kperkins411
commited on
Commit
•
95c6b38
1
Parent(s):
5e5d94c
Add new SentenceTransformer model.
Browse files- 1_Pooling/config.json +10 -0
- README.md +552 -0
- config.json +26 -0
- config_sentence_transformers.json +10 -0
- model.safetensors +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +37 -0
- tokenizer.json +0 -0
- tokenizer_config.json +64 -0
- vocab.txt +0 -0
1_Pooling/config.json
ADDED
@@ -0,0 +1,10 @@
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{
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"word_embedding_dimension": 384,
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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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@@ -0,0 +1,552 @@
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1 |
+
---
|
2 |
+
datasets: []
|
3 |
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language: []
|
4 |
+
library_name: sentence-transformers
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5 |
+
metrics:
|
6 |
+
- cosine_accuracy
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7 |
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- dot_accuracy
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8 |
+
- manhattan_accuracy
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9 |
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- euclidean_accuracy
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10 |
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- max_accuracy
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11 |
+
pipeline_tag: sentence-similarity
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12 |
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tags:
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13 |
+
- sentence-transformers
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14 |
+
- sentence-similarity
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+
- feature-extraction
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16 |
+
- generated_from_trainer
|
17 |
+
- dataset_size:32621
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18 |
+
- loss:TripletLoss
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19 |
+
widget:
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20 |
+
- source_sentence: 6.2 either party may terminate this agreement for cause if the
|
21 |
+
other party fails to perform any material provision of this agreement or commits
|
22 |
+
a material breach of this agreement which is not corrected within [***] after
|
23 |
+
receiving written notice of the failure or breach. except that if the default
|
24 |
+
is by 6 supplier that creates an immediate public food safety risk, pnc may terminate
|
25 |
+
this agreement immediately without regard to any period for correction.
|
26 |
+
sentences:
|
27 |
+
- what constitutes a material violation under the default provision?
|
28 |
+
- '8.3 termination for cause. this agreement may be terminated by a party ----------------------
|
29 |
+
for cause immediately upon the occurrence of and in accordance with the following:
|
30 |
+
(a) insolvency event. either may terminate this agreement by delivering written
|
31 |
+
notice to the other party upon the occurrence of any of the following events:
|
32 |
+
(i) a receiver is appointed for either party or its property; (ii) either makes
|
33 |
+
a general assignment for the benefit of its creditors; (iii) either party commences,
|
34 |
+
or has commenced against it, proceedings under any bankruptcy, insolvency or debtor''s
|
35 |
+
relief law, which proceedings are not dismissed within sixty (60) days; or (iv)
|
36 |
+
either party is liquidated or source: rae systems inc, 10-q, 11/14/2000 dissolved.
|
37 |
+
(b) change of control. in the event more that there is a change in ownership representing
|
38 |
+
fifty percent (50%) or more of the equity ownership of either party, the other
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39 |
+
party may, at its option, terminate this agreement upon written notice. (c) default.
|
40 |
+
either party may terminate this agreement effective upon written notice to the
|
41 |
+
other if the other party violates any covenant, agreement, representation or warranty
|
42 |
+
contained herein in any material respect or defaults or fails to perform any of
|
43 |
+
its obligations or agreements hereunder in any material respect, which violation,
|
44 |
+
default or failure is not cured within thirty (30) days after notice thereof from
|
45 |
+
the non-defaulting party stating its intention to terminate this agreement by
|
46 |
+
reason thereof.'
|
47 |
+
- does chinese law supersede international regulations?
|
48 |
+
- source_sentence: (a) member specifically acknowledges that, pursuant to the franchise
|
49 |
+
agreement, and by virtue of its position with franchisee, member will receive
|
50 |
+
valuable specialized training and confidential information, including, without
|
51 |
+
limitation, information regarding the operational, sales, promotional, and marketing
|
52 |
+
methods and techniques of franchisor and the system.
|
53 |
+
sentences:
|
54 |
+
- 1. confidential information. member shall not, during the term of the franchise
|
55 |
+
agreement or thereafter, communicate, divulge or use, for any purpose other than
|
56 |
+
the operation of the franchised business, any confidential information, knowledge,
|
57 |
+
trade secrets or know-how which may be communicated to member or which member
|
58 |
+
may learn by virtue of member's relationship with franchisee. all information,
|
59 |
+
knowledge and know-how relating to franchisor, its business plans, franchised
|
60 |
+
businesses, or the system ("confidential information") is deemed confidential,
|
61 |
+
except for information that member can demonstrate came to member's attention
|
62 |
+
by lawful means prior to disclosure to member; or which, at the time of the disclosure
|
63 |
+
to member, had become a part of the public domain.
|
64 |
+
- can the member use trade secrets for purposes outside of operating the franchised
|
65 |
+
business?
|
66 |
+
- is written consent from party a mandatory for party b's assignment?
|
67 |
+
- source_sentence: 'ad networks we may feature advertising within our service. the
|
68 |
+
advertisers may collect and use information about you, such as your service session
|
69 |
+
activity, device identifier, mac address, imei, geo-location information and ip
|
70 |
+
address. they may use this information to provide advertisements of interest to
|
71 |
+
you. please refer to our list of partners within the services and for more information
|
72 |
+
on how to opt out at: http://www.supercell.net/partner-opt-out.'
|
73 |
+
sentences:
|
74 |
+
- what is the designation for the type of data that pertains to a person's confidential
|
75 |
+
and unique identifiers, including their electronic mail details and connections
|
76 |
+
within online platforms?
|
77 |
+
- which entities constitute 'ad partners' as mentioned in the clause?
|
78 |
+
- how we use data collection tools and online advertising under armour uses cookies
|
79 |
+
and other data collection tools like web beacons to collect data that help us
|
80 |
+
personalize your use of our websites and mobile applications. we also work with
|
81 |
+
a variety of advertisers, advertising networks, advertising servers, and analytics
|
82 |
+
companies ("ad partners") that use various technologies including cookies to collect
|
83 |
+
data about your use of the services (such as pages visited, ads viewed or clicked
|
84 |
+
on) so that we and our ad partners deliver ads to you based on your interests
|
85 |
+
and online activities.
|
86 |
+
- source_sentence: third-party vendors, including google, use cookies to serve ads
|
87 |
+
based on a user's prior visits to our website and other websites. google's use
|
88 |
+
of advertising cookies enables it and its partners to serve ads based on visits
|
89 |
+
to our site or other sites on the internet. you can opt out of personalized advertising
|
90 |
+
by visiting google's ads settings. alternately, you can opt out of other third-party
|
91 |
+
vendors' uses of cookies by visiting the digital advertising alliance's (daa)
|
92 |
+
opt out page at http://www.aboutads.info/choices or http://www.aboutads.info/appchoices.
|
93 |
+
to find out more about how google uses data it collects please visit google privacy
|
94 |
+
& principals.
|
95 |
+
sentences:
|
96 |
+
- google, as a third party vendor, uses cookies to serve ads on our site. google's
|
97 |
+
use of the dart cookie enables it to serve ads to our users based on their visit
|
98 |
+
to our site and other sites on the internet. users may opt out of the use of the
|
99 |
+
dart cookie by visiting the google ad and content network privacy policy.
|
100 |
+
- is google considered a third-party vendor in this context?
|
101 |
+
- what are the obligations of the henry film and entertainment corporation under
|
102 |
+
this agreement?
|
103 |
+
- source_sentence: sponsor acknowledges and agrees that, notwithstanding the grant
|
104 |
+
of exclusivity set forth in this section 4, team shall have the right to solicit
|
105 |
+
and enter into sponsorships with other parties that are not known primarily or
|
106 |
+
exclusively as suppliers or providers of any product or service within the product
|
107 |
+
and services category.
|
108 |
+
sentences:
|
109 |
+
- what constitutes a 'purchase' under the revenue-sharing agreement?
|
110 |
+
- for the avoidance of doubt, the parties acknowledge that the foregoing restriction
|
111 |
+
applies only to persistent sponsorship placement as judged by sponsor at its discretion,
|
112 |
+
and not to run-of-site banner advertisements or other rotating promotional placements.
|
113 |
+
- what does 'foregoing restriction' refer to specifically within the context of
|
114 |
+
sponsorships?
|
115 |
+
model-index:
|
116 |
+
- name: SentenceTransformer
|
117 |
+
results:
|
118 |
+
- task:
|
119 |
+
type: triplet
|
120 |
+
name: Triplet
|
121 |
+
dataset:
|
122 |
+
name: all nli dev
|
123 |
+
type: all-nli-dev
|
124 |
+
metrics:
|
125 |
+
- type: cosine_accuracy
|
126 |
+
value: 0.5286745157614888
|
127 |
+
name: Cosine Accuracy
|
128 |
+
- type: dot_accuracy
|
129 |
+
value: 0.47322445879225217
|
130 |
+
name: Dot Accuracy
|
131 |
+
- type: manhattan_accuracy
|
132 |
+
value: 0.5104443600455754
|
133 |
+
name: Manhattan Accuracy
|
134 |
+
- type: euclidean_accuracy
|
135 |
+
value: 0.5142423091530574
|
136 |
+
name: Euclidean Accuracy
|
137 |
+
- type: max_accuracy
|
138 |
+
value: 0.5286745157614888
|
139 |
+
name: Max Accuracy
|
140 |
+
- task:
|
141 |
+
type: triplet
|
142 |
+
name: Triplet
|
143 |
+
dataset:
|
144 |
+
name: all nli test
|
145 |
+
type: all-nli-test
|
146 |
+
metrics:
|
147 |
+
- type: cosine_accuracy
|
148 |
+
value: 0.529054310672237
|
149 |
+
name: Cosine Accuracy
|
150 |
+
- type: dot_accuracy
|
151 |
+
value: 0.470945689327763
|
152 |
+
name: Dot Accuracy
|
153 |
+
- type: manhattan_accuracy
|
154 |
+
value: 0.5100645651348272
|
155 |
+
name: Manhattan Accuracy
|
156 |
+
- type: euclidean_accuracy
|
157 |
+
value: 0.515381693885302
|
158 |
+
name: Euclidean Accuracy
|
159 |
+
- type: max_accuracy
|
160 |
+
value: 0.529054310672237
|
161 |
+
name: Max Accuracy
|
162 |
+
---
|
163 |
+
|
164 |
+
# SentenceTransformer
|
165 |
+
|
166 |
+
This is a [sentence-transformers](https://www.SBERT.net) model trained. 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.
|
167 |
+
|
168 |
+
## Model Details
|
169 |
+
|
170 |
+
### Model Description
|
171 |
+
- **Model Type:** Sentence Transformer
|
172 |
+
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
|
173 |
+
- **Maximum Sequence Length:** 512 tokens
|
174 |
+
- **Output Dimensionality:** 384 tokens
|
175 |
+
- **Similarity Function:** Cosine Similarity
|
176 |
+
<!-- - **Training Dataset:** Unknown -->
|
177 |
+
<!-- - **Language:** Unknown -->
|
178 |
+
<!-- - **License:** Unknown -->
|
179 |
+
|
180 |
+
### Model Sources
|
181 |
+
|
182 |
+
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
183 |
+
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
|
184 |
+
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
185 |
+
|
186 |
+
### Full Model Architecture
|
187 |
+
|
188 |
+
```
|
189 |
+
SentenceTransformer(
|
190 |
+
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
|
191 |
+
(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})
|
192 |
+
)
|
193 |
+
```
|
194 |
+
|
195 |
+
## Usage
|
196 |
+
|
197 |
+
### Direct Usage (Sentence Transformers)
|
198 |
+
|
199 |
+
First install the Sentence Transformers library:
|
200 |
+
|
201 |
+
```bash
|
202 |
+
pip install -U sentence-transformers
|
203 |
+
```
|
204 |
+
|
205 |
+
Then you can load this model and run inference.
|
206 |
+
```python
|
207 |
+
from sentence_transformers import SentenceTransformer
|
208 |
+
|
209 |
+
# Download from the 🤗 Hub
|
210 |
+
model = SentenceTransformer("kperkins411/multi-qa-MiniLM-L6-cos-v1_triplet")
|
211 |
+
# Run inference
|
212 |
+
sentences = [
|
213 |
+
'sponsor acknowledges and agrees that, notwithstanding the grant of exclusivity set forth in this section 4, team shall have the right to solicit and enter into sponsorships with other parties that are not known primarily or exclusively as suppliers or providers of any product or service within the product and services category.',
|
214 |
+
"what does 'foregoing restriction' refer to specifically within the context of sponsorships?",
|
215 |
+
'for the avoidance of doubt, the parties acknowledge that the foregoing restriction applies only to persistent sponsorship placement as judged by sponsor at its discretion, and not to run-of-site banner advertisements or other rotating promotional placements.',
|
216 |
+
]
|
217 |
+
embeddings = model.encode(sentences)
|
218 |
+
print(embeddings.shape)
|
219 |
+
# [3, 384]
|
220 |
+
|
221 |
+
# Get the similarity scores for the embeddings
|
222 |
+
similarities = model.similarity(embeddings, embeddings)
|
223 |
+
print(similarities.shape)
|
224 |
+
# [3, 3]
|
225 |
+
```
|
226 |
+
|
227 |
+
<!--
|
228 |
+
### Direct Usage (Transformers)
|
229 |
+
|
230 |
+
<details><summary>Click to see the direct usage in Transformers</summary>
|
231 |
+
|
232 |
+
</details>
|
233 |
+
-->
|
234 |
+
|
235 |
+
<!--
|
236 |
+
### Downstream Usage (Sentence Transformers)
|
237 |
+
|
238 |
+
You can finetune this model on your own dataset.
|
239 |
+
|
240 |
+
<details><summary>Click to expand</summary>
|
241 |
+
|
242 |
+
</details>
|
243 |
+
-->
|
244 |
+
|
245 |
+
<!--
|
246 |
+
### Out-of-Scope Use
|
247 |
+
|
248 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
249 |
+
-->
|
250 |
+
|
251 |
+
## Evaluation
|
252 |
+
|
253 |
+
### Metrics
|
254 |
+
|
255 |
+
#### Triplet
|
256 |
+
* Dataset: `all-nli-dev`
|
257 |
+
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
|
258 |
+
|
259 |
+
| Metric | Value |
|
260 |
+
|:-------------------|:-----------|
|
261 |
+
| cosine_accuracy | 0.5287 |
|
262 |
+
| dot_accuracy | 0.4732 |
|
263 |
+
| manhattan_accuracy | 0.5104 |
|
264 |
+
| euclidean_accuracy | 0.5142 |
|
265 |
+
| **max_accuracy** | **0.5287** |
|
266 |
+
|
267 |
+
#### Triplet
|
268 |
+
* Dataset: `all-nli-test`
|
269 |
+
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
|
270 |
+
|
271 |
+
| Metric | Value |
|
272 |
+
|:-------------------|:-----------|
|
273 |
+
| cosine_accuracy | 0.5291 |
|
274 |
+
| dot_accuracy | 0.4709 |
|
275 |
+
| manhattan_accuracy | 0.5101 |
|
276 |
+
| euclidean_accuracy | 0.5154 |
|
277 |
+
| **max_accuracy** | **0.5291** |
|
278 |
+
|
279 |
+
<!--
|
280 |
+
## Bias, Risks and Limitations
|
281 |
+
|
282 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
283 |
+
-->
|
284 |
+
|
285 |
+
<!--
|
286 |
+
### Recommendations
|
287 |
+
|
288 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
289 |
+
-->
|
290 |
+
|
291 |
+
## Training Details
|
292 |
+
|
293 |
+
### Training Dataset
|
294 |
+
|
295 |
+
#### Unnamed Dataset
|
296 |
+
|
297 |
+
|
298 |
+
* Size: 32,621 training samples
|
299 |
+
* Columns: <code>negative</code>, <code>anchor</code>, and <code>positive</code>
|
300 |
+
* Approximate statistics based on the first 1000 samples:
|
301 |
+
| | negative | anchor | positive |
|
302 |
+
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
|
303 |
+
| type | string | string | string |
|
304 |
+
| details | <ul><li>min: 6 tokens</li><li>mean: 80.74 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 17.19 tokens</li><li>max: 167 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 101.64 tokens</li><li>max: 512 tokens</li></ul> |
|
305 |
+
* Samples:
|
306 |
+
| negative | anchor | positive |
|
307 |
+
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
308 |
+
| <code>c. the obligations specified in this article shall not apply to information for which the receiving party can reasonably demonstrate that such information: iii. becomes known to the receiving party through disclosure by sources other than the disclosing party, having a right to disclose such information,</code> | <code>what safeguards are in place to protect the information obtained from third-party sources?</code> | <code>information we collect from other sources we may also receive information from other sources and combine that with information we collect through our services. for example: if you choose to link, create, or log in to your uber account with a payment provider (e.g., google wallet) or social media service (e.g., facebook), or if you engage with a separate app or website that uses our api (or whose api we use), we may receive information about you or your connections from that site or app.</code> |
|
309 |
+
| <code>3.2 manufacturing standards the manufacturer covenants that it is and will remain for the term of this agreement in compliance with all international standards in production and manufacturing.</code> | <code>is there a guarantee from the manufacturers regarding the conformity of the items to the mutually approved written standards for a certain duration?</code> | <code>each of the suppliers warrants that the products shall comply with the specifications and documentation agreed by the relevant supplier and the company in writing that is applicable to such products for the warranty period.</code> |
|
310 |
+
| <code>planetcad hereby grants to dassault systemes a fully-paid, non-exclusive, worldwide, revocable limited license to the server software and infrastructure for the sole purpose of (i) hosting the co-branded service and (ii) fulfilling its<omitted>obligations under this agreement.</code> | <code>what type of authorization has the video conferencing service provided to the british virgin islands-based entity and its associated organization regarding their intellectual property, with respect to the customized software and web platform, including the conditions for customer access to enhanced functionalities that incur additional charges?</code> | <code>skype hereby grants to online bvi and the company a limited, non-exclusive, non-sublicensable (except as set forth herein), non-transferable, non-assignable (except as provided in section 14.4), royalty-free (but subject to the provisions of section 5), license during the term to use, market, provide access to, promote, reproduce and display the skype intellectual property solely (i) as incorporated in the company-skype branded application and/or the company-skype toolbar, and (ii) as incorporated in, for the development of, and for transmission pursuant to this agreement of, the company-skype branded content and the company-skype branded web site, in each case for the sole purposes (unless otherwise mutually agreed by the parties) of promoting and distributing, pursuant to this agreement, the company-skype branded application, the company-skype toolbar, the company-skype branded content and the company-skype branded web site in the territory; (a) provided, that it is understood that the company-skype branded customers will have the right under the eula to use the company- skype branded application and the company-skype toolbar and will have the right to access the company-skype branded content, the company-skype branded web site and the online bvi web site through the internet and to otherwise receive support from the company anywhere in the world, and that the company shall be permitted to provide access to and reproduce and display the skype intellectual property through the internet anywhere in the world, and (b) provided further, that online bvi and the company shall ensure that no company-skype branded customer (or potential company-skype branded customer) shall be permitted to access, using the company-skype branded application or the company-skype toolbar or through the company-skype branded web site, any skype premium features requiring payment by the company-skype branded customer (or potential company-skype branded customer), including, but not limited to, skypein, skypeout, or skype plus, unless such company-skype branded customer (or potential company-skype branded customer) uses the payment methods made available by the company pursuant to section 2.5 for the purchase of such premium features.</code> |
|
311 |
+
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
|
312 |
+
```json
|
313 |
+
{
|
314 |
+
"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
|
315 |
+
"triplet_margin": 5
|
316 |
+
}
|
317 |
+
```
|
318 |
+
|
319 |
+
### Evaluation Dataset
|
320 |
+
|
321 |
+
#### Unnamed Dataset
|
322 |
+
|
323 |
+
|
324 |
+
* Size: 2,641 evaluation samples
|
325 |
+
* Columns: <code>negative</code>, <code>anchor</code>, and <code>positive</code>
|
326 |
+
* Approximate statistics based on the first 1000 samples:
|
327 |
+
| | negative | anchor | positive |
|
328 |
+
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
|
329 |
+
| type | string | string | string |
|
330 |
+
| details | <ul><li>min: 6 tokens</li><li>mean: 83.63 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.61 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 98.17 tokens</li><li>max: 512 tokens</li></ul> |
|
331 |
+
* Samples:
|
332 |
+
| negative | anchor | positive |
|
333 |
+
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
334 |
+
| <code>this agreement shall be governed by, and construed in accordance with the law of the state of new york.</code> | <code>are there any exceptions to the governing law stated?</code> | <code>this agreement shall be governed by the laws of the state of california, without regard to the conflicts of law provisions of any jurisdiction.</code> |
|
335 |
+
| <code>you consent to the third party use, sharing and transfer of your personal information (both inside and outside of your jurisdiction) as described in this section. these third parties will use personal information to provide services to us and for their own internal use, including analytics use. we allow third parties such as analytics providers and advertising partners to collect your personal information over time and across different websites or online services when you use our services.</code> | <code>collection of personal data legal basis?</code> | <code>15. notice for malaysia residents close in view of the implementation of the personal data protection act 2010 ("act"), sony mobile recognises the need to process all personal data obtained in a lawful and appropriate manner. the legal responsibility for compliance with the act lies with sony mobile, which is the "data user" under the act. compliance with this privacy policy and the act is the responsibility of all employees of sony mobile. as and when sony mobile is required to collect personal data, sony mobile and its employees must abide by the requirements of this privacy policy and the act. in the context of the act, "processing" is defined as including the collection, recording, holding or storing of personal data which includes, inter alia, nric numbers, home address and contact details.</code> |
|
336 |
+
| <code>you can prevent peel from showing you targeted ads by sending an email to [email protected] and asking to opt-out of targeted advertising. opting-out will only prevent targeted ads from being displayed so you may continue to see generic (non-targeted) ads from peel after you opt-out. for more information on interest-based ads or to stop use of tracking technologies for these purposes, go to www.aboutads.info or www.networkadvertising.org.</code> | <code>how does one opt out from third-party analytics providers?</code> | <code>when you use our services, we collect the following information: information about your device (including device model, os version and operator's name), time and date of the connection to the game and/or service, ip or mac address, international mobile equipment id (imei), android id, device mac address, cookie information. we also from time-to-time use services provided by third party companies that might collect information from you, and you can opt-out from this. follow the directions provided by our other third party analytics provider located at http://www.flurry.com/user-opt-out.html, https://help.chartboost.com/legal/privacy, http://privacy.adcolony.com/, http://info.tapjoy.com/about-tapjoy/privacy-policy/, http://sponsorpay.com/. if you "opt out" with our third party analytics providers, that action is specific to the information we collect specifically for that provider, and does not limit our ability to collect information from you, under the terms of this privacy policy, for other third parties.</code> |
|
337 |
+
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
|
338 |
+
```json
|
339 |
+
{
|
340 |
+
"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
|
341 |
+
"triplet_margin": 5
|
342 |
+
}
|
343 |
+
```
|
344 |
+
|
345 |
+
### Training Hyperparameters
|
346 |
+
#### Non-Default Hyperparameters
|
347 |
+
|
348 |
+
- `eval_strategy`: steps
|
349 |
+
- `per_device_train_batch_size`: 64
|
350 |
+
- `per_device_eval_batch_size`: 64
|
351 |
+
- `learning_rate`: 2e-05
|
352 |
+
- `num_train_epochs`: 4
|
353 |
+
- `warmup_ratio`: 0.1
|
354 |
+
- `fp16`: True
|
355 |
+
- `batch_sampler`: no_duplicates
|
356 |
+
|
357 |
+
#### All Hyperparameters
|
358 |
+
<details><summary>Click to expand</summary>
|
359 |
+
|
360 |
+
- `overwrite_output_dir`: False
|
361 |
+
- `do_predict`: False
|
362 |
+
- `eval_strategy`: steps
|
363 |
+
- `prediction_loss_only`: True
|
364 |
+
- `per_device_train_batch_size`: 64
|
365 |
+
- `per_device_eval_batch_size`: 64
|
366 |
+
- `per_gpu_train_batch_size`: None
|
367 |
+
- `per_gpu_eval_batch_size`: None
|
368 |
+
- `gradient_accumulation_steps`: 1
|
369 |
+
- `eval_accumulation_steps`: None
|
370 |
+
- `learning_rate`: 2e-05
|
371 |
+
- `weight_decay`: 0.0
|
372 |
+
- `adam_beta1`: 0.9
|
373 |
+
- `adam_beta2`: 0.999
|
374 |
+
- `adam_epsilon`: 1e-08
|
375 |
+
- `max_grad_norm`: 1.0
|
376 |
+
- `num_train_epochs`: 4
|
377 |
+
- `max_steps`: -1
|
378 |
+
- `lr_scheduler_type`: linear
|
379 |
+
- `lr_scheduler_kwargs`: {}
|
380 |
+
- `warmup_ratio`: 0.1
|
381 |
+
- `warmup_steps`: 0
|
382 |
+
- `log_level`: passive
|
383 |
+
- `log_level_replica`: warning
|
384 |
+
- `log_on_each_node`: True
|
385 |
+
- `logging_nan_inf_filter`: True
|
386 |
+
- `save_safetensors`: True
|
387 |
+
- `save_on_each_node`: False
|
388 |
+
- `save_only_model`: False
|
389 |
+
- `restore_callback_states_from_checkpoint`: False
|
390 |
+
- `no_cuda`: False
|
391 |
+
- `use_cpu`: False
|
392 |
+
- `use_mps_device`: False
|
393 |
+
- `seed`: 42
|
394 |
+
- `data_seed`: None
|
395 |
+
- `jit_mode_eval`: False
|
396 |
+
- `use_ipex`: False
|
397 |
+
- `bf16`: False
|
398 |
+
- `fp16`: True
|
399 |
+
- `fp16_opt_level`: O1
|
400 |
+
- `half_precision_backend`: auto
|
401 |
+
- `bf16_full_eval`: False
|
402 |
+
- `fp16_full_eval`: False
|
403 |
+
- `tf32`: None
|
404 |
+
- `local_rank`: 0
|
405 |
+
- `ddp_backend`: None
|
406 |
+
- `tpu_num_cores`: None
|
407 |
+
- `tpu_metrics_debug`: False
|
408 |
+
- `debug`: []
|
409 |
+
- `dataloader_drop_last`: False
|
410 |
+
- `dataloader_num_workers`: 0
|
411 |
+
- `dataloader_prefetch_factor`: None
|
412 |
+
- `past_index`: -1
|
413 |
+
- `disable_tqdm`: False
|
414 |
+
- `remove_unused_columns`: True
|
415 |
+
- `label_names`: None
|
416 |
+
- `load_best_model_at_end`: False
|
417 |
+
- `ignore_data_skip`: False
|
418 |
+
- `fsdp`: []
|
419 |
+
- `fsdp_min_num_params`: 0
|
420 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
421 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
422 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
423 |
+
- `deepspeed`: None
|
424 |
+
- `label_smoothing_factor`: 0.0
|
425 |
+
- `optim`: adamw_torch
|
426 |
+
- `optim_args`: None
|
427 |
+
- `adafactor`: False
|
428 |
+
- `group_by_length`: False
|
429 |
+
- `length_column_name`: length
|
430 |
+
- `ddp_find_unused_parameters`: None
|
431 |
+
- `ddp_bucket_cap_mb`: None
|
432 |
+
- `ddp_broadcast_buffers`: False
|
433 |
+
- `dataloader_pin_memory`: True
|
434 |
+
- `dataloader_persistent_workers`: False
|
435 |
+
- `skip_memory_metrics`: True
|
436 |
+
- `use_legacy_prediction_loop`: False
|
437 |
+
- `push_to_hub`: False
|
438 |
+
- `resume_from_checkpoint`: None
|
439 |
+
- `hub_model_id`: None
|
440 |
+
- `hub_strategy`: every_save
|
441 |
+
- `hub_private_repo`: False
|
442 |
+
- `hub_always_push`: False
|
443 |
+
- `gradient_checkpointing`: False
|
444 |
+
- `gradient_checkpointing_kwargs`: None
|
445 |
+
- `include_inputs_for_metrics`: False
|
446 |
+
- `eval_do_concat_batches`: True
|
447 |
+
- `fp16_backend`: auto
|
448 |
+
- `push_to_hub_model_id`: None
|
449 |
+
- `push_to_hub_organization`: None
|
450 |
+
- `mp_parameters`:
|
451 |
+
- `auto_find_batch_size`: False
|
452 |
+
- `full_determinism`: False
|
453 |
+
- `torchdynamo`: None
|
454 |
+
- `ray_scope`: last
|
455 |
+
- `ddp_timeout`: 1800
|
456 |
+
- `torch_compile`: False
|
457 |
+
- `torch_compile_backend`: None
|
458 |
+
- `torch_compile_mode`: None
|
459 |
+
- `dispatch_batches`: None
|
460 |
+
- `split_batches`: None
|
461 |
+
- `include_tokens_per_second`: False
|
462 |
+
- `include_num_input_tokens_seen`: False
|
463 |
+
- `neftune_noise_alpha`: None
|
464 |
+
- `optim_target_modules`: None
|
465 |
+
- `batch_eval_metrics`: False
|
466 |
+
- `batch_sampler`: no_duplicates
|
467 |
+
- `multi_dataset_batch_sampler`: proportional
|
468 |
+
|
469 |
+
</details>
|
470 |
+
|
471 |
+
### Training Logs
|
472 |
+
| Epoch | Step | Training Loss | loss | all-nli-dev_max_accuracy | all-nli-test_max_accuracy |
|
473 |
+
|:------:|:----:|:-------------:|:------:|:------------------------:|:-------------------------:|
|
474 |
+
| 0 | 0 | - | - | 0.7235 | - |
|
475 |
+
| 0.1961 | 100 | 4.9029 | 3.1938 | 0.6058 | - |
|
476 |
+
| 0.3922 | 200 | 2.4204 | 1.5424 | 0.5507 | - |
|
477 |
+
| 0.5882 | 300 | 1.6076 | 1.0643 | 0.5344 | - |
|
478 |
+
| 0.7843 | 400 | 1.3142 | 0.8831 | 0.5351 | - |
|
479 |
+
| 0.9804 | 500 | 1.1919 | 0.7455 | 0.5435 | - |
|
480 |
+
| 1.1745 | 600 | 1.0824 | 0.6599 | 0.5427 | - |
|
481 |
+
| 1.3706 | 700 | 0.963 | 0.6360 | 0.5518 | - |
|
482 |
+
| 1.5667 | 800 | 0.8922 | 0.6131 | 0.5397 | - |
|
483 |
+
| 1.7627 | 900 | 0.8417 | 0.5900 | 0.5302 | - |
|
484 |
+
| 1.9588 | 1000 | 0.8165 | 0.5662 | 0.5253 | - |
|
485 |
+
| 2.1529 | 1100 | 0.7774 | 0.5192 | 0.5177 | - |
|
486 |
+
| 2.3490 | 1200 | 0.7394 | 0.5158 | 0.5363 | - |
|
487 |
+
| 2.5451 | 1300 | 0.7003 | 0.5185 | 0.5363 | - |
|
488 |
+
| 2.7412 | 1400 | 0.6636 | 0.5004 | 0.5310 | - |
|
489 |
+
| 2.9373 | 1500 | 0.6586 | 0.4872 | 0.5302 | - |
|
490 |
+
| 3.1314 | 1600 | 0.6831 | 0.4687 | 0.5306 | - |
|
491 |
+
| 3.3275 | 1700 | 0.6494 | 0.4667 | 0.5268 | - |
|
492 |
+
| 3.5235 | 1800 | 0.624 | 0.4750 | 0.5321 | - |
|
493 |
+
| 3.7196 | 1900 | 0.6035 | 0.4735 | 0.5264 | - |
|
494 |
+
| 3.9157 | 2000 | 0.6136 | 0.4679 | 0.5287 | - |
|
495 |
+
| 3.9941 | 2040 | - | - | - | 0.5291 |
|
496 |
+
|
497 |
+
|
498 |
+
### Framework Versions
|
499 |
+
- Python: 3.11.9
|
500 |
+
- Sentence Transformers: 3.0.1
|
501 |
+
- Transformers: 4.41.2
|
502 |
+
- PyTorch: 2.1.2+cu121
|
503 |
+
- Accelerate: 0.31.0
|
504 |
+
- Datasets: 2.19.1
|
505 |
+
- Tokenizers: 0.19.1
|
506 |
+
|
507 |
+
## Citation
|
508 |
+
|
509 |
+
### BibTeX
|
510 |
+
|
511 |
+
#### Sentence Transformers
|
512 |
+
```bibtex
|
513 |
+
@inproceedings{reimers-2019-sentence-bert,
|
514 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
515 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
516 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
517 |
+
month = "11",
|
518 |
+
year = "2019",
|
519 |
+
publisher = "Association for Computational Linguistics",
|
520 |
+
url = "https://arxiv.org/abs/1908.10084",
|
521 |
+
}
|
522 |
+
```
|
523 |
+
|
524 |
+
#### TripletLoss
|
525 |
+
```bibtex
|
526 |
+
@misc{hermans2017defense,
|
527 |
+
title={In Defense of the Triplet Loss for Person Re-Identification},
|
528 |
+
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
|
529 |
+
year={2017},
|
530 |
+
eprint={1703.07737},
|
531 |
+
archivePrefix={arXiv},
|
532 |
+
primaryClass={cs.CV}
|
533 |
+
}
|
534 |
+
```
|
535 |
+
|
536 |
+
<!--
|
537 |
+
## Glossary
|
538 |
+
|
539 |
+
*Clearly define terms in order to be accessible across audiences.*
|
540 |
+
-->
|
541 |
+
|
542 |
+
<!--
|
543 |
+
## Model Card Authors
|
544 |
+
|
545 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
546 |
+
-->
|
547 |
+
|
548 |
+
<!--
|
549 |
+
## Model Card Contact
|
550 |
+
|
551 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
552 |
+
-->
|
config.json
ADDED
@@ -0,0 +1,26 @@
|
|
|
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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": "./models/multi-qa-MiniLM-L6-cos-v1",
|
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": 6,
|
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": 30522
|
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": "2.1.2+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:c729929d68afa140f1f2dc93b788384f2e8a9a4f95cc09856a02616acfc0c7d8
|
3 |
+
size 90864192
|
modules.json
ADDED
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
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": 512,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
special_tokens_map.json
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"cls_token": {
|
3 |
+
"content": "[CLS]",
|
4 |
+
"lstrip": false,
|
5 |
+
"normalized": false,
|
6 |
+
"rstrip": false,
|
7 |
+
"single_word": false
|
8 |
+
},
|
9 |
+
"mask_token": {
|
10 |
+
"content": "[MASK]",
|
11 |
+
"lstrip": false,
|
12 |
+
"normalized": false,
|
13 |
+
"rstrip": false,
|
14 |
+
"single_word": false
|
15 |
+
},
|
16 |
+
"pad_token": {
|
17 |
+
"content": "[PAD]",
|
18 |
+
"lstrip": false,
|
19 |
+
"normalized": false,
|
20 |
+
"rstrip": false,
|
21 |
+
"single_word": false
|
22 |
+
},
|
23 |
+
"sep_token": {
|
24 |
+
"content": "[SEP]",
|
25 |
+
"lstrip": false,
|
26 |
+
"normalized": false,
|
27 |
+
"rstrip": false,
|
28 |
+
"single_word": false
|
29 |
+
},
|
30 |
+
"unk_token": {
|
31 |
+
"content": "[UNK]",
|
32 |
+
"lstrip": false,
|
33 |
+
"normalized": false,
|
34 |
+
"rstrip": false,
|
35 |
+
"single_word": false
|
36 |
+
}
|
37 |
+
}
|
tokenizer.json
ADDED
The diff for this file is too large to render.
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|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,64 @@
|
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|
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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": "[PAD]",
|
5 |
+
"lstrip": false,
|
6 |
+
"normalized": false,
|
7 |
+
"rstrip": false,
|
8 |
+
"single_word": false,
|
9 |
+
"special": true
|
10 |
+
},
|
11 |
+
"100": {
|
12 |
+
"content": "[UNK]",
|
13 |
+
"lstrip": false,
|
14 |
+
"normalized": false,
|
15 |
+
"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
+
"101": {
|
20 |
+
"content": "[CLS]",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
},
|
27 |
+
"102": {
|
28 |
+
"content": "[SEP]",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"103": {
|
36 |
+
"content": "[MASK]",
|
37 |
+
"lstrip": false,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": true
|
42 |
+
}
|
43 |
+
},
|
44 |
+
"clean_up_tokenization_spaces": true,
|
45 |
+
"cls_token": "[CLS]",
|
46 |
+
"do_basic_tokenize": true,
|
47 |
+
"do_lower_case": true,
|
48 |
+
"mask_token": "[MASK]",
|
49 |
+
"max_length": 250,
|
50 |
+
"model_max_length": 512,
|
51 |
+
"never_split": null,
|
52 |
+
"pad_to_multiple_of": null,
|
53 |
+
"pad_token": "[PAD]",
|
54 |
+
"pad_token_type_id": 0,
|
55 |
+
"padding_side": "right",
|
56 |
+
"sep_token": "[SEP]",
|
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 |
+
}
|
vocab.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|