Add new SentenceTransformer model.
Browse files- README.md +526 -164
- checkpoint-933/1_Pooling/config.json +10 -0
- checkpoint-933/README.md +563 -0
- checkpoint-933/added_tokens.json +3 -0
- checkpoint-933/config.json +33 -0
- checkpoint-933/config_sentence_transformers.json +10 -0
- checkpoint-933/model.safetensors +3 -0
- checkpoint-933/modules.json +14 -0
- checkpoint-933/optimizer.pt +3 -0
- checkpoint-933/rng_state.pth +3 -0
- checkpoint-933/scheduler.pt +3 -0
- checkpoint-933/sentence_bert_config.json +4 -0
- checkpoint-933/special_tokens_map.json +15 -0
- checkpoint-933/spm.model +3 -0
- checkpoint-933/tokenizer.json +0 -0
- checkpoint-933/tokenizer_config.json +65 -0
- checkpoint-933/trainer_state.json +83 -0
- checkpoint-933/training_args.bin +3 -0
- model.safetensors +1 -1
- runs/Sep19_22-03-26_default/events.out.tfevents.1726783408.default.3567.0 +3 -0
README.md
CHANGED
@@ -1,201 +1,563 @@
|
|
1 |
---
|
2 |
base_model: colorfulscoop/sbert-base-ja
|
3 |
-
|
4 |
-
|
5 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
6 |
---
|
7 |
|
8 |
-
#
|
9 |
-
|
10 |
-
<!-- Provide a quick summary of what the model is/does. -->
|
11 |
-
|
12 |
|
|
|
13 |
|
14 |
## Model Details
|
15 |
|
16 |
### Model Description
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
17 |
|
18 |
-
|
19 |
-
|
20 |
-
Generates similarity embeddings
|
21 |
-
|
22 |
-
- **Developed by:** [More Information Needed]
|
23 |
-
- **Funded by [optional]:** [More Information Needed]
|
24 |
-
- **Shared by [optional]:** [More Information Needed]
|
25 |
-
- **Model type:** [More Information Needed]
|
26 |
-
- **Language(s) (NLP):** ja
|
27 |
-
- **License:** cc-by-sa-4.0
|
28 |
-
- **Finetuned from model [optional]:** colorfulscoop/sbert-base-ja
|
29 |
-
|
30 |
-
### Model Sources [optional]
|
31 |
-
|
32 |
-
<!-- Provide the basic links for the model. -->
|
33 |
-
|
34 |
-
- **Repository:** [More Information Needed]
|
35 |
-
- **Paper [optional]:** [More Information Needed]
|
36 |
-
- **Demo [optional]:** [More Information Needed]
|
37 |
-
|
38 |
-
## Uses
|
39 |
-
|
40 |
-
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
|
41 |
|
42 |
-
|
|
|
|
|
43 |
|
44 |
-
|
45 |
|
46 |
-
|
|
|
|
|
|
|
|
|
|
|
47 |
|
48 |
-
|
49 |
|
50 |
-
|
51 |
|
52 |
-
|
53 |
|
54 |
-
|
55 |
-
|
56 |
-
|
57 |
-
|
58 |
-
[More Information Needed]
|
59 |
|
60 |
-
|
|
|
|
|
61 |
|
62 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
63 |
|
64 |
-
|
65 |
-
|
66 |
-
|
|
|
|
|
67 |
|
68 |
-
<!--
|
|
|
69 |
|
70 |
-
|
71 |
|
72 |
-
|
73 |
-
|
74 |
-
Use the code below to get started with the model.
|
75 |
-
|
76 |
-
[More Information Needed]
|
77 |
-
|
78 |
-
## Training Details
|
79 |
|
80 |
-
|
|
|
81 |
|
82 |
-
|
83 |
|
84 |
-
|
85 |
|
86 |
-
|
|
|
87 |
|
88 |
-
<!--
|
89 |
-
|
90 |
-
#### Preprocessing [optional]
|
91 |
-
|
92 |
-
[More Information Needed]
|
93 |
-
|
94 |
-
|
95 |
-
#### Training Hyperparameters
|
96 |
-
|
97 |
-
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
|
98 |
-
|
99 |
-
#### Speeds, Sizes, Times [optional]
|
100 |
-
|
101 |
-
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
|
102 |
|
103 |
-
|
|
|
104 |
|
105 |
## Evaluation
|
106 |
|
107 |
-
|
108 |
-
|
109 |
-
|
110 |
-
|
111 |
-
|
112 |
-
|
113 |
-
|
114 |
-
|
115 |
-
|
116 |
-
|
117 |
-
|
118 |
-
|
119 |
-
|
120 |
-
|
121 |
-
|
122 |
-
|
123 |
-
|
124 |
-
|
125 |
-
|
126 |
-
|
127 |
-
|
128 |
-
|
129 |
-
|
130 |
-
|
131 |
-
|
132 |
-
|
133 |
-
|
134 |
-
|
135 |
-
|
136 |
-
|
137 |
-
|
138 |
-
|
139 |
-
|
140 |
-
|
141 |
-
|
142 |
-
|
143 |
-
|
144 |
-
|
145 |
-
|
146 |
-
|
147 |
-
|
148 |
-
|
149 |
-
|
150 |
-
|
151 |
-
|
152 |
-
|
153 |
-
|
154 |
-
|
155 |
-
|
156 |
-
|
157 |
-
|
158 |
-
|
159 |
-
[More Information Needed]
|
160 |
-
|
161 |
-
### Compute Infrastructure
|
162 |
-
|
163 |
-
[More Information Needed]
|
164 |
-
|
165 |
-
#### Hardware
|
166 |
-
|
167 |
-
[More Information Needed]
|
168 |
-
|
169 |
-
#### Software
|
170 |
-
|
171 |
-
[More Information Needed]
|
172 |
-
|
173 |
-
## Citation [optional]
|
174 |
-
|
175 |
-
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
176 |
-
|
177 |
-
**BibTeX:**
|
178 |
-
|
179 |
-
[More Information Needed]
|
180 |
-
|
181 |
-
**APA:**
|
182 |
-
|
183 |
-
[More Information Needed]
|
184 |
-
|
185 |
-
## Glossary [optional]
|
186 |
-
|
187 |
-
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
188 |
-
|
189 |
-
[More Information Needed]
|
190 |
-
|
191 |
-
## More Information [optional]
|
192 |
-
|
193 |
-
[More Information Needed]
|
194 |
|
195 |
-
|
|
|
196 |
|
197 |
-
|
198 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
199 |
## Model Card Contact
|
200 |
|
201 |
-
|
|
|
|
1 |
---
|
2 |
base_model: colorfulscoop/sbert-base-ja
|
3 |
+
library_name: sentence-transformers
|
4 |
+
metrics:
|
5 |
+
- cosine_accuracy
|
6 |
+
- cosine_accuracy_threshold
|
7 |
+
- cosine_f1
|
8 |
+
- cosine_f1_threshold
|
9 |
+
- cosine_precision
|
10 |
+
- cosine_recall
|
11 |
+
- cosine_ap
|
12 |
+
- dot_accuracy
|
13 |
+
- dot_accuracy_threshold
|
14 |
+
- dot_f1
|
15 |
+
- dot_f1_threshold
|
16 |
+
- dot_precision
|
17 |
+
- dot_recall
|
18 |
+
- dot_ap
|
19 |
+
- manhattan_accuracy
|
20 |
+
- manhattan_accuracy_threshold
|
21 |
+
- manhattan_f1
|
22 |
+
- manhattan_f1_threshold
|
23 |
+
- manhattan_precision
|
24 |
+
- manhattan_recall
|
25 |
+
- manhattan_ap
|
26 |
+
- euclidean_accuracy
|
27 |
+
- euclidean_accuracy_threshold
|
28 |
+
- euclidean_f1
|
29 |
+
- euclidean_f1_threshold
|
30 |
+
- euclidean_precision
|
31 |
+
- euclidean_recall
|
32 |
+
- euclidean_ap
|
33 |
+
- max_accuracy
|
34 |
+
- max_accuracy_threshold
|
35 |
+
- max_f1
|
36 |
+
- max_f1_threshold
|
37 |
+
- max_precision
|
38 |
+
- max_recall
|
39 |
+
- max_ap
|
40 |
+
pipeline_tag: sentence-similarity
|
41 |
+
tags:
|
42 |
+
- sentence-transformers
|
43 |
+
- sentence-similarity
|
44 |
+
- feature-extraction
|
45 |
+
- generated_from_trainer
|
46 |
+
- dataset_size:7464
|
47 |
+
- loss:CosineSimilarityLoss
|
48 |
+
widget:
|
49 |
+
- source_sentence: なにか思い出せることは?
|
50 |
+
sentences:
|
51 |
+
- なんて言った?
|
52 |
+
- なにが欲しい?
|
53 |
+
- いつ失くしたの?
|
54 |
+
- source_sentence: 木にスカーフがひっかかってる?
|
55 |
+
sentences:
|
56 |
+
- 物の姿を変えられる魔法
|
57 |
+
- 木にスカーフがひっかかってる?
|
58 |
+
- あの木の上のやつ、スカーフ?
|
59 |
+
- source_sentence: どんなスカーフ?
|
60 |
+
sentences:
|
61 |
+
- お気に入りの食べ物は?
|
62 |
+
- くさい
|
63 |
+
- もしもし
|
64 |
+
- source_sentence: どうやったら猫が話せる?
|
65 |
+
sentences:
|
66 |
+
- どっちがいいと思う?
|
67 |
+
- ワゴンにスカーフはあった?
|
68 |
+
- 井戸へ行ったことある?
|
69 |
+
- source_sentence: なにか思い出せることは?
|
70 |
+
sentences:
|
71 |
+
- 村人はどんな呪文が使えるの?
|
72 |
+
- 町って?
|
73 |
+
- キミならどっちがいい?
|
74 |
+
model-index:
|
75 |
+
- name: SentenceTransformer based on colorfulscoop/sbert-base-ja
|
76 |
+
results:
|
77 |
+
- task:
|
78 |
+
type: binary-classification
|
79 |
+
name: Binary Classification
|
80 |
+
dataset:
|
81 |
+
name: custom arc semantics data jp
|
82 |
+
type: custom-arc-semantics-data-jp
|
83 |
+
metrics:
|
84 |
+
- type: cosine_accuracy
|
85 |
+
value: 0.9705409748259239
|
86 |
+
name: Cosine Accuracy
|
87 |
+
- type: cosine_accuracy_threshold
|
88 |
+
value: 0.5118279457092285
|
89 |
+
name: Cosine Accuracy Threshold
|
90 |
+
- type: cosine_f1
|
91 |
+
value: 0.9102773246329527
|
92 |
+
name: Cosine F1
|
93 |
+
- type: cosine_f1_threshold
|
94 |
+
value: 0.45031607151031494
|
95 |
+
name: Cosine F1 Threshold
|
96 |
+
- type: cosine_precision
|
97 |
+
value: 0.8857142857142857
|
98 |
+
name: Cosine Precision
|
99 |
+
- type: cosine_recall
|
100 |
+
value: 0.9362416107382551
|
101 |
+
name: Cosine Recall
|
102 |
+
- type: cosine_ap
|
103 |
+
value: 0.9236294738598163
|
104 |
+
name: Cosine Ap
|
105 |
+
- type: dot_accuracy
|
106 |
+
value: 0.9694697375468666
|
107 |
+
name: Dot Accuracy
|
108 |
+
- type: dot_accuracy_threshold
|
109 |
+
value: 251.2455596923828
|
110 |
+
name: Dot Accuracy Threshold
|
111 |
+
- type: dot_f1
|
112 |
+
value: 0.9060955518945634
|
113 |
+
name: Dot F1
|
114 |
+
- type: dot_f1_threshold
|
115 |
+
value: 246.36648559570312
|
116 |
+
name: Dot F1 Threshold
|
117 |
+
- type: dot_precision
|
118 |
+
value: 0.889967637540453
|
119 |
+
name: Dot Precision
|
120 |
+
- type: dot_recall
|
121 |
+
value: 0.9228187919463087
|
122 |
+
name: Dot Recall
|
123 |
+
- type: dot_ap
|
124 |
+
value: 0.9196731890884118
|
125 |
+
name: Dot Ap
|
126 |
+
- type: manhattan_accuracy
|
127 |
+
value: 0.9716122121049813
|
128 |
+
name: Manhattan Accuracy
|
129 |
+
- type: manhattan_accuracy_threshold
|
130 |
+
value: 514.571533203125
|
131 |
+
name: Manhattan Accuracy Threshold
|
132 |
+
- type: manhattan_f1
|
133 |
+
value: 0.9132569558101473
|
134 |
+
name: Manhattan F1
|
135 |
+
- type: manhattan_f1_threshold
|
136 |
+
value: 514.571533203125
|
137 |
+
name: Manhattan F1 Threshold
|
138 |
+
- type: manhattan_precision
|
139 |
+
value: 0.8913738019169329
|
140 |
+
name: Manhattan Precision
|
141 |
+
- type: manhattan_recall
|
142 |
+
value: 0.9362416107382551
|
143 |
+
name: Manhattan Recall
|
144 |
+
- type: manhattan_ap
|
145 |
+
value: 0.9255015709844487
|
146 |
+
name: Manhattan Ap
|
147 |
+
- type: euclidean_accuracy
|
148 |
+
value: 0.9721478307445099
|
149 |
+
name: Euclidean Accuracy
|
150 |
+
- type: euclidean_accuracy_threshold
|
151 |
+
value: 23.195274353027344
|
152 |
+
name: Euclidean Accuracy Threshold
|
153 |
+
- type: euclidean_f1
|
154 |
+
value: 0.9147540983606558
|
155 |
+
name: Euclidean F1
|
156 |
+
- type: euclidean_f1_threshold
|
157 |
+
value: 23.195274353027344
|
158 |
+
name: Euclidean F1 Threshold
|
159 |
+
- type: euclidean_precision
|
160 |
+
value: 0.8942307692307693
|
161 |
+
name: Euclidean Precision
|
162 |
+
- type: euclidean_recall
|
163 |
+
value: 0.9362416107382551
|
164 |
+
name: Euclidean Recall
|
165 |
+
- type: euclidean_ap
|
166 |
+
value: 0.9259440018381992
|
167 |
+
name: Euclidean Ap
|
168 |
+
- type: max_accuracy
|
169 |
+
value: 0.9721478307445099
|
170 |
+
name: Max Accuracy
|
171 |
+
- type: max_accuracy_threshold
|
172 |
+
value: 514.571533203125
|
173 |
+
name: Max Accuracy Threshold
|
174 |
+
- type: max_f1
|
175 |
+
value: 0.9147540983606558
|
176 |
+
name: Max F1
|
177 |
+
- type: max_f1_threshold
|
178 |
+
value: 514.571533203125
|
179 |
+
name: Max F1 Threshold
|
180 |
+
- type: max_precision
|
181 |
+
value: 0.8942307692307693
|
182 |
+
name: Max Precision
|
183 |
+
- type: max_recall
|
184 |
+
value: 0.9362416107382551
|
185 |
+
name: Max Recall
|
186 |
+
- type: max_ap
|
187 |
+
value: 0.9259440018381992
|
188 |
+
name: Max Ap
|
189 |
---
|
190 |
|
191 |
+
# SentenceTransformer based on colorfulscoop/sbert-base-ja
|
|
|
|
|
|
|
192 |
|
193 |
+
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
|
194 |
|
195 |
## Model Details
|
196 |
|
197 |
### Model Description
|
198 |
+
- **Model Type:** Sentence Transformer
|
199 |
+
- **Base model:** [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) <!-- at revision ecb8a98cd5176719ff7ab0d770a27420118732cf -->
|
200 |
+
- **Maximum Sequence Length:** 512 tokens
|
201 |
+
- **Output Dimensionality:** 768 tokens
|
202 |
+
- **Similarity Function:** Cosine Similarity
|
203 |
+
<!-- - **Training Dataset:** Unknown -->
|
204 |
+
<!-- - **Language:** Unknown -->
|
205 |
+
<!-- - **License:** Unknown -->
|
206 |
|
207 |
+
### Model Sources
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
208 |
|
209 |
+
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
210 |
+
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
|
211 |
+
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
212 |
|
213 |
+
### Full Model Architecture
|
214 |
|
215 |
+
```
|
216 |
+
SentenceTransformer(
|
217 |
+
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
|
218 |
+
(1): Pooling({'word_embedding_dimension': 768, '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})
|
219 |
+
)
|
220 |
+
```
|
221 |
|
222 |
+
## Usage
|
223 |
|
224 |
+
### Direct Usage (Sentence Transformers)
|
225 |
|
226 |
+
First install the Sentence Transformers library:
|
227 |
|
228 |
+
```bash
|
229 |
+
pip install -U sentence-transformers
|
230 |
+
```
|
|
|
|
|
231 |
|
232 |
+
Then you can load this model and run inference.
|
233 |
+
```python
|
234 |
+
from sentence_transformers import SentenceTransformer
|
235 |
|
236 |
+
# Download from the 🤗 Hub
|
237 |
+
model = SentenceTransformer("sentence_transformers_model_id")
|
238 |
+
# Run inference
|
239 |
+
sentences = [
|
240 |
+
'なにか思い出せることは?',
|
241 |
+
'村人はどんな呪文が使えるの?',
|
242 |
+
'キミならどっちがいい?',
|
243 |
+
]
|
244 |
+
embeddings = model.encode(sentences)
|
245 |
+
print(embeddings.shape)
|
246 |
+
# [3, 768]
|
247 |
|
248 |
+
# Get the similarity scores for the embeddings
|
249 |
+
similarities = model.similarity(embeddings, embeddings)
|
250 |
+
print(similarities.shape)
|
251 |
+
# [3, 3]
|
252 |
+
```
|
253 |
|
254 |
+
<!--
|
255 |
+
### Direct Usage (Transformers)
|
256 |
|
257 |
+
<details><summary>Click to see the direct usage in Transformers</summary>
|
258 |
|
259 |
+
</details>
|
260 |
+
-->
|
|
|
|
|
|
|
|
|
|
|
261 |
|
262 |
+
<!--
|
263 |
+
### Downstream Usage (Sentence Transformers)
|
264 |
|
265 |
+
You can finetune this model on your own dataset.
|
266 |
|
267 |
+
<details><summary>Click to expand</summary>
|
268 |
|
269 |
+
</details>
|
270 |
+
-->
|
271 |
|
272 |
+
<!--
|
273 |
+
### Out-of-Scope Use
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
274 |
|
275 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
276 |
+
-->
|
277 |
|
278 |
## Evaluation
|
279 |
|
280 |
+
### Metrics
|
281 |
+
|
282 |
+
#### Binary Classification
|
283 |
+
* Dataset: `custom-arc-semantics-data-jp`
|
284 |
+
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
|
285 |
+
|
286 |
+
| Metric | Value |
|
287 |
+
|:-----------------------------|:-----------|
|
288 |
+
| cosine_accuracy | 0.9705 |
|
289 |
+
| cosine_accuracy_threshold | 0.5118 |
|
290 |
+
| cosine_f1 | 0.9103 |
|
291 |
+
| cosine_f1_threshold | 0.4503 |
|
292 |
+
| cosine_precision | 0.8857 |
|
293 |
+
| cosine_recall | 0.9362 |
|
294 |
+
| cosine_ap | 0.9236 |
|
295 |
+
| dot_accuracy | 0.9695 |
|
296 |
+
| dot_accuracy_threshold | 251.2456 |
|
297 |
+
| dot_f1 | 0.9061 |
|
298 |
+
| dot_f1_threshold | 246.3665 |
|
299 |
+
| dot_precision | 0.89 |
|
300 |
+
| dot_recall | 0.9228 |
|
301 |
+
| dot_ap | 0.9197 |
|
302 |
+
| manhattan_accuracy | 0.9716 |
|
303 |
+
| manhattan_accuracy_threshold | 514.5715 |
|
304 |
+
| manhattan_f1 | 0.9133 |
|
305 |
+
| manhattan_f1_threshold | 514.5715 |
|
306 |
+
| manhattan_precision | 0.8914 |
|
307 |
+
| manhattan_recall | 0.9362 |
|
308 |
+
| manhattan_ap | 0.9255 |
|
309 |
+
| euclidean_accuracy | 0.9721 |
|
310 |
+
| euclidean_accuracy_threshold | 23.1953 |
|
311 |
+
| euclidean_f1 | 0.9148 |
|
312 |
+
| euclidean_f1_threshold | 23.1953 |
|
313 |
+
| euclidean_precision | 0.8942 |
|
314 |
+
| euclidean_recall | 0.9362 |
|
315 |
+
| euclidean_ap | 0.9259 |
|
316 |
+
| max_accuracy | 0.9721 |
|
317 |
+
| max_accuracy_threshold | 514.5715 |
|
318 |
+
| max_f1 | 0.9148 |
|
319 |
+
| max_f1_threshold | 514.5715 |
|
320 |
+
| max_precision | 0.8942 |
|
321 |
+
| max_recall | 0.9362 |
|
322 |
+
| **max_ap** | **0.9259** |
|
323 |
+
|
324 |
+
<!--
|
325 |
+
## Bias, Risks and Limitations
|
326 |
+
|
327 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
328 |
+
-->
|
329 |
+
|
330 |
+
<!--
|
331 |
+
### Recommendations
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
332 |
|
333 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
334 |
+
-->
|
335 |
|
336 |
+
## Training Details
|
337 |
|
338 |
+
### Training Dataset
|
339 |
+
|
340 |
+
#### Unnamed Dataset
|
341 |
+
|
342 |
+
|
343 |
+
* Size: 7,464 training samples
|
344 |
+
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
|
345 |
+
* Approximate statistics based on the first 1000 samples:
|
346 |
+
| | text1 | text2 | label |
|
347 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
|
348 |
+
| type | string | string | int |
|
349 |
+
| details | <ul><li>min: 4 tokens</li><li>mean: 8.32 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.93 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>0: ~86.30%</li><li>1: ~13.70%</li></ul> |
|
350 |
+
* Samples:
|
351 |
+
| text1 | text2 | label |
|
352 |
+
|:------------------------------|:-------------------------|:---------------|
|
353 |
+
| <code>昨日夕飯にチキンヌードル食べた?</code> | <code>何か企んでる?</code> | <code>0</code> |
|
354 |
+
| <code>どっちも欲しくない</code> | <code>お気に入りの食べ物は?</code> | <code>0</code> |
|
355 |
+
| <code>見た目を変える魔法</code> | <code>物の姿を変えられる魔法</code> | <code>1</code> |
|
356 |
+
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
|
357 |
+
```json
|
358 |
+
{
|
359 |
+
"loss_fct": "torch.nn.modules.loss.MSELoss"
|
360 |
+
}
|
361 |
+
```
|
362 |
+
|
363 |
+
### Evaluation Dataset
|
364 |
+
|
365 |
+
#### Unnamed Dataset
|
366 |
+
|
367 |
+
|
368 |
+
* Size: 1,867 evaluation samples
|
369 |
+
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
|
370 |
+
* Approximate statistics based on the first 1000 samples:
|
371 |
+
| | text1 | text2 | label |
|
372 |
+
|:--------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:------------------------------------------------|
|
373 |
+
| type | string | string | int |
|
374 |
+
| details | <ul><li>min: 4 tokens</li><li>mean: 8.24 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.1 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>0: ~83.40%</li><li>1: ~16.60%</li></ul> |
|
375 |
+
* Samples:
|
376 |
+
| text1 | text2 | label |
|
377 |
+
|:------------------------------|:-------------------------|:---------------|
|
378 |
+
| <code>例えば?</code> | <code>どうも</code> | <code>0</code> |
|
379 |
+
| <code>何を作ったの?</code> | <code>君は何でここにいるの?</code> | <code>0</code> |
|
380 |
+
| <code>昨日夕飯にビーフシチュー食べた?</code> | <code>屋根裏って?</code> | <code>0</code> |
|
381 |
+
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
|
382 |
+
```json
|
383 |
+
{
|
384 |
+
"loss_fct": "torch.nn.modules.loss.MSELoss"
|
385 |
+
}
|
386 |
+
```
|
387 |
+
|
388 |
+
### Training Hyperparameters
|
389 |
+
#### Non-Default Hyperparameters
|
390 |
+
|
391 |
+
- `eval_strategy`: epoch
|
392 |
+
- `learning_rate`: 2e-05
|
393 |
+
- `num_train_epochs`: 1
|
394 |
+
- `warmup_ratio`: 0.4
|
395 |
+
- `fp16`: True
|
396 |
+
- `batch_sampler`: no_duplicates
|
397 |
+
|
398 |
+
#### All Hyperparameters
|
399 |
+
<details><summary>Click to expand</summary>
|
400 |
+
|
401 |
+
- `overwrite_output_dir`: False
|
402 |
+
- `do_predict`: False
|
403 |
+
- `eval_strategy`: epoch
|
404 |
+
- `prediction_loss_only`: True
|
405 |
+
- `per_device_train_batch_size`: 8
|
406 |
+
- `per_device_eval_batch_size`: 8
|
407 |
+
- `per_gpu_train_batch_size`: None
|
408 |
+
- `per_gpu_eval_batch_size`: None
|
409 |
+
- `gradient_accumulation_steps`: 1
|
410 |
+
- `eval_accumulation_steps`: None
|
411 |
+
- `torch_empty_cache_steps`: None
|
412 |
+
- `learning_rate`: 2e-05
|
413 |
+
- `weight_decay`: 0.0
|
414 |
+
- `adam_beta1`: 0.9
|
415 |
+
- `adam_beta2`: 0.999
|
416 |
+
- `adam_epsilon`: 1e-08
|
417 |
+
- `max_grad_norm`: 1.0
|
418 |
+
- `num_train_epochs`: 1
|
419 |
+
- `max_steps`: -1
|
420 |
+
- `lr_scheduler_type`: linear
|
421 |
+
- `lr_scheduler_kwargs`: {}
|
422 |
+
- `warmup_ratio`: 0.4
|
423 |
+
- `warmup_steps`: 0
|
424 |
+
- `log_level`: passive
|
425 |
+
- `log_level_replica`: warning
|
426 |
+
- `log_on_each_node`: True
|
427 |
+
- `logging_nan_inf_filter`: True
|
428 |
+
- `save_safetensors`: True
|
429 |
+
- `save_on_each_node`: False
|
430 |
+
- `save_only_model`: False
|
431 |
+
- `restore_callback_states_from_checkpoint`: False
|
432 |
+
- `no_cuda`: False
|
433 |
+
- `use_cpu`: False
|
434 |
+
- `use_mps_device`: False
|
435 |
+
- `seed`: 42
|
436 |
+
- `data_seed`: None
|
437 |
+
- `jit_mode_eval`: False
|
438 |
+
- `use_ipex`: False
|
439 |
+
- `bf16`: False
|
440 |
+
- `fp16`: True
|
441 |
+
- `fp16_opt_level`: O1
|
442 |
+
- `half_precision_backend`: auto
|
443 |
+
- `bf16_full_eval`: False
|
444 |
+
- `fp16_full_eval`: False
|
445 |
+
- `tf32`: None
|
446 |
+
- `local_rank`: 0
|
447 |
+
- `ddp_backend`: None
|
448 |
+
- `tpu_num_cores`: None
|
449 |
+
- `tpu_metrics_debug`: False
|
450 |
+
- `debug`: []
|
451 |
+
- `dataloader_drop_last`: False
|
452 |
+
- `dataloader_num_workers`: 0
|
453 |
+
- `dataloader_prefetch_factor`: None
|
454 |
+
- `past_index`: -1
|
455 |
+
- `disable_tqdm`: False
|
456 |
+
- `remove_unused_columns`: True
|
457 |
+
- `label_names`: None
|
458 |
+
- `load_best_model_at_end`: False
|
459 |
+
- `ignore_data_skip`: False
|
460 |
+
- `fsdp`: []
|
461 |
+
- `fsdp_min_num_params`: 0
|
462 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
463 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
464 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
465 |
+
- `deepspeed`: None
|
466 |
+
- `label_smoothing_factor`: 0.0
|
467 |
+
- `optim`: adamw_torch
|
468 |
+
- `optim_args`: None
|
469 |
+
- `adafactor`: False
|
470 |
+
- `group_by_length`: False
|
471 |
+
- `length_column_name`: length
|
472 |
+
- `ddp_find_unused_parameters`: None
|
473 |
+
- `ddp_bucket_cap_mb`: None
|
474 |
+
- `ddp_broadcast_buffers`: False
|
475 |
+
- `dataloader_pin_memory`: True
|
476 |
+
- `dataloader_persistent_workers`: False
|
477 |
+
- `skip_memory_metrics`: True
|
478 |
+
- `use_legacy_prediction_loop`: False
|
479 |
+
- `push_to_hub`: False
|
480 |
+
- `resume_from_checkpoint`: None
|
481 |
+
- `hub_model_id`: None
|
482 |
+
- `hub_strategy`: every_save
|
483 |
+
- `hub_private_repo`: False
|
484 |
+
- `hub_always_push`: False
|
485 |
+
- `gradient_checkpointing`: False
|
486 |
+
- `gradient_checkpointing_kwargs`: None
|
487 |
+
- `include_inputs_for_metrics`: False
|
488 |
+
- `eval_do_concat_batches`: True
|
489 |
+
- `fp16_backend`: auto
|
490 |
+
- `push_to_hub_model_id`: None
|
491 |
+
- `push_to_hub_organization`: None
|
492 |
+
- `mp_parameters`:
|
493 |
+
- `auto_find_batch_size`: False
|
494 |
+
- `full_determinism`: False
|
495 |
+
- `torchdynamo`: None
|
496 |
+
- `ray_scope`: last
|
497 |
+
- `ddp_timeout`: 1800
|
498 |
+
- `torch_compile`: False
|
499 |
+
- `torch_compile_backend`: None
|
500 |
+
- `torch_compile_mode`: None
|
501 |
+
- `dispatch_batches`: None
|
502 |
+
- `split_batches`: None
|
503 |
+
- `include_tokens_per_second`: False
|
504 |
+
- `include_num_input_tokens_seen`: False
|
505 |
+
- `neftune_noise_alpha`: None
|
506 |
+
- `optim_target_modules`: None
|
507 |
+
- `batch_eval_metrics`: False
|
508 |
+
- `eval_on_start`: False
|
509 |
+
- `eval_use_gather_object`: False
|
510 |
+
- `batch_sampler`: no_duplicates
|
511 |
+
- `multi_dataset_batch_sampler`: proportional
|
512 |
+
|
513 |
+
</details>
|
514 |
+
|
515 |
+
### Training Logs
|
516 |
+
| Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
|
517 |
+
|:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
|
518 |
+
| 1.0 | 933 | 0.0601 | 0.0303 | 0.9259 |
|
519 |
+
|
520 |
+
|
521 |
+
### Framework Versions
|
522 |
+
- Python: 3.10.14
|
523 |
+
- Sentence Transformers: 3.1.1
|
524 |
+
- Transformers: 4.44.2
|
525 |
+
- PyTorch: 2.4.1+cu121
|
526 |
+
- Accelerate: 0.34.2
|
527 |
+
- Datasets: 2.20.0
|
528 |
+
- Tokenizers: 0.19.1
|
529 |
+
|
530 |
+
## Citation
|
531 |
+
|
532 |
+
### BibTeX
|
533 |
+
|
534 |
+
#### Sentence Transformers
|
535 |
+
```bibtex
|
536 |
+
@inproceedings{reimers-2019-sentence-bert,
|
537 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
538 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
539 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
540 |
+
month = "11",
|
541 |
+
year = "2019",
|
542 |
+
publisher = "Association for Computational Linguistics",
|
543 |
+
url = "https://arxiv.org/abs/1908.10084",
|
544 |
+
}
|
545 |
+
```
|
546 |
+
|
547 |
+
<!--
|
548 |
+
## Glossary
|
549 |
+
|
550 |
+
*Clearly define terms in order to be accessible across audiences.*
|
551 |
+
-->
|
552 |
+
|
553 |
+
<!--
|
554 |
+
## Model Card Authors
|
555 |
+
|
556 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
557 |
+
-->
|
558 |
+
|
559 |
+
<!--
|
560 |
## Model Card Contact
|
561 |
|
562 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
563 |
+
-->
|
checkpoint-933/1_Pooling/config.json
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"word_embedding_dimension": 768,
|
3 |
+
"pooling_mode_cls_token": false,
|
4 |
+
"pooling_mode_mean_tokens": true,
|
5 |
+
"pooling_mode_max_tokens": false,
|
6 |
+
"pooling_mode_mean_sqrt_len_tokens": false,
|
7 |
+
"pooling_mode_weightedmean_tokens": false,
|
8 |
+
"pooling_mode_lasttoken": false,
|
9 |
+
"include_prompt": true
|
10 |
+
}
|
checkpoint-933/README.md
ADDED
@@ -0,0 +1,563 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
base_model: colorfulscoop/sbert-base-ja
|
3 |
+
library_name: sentence-transformers
|
4 |
+
metrics:
|
5 |
+
- cosine_accuracy
|
6 |
+
- cosine_accuracy_threshold
|
7 |
+
- cosine_f1
|
8 |
+
- cosine_f1_threshold
|
9 |
+
- cosine_precision
|
10 |
+
- cosine_recall
|
11 |
+
- cosine_ap
|
12 |
+
- dot_accuracy
|
13 |
+
- dot_accuracy_threshold
|
14 |
+
- dot_f1
|
15 |
+
- dot_f1_threshold
|
16 |
+
- dot_precision
|
17 |
+
- dot_recall
|
18 |
+
- dot_ap
|
19 |
+
- manhattan_accuracy
|
20 |
+
- manhattan_accuracy_threshold
|
21 |
+
- manhattan_f1
|
22 |
+
- manhattan_f1_threshold
|
23 |
+
- manhattan_precision
|
24 |
+
- manhattan_recall
|
25 |
+
- manhattan_ap
|
26 |
+
- euclidean_accuracy
|
27 |
+
- euclidean_accuracy_threshold
|
28 |
+
- euclidean_f1
|
29 |
+
- euclidean_f1_threshold
|
30 |
+
- euclidean_precision
|
31 |
+
- euclidean_recall
|
32 |
+
- euclidean_ap
|
33 |
+
- max_accuracy
|
34 |
+
- max_accuracy_threshold
|
35 |
+
- max_f1
|
36 |
+
- max_f1_threshold
|
37 |
+
- max_precision
|
38 |
+
- max_recall
|
39 |
+
- max_ap
|
40 |
+
pipeline_tag: sentence-similarity
|
41 |
+
tags:
|
42 |
+
- sentence-transformers
|
43 |
+
- sentence-similarity
|
44 |
+
- feature-extraction
|
45 |
+
- generated_from_trainer
|
46 |
+
- dataset_size:7464
|
47 |
+
- loss:CosineSimilarityLoss
|
48 |
+
widget:
|
49 |
+
- source_sentence: なにか思い出せることは?
|
50 |
+
sentences:
|
51 |
+
- なんて言った?
|
52 |
+
- なにが欲しい?
|
53 |
+
- いつ失くしたの?
|
54 |
+
- source_sentence: 木にスカーフがひっかかってる?
|
55 |
+
sentences:
|
56 |
+
- 物の姿を変えられる魔法
|
57 |
+
- 木にスカーフがひっかかってる?
|
58 |
+
- あの木の上のやつ、スカーフ?
|
59 |
+
- source_sentence: どんなスカーフ?
|
60 |
+
sentences:
|
61 |
+
- お気に入りの食べ物は?
|
62 |
+
- くさい
|
63 |
+
- もしもし
|
64 |
+
- source_sentence: どうやったら猫が話せる?
|
65 |
+
sentences:
|
66 |
+
- どっちがいいと思う?
|
67 |
+
- ワゴンにスカーフはあった?
|
68 |
+
- 井戸へ行ったことある?
|
69 |
+
- source_sentence: なにか思い出せることは?
|
70 |
+
sentences:
|
71 |
+
- 村人はどんな呪文が使えるの?
|
72 |
+
- 町って?
|
73 |
+
- キミならどっちがいい?
|
74 |
+
model-index:
|
75 |
+
- name: SentenceTransformer based on colorfulscoop/sbert-base-ja
|
76 |
+
results:
|
77 |
+
- task:
|
78 |
+
type: binary-classification
|
79 |
+
name: Binary Classification
|
80 |
+
dataset:
|
81 |
+
name: custom arc semantics data jp
|
82 |
+
type: custom-arc-semantics-data-jp
|
83 |
+
metrics:
|
84 |
+
- type: cosine_accuracy
|
85 |
+
value: 0.9705409748259239
|
86 |
+
name: Cosine Accuracy
|
87 |
+
- type: cosine_accuracy_threshold
|
88 |
+
value: 0.5118279457092285
|
89 |
+
name: Cosine Accuracy Threshold
|
90 |
+
- type: cosine_f1
|
91 |
+
value: 0.9102773246329527
|
92 |
+
name: Cosine F1
|
93 |
+
- type: cosine_f1_threshold
|
94 |
+
value: 0.45031607151031494
|
95 |
+
name: Cosine F1 Threshold
|
96 |
+
- type: cosine_precision
|
97 |
+
value: 0.8857142857142857
|
98 |
+
name: Cosine Precision
|
99 |
+
- type: cosine_recall
|
100 |
+
value: 0.9362416107382551
|
101 |
+
name: Cosine Recall
|
102 |
+
- type: cosine_ap
|
103 |
+
value: 0.9236294738598163
|
104 |
+
name: Cosine Ap
|
105 |
+
- type: dot_accuracy
|
106 |
+
value: 0.9694697375468666
|
107 |
+
name: Dot Accuracy
|
108 |
+
- type: dot_accuracy_threshold
|
109 |
+
value: 251.2455596923828
|
110 |
+
name: Dot Accuracy Threshold
|
111 |
+
- type: dot_f1
|
112 |
+
value: 0.9060955518945634
|
113 |
+
name: Dot F1
|
114 |
+
- type: dot_f1_threshold
|
115 |
+
value: 246.36648559570312
|
116 |
+
name: Dot F1 Threshold
|
117 |
+
- type: dot_precision
|
118 |
+
value: 0.889967637540453
|
119 |
+
name: Dot Precision
|
120 |
+
- type: dot_recall
|
121 |
+
value: 0.9228187919463087
|
122 |
+
name: Dot Recall
|
123 |
+
- type: dot_ap
|
124 |
+
value: 0.9196731890884118
|
125 |
+
name: Dot Ap
|
126 |
+
- type: manhattan_accuracy
|
127 |
+
value: 0.9716122121049813
|
128 |
+
name: Manhattan Accuracy
|
129 |
+
- type: manhattan_accuracy_threshold
|
130 |
+
value: 514.571533203125
|
131 |
+
name: Manhattan Accuracy Threshold
|
132 |
+
- type: manhattan_f1
|
133 |
+
value: 0.9132569558101473
|
134 |
+
name: Manhattan F1
|
135 |
+
- type: manhattan_f1_threshold
|
136 |
+
value: 514.571533203125
|
137 |
+
name: Manhattan F1 Threshold
|
138 |
+
- type: manhattan_precision
|
139 |
+
value: 0.8913738019169329
|
140 |
+
name: Manhattan Precision
|
141 |
+
- type: manhattan_recall
|
142 |
+
value: 0.9362416107382551
|
143 |
+
name: Manhattan Recall
|
144 |
+
- type: manhattan_ap
|
145 |
+
value: 0.9255015709844487
|
146 |
+
name: Manhattan Ap
|
147 |
+
- type: euclidean_accuracy
|
148 |
+
value: 0.9721478307445099
|
149 |
+
name: Euclidean Accuracy
|
150 |
+
- type: euclidean_accuracy_threshold
|
151 |
+
value: 23.195274353027344
|
152 |
+
name: Euclidean Accuracy Threshold
|
153 |
+
- type: euclidean_f1
|
154 |
+
value: 0.9147540983606558
|
155 |
+
name: Euclidean F1
|
156 |
+
- type: euclidean_f1_threshold
|
157 |
+
value: 23.195274353027344
|
158 |
+
name: Euclidean F1 Threshold
|
159 |
+
- type: euclidean_precision
|
160 |
+
value: 0.8942307692307693
|
161 |
+
name: Euclidean Precision
|
162 |
+
- type: euclidean_recall
|
163 |
+
value: 0.9362416107382551
|
164 |
+
name: Euclidean Recall
|
165 |
+
- type: euclidean_ap
|
166 |
+
value: 0.9259440018381992
|
167 |
+
name: Euclidean Ap
|
168 |
+
- type: max_accuracy
|
169 |
+
value: 0.9721478307445099
|
170 |
+
name: Max Accuracy
|
171 |
+
- type: max_accuracy_threshold
|
172 |
+
value: 514.571533203125
|
173 |
+
name: Max Accuracy Threshold
|
174 |
+
- type: max_f1
|
175 |
+
value: 0.9147540983606558
|
176 |
+
name: Max F1
|
177 |
+
- type: max_f1_threshold
|
178 |
+
value: 514.571533203125
|
179 |
+
name: Max F1 Threshold
|
180 |
+
- type: max_precision
|
181 |
+
value: 0.8942307692307693
|
182 |
+
name: Max Precision
|
183 |
+
- type: max_recall
|
184 |
+
value: 0.9362416107382551
|
185 |
+
name: Max Recall
|
186 |
+
- type: max_ap
|
187 |
+
value: 0.9259440018381992
|
188 |
+
name: Max Ap
|
189 |
+
---
|
190 |
+
|
191 |
+
# SentenceTransformer based on colorfulscoop/sbert-base-ja
|
192 |
+
|
193 |
+
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
|
194 |
+
|
195 |
+
## Model Details
|
196 |
+
|
197 |
+
### Model Description
|
198 |
+
- **Model Type:** Sentence Transformer
|
199 |
+
- **Base model:** [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) <!-- at revision ecb8a98cd5176719ff7ab0d770a27420118732cf -->
|
200 |
+
- **Maximum Sequence Length:** 512 tokens
|
201 |
+
- **Output Dimensionality:** 768 tokens
|
202 |
+
- **Similarity Function:** Cosine Similarity
|
203 |
+
<!-- - **Training Dataset:** Unknown -->
|
204 |
+
<!-- - **Language:** Unknown -->
|
205 |
+
<!-- - **License:** Unknown -->
|
206 |
+
|
207 |
+
### Model Sources
|
208 |
+
|
209 |
+
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
210 |
+
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
|
211 |
+
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
212 |
+
|
213 |
+
### Full Model Architecture
|
214 |
+
|
215 |
+
```
|
216 |
+
SentenceTransformer(
|
217 |
+
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
|
218 |
+
(1): Pooling({'word_embedding_dimension': 768, '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})
|
219 |
+
)
|
220 |
+
```
|
221 |
+
|
222 |
+
## Usage
|
223 |
+
|
224 |
+
### Direct Usage (Sentence Transformers)
|
225 |
+
|
226 |
+
First install the Sentence Transformers library:
|
227 |
+
|
228 |
+
```bash
|
229 |
+
pip install -U sentence-transformers
|
230 |
+
```
|
231 |
+
|
232 |
+
Then you can load this model and run inference.
|
233 |
+
```python
|
234 |
+
from sentence_transformers import SentenceTransformer
|
235 |
+
|
236 |
+
# Download from the 🤗 Hub
|
237 |
+
model = SentenceTransformer("sentence_transformers_model_id")
|
238 |
+
# Run inference
|
239 |
+
sentences = [
|
240 |
+
'なにか思い出せることは?',
|
241 |
+
'村人はどんな呪文が使えるの?',
|
242 |
+
'キミならどっちがいい?',
|
243 |
+
]
|
244 |
+
embeddings = model.encode(sentences)
|
245 |
+
print(embeddings.shape)
|
246 |
+
# [3, 768]
|
247 |
+
|
248 |
+
# Get the similarity scores for the embeddings
|
249 |
+
similarities = model.similarity(embeddings, embeddings)
|
250 |
+
print(similarities.shape)
|
251 |
+
# [3, 3]
|
252 |
+
```
|
253 |
+
|
254 |
+
<!--
|
255 |
+
### Direct Usage (Transformers)
|
256 |
+
|
257 |
+
<details><summary>Click to see the direct usage in Transformers</summary>
|
258 |
+
|
259 |
+
</details>
|
260 |
+
-->
|
261 |
+
|
262 |
+
<!--
|
263 |
+
### Downstream Usage (Sentence Transformers)
|
264 |
+
|
265 |
+
You can finetune this model on your own dataset.
|
266 |
+
|
267 |
+
<details><summary>Click to expand</summary>
|
268 |
+
|
269 |
+
</details>
|
270 |
+
-->
|
271 |
+
|
272 |
+
<!--
|
273 |
+
### Out-of-Scope Use
|
274 |
+
|
275 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
276 |
+
-->
|
277 |
+
|
278 |
+
## Evaluation
|
279 |
+
|
280 |
+
### Metrics
|
281 |
+
|
282 |
+
#### Binary Classification
|
283 |
+
* Dataset: `custom-arc-semantics-data-jp`
|
284 |
+
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
|
285 |
+
|
286 |
+
| Metric | Value |
|
287 |
+
|:-----------------------------|:-----------|
|
288 |
+
| cosine_accuracy | 0.9705 |
|
289 |
+
| cosine_accuracy_threshold | 0.5118 |
|
290 |
+
| cosine_f1 | 0.9103 |
|
291 |
+
| cosine_f1_threshold | 0.4503 |
|
292 |
+
| cosine_precision | 0.8857 |
|
293 |
+
| cosine_recall | 0.9362 |
|
294 |
+
| cosine_ap | 0.9236 |
|
295 |
+
| dot_accuracy | 0.9695 |
|
296 |
+
| dot_accuracy_threshold | 251.2456 |
|
297 |
+
| dot_f1 | 0.9061 |
|
298 |
+
| dot_f1_threshold | 246.3665 |
|
299 |
+
| dot_precision | 0.89 |
|
300 |
+
| dot_recall | 0.9228 |
|
301 |
+
| dot_ap | 0.9197 |
|
302 |
+
| manhattan_accuracy | 0.9716 |
|
303 |
+
| manhattan_accuracy_threshold | 514.5715 |
|
304 |
+
| manhattan_f1 | 0.9133 |
|
305 |
+
| manhattan_f1_threshold | 514.5715 |
|
306 |
+
| manhattan_precision | 0.8914 |
|
307 |
+
| manhattan_recall | 0.9362 |
|
308 |
+
| manhattan_ap | 0.9255 |
|
309 |
+
| euclidean_accuracy | 0.9721 |
|
310 |
+
| euclidean_accuracy_threshold | 23.1953 |
|
311 |
+
| euclidean_f1 | 0.9148 |
|
312 |
+
| euclidean_f1_threshold | 23.1953 |
|
313 |
+
| euclidean_precision | 0.8942 |
|
314 |
+
| euclidean_recall | 0.9362 |
|
315 |
+
| euclidean_ap | 0.9259 |
|
316 |
+
| max_accuracy | 0.9721 |
|
317 |
+
| max_accuracy_threshold | 514.5715 |
|
318 |
+
| max_f1 | 0.9148 |
|
319 |
+
| max_f1_threshold | 514.5715 |
|
320 |
+
| max_precision | 0.8942 |
|
321 |
+
| max_recall | 0.9362 |
|
322 |
+
| **max_ap** | **0.9259** |
|
323 |
+
|
324 |
+
<!--
|
325 |
+
## Bias, Risks and Limitations
|
326 |
+
|
327 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
328 |
+
-->
|
329 |
+
|
330 |
+
<!--
|
331 |
+
### Recommendations
|
332 |
+
|
333 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
334 |
+
-->
|
335 |
+
|
336 |
+
## Training Details
|
337 |
+
|
338 |
+
### Training Dataset
|
339 |
+
|
340 |
+
#### Unnamed Dataset
|
341 |
+
|
342 |
+
|
343 |
+
* Size: 7,464 training samples
|
344 |
+
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
|
345 |
+
* Approximate statistics based on the first 1000 samples:
|
346 |
+
| | text1 | text2 | label |
|
347 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
|
348 |
+
| type | string | string | int |
|
349 |
+
| details | <ul><li>min: 4 tokens</li><li>mean: 8.32 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.93 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>0: ~86.30%</li><li>1: ~13.70%</li></ul> |
|
350 |
+
* Samples:
|
351 |
+
| text1 | text2 | label |
|
352 |
+
|:------------------------------|:-------------------------|:---------------|
|
353 |
+
| <code>昨日夕飯にチキンヌードル食べた?</code> | <code>何か企んでる?</code> | <code>0</code> |
|
354 |
+
| <code>どっちも欲しくない</code> | <code>お気に入りの食べ物は?</code> | <code>0</code> |
|
355 |
+
| <code>見た目を変える魔法</code> | <code>物の姿を変えられる魔法</code> | <code>1</code> |
|
356 |
+
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
|
357 |
+
```json
|
358 |
+
{
|
359 |
+
"loss_fct": "torch.nn.modules.loss.MSELoss"
|
360 |
+
}
|
361 |
+
```
|
362 |
+
|
363 |
+
### Evaluation Dataset
|
364 |
+
|
365 |
+
#### Unnamed Dataset
|
366 |
+
|
367 |
+
|
368 |
+
* Size: 1,867 evaluation samples
|
369 |
+
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
|
370 |
+
* Approximate statistics based on the first 1000 samples:
|
371 |
+
| | text1 | text2 | label |
|
372 |
+
|:--------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:------------------------------------------------|
|
373 |
+
| type | string | string | int |
|
374 |
+
| details | <ul><li>min: 4 tokens</li><li>mean: 8.24 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.1 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>0: ~83.40%</li><li>1: ~16.60%</li></ul> |
|
375 |
+
* Samples:
|
376 |
+
| text1 | text2 | label |
|
377 |
+
|:------------------------------|:-------------------------|:---------------|
|
378 |
+
| <code>例えば?</code> | <code>どうも</code> | <code>0</code> |
|
379 |
+
| <code>何を作ったの?</code> | <code>君は何でここにいるの?</code> | <code>0</code> |
|
380 |
+
| <code>昨日夕飯にビーフシチュー食べた?</code> | <code>屋根裏って?</code> | <code>0</code> |
|
381 |
+
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
|
382 |
+
```json
|
383 |
+
{
|
384 |
+
"loss_fct": "torch.nn.modules.loss.MSELoss"
|
385 |
+
}
|
386 |
+
```
|
387 |
+
|
388 |
+
### Training Hyperparameters
|
389 |
+
#### Non-Default Hyperparameters
|
390 |
+
|
391 |
+
- `eval_strategy`: epoch
|
392 |
+
- `learning_rate`: 2e-05
|
393 |
+
- `num_train_epochs`: 1
|
394 |
+
- `warmup_ratio`: 0.4
|
395 |
+
- `fp16`: True
|
396 |
+
- `batch_sampler`: no_duplicates
|
397 |
+
|
398 |
+
#### All Hyperparameters
|
399 |
+
<details><summary>Click to expand</summary>
|
400 |
+
|
401 |
+
- `overwrite_output_dir`: False
|
402 |
+
- `do_predict`: False
|
403 |
+
- `eval_strategy`: epoch
|
404 |
+
- `prediction_loss_only`: True
|
405 |
+
- `per_device_train_batch_size`: 8
|
406 |
+
- `per_device_eval_batch_size`: 8
|
407 |
+
- `per_gpu_train_batch_size`: None
|
408 |
+
- `per_gpu_eval_batch_size`: None
|
409 |
+
- `gradient_accumulation_steps`: 1
|
410 |
+
- `eval_accumulation_steps`: None
|
411 |
+
- `torch_empty_cache_steps`: None
|
412 |
+
- `learning_rate`: 2e-05
|
413 |
+
- `weight_decay`: 0.0
|
414 |
+
- `adam_beta1`: 0.9
|
415 |
+
- `adam_beta2`: 0.999
|
416 |
+
- `adam_epsilon`: 1e-08
|
417 |
+
- `max_grad_norm`: 1.0
|
418 |
+
- `num_train_epochs`: 1
|
419 |
+
- `max_steps`: -1
|
420 |
+
- `lr_scheduler_type`: linear
|
421 |
+
- `lr_scheduler_kwargs`: {}
|
422 |
+
- `warmup_ratio`: 0.4
|
423 |
+
- `warmup_steps`: 0
|
424 |
+
- `log_level`: passive
|
425 |
+
- `log_level_replica`: warning
|
426 |
+
- `log_on_each_node`: True
|
427 |
+
- `logging_nan_inf_filter`: True
|
428 |
+
- `save_safetensors`: True
|
429 |
+
- `save_on_each_node`: False
|
430 |
+
- `save_only_model`: False
|
431 |
+
- `restore_callback_states_from_checkpoint`: False
|
432 |
+
- `no_cuda`: False
|
433 |
+
- `use_cpu`: False
|
434 |
+
- `use_mps_device`: False
|
435 |
+
- `seed`: 42
|
436 |
+
- `data_seed`: None
|
437 |
+
- `jit_mode_eval`: False
|
438 |
+
- `use_ipex`: False
|
439 |
+
- `bf16`: False
|
440 |
+
- `fp16`: True
|
441 |
+
- `fp16_opt_level`: O1
|
442 |
+
- `half_precision_backend`: auto
|
443 |
+
- `bf16_full_eval`: False
|
444 |
+
- `fp16_full_eval`: False
|
445 |
+
- `tf32`: None
|
446 |
+
- `local_rank`: 0
|
447 |
+
- `ddp_backend`: None
|
448 |
+
- `tpu_num_cores`: None
|
449 |
+
- `tpu_metrics_debug`: False
|
450 |
+
- `debug`: []
|
451 |
+
- `dataloader_drop_last`: False
|
452 |
+
- `dataloader_num_workers`: 0
|
453 |
+
- `dataloader_prefetch_factor`: None
|
454 |
+
- `past_index`: -1
|
455 |
+
- `disable_tqdm`: False
|
456 |
+
- `remove_unused_columns`: True
|
457 |
+
- `label_names`: None
|
458 |
+
- `load_best_model_at_end`: False
|
459 |
+
- `ignore_data_skip`: False
|
460 |
+
- `fsdp`: []
|
461 |
+
- `fsdp_min_num_params`: 0
|
462 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
463 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
464 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
465 |
+
- `deepspeed`: None
|
466 |
+
- `label_smoothing_factor`: 0.0
|
467 |
+
- `optim`: adamw_torch
|
468 |
+
- `optim_args`: None
|
469 |
+
- `adafactor`: False
|
470 |
+
- `group_by_length`: False
|
471 |
+
- `length_column_name`: length
|
472 |
+
- `ddp_find_unused_parameters`: None
|
473 |
+
- `ddp_bucket_cap_mb`: None
|
474 |
+
- `ddp_broadcast_buffers`: False
|
475 |
+
- `dataloader_pin_memory`: True
|
476 |
+
- `dataloader_persistent_workers`: False
|
477 |
+
- `skip_memory_metrics`: True
|
478 |
+
- `use_legacy_prediction_loop`: False
|
479 |
+
- `push_to_hub`: False
|
480 |
+
- `resume_from_checkpoint`: None
|
481 |
+
- `hub_model_id`: None
|
482 |
+
- `hub_strategy`: every_save
|
483 |
+
- `hub_private_repo`: False
|
484 |
+
- `hub_always_push`: False
|
485 |
+
- `gradient_checkpointing`: False
|
486 |
+
- `gradient_checkpointing_kwargs`: None
|
487 |
+
- `include_inputs_for_metrics`: False
|
488 |
+
- `eval_do_concat_batches`: True
|
489 |
+
- `fp16_backend`: auto
|
490 |
+
- `push_to_hub_model_id`: None
|
491 |
+
- `push_to_hub_organization`: None
|
492 |
+
- `mp_parameters`:
|
493 |
+
- `auto_find_batch_size`: False
|
494 |
+
- `full_determinism`: False
|
495 |
+
- `torchdynamo`: None
|
496 |
+
- `ray_scope`: last
|
497 |
+
- `ddp_timeout`: 1800
|
498 |
+
- `torch_compile`: False
|
499 |
+
- `torch_compile_backend`: None
|
500 |
+
- `torch_compile_mode`: None
|
501 |
+
- `dispatch_batches`: None
|
502 |
+
- `split_batches`: None
|
503 |
+
- `include_tokens_per_second`: False
|
504 |
+
- `include_num_input_tokens_seen`: False
|
505 |
+
- `neftune_noise_alpha`: None
|
506 |
+
- `optim_target_modules`: None
|
507 |
+
- `batch_eval_metrics`: False
|
508 |
+
- `eval_on_start`: False
|
509 |
+
- `eval_use_gather_object`: False
|
510 |
+
- `batch_sampler`: no_duplicates
|
511 |
+
- `multi_dataset_batch_sampler`: proportional
|
512 |
+
|
513 |
+
</details>
|
514 |
+
|
515 |
+
### Training Logs
|
516 |
+
| Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
|
517 |
+
|:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
|
518 |
+
| 1.0 | 933 | 0.0601 | 0.0303 | 0.9259 |
|
519 |
+
|
520 |
+
|
521 |
+
### Framework Versions
|
522 |
+
- Python: 3.10.14
|
523 |
+
- Sentence Transformers: 3.1.1
|
524 |
+
- Transformers: 4.44.2
|
525 |
+
- PyTorch: 2.4.1+cu121
|
526 |
+
- Accelerate: 0.34.2
|
527 |
+
- Datasets: 2.20.0
|
528 |
+
- Tokenizers: 0.19.1
|
529 |
+
|
530 |
+
## Citation
|
531 |
+
|
532 |
+
### BibTeX
|
533 |
+
|
534 |
+
#### Sentence Transformers
|
535 |
+
```bibtex
|
536 |
+
@inproceedings{reimers-2019-sentence-bert,
|
537 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
538 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
539 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
540 |
+
month = "11",
|
541 |
+
year = "2019",
|
542 |
+
publisher = "Association for Computational Linguistics",
|
543 |
+
url = "https://arxiv.org/abs/1908.10084",
|
544 |
+
}
|
545 |
+
```
|
546 |
+
|
547 |
+
<!--
|
548 |
+
## Glossary
|
549 |
+
|
550 |
+
*Clearly define terms in order to be accessible across audiences.*
|
551 |
+
-->
|
552 |
+
|
553 |
+
<!--
|
554 |
+
## Model Card Authors
|
555 |
+
|
556 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
557 |
+
-->
|
558 |
+
|
559 |
+
<!--
|
560 |
+
## Model Card Contact
|
561 |
+
|
562 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
563 |
+
-->
|
checkpoint-933/added_tokens.json
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"[PAD]": 32000
|
3 |
+
}
|
checkpoint-933/config.json
ADDED
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "colorfulscoop/sbert-base-ja",
|
3 |
+
"architectures": [
|
4 |
+
"BertModel"
|
5 |
+
],
|
6 |
+
"attention_probs_dropout_prob": 0.1,
|
7 |
+
"bos_token_id": 2,
|
8 |
+
"classifier_dropout": null,
|
9 |
+
"cls_token_id": 2,
|
10 |
+
"eos_token_id": 3,
|
11 |
+
"gradient_checkpointing": false,
|
12 |
+
"hidden_act": "gelu",
|
13 |
+
"hidden_dropout_prob": 0.1,
|
14 |
+
"hidden_size": 768,
|
15 |
+
"initializer_range": 0.02,
|
16 |
+
"intermediate_size": 3072,
|
17 |
+
"layer_norm_eps": 1e-12,
|
18 |
+
"mask_token_id": 4,
|
19 |
+
"max_position_embeddings": 512,
|
20 |
+
"model_type": "bert",
|
21 |
+
"num_attention_heads": 12,
|
22 |
+
"num_hidden_layers": 12,
|
23 |
+
"pad_token_id": 0,
|
24 |
+
"position_embedding_type": "absolute",
|
25 |
+
"sep_token_id": 3,
|
26 |
+
"tokenizer_class": "DebertaV2Tokenizer",
|
27 |
+
"torch_dtype": "float32",
|
28 |
+
"transformers_version": "4.44.2",
|
29 |
+
"type_vocab_size": 2,
|
30 |
+
"unk_token_id": 1,
|
31 |
+
"use_cache": true,
|
32 |
+
"vocab_size": 32000
|
33 |
+
}
|
checkpoint-933/config_sentence_transformers.json
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "3.1.1",
|
4 |
+
"transformers": "4.44.2",
|
5 |
+
"pytorch": "2.4.1+cu121"
|
6 |
+
},
|
7 |
+
"prompts": {},
|
8 |
+
"default_prompt_name": null,
|
9 |
+
"similarity_fn_name": null
|
10 |
+
}
|
checkpoint-933/model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6758040556e3bed519545360ca6a8f7d1c3ff78255d7efbc798100fd8d614605
|
3 |
+
size 442491744
|
checkpoint-933/modules.json
ADDED
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
[
|
2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
+
"name": "0",
|
5 |
+
"path": "",
|
6 |
+
"type": "sentence_transformers.models.Transformer"
|
7 |
+
},
|
8 |
+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
}
|
14 |
+
]
|
checkpoint-933/optimizer.pt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:4f76ead72c4689b772f64f31fc106718bfcaf4307dcd8502d294f672594af69c
|
3 |
+
size 880373306
|
checkpoint-933/rng_state.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3fbf81be34d062a44e2dcc657dd3503e2a438f425d97b38baa29179afc6ff94a
|
3 |
+
size 13990
|
checkpoint-933/scheduler.pt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:053c342b66b6e453e6ad0b9ef5f6adae981132986ee181a29d59722609f26a43
|
3 |
+
size 1064
|
checkpoint-933/sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 512,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
checkpoint-933/special_tokens_map.json
ADDED
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"bos_token": "[CLS]",
|
3 |
+
"cls_token": "[CLS]",
|
4 |
+
"eos_token": "[SEP]",
|
5 |
+
"mask_token": "[MASK]",
|
6 |
+
"pad_token": "<pad>",
|
7 |
+
"sep_token": "[SEP]",
|
8 |
+
"unk_token": {
|
9 |
+
"content": "<unk>",
|
10 |
+
"lstrip": false,
|
11 |
+
"normalized": true,
|
12 |
+
"rstrip": false,
|
13 |
+
"single_word": false
|
14 |
+
}
|
15 |
+
}
|
checkpoint-933/spm.model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d6467857b4b0c77ded9bac7ad2fb5c16eb64e17e417ce46624dacac2bbb404fc
|
3 |
+
size 802713
|
checkpoint-933/tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
checkpoint-933/tokenizer_config.json
ADDED
@@ -0,0 +1,65 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 |
+
"1": {
|
12 |
+
"content": "<unk>",
|
13 |
+
"lstrip": false,
|
14 |
+
"normalized": true,
|
15 |
+
"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
+
"2": {
|
20 |
+
"content": "[CLS]",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": false
|
26 |
+
},
|
27 |
+
"3": {
|
28 |
+
"content": "[SEP]",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": false
|
34 |
+
},
|
35 |
+
"4": {
|
36 |
+
"content": "[MASK]",
|
37 |
+
"lstrip": false,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": false
|
42 |
+
},
|
43 |
+
"32000": {
|
44 |
+
"content": "[PAD]",
|
45 |
+
"lstrip": false,
|
46 |
+
"normalized": true,
|
47 |
+
"rstrip": false,
|
48 |
+
"single_word": false,
|
49 |
+
"special": false
|
50 |
+
}
|
51 |
+
},
|
52 |
+
"bos_token": "[CLS]",
|
53 |
+
"clean_up_tokenization_spaces": true,
|
54 |
+
"cls_token": "[CLS]",
|
55 |
+
"do_lower_case": false,
|
56 |
+
"eos_token": "[SEP]",
|
57 |
+
"mask_token": "[MASK]",
|
58 |
+
"model_max_length": 512,
|
59 |
+
"pad_token": "<pad>",
|
60 |
+
"sep_token": "[SEP]",
|
61 |
+
"sp_model_kwargs": {},
|
62 |
+
"split_by_punct": false,
|
63 |
+
"tokenizer_class": "DebertaV2Tokenizer",
|
64 |
+
"unk_token": "<unk>"
|
65 |
+
}
|
checkpoint-933/trainer_state.json
ADDED
@@ -0,0 +1,83 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"best_metric": null,
|
3 |
+
"best_model_checkpoint": null,
|
4 |
+
"epoch": 1.0,
|
5 |
+
"eval_steps": 10,
|
6 |
+
"global_step": 933,
|
7 |
+
"is_hyper_param_search": false,
|
8 |
+
"is_local_process_zero": true,
|
9 |
+
"is_world_process_zero": true,
|
10 |
+
"log_history": [
|
11 |
+
{
|
12 |
+
"epoch": 1.0,
|
13 |
+
"grad_norm": 0.4457938075065613,
|
14 |
+
"learning_rate": 0.0,
|
15 |
+
"loss": 0.0601,
|
16 |
+
"step": 933
|
17 |
+
},
|
18 |
+
{
|
19 |
+
"epoch": 1.0,
|
20 |
+
"eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9705409748259239,
|
21 |
+
"eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5118279457092285,
|
22 |
+
"eval_custom-arc-semantics-data-jp_cosine_ap": 0.9236294738598163,
|
23 |
+
"eval_custom-arc-semantics-data-jp_cosine_f1": 0.9102773246329527,
|
24 |
+
"eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.45031607151031494,
|
25 |
+
"eval_custom-arc-semantics-data-jp_cosine_precision": 0.8857142857142857,
|
26 |
+
"eval_custom-arc-semantics-data-jp_cosine_recall": 0.9362416107382551,
|
27 |
+
"eval_custom-arc-semantics-data-jp_dot_accuracy": 0.9694697375468666,
|
28 |
+
"eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 251.2455596923828,
|
29 |
+
"eval_custom-arc-semantics-data-jp_dot_ap": 0.9196731890884118,
|
30 |
+
"eval_custom-arc-semantics-data-jp_dot_f1": 0.9060955518945634,
|
31 |
+
"eval_custom-arc-semantics-data-jp_dot_f1_threshold": 246.36648559570312,
|
32 |
+
"eval_custom-arc-semantics-data-jp_dot_precision": 0.889967637540453,
|
33 |
+
"eval_custom-arc-semantics-data-jp_dot_recall": 0.9228187919463087,
|
34 |
+
"eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9721478307445099,
|
35 |
+
"eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 23.195274353027344,
|
36 |
+
"eval_custom-arc-semantics-data-jp_euclidean_ap": 0.9259440018381992,
|
37 |
+
"eval_custom-arc-semantics-data-jp_euclidean_f1": 0.9147540983606558,
|
38 |
+
"eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 23.195274353027344,
|
39 |
+
"eval_custom-arc-semantics-data-jp_euclidean_precision": 0.8942307692307693,
|
40 |
+
"eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9362416107382551,
|
41 |
+
"eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9716122121049813,
|
42 |
+
"eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 514.571533203125,
|
43 |
+
"eval_custom-arc-semantics-data-jp_manhattan_ap": 0.9255015709844487,
|
44 |
+
"eval_custom-arc-semantics-data-jp_manhattan_f1": 0.9132569558101473,
|
45 |
+
"eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 514.571533203125,
|
46 |
+
"eval_custom-arc-semantics-data-jp_manhattan_precision": 0.8913738019169329,
|
47 |
+
"eval_custom-arc-semantics-data-jp_manhattan_recall": 0.9362416107382551,
|
48 |
+
"eval_custom-arc-semantics-data-jp_max_accuracy": 0.9721478307445099,
|
49 |
+
"eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 514.571533203125,
|
50 |
+
"eval_custom-arc-semantics-data-jp_max_ap": 0.9259440018381992,
|
51 |
+
"eval_custom-arc-semantics-data-jp_max_f1": 0.9147540983606558,
|
52 |
+
"eval_custom-arc-semantics-data-jp_max_f1_threshold": 514.571533203125,
|
53 |
+
"eval_custom-arc-semantics-data-jp_max_precision": 0.8942307692307693,
|
54 |
+
"eval_custom-arc-semantics-data-jp_max_recall": 0.9362416107382551,
|
55 |
+
"eval_loss": 0.030283836647868156,
|
56 |
+
"eval_runtime": 238.5188,
|
57 |
+
"eval_samples_per_second": 7.827,
|
58 |
+
"eval_steps_per_second": 0.981,
|
59 |
+
"step": 933
|
60 |
+
}
|
61 |
+
],
|
62 |
+
"logging_steps": 500,
|
63 |
+
"max_steps": 933,
|
64 |
+
"num_input_tokens_seen": 0,
|
65 |
+
"num_train_epochs": 1,
|
66 |
+
"save_steps": 100,
|
67 |
+
"stateful_callbacks": {
|
68 |
+
"TrainerControl": {
|
69 |
+
"args": {
|
70 |
+
"should_epoch_stop": false,
|
71 |
+
"should_evaluate": false,
|
72 |
+
"should_log": false,
|
73 |
+
"should_save": true,
|
74 |
+
"should_training_stop": true
|
75 |
+
},
|
76 |
+
"attributes": {}
|
77 |
+
}
|
78 |
+
},
|
79 |
+
"total_flos": 0.0,
|
80 |
+
"train_batch_size": 8,
|
81 |
+
"trial_name": null,
|
82 |
+
"trial_params": null
|
83 |
+
}
|
checkpoint-933/training_args.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:183d9908193d1ee6d3162bb02d34344be732b5c1f0df4a2c1f5c6d3be91b5cd4
|
3 |
+
size 5432
|
model.safetensors
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 442491744
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6758040556e3bed519545360ca6a8f7d1c3ff78255d7efbc798100fd8d614605
|
3 |
size 442491744
|
runs/Sep19_22-03-26_default/events.out.tfevents.1726783408.default.3567.0
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:e5289ac2fdee49b730acbdfad03fc3a7aa20fd0d669c155971393539406aa7b9
|
3 |
+
size 8199
|