Add new SentenceTransformer model.
Browse files
README.md
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---
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base_model: colorfulscoop/sbert-base-ja
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---
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#
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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Generates similarity embeddings
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** ja
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- **License:** cc-by-sa-4.0
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- **Finetuned from model [optional]:** colorfulscoop/sbert-base-ja
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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[More Information Needed]
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<!--
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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<!--
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Contact
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---
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base_model: colorfulscoop/sbert-base-ja
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library_name: sentence-transformers
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metrics:
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- cosine_accuracy
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- cosine_accuracy_threshold
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- cosine_f1
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- cosine_f1_threshold
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- cosine_precision
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- cosine_recall
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- cosine_ap
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- dot_accuracy
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- dot_accuracy_threshold
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- dot_f1
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- dot_f1_threshold
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- dot_precision
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- dot_recall
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- dot_ap
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- manhattan_accuracy
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- manhattan_accuracy_threshold
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- manhattan_f1
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- manhattan_f1_threshold
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- manhattan_precision
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- manhattan_recall
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- manhattan_ap
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- euclidean_accuracy
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- euclidean_accuracy_threshold
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- euclidean_f1
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- euclidean_f1_threshold
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- euclidean_precision
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- euclidean_recall
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- euclidean_ap
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- max_accuracy
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- max_accuracy_threshold
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- max_f1
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- max_f1_threshold
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- max_precision
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- max_recall
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- max_ap
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- generated_from_trainer
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- dataset_size:401
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- loss:CosineSimilarityLoss
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widget:
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- source_sentence: 昨夜何を食べたの?
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sentences:
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- 昨日作ったのはチキンヌードル?
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- 村長がどうしたの?
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- 雲より高くってどこ?
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- source_sentence: ナイトスタンドにある?
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sentences:
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- わかんない
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- スリッパ履いた?
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- だめじゃん
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- source_sentence: センパイ
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sentences:
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- 昨日の夜は暑かった
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- 調子はどう?
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- トーチ
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- source_sentence: 村人はどんな呪文を使うの?
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sentences:
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- スパイク
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- キミはどんな魔法を使うの?
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- うさんくさい
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- source_sentence: 祭壇の些細な違和感ってどういう意味?
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sentences:
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- 青いオーブがどこにあるか知ってる?
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- 赤い染みが皿にあった
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- これが花
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model-index:
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- name: SentenceTransformer based on colorfulscoop/sbert-base-ja
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results:
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- task:
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type: binary-classification
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name: Binary Classification
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dataset:
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name: custom arc semantics data jp
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type: custom-arc-semantics-data-jp
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metrics:
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- type: cosine_accuracy
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value: 0.8855721393034826
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name: Cosine Accuracy
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- type: cosine_accuracy_threshold
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value: 0.6970740556716919
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name: Cosine Accuracy Threshold
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- type: cosine_f1
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value: 0.7766990291262137
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name: Cosine F1
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- type: cosine_f1_threshold
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value: 0.6637545228004456
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name: Cosine F1 Threshold
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- type: cosine_precision
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value: 0.8163265306122449
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name: Cosine Precision
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- type: cosine_recall
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value: 0.7407407407407407
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name: Cosine Recall
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- type: cosine_ap
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value: 0.6606381605892593
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name: Cosine Ap
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- type: dot_accuracy
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value: 0.8805970149253731
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name: Dot Accuracy
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- type: dot_accuracy_threshold
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value: 378.6933898925781
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name: Dot Accuracy Threshold
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- type: dot_f1
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value: 0.7647058823529411
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name: Dot F1
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- type: dot_f1_threshold
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value: 378.6933898925781
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name: Dot F1 Threshold
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- type: dot_precision
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value: 0.8125
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name: Dot Precision
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- type: dot_recall
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value: 0.7222222222222222
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name: Dot Recall
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- type: dot_ap
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value: 0.6865123266544332
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name: Dot Ap
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- type: manhattan_accuracy
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value: 0.8855721393034826
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name: Manhattan Accuracy
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- type: manhattan_accuracy_threshold
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value: 407.9349365234375
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name: Manhattan Accuracy Threshold
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- type: manhattan_f1
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value: 0.7766990291262137
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name: Manhattan F1
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- type: manhattan_f1_threshold
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value: 426.941650390625
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name: Manhattan F1 Threshold
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- type: manhattan_precision
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value: 0.8163265306122449
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name: Manhattan Precision
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- type: manhattan_recall
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value: 0.7407407407407407
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name: Manhattan Recall
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- type: manhattan_ap
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value: 0.6609390536301427
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name: Manhattan Ap
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- type: euclidean_accuracy
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value: 0.8855721393034826
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name: Euclidean Accuracy
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- type: euclidean_accuracy_threshold
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+
value: 18.663713455200195
|
152 |
+
name: Euclidean Accuracy Threshold
|
153 |
+
- type: euclidean_f1
|
154 |
+
value: 0.7766990291262137
|
155 |
+
name: Euclidean F1
|
156 |
+
- type: euclidean_f1_threshold
|
157 |
+
value: 19.35655975341797
|
158 |
+
name: Euclidean F1 Threshold
|
159 |
+
- type: euclidean_precision
|
160 |
+
value: 0.8163265306122449
|
161 |
+
name: Euclidean Precision
|
162 |
+
- type: euclidean_recall
|
163 |
+
value: 0.7407407407407407
|
164 |
+
name: Euclidean Recall
|
165 |
+
- type: euclidean_ap
|
166 |
+
value: 0.6602743223356511
|
167 |
+
name: Euclidean Ap
|
168 |
+
- type: max_accuracy
|
169 |
+
value: 0.8855721393034826
|
170 |
+
name: Max Accuracy
|
171 |
+
- type: max_accuracy_threshold
|
172 |
+
value: 407.9349365234375
|
173 |
+
name: Max Accuracy Threshold
|
174 |
+
- type: max_f1
|
175 |
+
value: 0.7766990291262137
|
176 |
+
name: Max F1
|
177 |
+
- type: max_f1_threshold
|
178 |
+
value: 426.941650390625
|
179 |
+
name: Max F1 Threshold
|
180 |
+
- type: max_precision
|
181 |
+
value: 0.8163265306122449
|
182 |
+
name: Max Precision
|
183 |
+
- type: max_recall
|
184 |
+
value: 0.7407407407407407
|
185 |
+
name: Max Recall
|
186 |
+
- type: max_ap
|
187 |
+
value: 0.6865123266544332
|
188 |
+
name: Max Ap
|
189 |
---
|
190 |
|
191 |
+
# SentenceTransformer based on colorfulscoop/sbert-base-ja
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|
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|
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+
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) on the csv dataset. 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.
|
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## Model Details
|
196 |
|
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### Model Description
|
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+
- **Model Type:** Sentence Transformer
|
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+
- **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
|
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+
- **Similarity Function:** Cosine Similarity
|
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+
- **Training Dataset:**
|
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+
- csv
|
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+
<!-- - **Language:** Unknown -->
|
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+
<!-- - **License:** Unknown -->
|
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|
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+
### Model Sources
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+
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
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+
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
|
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+
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
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|
214 |
+
### Full Model Architecture
|
215 |
|
216 |
+
```
|
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+
SentenceTransformer(
|
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+
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
|
219 |
+
(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})
|
220 |
+
)
|
221 |
+
```
|
222 |
|
223 |
+
## Usage
|
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|
225 |
+
### Direct Usage (Sentence Transformers)
|
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|
227 |
+
First install the Sentence Transformers library:
|
228 |
|
229 |
+
```bash
|
230 |
+
pip install -U sentence-transformers
|
231 |
+
```
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|
232 |
|
233 |
+
Then you can load this model and run inference.
|
234 |
+
```python
|
235 |
+
from sentence_transformers import SentenceTransformer
|
236 |
|
237 |
+
# Download from the 🤗 Hub
|
238 |
+
model = SentenceTransformer("sentence_transformers_model_id")
|
239 |
+
# Run inference
|
240 |
+
sentences = [
|
241 |
+
'祭壇の些細な違和感ってどういう意味?',
|
242 |
+
'青いオーブがどこにあるか知ってる?',
|
243 |
+
'これが花',
|
244 |
+
]
|
245 |
+
embeddings = model.encode(sentences)
|
246 |
+
print(embeddings.shape)
|
247 |
+
# [3, 768]
|
248 |
|
249 |
+
# Get the similarity scores for the embeddings
|
250 |
+
similarities = model.similarity(embeddings, embeddings)
|
251 |
+
print(similarities.shape)
|
252 |
+
# [3, 3]
|
253 |
+
```
|
254 |
|
255 |
+
<!--
|
256 |
+
### Direct Usage (Transformers)
|
257 |
|
258 |
+
<details><summary>Click to see the direct usage in Transformers</summary>
|
259 |
|
260 |
+
</details>
|
261 |
+
-->
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|
262 |
|
263 |
+
<!--
|
264 |
+
### Downstream Usage (Sentence Transformers)
|
265 |
|
266 |
+
You can finetune this model on your own dataset.
|
267 |
|
268 |
+
<details><summary>Click to expand</summary>
|
269 |
|
270 |
+
</details>
|
271 |
+
-->
|
272 |
|
273 |
+
<!--
|
274 |
+
### Out-of-Scope Use
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|
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+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
277 |
+
-->
|
278 |
|
279 |
## Evaluation
|
280 |
|
281 |
+
### Metrics
|
282 |
+
|
283 |
+
#### Binary Classification
|
284 |
+
* Dataset: `custom-arc-semantics-data-jp`
|
285 |
+
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
|
286 |
+
|
287 |
+
| Metric | Value |
|
288 |
+
|:-----------------------------|:-----------|
|
289 |
+
| cosine_accuracy | 0.8856 |
|
290 |
+
| cosine_accuracy_threshold | 0.6971 |
|
291 |
+
| cosine_f1 | 0.7767 |
|
292 |
+
| cosine_f1_threshold | 0.6638 |
|
293 |
+
| cosine_precision | 0.8163 |
|
294 |
+
| cosine_recall | 0.7407 |
|
295 |
+
| cosine_ap | 0.6606 |
|
296 |
+
| dot_accuracy | 0.8806 |
|
297 |
+
| dot_accuracy_threshold | 378.6934 |
|
298 |
+
| dot_f1 | 0.7647 |
|
299 |
+
| dot_f1_threshold | 378.6934 |
|
300 |
+
| dot_precision | 0.8125 |
|
301 |
+
| dot_recall | 0.7222 |
|
302 |
+
| dot_ap | 0.6865 |
|
303 |
+
| manhattan_accuracy | 0.8856 |
|
304 |
+
| manhattan_accuracy_threshold | 407.9349 |
|
305 |
+
| manhattan_f1 | 0.7767 |
|
306 |
+
| manhattan_f1_threshold | 426.9417 |
|
307 |
+
| manhattan_precision | 0.8163 |
|
308 |
+
| manhattan_recall | 0.7407 |
|
309 |
+
| manhattan_ap | 0.6609 |
|
310 |
+
| euclidean_accuracy | 0.8856 |
|
311 |
+
| euclidean_accuracy_threshold | 18.6637 |
|
312 |
+
| euclidean_f1 | 0.7767 |
|
313 |
+
| euclidean_f1_threshold | 19.3566 |
|
314 |
+
| euclidean_precision | 0.8163 |
|
315 |
+
| euclidean_recall | 0.7407 |
|
316 |
+
| euclidean_ap | 0.6603 |
|
317 |
+
| max_accuracy | 0.8856 |
|
318 |
+
| max_accuracy_threshold | 407.9349 |
|
319 |
+
| max_f1 | 0.7767 |
|
320 |
+
| max_f1_threshold | 426.9417 |
|
321 |
+
| max_precision | 0.8163 |
|
322 |
+
| max_recall | 0.7407 |
|
323 |
+
| **max_ap** | **0.6865** |
|
324 |
+
|
325 |
+
<!--
|
326 |
+
## Bias, Risks and Limitations
|
327 |
+
|
328 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
329 |
+
-->
|
330 |
+
|
331 |
+
<!--
|
332 |
+
### Recommendations
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|
333 |
|
334 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
335 |
+
-->
|
336 |
|
337 |
+
## Training Details
|
338 |
|
339 |
+
### Training Dataset
|
340 |
+
|
341 |
+
#### csv
|
342 |
+
|
343 |
+
* Dataset: csv
|
344 |
+
* Size: 401 training samples
|
345 |
+
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
|
346 |
+
* Approximate statistics based on the first 401 samples:
|
347 |
+
| | text1 | text2 | label |
|
348 |
+
|:--------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:------------------------------------------------|
|
349 |
+
| type | string | string | int |
|
350 |
+
| details | <ul><li>min: 4 tokens</li><li>mean: 8.34 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.9 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~67.00%</li><li>1: ~33.00%</li></ul> |
|
351 |
+
* Samples:
|
352 |
+
| text1 | text2 | label |
|
353 |
+
|:-----------------------------------|:-------------------------|:---------------|
|
354 |
+
| <code>雲より高くってどういう意味?</code> | <code>猫好き</code> | <code>0</code> |
|
355 |
+
| <code>花の囁きってなに?</code> | <code>リリアンについて教えて</code> | <code>0</code> |
|
356 |
+
| <code>リリアンってものの姿を変える魔法を使える?</code> | <code>どんな魔法なの?</code> | <code>0</code> |
|
357 |
+
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
|
358 |
+
```json
|
359 |
+
{
|
360 |
+
"loss_fct": "torch.nn.modules.loss.MSELoss"
|
361 |
+
}
|
362 |
+
```
|
363 |
+
|
364 |
+
### Evaluation Dataset
|
365 |
+
|
366 |
+
#### csv
|
367 |
+
|
368 |
+
* Dataset: csv
|
369 |
+
* Size: 401 evaluation samples
|
370 |
+
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
|
371 |
+
* Approximate statistics based on the first 401 samples:
|
372 |
+
| | text1 | text2 | label |
|
373 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
|
374 |
+
| type | string | string | int |
|
375 |
+
| details | <ul><li>min: 4 tokens</li><li>mean: 8.23 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.46 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~73.13%</li><li>1: ~26.87%</li></ul> |
|
376 |
+
* Samples:
|
377 |
+
| text1 | text2 | label |
|
378 |
+
|:-----------------------------|:------------------------------|:---------------|
|
379 |
+
| <code>棚からトマトがなくなってたから</code> | <code>トマトが棚からなくなっていたから</code> | <code>1</code> |
|
380 |
+
| <code>欲しくない</code> | <code>家の中へ行こう</code> | <code>0</code> |
|
381 |
+
| <code>昨日は何を作ったの?</code> | <code>ビーフシチュー食べた?</code> | <code>0</code> |
|
382 |
+
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
|
383 |
+
```json
|
384 |
+
{
|
385 |
+
"loss_fct": "torch.nn.modules.loss.MSELoss"
|
386 |
+
}
|
387 |
+
```
|
388 |
+
|
389 |
+
### Training Hyperparameters
|
390 |
+
#### Non-Default Hyperparameters
|
391 |
+
|
392 |
+
- `eval_strategy`: epoch
|
393 |
+
- `learning_rate`: 2e-05
|
394 |
+
- `num_train_epochs`: 10
|
395 |
+
- `warmup_ratio`: 0.4
|
396 |
+
- `fp16`: True
|
397 |
+
- `batch_sampler`: no_duplicates
|
398 |
+
|
399 |
+
#### All Hyperparameters
|
400 |
+
<details><summary>Click to expand</summary>
|
401 |
+
|
402 |
+
- `overwrite_output_dir`: False
|
403 |
+
- `do_predict`: False
|
404 |
+
- `eval_strategy`: epoch
|
405 |
+
- `prediction_loss_only`: True
|
406 |
+
- `per_device_train_batch_size`: 8
|
407 |
+
- `per_device_eval_batch_size`: 8
|
408 |
+
- `per_gpu_train_batch_size`: None
|
409 |
+
- `per_gpu_eval_batch_size`: None
|
410 |
+
- `gradient_accumulation_steps`: 1
|
411 |
+
- `eval_accumulation_steps`: None
|
412 |
+
- `torch_empty_cache_steps`: None
|
413 |
+
- `learning_rate`: 2e-05
|
414 |
+
- `weight_decay`: 0.0
|
415 |
+
- `adam_beta1`: 0.9
|
416 |
+
- `adam_beta2`: 0.999
|
417 |
+
- `adam_epsilon`: 1e-08
|
418 |
+
- `max_grad_norm`: 1.0
|
419 |
+
- `num_train_epochs`: 10
|
420 |
+
- `max_steps`: -1
|
421 |
+
- `lr_scheduler_type`: linear
|
422 |
+
- `lr_scheduler_kwargs`: {}
|
423 |
+
- `warmup_ratio`: 0.4
|
424 |
+
- `warmup_steps`: 0
|
425 |
+
- `log_level`: passive
|
426 |
+
- `log_level_replica`: warning
|
427 |
+
- `log_on_each_node`: True
|
428 |
+
- `logging_nan_inf_filter`: True
|
429 |
+
- `save_safetensors`: True
|
430 |
+
- `save_on_each_node`: False
|
431 |
+
- `save_only_model`: False
|
432 |
+
- `restore_callback_states_from_checkpoint`: False
|
433 |
+
- `no_cuda`: False
|
434 |
+
- `use_cpu`: False
|
435 |
+
- `use_mps_device`: False
|
436 |
+
- `seed`: 42
|
437 |
+
- `data_seed`: None
|
438 |
+
- `jit_mode_eval`: False
|
439 |
+
- `use_ipex`: False
|
440 |
+
- `bf16`: False
|
441 |
+
- `fp16`: True
|
442 |
+
- `fp16_opt_level`: O1
|
443 |
+
- `half_precision_backend`: auto
|
444 |
+
- `bf16_full_eval`: False
|
445 |
+
- `fp16_full_eval`: False
|
446 |
+
- `tf32`: None
|
447 |
+
- `local_rank`: 0
|
448 |
+
- `ddp_backend`: None
|
449 |
+
- `tpu_num_cores`: None
|
450 |
+
- `tpu_metrics_debug`: False
|
451 |
+
- `debug`: []
|
452 |
+
- `dataloader_drop_last`: False
|
453 |
+
- `dataloader_num_workers`: 0
|
454 |
+
- `dataloader_prefetch_factor`: None
|
455 |
+
- `past_index`: -1
|
456 |
+
- `disable_tqdm`: False
|
457 |
+
- `remove_unused_columns`: True
|
458 |
+
- `label_names`: None
|
459 |
+
- `load_best_model_at_end`: False
|
460 |
+
- `ignore_data_skip`: False
|
461 |
+
- `fsdp`: []
|
462 |
+
- `fsdp_min_num_params`: 0
|
463 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
464 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
465 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
466 |
+
- `deepspeed`: None
|
467 |
+
- `label_smoothing_factor`: 0.0
|
468 |
+
- `optim`: adamw_torch
|
469 |
+
- `optim_args`: None
|
470 |
+
- `adafactor`: False
|
471 |
+
- `group_by_length`: False
|
472 |
+
- `length_column_name`: length
|
473 |
+
- `ddp_find_unused_parameters`: None
|
474 |
+
- `ddp_bucket_cap_mb`: None
|
475 |
+
- `ddp_broadcast_buffers`: False
|
476 |
+
- `dataloader_pin_memory`: True
|
477 |
+
- `dataloader_persistent_workers`: False
|
478 |
+
- `skip_memory_metrics`: True
|
479 |
+
- `use_legacy_prediction_loop`: False
|
480 |
+
- `push_to_hub`: False
|
481 |
+
- `resume_from_checkpoint`: None
|
482 |
+
- `hub_model_id`: None
|
483 |
+
- `hub_strategy`: every_save
|
484 |
+
- `hub_private_repo`: False
|
485 |
+
- `hub_always_push`: False
|
486 |
+
- `gradient_checkpointing`: False
|
487 |
+
- `gradient_checkpointing_kwargs`: None
|
488 |
+
- `include_inputs_for_metrics`: False
|
489 |
+
- `eval_do_concat_batches`: True
|
490 |
+
- `fp16_backend`: auto
|
491 |
+
- `push_to_hub_model_id`: None
|
492 |
+
- `push_to_hub_organization`: None
|
493 |
+
- `mp_parameters`:
|
494 |
+
- `auto_find_batch_size`: False
|
495 |
+
- `full_determinism`: False
|
496 |
+
- `torchdynamo`: None
|
497 |
+
- `ray_scope`: last
|
498 |
+
- `ddp_timeout`: 1800
|
499 |
+
- `torch_compile`: False
|
500 |
+
- `torch_compile_backend`: None
|
501 |
+
- `torch_compile_mode`: None
|
502 |
+
- `dispatch_batches`: None
|
503 |
+
- `split_batches`: None
|
504 |
+
- `include_tokens_per_second`: False
|
505 |
+
- `include_num_input_tokens_seen`: False
|
506 |
+
- `neftune_noise_alpha`: None
|
507 |
+
- `optim_target_modules`: None
|
508 |
+
- `batch_eval_metrics`: False
|
509 |
+
- `eval_on_start`: False
|
510 |
+
- `eval_use_gather_object`: False
|
511 |
+
- `batch_sampler`: no_duplicates
|
512 |
+
- `multi_dataset_batch_sampler`: proportional
|
513 |
+
|
514 |
+
</details>
|
515 |
+
|
516 |
+
### Training Logs
|
517 |
+
| Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
|
518 |
+
|:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
|
519 |
+
| 1.0 | 25 | 0.2393 | 0.2390 | 0.5655 |
|
520 |
+
| 2.0 | 50 | 0.1724 | 0.1689 | 0.6075 |
|
521 |
+
| 3.0 | 75 | 0.1046 | 0.1326 | 0.6478 |
|
522 |
+
| 4.0 | 100 | 0.062 | 0.1183 | 0.6618 |
|
523 |
+
| 5.0 | 125 | 0.0349 | 0.1158 | 0.6683 |
|
524 |
+
| 6.0 | 150 | 0.0258 | 0.1142 | 0.6772 |
|
525 |
+
| 7.0 | 175 | 0.0211 | 0.1168 | 0.6739 |
|
526 |
+
| 8.0 | 200 | 0.0204 | 0.1180 | 0.6765 |
|
527 |
+
| 9.0 | 225 | 0.0194 | 0.1178 | 0.6869 |
|
528 |
+
| 10.0 | 250 | 0.0185 | 0.1180 | 0.6865 |
|
529 |
+
|
530 |
+
|
531 |
+
### Framework Versions
|
532 |
+
- Python: 3.10.14
|
533 |
+
- Sentence Transformers: 3.1.0
|
534 |
+
- Transformers: 4.44.2
|
535 |
+
- PyTorch: 2.4.1+cu121
|
536 |
+
- Accelerate: 0.34.2
|
537 |
+
- Datasets: 2.20.0
|
538 |
+
- Tokenizers: 0.19.1
|
539 |
+
|
540 |
+
## Citation
|
541 |
+
|
542 |
+
### BibTeX
|
543 |
+
|
544 |
+
#### Sentence Transformers
|
545 |
+
```bibtex
|
546 |
+
@inproceedings{reimers-2019-sentence-bert,
|
547 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
548 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
549 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
550 |
+
month = "11",
|
551 |
+
year = "2019",
|
552 |
+
publisher = "Association for Computational Linguistics",
|
553 |
+
url = "https://arxiv.org/abs/1908.10084",
|
554 |
+
}
|
555 |
+
```
|
556 |
+
|
557 |
+
<!--
|
558 |
+
## Glossary
|
559 |
+
|
560 |
+
*Clearly define terms in order to be accessible across audiences.*
|
561 |
+
-->
|
562 |
+
|
563 |
+
<!--
|
564 |
+
## Model Card Authors
|
565 |
+
|
566 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
567 |
+
-->
|
568 |
+
|
569 |
+
<!--
|
570 |
## Model Card Contact
|
571 |
|
572 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
573 |
+
-->
|
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