: ~0.50%</li><li>182: ~0.30%</li><li>183: ~0.40%</li><li>184: ~0.20%</li><li>185: ~0.50%</li><li>186: ~0.50%</li><li>187: ~0.50%</li><li>188: ~0.20%</li><li>189: ~0.50%</li><li>191: ~0.20%</li><li>192: ~0.50%</li><li>193: ~0.20%</li><li>194: ~0.20%</li><li>195: ~0.40%</li><li>196: ~0.20%</li><li>197: ~0.20%</li><li>198: ~0.50%</li><li>199: ~0.40%</li><li>200: ~0.50%</li><li>201: ~0.50%</li><li>202: ~0.50%</li><li>203: ~0.40%</li><li>205: ~0.50%</li><li>206: ~0.50%</li><li>207: ~0.50%</li><li>208: ~0.50%</li><li>209: ~0.50%</li><li>210: ~0.50%</li><li>211: ~0.40%</li><li>212: ~0.50%</li><li>213: ~0.50%</li><li>214: ~0.20%</li><li>215: ~0.20%</li><li>216: ~0.50%</li><li>217: ~0.50%</li><li>218: ~0.50%</li><li>219: ~0.30%</li><li>220: ~0.50%</li><li>221: ~0.50%</li><li>223: ~0.50%</li><li>224: ~0.50%</li><li>225: ~0.50%</li><li>227: ~0.20%</li><li>228: ~0.50%</li><li>230: ~0.20%</li><li>231: ~0.50%</li><li>232: ~0.50%</li><li>234: ~0.30%</li><li>235: ~0.40%</li><li>236: ~0.50%</li><li>237: ~0.50%</li><li>238: ~0.20%</li><li>239: ~0.50%</li><li>240: ~0.20%</li><li>241: ~0.50%</li><li>242: ~0.20%</li><li>243: ~0.20%</li><li>244: ~0.20%</li><li>246: ~0.50%</li><li>247: ~0.20%</li><li>248: ~0.50%</li><li>249: ~0.50%</li><li>250: ~0.40%</li><li>251: ~0.50%</li><li>252: ~0.50%</li><li>253: ~0.20%</li><li>254: ~0.50%</li><li>255: ~0.50%</li><li>256: ~0.50%</li><li>257: ~0.50%</li><li>258: ~0.20%</li><li>259: ~0.20%</li><li>260: ~0.50%</li><li>261: ~0.50%</li><li>262: ~0.20%</li><li>263: ~0.40%</li><li>264: ~0.50%</li><li>265: ~0.50%</li><li>266: ~0.50%</li><li>267: ~0.50%</li><li>268: ~0.20%</li><li>269: ~0.20%</li><li>270: ~0.40%</li><li>272: ~0.30%</li><li>273: ~0.20%</li><li>274: ~0.50%</li><li>275: ~0.50%</li><li>276: ~0.40%</li><li>277: ~0.30%</li><li>278: ~0.30%</li></ul> |
* Samples:
| sentence | label |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>Supplenze: come funzionano i contratti fino al 31 dicembre 2024 e il calcolo del punteggio?</code> | <code>0</code> |
| <code>Docente non abilitato assunto a tempo determinato da concorso PNRR1: in quale scuola "andrò a finire" se nella mia si perde un posto?</code> | <code>0</code> |
| <code>Docenti non abilitati nominati dopo il 31 agosto da graduatorie pubblicate prima: otterranno sede di titolarità all’esito delle operazioni di mobilità [Chiarimenti]</code> | <code>0</code> |
* Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 32
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.25
- `seed`: 17
- `data_seed`: 17
- `bf16`: True
- `batch_sampler`: group_by_label
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.25
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 17
- `data_seed`: 17
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: group_by_label
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | average_precision |
|:------:|:-----:|:-------------:|:---------------:|:-----------------:|
| 0.0002 | 1 | 0.0521 | - | - |
| 0.8002 | 3824 | 0.0195 | - | - |
| 1.7732 | 7648 | 0.0071 | - | - |
| 2.7462 | 11472 | 0.0052 | - | - |
| 3.7192 | 15296 | 0.0046 | - | - |
| 3.8272 | 15812 | - | 0.0011 | 0.5284 |
### Framework Versions
- Python: 3.12.8
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### BatchAllTripletLoss
```bibtex
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
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