Push model using huggingface_hub.
Browse files- .gitattributes +1 -0
- 1_Pooling/config.json +10 -0
- README.md +862 -0
- config.json +28 -0
- config_sentence_transformers.json +9 -0
- config_setfit.json +49 -0
- model.safetensors +3 -0
- model_head.pkl +3 -0
- modules.json +20 -0
- sentence_bert_config.json +4 -0
- sentencepiece.bpe.model +3 -0
- special_tokens_map.json +51 -0
- tokenizer.json +3 -0
- tokenizer_config.json +55 -0
.gitattributes
CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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+
tokenizer.json filter=lfs diff=lfs merge=lfs -text
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1_Pooling/config.json
ADDED
@@ -0,0 +1,10 @@
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{
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"word_embedding_dimension": 1024,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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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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}
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README.md
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1 |
+
---
|
2 |
+
library_name: setfit
|
3 |
+
tags:
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4 |
+
- setfit
|
5 |
+
- sentence-transformers
|
6 |
+
- text-classification
|
7 |
+
- generated_from_setfit_trainer
|
8 |
+
metrics:
|
9 |
+
- accuracy
|
10 |
+
widget:
|
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+
- text: prlv sepa ecole montaigne cotisation scolaire
|
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+
- text: facture carte du pharmacie pont neuf carte
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+
- text: virement sortant facture soleil energie
|
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+
- text: leçon de surf hossegor surf club carte
|
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+
- text: virement initie application mobile vers comptes joints
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+
pipeline_tag: text-classification
|
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+
inference: true
|
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+
model-index:
|
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+
- name: SetFit
|
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+
results:
|
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+
- task:
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type: text-classification
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name: Text Classification
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24 |
+
dataset:
|
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name: Unknown
|
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+
type: unknown
|
27 |
+
split: test
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+
metrics:
|
29 |
+
- type: accuracy
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30 |
+
value: 0.7007575757575758
|
31 |
+
name: Accuracy
|
32 |
+
---
|
33 |
+
|
34 |
+
# SetFit
|
35 |
+
|
36 |
+
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
|
37 |
+
|
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+
The model has been trained using an efficient few-shot learning technique that involves:
|
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+
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+
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+
2. Training a classification head with features from the fine-tuned Sentence Transformer.
|
42 |
+
|
43 |
+
## Model Details
|
44 |
+
|
45 |
+
### Model Description
|
46 |
+
- **Model Type:** SetFit
|
47 |
+
<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
|
48 |
+
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
|
49 |
+
- **Maximum Sequence Length:** 512 tokens
|
50 |
+
- **Number of Classes:** 44 classes
|
51 |
+
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
|
52 |
+
<!-- - **Language:** Unknown -->
|
53 |
+
<!-- - **License:** Unknown -->
|
54 |
+
|
55 |
+
### Model Sources
|
56 |
+
|
57 |
+
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
|
58 |
+
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
|
59 |
+
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
|
60 |
+
|
61 |
+
### Model Labels
|
62 |
+
| Label | Examples |
|
63 |
+
|:-------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
64 |
+
| Other / kids | <ul><li>'cantine scolaire mars paiement carte'</li><li>'virement recu alouette garde d enfants'</li><li>'achat carte couture kids atelier carte'</li></ul> |
|
65 |
+
| Bank services / withdrawal | <ul><li>'retrait dab banque du peuple lille carte fr'</li><li>'retrait dab megastore electronix carte'</li><li>'retrait dab banqueville centre carte'</li></ul> |
|
66 |
+
| Housing / rent | <ul><li>'reglement loyer f nice carte'</li><li>'paiement loyer duplex nantes carte'</li><li>'loyer t bordeaux chartrons carte'</li></ul> |
|
67 |
+
| Leisure & Entertainment / sports & hobbies | <ul><li>'abonnement golf de roncevaux avril carte'</li><li>'paiement en ligne du adidas fr carte'</li><li>'cours de tennis indoor club fontenay carte'</li></ul> |
|
68 |
+
| Transportation / car loan & leasing | <ul><li>'prelevement leaseplan citroen c'</li><li>'loyer mensuel volkswagen polo carte'</li><li>'pret vehicule citroen c carte'</li></ul> |
|
69 |
+
| Healthy & Beauty / veterinary | <ul><li>'urgence digestive bibou urgencyvet carte'</li><li>'echoradiographie minette carte'</li><li>'consultation dermatologique canine vetderm carte'</li></ul> |
|
70 |
+
| Transportation / taxi & carpool | <ul><li>'facture carte du grab bangkok carte tha thb commission'</li><li>'prlv sepa bolt transport carte'</li><li>'facture carte du kakao taxi seoul carte kor krw commission'</li></ul> |
|
71 |
+
| Healthy & Beauty / doctor fees | <ul><li>'consultation dr lemoine carte'</li><li>'rdv pediatrie dr lenoir carte'</li><li>'rdv chirurgie esthetique dr martin carte'</li></ul> |
|
72 |
+
| Food & Drinks / eating out | <ul><li>'facture carte du vietnam delices strasbourg carte'</li><li>'facture carte du la cabane creole la reunion carte'</li><li>'facture carte du the green pub montpellier carte'</li></ul> |
|
73 |
+
| Transportation / other | <ul><li>'location velo elecgreen du paris carte'</li><li>'frais de lavage auto eco wash carte'</li><li>'frais post stationnement carte'</li></ul> |
|
74 |
+
| Healthy & Beauty / beauty & self-care | <ul><li>'achat lush cosmetiques lyon carte'</li><li>'facture carte du douglas beauty store carte'</li><li>'achat salon de coiffure elegance carte'</li></ul> |
|
75 |
+
| Bank services / other | <ul><li>'cotisation carte gold annuelle carte'</li><li>'frais emission duplicata rib iban carte'</li><li>'frais mise en place autorisation de decouvert carte'</li></ul> |
|
76 |
+
| Bank services / general fees | <ul><li>'frais de gestion compte epargne banqplus carte'</li><li>'abonnement service banque en ligne banqfacile carte'</li><li>'frais sur decouvert autorise'</li></ul> |
|
77 |
+
| Leisure & Entertainment / culture & events | <ul><li>'pass festival avignon off carte'</li><li>'reservation carte salon du livre paris carte'</li><li>'achat carte billet expo universselle carte'</li></ul> |
|
78 |
+
| Other / taxes | <ul><li>'taxe d amenagement projet xyz'</li><li>'taxe annuelle sur les locations meublees non professionnelles'</li><li>'impot sur le revenu prelevement sepa fiscal frzzz'</li></ul> |
|
79 |
+
| Housing / services & maintenance | <ul><li>'prlv sepa sos plombier paris'</li><li>'virement recu travaux peinture dupont michel'</li><li>'facture carte du vitrerie lumiere carte'</li></ul> |
|
80 |
+
| Housing / utilities & bills | <ul><li>'prlv sepa edf facture electricite'</li><li>'prlv sepa engie'</li><li>'facture carte du bouygues telecom carte'</li></ul> |
|
81 |
+
| Investment / real estate | <ul><li>'reservation studio montagne carte'</li><li>'virement sortant achat terrain bellevue carte'</li><li>'prlv sepa agence immobiliere commission vente'</li></ul> |
|
82 |
+
| Recurrent Payments / subscription | <ul><li>'abonnement annual magazine cuisine carte'</li><li>'abonnement plateforme d apprentissage en ligne skillex carte'</li><li>'adhesion annuelle amazon prime carte'</li></ul> |
|
83 |
+
| Other / other | <ul><li>'paiement service debrisage encombrants'</li><li>'stage de survie nature extreme carte'</li><li>'inscription marathon de paris'</li></ul> |
|
84 |
+
| Shopping / electronics & multimedia | <ul><li>'achat carte du gamerzone carte'</li><li>'facture carte du hightech store paris carte'</li><li>'achat en ligne hp store carte usa'</li></ul> |
|
85 |
+
| Bank services / transfers | <ul><li>'virement sortant facture soleil energie'</li><li>'transfer economies vers pel'</li><li>'virement initie application mobile vers comptes joints'</li></ul> |
|
86 |
+
| Investment / retirement & savings | <ul><li>'versement plan epargne retraite crédit agricole carte'</li><li>'achat parts cooperative eparco'</li><li>'souscription fonds pension'</li></ul> |
|
87 |
+
| Housing / other | <ul><li>'achat mobilier jardin bleutropic carte'</li><li>'douchette ecologique ecoflow carte'</li><li>'virement recu du remboursement depot de garantie'</li></ul> |
|
88 |
+
| Housing / house loan | <ul><li>'solde emprunt habitat fortuneo pret'</li><li>'remboursement emprunt logis credit agricole'</li><li>'transaction pret immo societe generale de prêteur frsge'</li></ul> |
|
89 |
+
| Recurrent Payments / other | <ul><li>'don mensuel a l ong environ actionfr transaction date'</li><li>'contribution trimestrielle revue culturelle lumières date'</li><li>'prlv sepa chess com premium'</li></ul> |
|
90 |
+
| Transportation / fuel | <ul><li>'facture carte du bp energy carte'</li><li>'depense s c agip lyon carte'</li><li>'debit carte circle k energy carte'</li></ul> |
|
91 |
+
| Other / pets | <ul><li>'debit automatique assurance chien fido protect'</li><li>'retrait dab ferme des lapinous carte commission'</li><li>'achat animalerie toutouplus lyon carte'</li></ul> |
|
92 |
+
| Transportation / maitenance | <ul><li>'reparation systeme de navigation gps auto tech dijon'</li><li>'vidange huile moteur garage du centre rennes'</li><li>'paiement carte du garage rénov clim reims carte'</li></ul> |
|
93 |
+
| Food & Drinks / groceries | <ul><li>'facture carte du fromagerie dupont carte'</li><li>'facture carte du cremerie des alpes carte'</li><li>'prlv sepa la ferme lorraine'</li></ul> |
|
94 |
+
| Recurrent Payments / insurance | <ul><li>'cotisation assurance multirisque domus secur frzzz'</li><li>'prelevement sepa assurance grand voyage worldtravel frzzz'</li><li>'prlv sepa assurance emprunteur bnp paribas'</li></ul> |
|
95 |
+
| Food & Drinks / other | <ul><li>'facture carte just press carte'</li><li>'achat card du tea box paris carte'</li><li>'facture carte du café de flore carte'</li></ul> |
|
96 |
+
| Recurrent Payments / loans | <ul><li>'prlv sepa monabanq pret amelioration habitat frzzz'</li><li>'prélèvement sepa credit agricole pret immoparc carte'</li><li>'prlv sepa sofinco pret conso carte'</li></ul> |
|
97 |
+
| Transportation / public transportation | <ul><li>'achat titres v ville de lille carte'</li><li>'navette aeroport orly bus carte'</li><li>'ticket voyages lyon transport carte'</li></ul> |
|
98 |
+
| Investment / securities | <ul><li>'souscription fonds pension carte'</li><li>'vente sicav monetaire carte'</li><li>'achat parts initiative europe carte'</li></ul> |
|
99 |
+
| Shopping / housing equipment | <ul><li>'paiement cb darty nice carte'</li><li>'achat au moulin des peintures nantes carte'</li><li>'achat cb castorama lille carte le'</li></ul> |
|
100 |
+
| Healthy & Beauty / other | <ul><li>'abonnement annuel gymnase formeplus carte'</li><li>'seance yoga luxe paris carte'</li><li>'soin relaxant doux jardin nantes carte'</li></ul> |
|
101 |
+
| Healthy & Beauty / pharmacy | <ul><li>'prlv sepa pharmacie azureech'</li><li>'facture carte du pharmacie centrale paris carte'</li><li>'achat pharmacie du parc carte le'</li></ul> |
|
102 |
+
| Shopping / clothing | <ul><li>'facture carte du levis store carte can cad commission'</li><li>'achat veste en cuir chez massimo dutti carte paris'</li><li>'facture carte converse paris carte'</li></ul> |
|
103 |
+
| Shopping / sporting goods | <ul><li>'achat decathlon besancon carte'</li><li>'achat carte nike store carte'</li><li>'paiement carte running store paris carte'</li></ul> |
|
104 |
+
| Leisure & Entertainment / travel | <ul><li>'facture carte du seaworld san diego carte'</li><li>'payment card louvre museum card'</li><li>'retrait dab cairo airport card egy egp commission'</li></ul> |
|
105 |
+
| Investment / other | <ul><li>'achat parts sociales cooperative agricole carte'</li><li>'achat actions ia revolution carte'</li><li>'investissement projet ecologique carte'</li></ul> |
|
106 |
+
| Leisure & Entertainment / other | <ul><li>'facture carte du hbo max carte usa'</li><li>'abonnement carte playstation plus carte eu'</li><li>'abonnement mensuel canal carte'</li></ul> |
|
107 |
+
| Shopping / other | <ul><li>'facture carte du fnac livres carte'</li><li>'facture carte du les tresors de sophie bordeaux carte'</li><li>'achat jouets et merveilles dijon carte'</li></ul> |
|
108 |
+
|
109 |
+
## Evaluation
|
110 |
+
|
111 |
+
### Metrics
|
112 |
+
| Label | Accuracy |
|
113 |
+
|:--------|:---------|
|
114 |
+
| **all** | 0.7008 |
|
115 |
+
|
116 |
+
## Uses
|
117 |
+
|
118 |
+
### Direct Use for Inference
|
119 |
+
|
120 |
+
First install the SetFit library:
|
121 |
+
|
122 |
+
```bash
|
123 |
+
pip install setfit
|
124 |
+
```
|
125 |
+
|
126 |
+
Then you can load this model and run inference.
|
127 |
+
|
128 |
+
```python
|
129 |
+
from setfit import SetFitModel
|
130 |
+
|
131 |
+
# Download from the 🤗 Hub
|
132 |
+
model = SetFitModel.from_pretrained("HEN10/setfit-particular-transaction-solon-embeddings-labels-large-v4")
|
133 |
+
# Run inference
|
134 |
+
preds = model("leçon de surf hossegor surf club carte")
|
135 |
+
```
|
136 |
+
|
137 |
+
<!--
|
138 |
+
### Downstream Use
|
139 |
+
|
140 |
+
*List how someone could finetune this model on their own dataset.*
|
141 |
+
-->
|
142 |
+
|
143 |
+
<!--
|
144 |
+
### Out-of-Scope Use
|
145 |
+
|
146 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
147 |
+
-->
|
148 |
+
|
149 |
+
<!--
|
150 |
+
## Bias, Risks and Limitations
|
151 |
+
|
152 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
153 |
+
-->
|
154 |
+
|
155 |
+
<!--
|
156 |
+
### Recommendations
|
157 |
+
|
158 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
159 |
+
-->
|
160 |
+
|
161 |
+
## Training Details
|
162 |
+
|
163 |
+
### Training Set Metrics
|
164 |
+
| Training set | Min | Median | Max |
|
165 |
+
|:-------------|:----|:-------|:----|
|
166 |
+
| Word count | 3 | 5.9159 | 12 |
|
167 |
+
|
168 |
+
| Label | Training Sample Count |
|
169 |
+
|:-------------------------------------------|:----------------------|
|
170 |
+
| Housing / rent | 20 |
|
171 |
+
| Housing / house loan | 20 |
|
172 |
+
| Housing / utilities & bills | 20 |
|
173 |
+
| Housing / services & maintenance | 20 |
|
174 |
+
| Housing / other | 20 |
|
175 |
+
| Food & Drinks / groceries | 20 |
|
176 |
+
| Food & Drinks / eating out | 20 |
|
177 |
+
| Food & Drinks / other | 20 |
|
178 |
+
| Leisure & Entertainment / sports & hobbies | 20 |
|
179 |
+
| Leisure & Entertainment / culture & events | 20 |
|
180 |
+
| Leisure & Entertainment / travel | 20 |
|
181 |
+
| Leisure & Entertainment / other | 20 |
|
182 |
+
| Transportation / car loan & leasing | 20 |
|
183 |
+
| Transportation / fuel | 20 |
|
184 |
+
| Transportation / public transportation | 20 |
|
185 |
+
| Transportation / taxi & carpool | 20 |
|
186 |
+
| Transportation / maitenance | 20 |
|
187 |
+
| Transportation / other | 20 |
|
188 |
+
| Recurrent Payments / loans | 20 |
|
189 |
+
| Recurrent Payments / insurance | 20 |
|
190 |
+
| Recurrent Payments / subscription | 20 |
|
191 |
+
| Recurrent Payments / other | 20 |
|
192 |
+
| Investment / securities | 20 |
|
193 |
+
| Investment / retirement & savings | 20 |
|
194 |
+
| Investment / real estate | 20 |
|
195 |
+
| Investment / other | 20 |
|
196 |
+
| Shopping / clothing | 20 |
|
197 |
+
| Shopping / electronics & multimedia | 20 |
|
198 |
+
| Shopping / sporting goods | 20 |
|
199 |
+
| Shopping / housing equipment | 20 |
|
200 |
+
| Shopping / other | 20 |
|
201 |
+
| Healthy & Beauty / doctor fees | 20 |
|
202 |
+
| Healthy & Beauty / pharmacy | 20 |
|
203 |
+
| Healthy & Beauty / beauty & self-care | 20 |
|
204 |
+
| Healthy & Beauty / veterinary | 20 |
|
205 |
+
| Healthy & Beauty / other | 20 |
|
206 |
+
| Bank services / transfers | 20 |
|
207 |
+
| Bank services / withdrawal | 20 |
|
208 |
+
| Bank services / general fees | 20 |
|
209 |
+
| Bank services / other | 20 |
|
210 |
+
| Other / taxes | 20 |
|
211 |
+
| Other / kids | 20 |
|
212 |
+
| Other / pets | 20 |
|
213 |
+
| Other / other | 20 |
|
214 |
+
|
215 |
+
### Training Hyperparameters
|
216 |
+
- batch_size: (26, 26)
|
217 |
+
- num_epochs: (1, 1)
|
218 |
+
- max_steps: -1
|
219 |
+
- sampling_strategy: oversampling
|
220 |
+
- body_learning_rate: (2e-05, 1e-05)
|
221 |
+
- head_learning_rate: 0.01
|
222 |
+
- loss: CosineSimilarityLoss
|
223 |
+
- distance_metric: cosine_distance
|
224 |
+
- margin: 0.25
|
225 |
+
- end_to_end: True
|
226 |
+
- use_amp: False
|
227 |
+
- warmup_proportion: 0.1
|
228 |
+
- seed: 6
|
229 |
+
- eval_max_steps: -1
|
230 |
+
- load_best_model_at_end: False
|
231 |
+
|
232 |
+
### Training Results
|
233 |
+
| Epoch | Step | Training Loss | Validation Loss |
|
234 |
+
|:------:|:-----:|:-------------:|:---------------:|
|
235 |
+
| 0.0000 | 1 | 0.2084 | - |
|
236 |
+
| 0.0012 | 50 | 0.2041 | - |
|
237 |
+
| 0.0000 | 1 | 0.1841 | - |
|
238 |
+
| 0.0017 | 50 | 0.219 | - |
|
239 |
+
| 0.0034 | 100 | 0.2197 | - |
|
240 |
+
| 0.0052 | 150 | 0.1724 | - |
|
241 |
+
| 0.0069 | 200 | 0.2291 | - |
|
242 |
+
| 0.0086 | 250 | 0.1693 | - |
|
243 |
+
| 0.0103 | 300 | 0.0832 | - |
|
244 |
+
| 0.0120 | 350 | 0.1414 | - |
|
245 |
+
| 0.0137 | 400 | 0.0989 | - |
|
246 |
+
| 0.0155 | 450 | 0.0962 | - |
|
247 |
+
| 0.0172 | 500 | 0.1132 | - |
|
248 |
+
| 0.0189 | 550 | 0.1 | - |
|
249 |
+
| 0.0206 | 600 | 0.0561 | - |
|
250 |
+
| 0.0223 | 650 | 0.0851 | - |
|
251 |
+
| 0.0240 | 700 | 0.0762 | - |
|
252 |
+
| 0.0258 | 750 | 0.0876 | - |
|
253 |
+
| 0.0275 | 800 | 0.0414 | - |
|
254 |
+
| 0.0292 | 850 | 0.0368 | - |
|
255 |
+
| 0.0309 | 900 | 0.0409 | - |
|
256 |
+
| 0.0326 | 950 | 0.0212 | - |
|
257 |
+
| 0.0344 | 1000 | 0.0175 | - |
|
258 |
+
| 0.0361 | 1050 | 0.05 | - |
|
259 |
+
| 0.0378 | 1100 | 0.0848 | - |
|
260 |
+
| 0.0395 | 1150 | 0.0549 | - |
|
261 |
+
| 0.0412 | 1200 | 0.0395 | - |
|
262 |
+
| 0.0429 | 1250 | 0.029 | - |
|
263 |
+
| 0.0447 | 1300 | 0.0047 | - |
|
264 |
+
| 0.0464 | 1350 | 0.0387 | - |
|
265 |
+
| 0.0481 | 1400 | 0.0268 | - |
|
266 |
+
| 0.0498 | 1450 | 0.0531 | - |
|
267 |
+
| 0.0515 | 1500 | 0.0038 | - |
|
268 |
+
| 0.0532 | 1550 | 0.0226 | - |
|
269 |
+
| 0.0550 | 1600 | 0.0349 | - |
|
270 |
+
| 0.0567 | 1650 | 0.0106 | - |
|
271 |
+
| 0.0584 | 1700 | 0.0049 | - |
|
272 |
+
| 0.0601 | 1750 | 0.0171 | - |
|
273 |
+
| 0.0618 | 1800 | 0.0066 | - |
|
274 |
+
| 0.0636 | 1850 | 0.0066 | - |
|
275 |
+
| 0.0653 | 1900 | 0.0039 | - |
|
276 |
+
| 0.0670 | 1950 | 0.0016 | - |
|
277 |
+
| 0.0687 | 2000 | 0.0414 | - |
|
278 |
+
| 0.0704 | 2050 | 0.0172 | - |
|
279 |
+
| 0.0721 | 2100 | 0.0039 | - |
|
280 |
+
| 0.0739 | 2150 | 0.0036 | - |
|
281 |
+
| 0.0756 | 2200 | 0.0334 | - |
|
282 |
+
| 0.0773 | 2250 | 0.0025 | - |
|
283 |
+
| 0.0790 | 2300 | 0.0022 | - |
|
284 |
+
| 0.0807 | 2350 | 0.0017 | - |
|
285 |
+
| 0.0825 | 2400 | 0.0015 | - |
|
286 |
+
| 0.0842 | 2450 | 0.0125 | - |
|
287 |
+
| 0.0859 | 2500 | 0.0023 | - |
|
288 |
+
| 0.0876 | 2550 | 0.0023 | - |
|
289 |
+
| 0.0893 | 2600 | 0.0013 | - |
|
290 |
+
| 0.0910 | 2650 | 0.0728 | - |
|
291 |
+
| 0.0928 | 2700 | 0.0141 | - |
|
292 |
+
| 0.0945 | 2750 | 0.0332 | - |
|
293 |
+
| 0.0962 | 2800 | 0.0632 | - |
|
294 |
+
| 0.0979 | 2850 | 0.0042 | - |
|
295 |
+
| 0.0996 | 2900 | 0.0117 | - |
|
296 |
+
| 0.1013 | 2950 | 0.0014 | - |
|
297 |
+
| 0.1031 | 3000 | 0.0013 | - |
|
298 |
+
| 0.1048 | 3050 | 0.0464 | - |
|
299 |
+
| 0.1065 | 3100 | 0.0031 | - |
|
300 |
+
| 0.1082 | 3150 | 0.0007 | - |
|
301 |
+
| 0.1099 | 3200 | 0.0008 | - |
|
302 |
+
| 0.1117 | 3250 | 0.001 | - |
|
303 |
+
| 0.1134 | 3300 | 0.001 | - |
|
304 |
+
| 0.1151 | 3350 | 0.0016 | - |
|
305 |
+
| 0.1168 | 3400 | 0.0006 | - |
|
306 |
+
| 0.1185 | 3450 | 0.0005 | - |
|
307 |
+
| 0.1202 | 3500 | 0.0006 | - |
|
308 |
+
| 0.1220 | 3550 | 0.0008 | - |
|
309 |
+
| 0.1237 | 3600 | 0.0368 | - |
|
310 |
+
| 0.1254 | 3650 | 0.0026 | - |
|
311 |
+
| 0.1271 | 3700 | 0.0372 | - |
|
312 |
+
| 0.1288 | 3750 | 0.0006 | - |
|
313 |
+
| 0.1305 | 3800 | 0.0005 | - |
|
314 |
+
| 0.1323 | 3850 | 0.0276 | - |
|
315 |
+
| 0.1340 | 3900 | 0.0007 | - |
|
316 |
+
| 0.1357 | 3950 | 0.0013 | - |
|
317 |
+
| 0.1374 | 4000 | 0.0008 | - |
|
318 |
+
| 0.1391 | 4050 | 0.0018 | - |
|
319 |
+
| 0.1409 | 4100 | 0.0292 | - |
|
320 |
+
| 0.1426 | 4150 | 0.0102 | - |
|
321 |
+
| 0.1443 | 4200 | 0.0093 | - |
|
322 |
+
| 0.1460 | 4250 | 0.0022 | - |
|
323 |
+
| 0.1477 | 4300 | 0.0032 | - |
|
324 |
+
| 0.1494 | 4350 | 0.001 | - |
|
325 |
+
| 0.1512 | 4400 | 0.0006 | - |
|
326 |
+
| 0.1529 | 4450 | 0.0007 | - |
|
327 |
+
| 0.1546 | 4500 | 0.0007 | - |
|
328 |
+
| 0.1563 | 4550 | 0.0007 | - |
|
329 |
+
| 0.1580 | 4600 | 0.0007 | - |
|
330 |
+
| 0.1597 | 4650 | 0.0011 | - |
|
331 |
+
| 0.1615 | 4700 | 0.0008 | - |
|
332 |
+
| 0.1632 | 4750 | 0.0374 | - |
|
333 |
+
| 0.1649 | 4800 | 0.0004 | - |
|
334 |
+
| 0.1666 | 4850 | 0.0008 | - |
|
335 |
+
| 0.1683 | 4900 | 0.005 | - |
|
336 |
+
| 0.1701 | 4950 | 0.0013 | - |
|
337 |
+
| 0.1718 | 5000 | 0.0016 | - |
|
338 |
+
| 0.1735 | 5050 | 0.0006 | - |
|
339 |
+
| 0.1752 | 5100 | 0.0007 | - |
|
340 |
+
| 0.1769 | 5150 | 0.0007 | - |
|
341 |
+
| 0.1786 | 5200 | 0.0004 | - |
|
342 |
+
| 0.1804 | 5250 | 0.0003 | - |
|
343 |
+
| 0.1821 | 5300 | 0.0004 | - |
|
344 |
+
| 0.1838 | 5350 | 0.0004 | - |
|
345 |
+
| 0.1855 | 5400 | 0.0002 | - |
|
346 |
+
| 0.1872 | 5450 | 0.036 | - |
|
347 |
+
| 0.1890 | 5500 | 0.0003 | - |
|
348 |
+
| 0.1907 | 5550 | 0.0003 | - |
|
349 |
+
| 0.1924 | 5600 | 0.0003 | - |
|
350 |
+
| 0.1941 | 5650 | 0.0006 | - |
|
351 |
+
| 0.1958 | 5700 | 0.0005 | - |
|
352 |
+
| 0.1975 | 5750 | 0.0057 | - |
|
353 |
+
| 0.1993 | 5800 | 0.0008 | - |
|
354 |
+
| 0.2010 | 5850 | 0.0002 | - |
|
355 |
+
| 0.2027 | 5900 | 0.0013 | - |
|
356 |
+
| 0.2044 | 5950 | 0.0004 | - |
|
357 |
+
| 0.2061 | 6000 | 0.0002 | - |
|
358 |
+
| 0.2078 | 6050 | 0.0002 | - |
|
359 |
+
| 0.2096 | 6100 | 0.0015 | - |
|
360 |
+
| 0.2113 | 6150 | 0.037 | - |
|
361 |
+
| 0.2130 | 6200 | 0.0003 | - |
|
362 |
+
| 0.2147 | 6250 | 0.0003 | - |
|
363 |
+
| 0.2164 | 6300 | 0.0002 | - |
|
364 |
+
| 0.2182 | 6350 | 0.0003 | - |
|
365 |
+
| 0.2199 | 6400 | 0.0005 | - |
|
366 |
+
| 0.2216 | 6450 | 0.0004 | - |
|
367 |
+
| 0.2233 | 6500 | 0.0042 | - |
|
368 |
+
| 0.2250 | 6550 | 0.0004 | - |
|
369 |
+
| 0.2267 | 6600 | 0.0006 | - |
|
370 |
+
| 0.2285 | 6650 | 0.0004 | - |
|
371 |
+
| 0.2302 | 6700 | 0.0005 | - |
|
372 |
+
| 0.2319 | 6750 | 0.0021 | - |
|
373 |
+
| 0.2336 | 6800 | 0.0003 | - |
|
374 |
+
| 0.2353 | 6850 | 0.0003 | - |
|
375 |
+
| 0.2370 | 6900 | 0.0005 | - |
|
376 |
+
| 0.2388 | 6950 | 0.0003 | - |
|
377 |
+
| 0.2405 | 7000 | 0.0002 | - |
|
378 |
+
| 0.2422 | 7050 | 0.0003 | - |
|
379 |
+
| 0.2439 | 7100 | 0.0004 | - |
|
380 |
+
| 0.2456 | 7150 | 0.0005 | - |
|
381 |
+
| 0.2474 | 7200 | 0.0005 | - |
|
382 |
+
| 0.2491 | 7250 | 0.001 | - |
|
383 |
+
| 0.2508 | 7300 | 0.0055 | - |
|
384 |
+
| 0.2525 | 7350 | 0.0005 | - |
|
385 |
+
| 0.2542 | 7400 | 0.0005 | - |
|
386 |
+
| 0.2559 | 7450 | 0.0007 | - |
|
387 |
+
| 0.2577 | 7500 | 0.0002 | - |
|
388 |
+
| 0.2594 | 7550 | 0.0745 | - |
|
389 |
+
| 0.2611 | 7600 | 0.0003 | - |
|
390 |
+
| 0.2628 | 7650 | 0.0002 | - |
|
391 |
+
| 0.2645 | 7700 | 0.0002 | - |
|
392 |
+
| 0.2662 | 7750 | 0.0004 | - |
|
393 |
+
| 0.2680 | 7800 | 0.0002 | - |
|
394 |
+
| 0.2697 | 7850 | 0.0002 | - |
|
395 |
+
| 0.2714 | 7900 | 0.0003 | - |
|
396 |
+
| 0.2731 | 7950 | 0.0002 | - |
|
397 |
+
| 0.2748 | 8000 | 0.0002 | - |
|
398 |
+
| 0.2766 | 8050 | 0.0003 | - |
|
399 |
+
| 0.2783 | 8100 | 0.0003 | - |
|
400 |
+
| 0.2800 | 8150 | 0.0313 | - |
|
401 |
+
| 0.2817 | 8200 | 0.0007 | - |
|
402 |
+
| 0.2834 | 8250 | 0.0002 | - |
|
403 |
+
| 0.2851 | 8300 | 0.0003 | - |
|
404 |
+
| 0.2869 | 8350 | 0.0003 | - |
|
405 |
+
| 0.2886 | 8400 | 0.0003 | - |
|
406 |
+
| 0.2903 | 8450 | 0.0002 | - |
|
407 |
+
| 0.2920 | 8500 | 0.0003 | - |
|
408 |
+
| 0.2937 | 8550 | 0.0154 | - |
|
409 |
+
| 0.2955 | 8600 | 0.0003 | - |
|
410 |
+
| 0.2972 | 8650 | 0.0005 | - |
|
411 |
+
| 0.2989 | 8700 | 0.0041 | - |
|
412 |
+
| 0.3006 | 8750 | 0.0003 | - |
|
413 |
+
| 0.3023 | 8800 | 0.0002 | - |
|
414 |
+
| 0.3040 | 8850 | 0.0003 | - |
|
415 |
+
| 0.3058 | 8900 | 0.0001 | - |
|
416 |
+
| 0.3075 | 8950 | 0.0005 | - |
|
417 |
+
| 0.3092 | 9000 | 0.0022 | - |
|
418 |
+
| 0.3109 | 9050 | 0.0002 | - |
|
419 |
+
| 0.3126 | 9100 | 0.0003 | - |
|
420 |
+
| 0.3143 | 9150 | 0.0002 | - |
|
421 |
+
| 0.3161 | 9200 | 0.0001 | - |
|
422 |
+
| 0.3178 | 9250 | 0.0002 | - |
|
423 |
+
| 0.3195 | 9300 | 0.0001 | - |
|
424 |
+
| 0.3212 | 9350 | 0.0002 | - |
|
425 |
+
| 0.3229 | 9400 | 0.0002 | - |
|
426 |
+
| 0.3247 | 9450 | 0.0003 | - |
|
427 |
+
| 0.3264 | 9500 | 0.0017 | - |
|
428 |
+
| 0.3281 | 9550 | 0.003 | - |
|
429 |
+
| 0.3298 | 9600 | 0.0039 | - |
|
430 |
+
| 0.3315 | 9650 | 0.0028 | - |
|
431 |
+
| 0.3332 | 9700 | 0.0037 | - |
|
432 |
+
| 0.3350 | 9750 | 0.0005 | - |
|
433 |
+
| 0.3367 | 9800 | 0.0352 | - |
|
434 |
+
| 0.3384 | 9850 | 0.0006 | - |
|
435 |
+
| 0.3401 | 9900 | 0.0006 | - |
|
436 |
+
| 0.3418 | 9950 | 0.0004 | - |
|
437 |
+
| 0.3435 | 10000 | 0.0002 | - |
|
438 |
+
| 0.3453 | 10050 | 0.0012 | - |
|
439 |
+
| 0.3470 | 10100 | 0.0002 | - |
|
440 |
+
| 0.3487 | 10150 | 0.0003 | - |
|
441 |
+
| 0.3504 | 10200 | 0.0002 | - |
|
442 |
+
| 0.3521 | 10250 | 0.0002 | - |
|
443 |
+
| 0.3539 | 10300 | 0.0004 | - |
|
444 |
+
| 0.3556 | 10350 | 0.0003 | - |
|
445 |
+
| 0.3573 | 10400 | 0.0003 | - |
|
446 |
+
| 0.3590 | 10450 | 0.0002 | - |
|
447 |
+
| 0.3607 | 10500 | 0.0004 | - |
|
448 |
+
| 0.3624 | 10550 | 0.0004 | - |
|
449 |
+
| 0.3642 | 10600 | 0.0371 | - |
|
450 |
+
| 0.3659 | 10650 | 0.0005 | - |
|
451 |
+
| 0.3676 | 10700 | 0.0236 | - |
|
452 |
+
| 0.3693 | 10750 | 0.0002 | - |
|
453 |
+
| 0.3710 | 10800 | 0.0002 | - |
|
454 |
+
| 0.3727 | 10850 | 0.0003 | - |
|
455 |
+
| 0.3745 | 10900 | 0.0004 | - |
|
456 |
+
| 0.3762 | 10950 | 0.0002 | - |
|
457 |
+
| 0.3779 | 11000 | 0.0002 | - |
|
458 |
+
| 0.3796 | 11050 | 0.0002 | - |
|
459 |
+
| 0.3813 | 11100 | 0.0001 | - |
|
460 |
+
| 0.3831 | 11150 | 0.0001 | - |
|
461 |
+
| 0.3848 | 11200 | 0.0002 | - |
|
462 |
+
| 0.3865 | 11250 | 0.0002 | - |
|
463 |
+
| 0.3882 | 11300 | 0.0001 | - |
|
464 |
+
| 0.3899 | 11350 | 0.0001 | - |
|
465 |
+
| 0.3916 | 11400 | 0.0351 | - |
|
466 |
+
| 0.3934 | 11450 | 0.0003 | - |
|
467 |
+
| 0.3951 | 11500 | 0.0001 | - |
|
468 |
+
| 0.3968 | 11550 | 0.0326 | - |
|
469 |
+
| 0.3985 | 11600 | 0.0001 | - |
|
470 |
+
| 0.4002 | 11650 | 0.0006 | - |
|
471 |
+
| 0.4020 | 11700 | 0.0002 | - |
|
472 |
+
| 0.4037 | 11750 | 0.0004 | - |
|
473 |
+
| 0.4054 | 11800 | 0.0002 | - |
|
474 |
+
| 0.4071 | 11850 | 0.0002 | - |
|
475 |
+
| 0.4088 | 11900 | 0.0001 | - |
|
476 |
+
| 0.4105 | 11950 | 0.0002 | - |
|
477 |
+
| 0.4123 | 12000 | 0.0002 | - |
|
478 |
+
| 0.4140 | 12050 | 0.0003 | - |
|
479 |
+
| 0.4157 | 12100 | 0.0003 | - |
|
480 |
+
| 0.4174 | 12150 | 0.0001 | - |
|
481 |
+
| 0.4191 | 12200 | 0.0001 | - |
|
482 |
+
| 0.4208 | 12250 | 0.0003 | - |
|
483 |
+
| 0.4226 | 12300 | 0.0001 | - |
|
484 |
+
| 0.4243 | 12350 | 0.0002 | - |
|
485 |
+
| 0.4260 | 12400 | 0.0003 | - |
|
486 |
+
| 0.4277 | 12450 | 0.0002 | - |
|
487 |
+
| 0.4294 | 12500 | 0.0002 | - |
|
488 |
+
| 0.4312 | 12550 | 0.0002 | - |
|
489 |
+
| 0.4329 | 12600 | 0.0002 | - |
|
490 |
+
| 0.4346 | 12650 | 0.0007 | - |
|
491 |
+
| 0.4363 | 12700 | 0.0002 | - |
|
492 |
+
| 0.4380 | 12750 | 0.0003 | - |
|
493 |
+
| 0.4397 | 12800 | 0.0001 | - |
|
494 |
+
| 0.4415 | 12850 | 0.0001 | - |
|
495 |
+
| 0.4432 | 12900 | 0.0002 | - |
|
496 |
+
| 0.4449 | 12950 | 0.001 | - |
|
497 |
+
| 0.4466 | 13000 | 0.0002 | - |
|
498 |
+
| 0.4483 | 13050 | 0.0002 | - |
|
499 |
+
| 0.4500 | 13100 | 0.0005 | - |
|
500 |
+
| 0.4518 | 13150 | 0.0002 | - |
|
501 |
+
| 0.4535 | 13200 | 0.0002 | - |
|
502 |
+
| 0.4552 | 13250 | 0.0001 | - |
|
503 |
+
| 0.4569 | 13300 | 0.0003 | - |
|
504 |
+
| 0.4586 | 13350 | 0.0013 | - |
|
505 |
+
| 0.4604 | 13400 | 0.0002 | - |
|
506 |
+
| 0.4621 | 13450 | 0.0372 | - |
|
507 |
+
| 0.4638 | 13500 | 0.0002 | - |
|
508 |
+
| 0.4655 | 13550 | 0.0003 | - |
|
509 |
+
| 0.4672 | 13600 | 0.0025 | - |
|
510 |
+
| 0.4689 | 13650 | 0.0002 | - |
|
511 |
+
| 0.4707 | 13700 | 0.0002 | - |
|
512 |
+
| 0.4724 | 13750 | 0.0001 | - |
|
513 |
+
| 0.4741 | 13800 | 0.0002 | - |
|
514 |
+
| 0.4758 | 13850 | 0.0001 | - |
|
515 |
+
| 0.4775 | 13900 | 0.0003 | - |
|
516 |
+
| 0.4792 | 13950 | 0.0026 | - |
|
517 |
+
| 0.4810 | 14000 | 0.0002 | - |
|
518 |
+
| 0.4827 | 14050 | 0.0002 | - |
|
519 |
+
| 0.4844 | 14100 | 0.0002 | - |
|
520 |
+
| 0.4861 | 14150 | 0.0002 | - |
|
521 |
+
| 0.4878 | 14200 | 0.0002 | - |
|
522 |
+
| 0.4896 | 14250 | 0.0002 | - |
|
523 |
+
| 0.4913 | 14300 | 0.0003 | - |
|
524 |
+
| 0.4930 | 14350 | 0.0002 | - |
|
525 |
+
| 0.4947 | 14400 | 0.0014 | - |
|
526 |
+
| 0.4964 | 14450 | 0.0002 | - |
|
527 |
+
| 0.4981 | 14500 | 0.0001 | - |
|
528 |
+
| 0.4999 | 14550 | 0.0002 | - |
|
529 |
+
| 0.5016 | 14600 | 0.0001 | - |
|
530 |
+
| 0.5033 | 14650 | 0.0002 | - |
|
531 |
+
| 0.5050 | 14700 | 0.0001 | - |
|
532 |
+
| 0.5067 | 14750 | 0.0002 | - |
|
533 |
+
| 0.5085 | 14800 | 0.0001 | - |
|
534 |
+
| 0.5102 | 14850 | 0.0001 | - |
|
535 |
+
| 0.5119 | 14900 | 0.0002 | - |
|
536 |
+
| 0.5136 | 14950 | 0.0001 | - |
|
537 |
+
| 0.5153 | 15000 | 0.0001 | - |
|
538 |
+
| 0.5170 | 15050 | 0.0001 | - |
|
539 |
+
| 0.5188 | 15100 | 0.0002 | - |
|
540 |
+
| 0.5205 | 15150 | 0.0002 | - |
|
541 |
+
| 0.5222 | 15200 | 0.0002 | - |
|
542 |
+
| 0.5239 | 15250 | 0.0001 | - |
|
543 |
+
| 0.5256 | 15300 | 0.0001 | - |
|
544 |
+
| 0.5273 | 15350 | 0.0001 | - |
|
545 |
+
| 0.5291 | 15400 | 0.0001 | - |
|
546 |
+
| 0.5308 | 15450 | 0.0001 | - |
|
547 |
+
| 0.5325 | 15500 | 0.0001 | - |
|
548 |
+
| 0.5342 | 15550 | 0.0001 | - |
|
549 |
+
| 0.5359 | 15600 | 0.0001 | - |
|
550 |
+
| 0.5377 | 15650 | 0.0001 | - |
|
551 |
+
| 0.5394 | 15700 | 0.0001 | - |
|
552 |
+
| 0.5411 | 15750 | 0.0001 | - |
|
553 |
+
| 0.5428 | 15800 | 0.0001 | - |
|
554 |
+
| 0.5445 | 15850 | 0.0002 | - |
|
555 |
+
| 0.5462 | 15900 | 0.0002 | - |
|
556 |
+
| 0.5480 | 15950 | 0.0001 | - |
|
557 |
+
| 0.5497 | 16000 | 0.0001 | - |
|
558 |
+
| 0.5514 | 16050 | 0.0001 | - |
|
559 |
+
| 0.5531 | 16100 | 0.0001 | - |
|
560 |
+
| 0.5548 | 16150 | 0.0001 | - |
|
561 |
+
| 0.5565 | 16200 | 0.0001 | - |
|
562 |
+
| 0.5583 | 16250 | 0.0001 | - |
|
563 |
+
| 0.5600 | 16300 | 0.0001 | - |
|
564 |
+
| 0.5617 | 16350 | 0.0001 | - |
|
565 |
+
| 0.5634 | 16400 | 0.0002 | - |
|
566 |
+
| 0.5651 | 16450 | 0.0001 | - |
|
567 |
+
| 0.5669 | 16500 | 0.0001 | - |
|
568 |
+
| 0.5686 | 16550 | 0.0001 | - |
|
569 |
+
| 0.5703 | 16600 | 0.0001 | - |
|
570 |
+
| 0.5720 | 16650 | 0.0002 | - |
|
571 |
+
| 0.5737 | 16700 | 0.0001 | - |
|
572 |
+
| 0.5754 | 16750 | 0.0001 | - |
|
573 |
+
| 0.5772 | 16800 | 0.0001 | - |
|
574 |
+
| 0.5789 | 16850 | 0.0001 | - |
|
575 |
+
| 0.5806 | 16900 | 0.0001 | - |
|
576 |
+
| 0.5823 | 16950 | 0.0001 | - |
|
577 |
+
| 0.5840 | 17000 | 0.0001 | - |
|
578 |
+
| 0.5857 | 17050 | 0.0002 | - |
|
579 |
+
| 0.5875 | 17100 | 0.0001 | - |
|
580 |
+
| 0.5892 | 17150 | 0.0001 | - |
|
581 |
+
| 0.5909 | 17200 | 0.0001 | - |
|
582 |
+
| 0.5926 | 17250 | 0.0001 | - |
|
583 |
+
| 0.5943 | 17300 | 0.0001 | - |
|
584 |
+
| 0.5961 | 17350 | 0.0001 | - |
|
585 |
+
| 0.5978 | 17400 | 0.0001 | - |
|
586 |
+
| 0.5995 | 17450 | 0.0001 | - |
|
587 |
+
| 0.6012 | 17500 | 0.0371 | - |
|
588 |
+
| 0.6029 | 17550 | 0.0001 | - |
|
589 |
+
| 0.6046 | 17600 | 0.0001 | - |
|
590 |
+
| 0.6064 | 17650 | 0.0001 | - |
|
591 |
+
| 0.6081 | 17700 | 0.0001 | - |
|
592 |
+
| 0.6098 | 17750 | 0.0001 | - |
|
593 |
+
| 0.6115 | 17800 | 0.0002 | - |
|
594 |
+
| 0.6132 | 17850 | 0.0007 | - |
|
595 |
+
| 0.6150 | 17900 | 0.0002 | - |
|
596 |
+
| 0.6167 | 17950 | 0.0001 | - |
|
597 |
+
| 0.6184 | 18000 | 0.0115 | - |
|
598 |
+
| 0.6201 | 18050 | 0.0001 | - |
|
599 |
+
| 0.6218 | 18100 | 0.0004 | - |
|
600 |
+
| 0.6235 | 18150 | 0.0002 | - |
|
601 |
+
| 0.6253 | 18200 | 0.0074 | - |
|
602 |
+
| 0.6270 | 18250 | 0.0325 | - |
|
603 |
+
| 0.6287 | 18300 | 0.0008 | - |
|
604 |
+
| 0.6304 | 18350 | 0.0007 | - |
|
605 |
+
| 0.6321 | 18400 | 0.0002 | - |
|
606 |
+
| 0.6338 | 18450 | 0.0005 | - |
|
607 |
+
| 0.6356 | 18500 | 0.0003 | - |
|
608 |
+
| 0.6373 | 18550 | 0.0003 | - |
|
609 |
+
| 0.6390 | 18600 | 0.0002 | - |
|
610 |
+
| 0.6407 | 18650 | 0.0003 | - |
|
611 |
+
| 0.6424 | 18700 | 0.0003 | - |
|
612 |
+
| 0.6442 | 18750 | 0.0002 | - |
|
613 |
+
| 0.6459 | 18800 | 0.0002 | - |
|
614 |
+
| 0.6476 | 18850 | 0.0002 | - |
|
615 |
+
| 0.6493 | 18900 | 0.0002 | - |
|
616 |
+
| 0.6510 | 18950 | 0.0001 | - |
|
617 |
+
| 0.6527 | 19000 | 0.0001 | - |
|
618 |
+
| 0.6545 | 19050 | 0.0003 | - |
|
619 |
+
| 0.6562 | 19100 | 0.0001 | - |
|
620 |
+
| 0.6579 | 19150 | 0.0001 | - |
|
621 |
+
| 0.6596 | 19200 | 0.0002 | - |
|
622 |
+
| 0.6613 | 19250 | 0.0002 | - |
|
623 |
+
| 0.6630 | 19300 | 0.0003 | - |
|
624 |
+
| 0.6648 | 19350 | 0.0186 | - |
|
625 |
+
| 0.6665 | 19400 | 0.0001 | - |
|
626 |
+
| 0.6682 | 19450 | 0.0002 | - |
|
627 |
+
| 0.6699 | 19500 | 0.0002 | - |
|
628 |
+
| 0.6716 | 19550 | 0.0001 | - |
|
629 |
+
| 0.6734 | 19600 | 0.0001 | - |
|
630 |
+
| 0.6751 | 19650 | 0.0001 | - |
|
631 |
+
| 0.6768 | 19700 | 0.0001 | - |
|
632 |
+
| 0.6785 | 19750 | 0.0001 | - |
|
633 |
+
| 0.6802 | 19800 | 0.0001 | - |
|
634 |
+
| 0.6819 | 19850 | 0.0001 | - |
|
635 |
+
| 0.6837 | 19900 | 0.0001 | - |
|
636 |
+
| 0.6854 | 19950 | 0.0371 | - |
|
637 |
+
| 0.6871 | 20000 | 0.0001 | - |
|
638 |
+
| 0.6888 | 20050 | 0.0001 | - |
|
639 |
+
| 0.6905 | 20100 | 0.0001 | - |
|
640 |
+
| 0.6922 | 20150 | 0.0001 | - |
|
641 |
+
| 0.6940 | 20200 | 0.0001 | - |
|
642 |
+
| 0.6957 | 20250 | 0.0001 | - |
|
643 |
+
| 0.6974 | 20300 | 0.0001 | - |
|
644 |
+
| 0.6991 | 20350 | 0.0001 | - |
|
645 |
+
| 0.7008 | 20400 | 0.0001 | - |
|
646 |
+
| 0.7026 | 20450 | 0.0001 | - |
|
647 |
+
| 0.7043 | 20500 | 0.0002 | - |
|
648 |
+
| 0.7060 | 20550 | 0.0001 | - |
|
649 |
+
| 0.7077 | 20600 | 0.0002 | - |
|
650 |
+
| 0.7094 | 20650 | 0.0001 | - |
|
651 |
+
| 0.7111 | 20700 | 0.0001 | - |
|
652 |
+
| 0.7129 | 20750 | 0.0001 | - |
|
653 |
+
| 0.7146 | 20800 | 0.0001 | - |
|
654 |
+
| 0.7163 | 20850 | 0.0001 | - |
|
655 |
+
| 0.7180 | 20900 | 0.0001 | - |
|
656 |
+
| 0.7197 | 20950 | 0.0001 | - |
|
657 |
+
| 0.7215 | 21000 | 0.0001 | - |
|
658 |
+
| 0.7232 | 21050 | 0.0001 | - |
|
659 |
+
| 0.7249 | 21100 | 0.0363 | - |
|
660 |
+
| 0.7266 | 21150 | 0.0001 | - |
|
661 |
+
| 0.7283 | 21200 | 0.0001 | - |
|
662 |
+
| 0.7300 | 21250 | 0.0001 | - |
|
663 |
+
| 0.7318 | 21300 | 0.0001 | - |
|
664 |
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| 0.7335 | 21350 | 0.0001 | - |
|
665 |
+
| 0.7352 | 21400 | 0.0001 | - |
|
666 |
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| 0.7369 | 21450 | 0.0001 | - |
|
667 |
+
| 0.7386 | 21500 | 0.0001 | - |
|
668 |
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| 0.7403 | 21550 | 0.0001 | - |
|
669 |
+
| 0.7421 | 21600 | 0.0001 | - |
|
670 |
+
| 0.7438 | 21650 | 0.0001 | - |
|
671 |
+
| 0.7455 | 21700 | 0.0001 | - |
|
672 |
+
| 0.7472 | 21750 | 0.0001 | - |
|
673 |
+
| 0.7489 | 21800 | 0.0001 | - |
|
674 |
+
| 0.7507 | 21850 | 0.0001 | - |
|
675 |
+
| 0.7524 | 21900 | 0.0001 | - |
|
676 |
+
| 0.7541 | 21950 | 0.0001 | - |
|
677 |
+
| 0.7558 | 22000 | 0.0001 | - |
|
678 |
+
| 0.7575 | 22050 | 0.0358 | - |
|
679 |
+
| 0.7592 | 22100 | 0.0007 | - |
|
680 |
+
| 0.7610 | 22150 | 0.0001 | - |
|
681 |
+
| 0.7627 | 22200 | 0.0001 | - |
|
682 |
+
| 0.7644 | 22250 | 0.0001 | - |
|
683 |
+
| 0.7661 | 22300 | 0.0001 | - |
|
684 |
+
| 0.7678 | 22350 | 0.0001 | - |
|
685 |
+
| 0.7695 | 22400 | 0.0368 | - |
|
686 |
+
| 0.7713 | 22450 | 0.0001 | - |
|
687 |
+
| 0.7730 | 22500 | 0.0001 | - |
|
688 |
+
| 0.7747 | 22550 | 0.0001 | - |
|
689 |
+
| 0.7764 | 22600 | 0.0001 | - |
|
690 |
+
| 0.7781 | 22650 | 0.0001 | - |
|
691 |
+
| 0.7799 | 22700 | 0.0003 | - |
|
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+
| 0.7816 | 22750 | 0.0001 | - |
|
693 |
+
| 0.7833 | 22800 | 0.0001 | - |
|
694 |
+
| 0.7850 | 22850 | 0.0001 | - |
|
695 |
+
| 0.7867 | 22900 | 0.0001 | - |
|
696 |
+
| 0.7884 | 22950 | 0.0001 | - |
|
697 |
+
| 0.7902 | 23000 | 0.0001 | - |
|
698 |
+
| 0.7919 | 23050 | 0.0001 | - |
|
699 |
+
| 0.7936 | 23100 | 0.0001 | - |
|
700 |
+
| 0.7953 | 23150 | 0.0001 | - |
|
701 |
+
| 0.7970 | 23200 | 0.0001 | - |
|
702 |
+
| 0.7987 | 23250 | 0.0001 | - |
|
703 |
+
| 0.8005 | 23300 | 0.0001 | - |
|
704 |
+
| 0.8022 | 23350 | 0.0001 | - |
|
705 |
+
| 0.8039 | 23400 | 0.0002 | - |
|
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+
| 0.8056 | 23450 | 0.0001 | - |
|
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+
| 0.8073 | 23500 | 0.0001 | - |
|
708 |
+
| 0.8091 | 23550 | 0.0001 | - |
|
709 |
+
| 0.8108 | 23600 | 0.0001 | - |
|
710 |
+
| 0.8125 | 23650 | 0.0001 | - |
|
711 |
+
| 0.8142 | 23700 | 0.0173 | - |
|
712 |
+
| 0.8159 | 23750 | 0.0001 | - |
|
713 |
+
| 0.8176 | 23800 | 0.0001 | - |
|
714 |
+
| 0.8194 | 23850 | 0.0001 | - |
|
715 |
+
| 0.8211 | 23900 | 0.0001 | - |
|
716 |
+
| 0.8228 | 23950 | 0.0001 | - |
|
717 |
+
| 0.8245 | 24000 | 0.0001 | - |
|
718 |
+
| 0.8262 | 24050 | 0.0001 | - |
|
719 |
+
| 0.8280 | 24100 | 0.0001 | - |
|
720 |
+
| 0.8297 | 24150 | 0.0001 | - |
|
721 |
+
| 0.8314 | 24200 | 0.0001 | - |
|
722 |
+
| 0.8331 | 24250 | 0.0001 | - |
|
723 |
+
| 0.8348 | 24300 | 0.0001 | - |
|
724 |
+
| 0.8365 | 24350 | 0.0001 | - |
|
725 |
+
| 0.8383 | 24400 | 0.0001 | - |
|
726 |
+
| 0.8400 | 24450 | 0.0001 | - |
|
727 |
+
| 0.8417 | 24500 | 0.0003 | - |
|
728 |
+
| 0.8434 | 24550 | 0.0002 | - |
|
729 |
+
| 0.8451 | 24600 | 0.0002 | - |
|
730 |
+
| 0.8468 | 24650 | 0.0001 | - |
|
731 |
+
| 0.8486 | 24700 | 0.0001 | - |
|
732 |
+
| 0.8503 | 24750 | 0.0001 | - |
|
733 |
+
| 0.8520 | 24800 | 0.0004 | - |
|
734 |
+
| 0.8537 | 24850 | 0.0001 | - |
|
735 |
+
| 0.8554 | 24900 | 0.0001 | - |
|
736 |
+
| 0.8572 | 24950 | 0.0001 | - |
|
737 |
+
| 0.8589 | 25000 | 0.0001 | - |
|
738 |
+
| 0.8606 | 25050 | 0.0001 | - |
|
739 |
+
| 0.8623 | 25100 | 0.0372 | - |
|
740 |
+
| 0.8640 | 25150 | 0.0001 | - |
|
741 |
+
| 0.8657 | 25200 | 0.0001 | - |
|
742 |
+
| 0.8675 | 25250 | 0.0001 | - |
|
743 |
+
| 0.8692 | 25300 | 0.0001 | - |
|
744 |
+
| 0.8709 | 25350 | 0.0001 | - |
|
745 |
+
| 0.8726 | 25400 | 0.0001 | - |
|
746 |
+
| 0.8743 | 25450 | 0.0001 | - |
|
747 |
+
| 0.8760 | 25500 | 0.0001 | - |
|
748 |
+
| 0.8778 | 25550 | 0.0002 | - |
|
749 |
+
| 0.8795 | 25600 | 0.0001 | - |
|
750 |
+
| 0.8812 | 25650 | 0.0001 | - |
|
751 |
+
| 0.8829 | 25700 | 0.0001 | - |
|
752 |
+
| 0.8846 | 25750 | 0.0001 | - |
|
753 |
+
| 0.8864 | 25800 | 0.0001 | - |
|
754 |
+
| 0.8881 | 25850 | 0.0001 | - |
|
755 |
+
| 0.8898 | 25900 | 0.0001 | - |
|
756 |
+
| 0.8915 | 25950 | 0.0001 | - |
|
757 |
+
| 0.8932 | 26000 | 0.0001 | - |
|
758 |
+
| 0.8949 | 26050 | 0.0001 | - |
|
759 |
+
| 0.8967 | 26100 | 0.0001 | - |
|
760 |
+
| 0.8984 | 26150 | 0.0001 | - |
|
761 |
+
| 0.9001 | 26200 | 0.0001 | - |
|
762 |
+
| 0.9018 | 26250 | 0.0001 | - |
|
763 |
+
| 0.9035 | 26300 | 0.0001 | - |
|
764 |
+
| 0.9052 | 26350 | 0.0001 | - |
|
765 |
+
| 0.9070 | 26400 | 0.0001 | - |
|
766 |
+
| 0.9087 | 26450 | 0.0001 | - |
|
767 |
+
| 0.9104 | 26500 | 0.0001 | - |
|
768 |
+
| 0.9121 | 26550 | 0.0001 | - |
|
769 |
+
| 0.9138 | 26600 | 0.0001 | - |
|
770 |
+
| 0.9156 | 26650 | 0.0001 | - |
|
771 |
+
| 0.9173 | 26700 | 0.0001 | - |
|
772 |
+
| 0.9190 | 26750 | 0.0001 | - |
|
773 |
+
| 0.9207 | 26800 | 0.0001 | - |
|
774 |
+
| 0.9224 | 26850 | 0.0001 | - |
|
775 |
+
| 0.9241 | 26900 | 0.0001 | - |
|
776 |
+
| 0.9259 | 26950 | 0.0001 | - |
|
777 |
+
| 0.9276 | 27000 | 0.0001 | - |
|
778 |
+
| 0.9293 | 27050 | 0.0001 | - |
|
779 |
+
| 0.9310 | 27100 | 0.0001 | - |
|
780 |
+
| 0.9327 | 27150 | 0.0001 | - |
|
781 |
+
| 0.9345 | 27200 | 0.0001 | - |
|
782 |
+
| 0.9362 | 27250 | 0.0001 | - |
|
783 |
+
| 0.9379 | 27300 | 0.0001 | - |
|
784 |
+
| 0.9396 | 27350 | 0.0001 | - |
|
785 |
+
| 0.9413 | 27400 | 0.0001 | - |
|
786 |
+
| 0.9430 | 27450 | 0.0001 | - |
|
787 |
+
| 0.9448 | 27500 | 0.0001 | - |
|
788 |
+
| 0.9465 | 27550 | 0.0001 | - |
|
789 |
+
| 0.9482 | 27600 | 0.0001 | - |
|
790 |
+
| 0.9499 | 27650 | 0.0001 | - |
|
791 |
+
| 0.9516 | 27700 | 0.0001 | - |
|
792 |
+
| 0.9533 | 27750 | 0.0001 | - |
|
793 |
+
| 0.9551 | 27800 | 0.0001 | - |
|
794 |
+
| 0.9568 | 27850 | 0.0001 | - |
|
795 |
+
| 0.9585 | 27900 | 0.0001 | - |
|
796 |
+
| 0.9602 | 27950 | 0.0001 | - |
|
797 |
+
| 0.9619 | 28000 | 0.0001 | - |
|
798 |
+
| 0.9637 | 28050 | 0.0001 | - |
|
799 |
+
| 0.9654 | 28100 | 0.0001 | - |
|
800 |
+
| 0.9671 | 28150 | 0.0001 | - |
|
801 |
+
| 0.9688 | 28200 | 0.0001 | - |
|
802 |
+
| 0.9705 | 28250 | 0.0001 | - |
|
803 |
+
| 0.9722 | 28300 | 0.0001 | - |
|
804 |
+
| 0.9740 | 28350 | 0.0001 | - |
|
805 |
+
| 0.9757 | 28400 | 0.0001 | - |
|
806 |
+
| 0.9774 | 28450 | 0.0001 | - |
|
807 |
+
| 0.9791 | 28500 | 0.0001 | - |
|
808 |
+
| 0.9808 | 28550 | 0.0001 | - |
|
809 |
+
| 0.9825 | 28600 | 0.0001 | - |
|
810 |
+
| 0.9843 | 28650 | 0.0001 | - |
|
811 |
+
| 0.9860 | 28700 | 0.0001 | - |
|
812 |
+
| 0.9877 | 28750 | 0.0001 | - |
|
813 |
+
| 0.9894 | 28800 | 0.0001 | - |
|
814 |
+
| 0.9911 | 28850 | 0.0001 | - |
|
815 |
+
| 0.9929 | 28900 | 0.0001 | - |
|
816 |
+
| 0.9946 | 28950 | 0.0001 | - |
|
817 |
+
| 0.9963 | 29000 | 0.0001 | - |
|
818 |
+
| 0.9980 | 29050 | 0.0374 | - |
|
819 |
+
| 0.9997 | 29100 | 0.0001 | - |
|
820 |
+
|
821 |
+
### Framework Versions
|
822 |
+
- Python: 3.10.13
|
823 |
+
- SetFit: 1.0.3
|
824 |
+
- Sentence Transformers: 2.6.1
|
825 |
+
- Transformers: 4.38.2
|
826 |
+
- PyTorch: 2.1.2
|
827 |
+
- Datasets: 2.17.0
|
828 |
+
- Tokenizers: 0.15.2
|
829 |
+
|
830 |
+
## Citation
|
831 |
+
|
832 |
+
### BibTeX
|
833 |
+
```bibtex
|
834 |
+
@article{https://doi.org/10.48550/arxiv.2209.11055,
|
835 |
+
doi = {10.48550/ARXIV.2209.11055},
|
836 |
+
url = {https://arxiv.org/abs/2209.11055},
|
837 |
+
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
|
838 |
+
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
|
839 |
+
title = {Efficient Few-Shot Learning Without Prompts},
|
840 |
+
publisher = {arXiv},
|
841 |
+
year = {2022},
|
842 |
+
copyright = {Creative Commons Attribution 4.0 International}
|
843 |
+
}
|
844 |
+
```
|
845 |
+
|
846 |
+
<!--
|
847 |
+
## Glossary
|
848 |
+
|
849 |
+
*Clearly define terms in order to be accessible across audiences.*
|
850 |
+
-->
|
851 |
+
|
852 |
+
<!--
|
853 |
+
## Model Card Authors
|
854 |
+
|
855 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
856 |
+
-->
|
857 |
+
|
858 |
+
<!--
|
859 |
+
## Model Card Contact
|
860 |
+
|
861 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
862 |
+
-->
|
config.json
ADDED
@@ -0,0 +1,28 @@
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|
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|
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|
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|
|
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|
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|
|
|
|
|
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|
|
|
|
|
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|
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|
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|
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|
|
1 |
+
{
|
2 |
+
"_name_or_path": "OrdalieTech/Solon-embeddings-large-0.1",
|
3 |
+
"architectures": [
|
4 |
+
"XLMRobertaModel"
|
5 |
+
],
|
6 |
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|
7 |
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"bos_token_id": 0,
|
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|
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|
10 |
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"hidden_act": "gelu",
|
11 |
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"hidden_dropout_prob": 0.1,
|
12 |
+
"hidden_size": 1024,
|
13 |
+
"initializer_range": 0.02,
|
14 |
+
"intermediate_size": 4096,
|
15 |
+
"layer_norm_eps": 1e-05,
|
16 |
+
"max_position_embeddings": 514,
|
17 |
+
"model_type": "xlm-roberta",
|
18 |
+
"num_attention_heads": 16,
|
19 |
+
"num_hidden_layers": 24,
|
20 |
+
"output_past": true,
|
21 |
+
"pad_token_id": 1,
|
22 |
+
"position_embedding_type": "absolute",
|
23 |
+
"torch_dtype": "float32",
|
24 |
+
"transformers_version": "4.38.2",
|
25 |
+
"type_vocab_size": 1,
|
26 |
+
"use_cache": true,
|
27 |
+
"vocab_size": 250002
|
28 |
+
}
|
config_sentence_transformers.json
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "2.2.2",
|
4 |
+
"transformers": "4.35.2",
|
5 |
+
"pytorch": "2.1.1+cu121"
|
6 |
+
},
|
7 |
+
"prompts": {},
|
8 |
+
"default_prompt_name": null
|
9 |
+
}
|
config_setfit.json
ADDED
@@ -0,0 +1,49 @@
|
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|
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|
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|
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|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"labels": [
|
3 |
+
"Housing / rent",
|
4 |
+
"Housing / house loan",
|
5 |
+
"Housing / utilities & bills",
|
6 |
+
"Housing / services & maintenance",
|
7 |
+
"Housing / other",
|
8 |
+
"Food & Drinks / groceries",
|
9 |
+
"Food & Drinks / eating out",
|
10 |
+
"Food & Drinks / other",
|
11 |
+
"Leisure & Entertainment / sports & hobbies",
|
12 |
+
"Leisure & Entertainment / culture & events",
|
13 |
+
"Leisure & Entertainment / travel",
|
14 |
+
"Leisure & Entertainment / other",
|
15 |
+
"Transportation / car loan & leasing",
|
16 |
+
"Transportation / fuel",
|
17 |
+
"Transportation / public transportation",
|
18 |
+
"Transportation / taxi & carpool",
|
19 |
+
"Transportation / maitenance",
|
20 |
+
"Transportation / other",
|
21 |
+
"Recurrent Payments / loans",
|
22 |
+
"Recurrent Payments / insurance",
|
23 |
+
"Recurrent Payments / subscription",
|
24 |
+
"Recurrent Payments / other",
|
25 |
+
"Investment / securities",
|
26 |
+
"Investment / retirement & savings",
|
27 |
+
"Investment / real estate",
|
28 |
+
"Investment / other",
|
29 |
+
"Shopping / clothing",
|
30 |
+
"Shopping / electronics & multimedia",
|
31 |
+
"Shopping / sporting goods",
|
32 |
+
"Shopping / housing equipment",
|
33 |
+
"Shopping / other",
|
34 |
+
"Healthy & Beauty / doctor fees",
|
35 |
+
"Healthy & Beauty / pharmacy",
|
36 |
+
"Healthy & Beauty / beauty & self-care",
|
37 |
+
"Healthy & Beauty / veterinary",
|
38 |
+
"Healthy & Beauty / other",
|
39 |
+
"Bank services / transfers",
|
40 |
+
"Bank services / withdrawal",
|
41 |
+
"Bank services / general fees",
|
42 |
+
"Bank services / other",
|
43 |
+
"Other / taxes",
|
44 |
+
"Other / kids",
|
45 |
+
"Other / pets",
|
46 |
+
"Other / other"
|
47 |
+
],
|
48 |
+
"normalize_embeddings": false
|
49 |
+
}
|
model.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:6be2ab6c35a9619e25123bade98e05bd4cf153eb07cb7305777ed407dcdfdd83
|
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size 2239607176
|
model_head.pkl
ADDED
@@ -0,0 +1,3 @@
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|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:83d5f8a40d26be4b68849f24bd3478da8db777888a7b1ed3f0a4ac16d4f1dddd
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size 369207
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modules.json
ADDED
@@ -0,0 +1,20 @@
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|
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[
|
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|
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"path": "",
|
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"type": "sentence_transformers.models.Transformer"
|
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|
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{
|
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"idx": 1,
|
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"name": "1",
|
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"path": "1_Pooling",
|
12 |
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"type": "sentence_transformers.models.Pooling"
|
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|
14 |
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{
|
15 |
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|
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"name": "2",
|
17 |
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"path": "2_Normalize",
|
18 |
+
"type": "sentence_transformers.models.Normalize"
|
19 |
+
}
|
20 |
+
]
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 512,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
sentencepiece.bpe.model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
|
3 |
+
size 5069051
|
special_tokens_map.json
ADDED
@@ -0,0 +1,51 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 sha256:39feb9863a378165ab9c5c689047203d789422966c0c58721c5309fd039a8edc
|
3 |
+
size 17083074
|
tokenizer_config.json
ADDED
@@ -0,0 +1,55 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 |
+
"eos_token": "</s>",
|
48 |
+
"mask_token": "<mask>",
|
49 |
+
"model_max_length": 512,
|
50 |
+
"pad_token": "<pad>",
|
51 |
+
"sep_token": "</s>",
|
52 |
+
"sp_model_kwargs": {},
|
53 |
+
"tokenizer_class": "XLMRobertaTokenizer",
|
54 |
+
"unk_token": "<unk>"
|
55 |
+
}
|