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---
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base_model: colorfulscoop/sbert-base-ja
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- cosine_accuracy_threshold
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- cosine_f1_threshold
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- cosine_precision
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- cosine_recall
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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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- manhattan_accuracy
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- manhattan_accuracy_threshold
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- manhattan_f1_threshold
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- manhattan_precision
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- max_f1_threshold
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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:53
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- loss:OnlineContrastiveLoss
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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: 0.6666666666666666
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name: Cosine Accuracy
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- type: cosine_accuracy_threshold
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value: 0.9126271605491638
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name: Cosine Accuracy Threshold
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- type: cosine_f1
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value: 0.8000000000000002
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name: Cosine F1
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- type: cosine_f1_threshold
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value: 0.8779952526092529
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name: Cosine F1 Threshold
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- type: cosine_precision
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value: 0.8
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name: Cosine Precision
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- type: cosine_recall
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value: 0.8
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name: Cosine Recall
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- type: cosine_ap
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value: 0.9266666666666665
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name: Cosine Ap
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- type: dot_accuracy
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value: 0.8333333333333334
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name: Dot Accuracy
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- type: dot_accuracy_threshold
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value: 504.3712158203125
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name: Dot Accuracy Threshold
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- type: dot_f1
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value: 0.888888888888889
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name: Dot F1
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- type: dot_f1_threshold
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value: 504.3712158203125
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name: Dot F1 Threshold
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- type: dot_precision
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value: 1.0
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name: Dot Precision
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- type: dot_recall
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value: 0.8
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name: Dot Recall
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- type: dot_ap
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value: 0.9666666666666666
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name: Dot Ap
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- type: manhattan_accuracy
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value: 0.6666666666666666
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name: Manhattan Accuracy
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- type: manhattan_accuracy_threshold
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value: 217.20816040039062
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name: Manhattan Accuracy Threshold
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- type: manhattan_f1
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value: 0.8000000000000002
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name: Manhattan F1
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- type: manhattan_f1_threshold
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value: 259.5025939941406
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name: Manhattan F1 Threshold
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- type: manhattan_precision
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value: 0.8
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name: Manhattan Precision
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- type: manhattan_recall
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value: 0.8
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name: Manhattan Recall
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- type: manhattan_ap
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value: 0.9266666666666665
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name: Manhattan Ap
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- type: euclidean_accuracy
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value: 0.6666666666666666
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name: Euclidean Accuracy
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- type: euclidean_accuracy_threshold
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value: 9.874061584472656
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name: Euclidean Accuracy Threshold
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- type: euclidean_f1
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value: 0.8000000000000002
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name: Euclidean F1
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- type: euclidean_f1_threshold
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value: 11.74197006225586
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name: Euclidean F1 Threshold
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- type: euclidean_precision
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value: 0.8
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name: Euclidean Precision
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- type: euclidean_recall
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value: 0.8
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name: Euclidean Recall
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- type: euclidean_ap
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value: 0.9266666666666665
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name: Euclidean Ap
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- type: max_accuracy
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value: 0.8333333333333334
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name: Max Accuracy
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- type: max_accuracy_threshold
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value: 504.3712158203125
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name: Max Accuracy Threshold
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- type: max_f1
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value: 0.888888888888889
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name: Max F1
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- type: max_f1_threshold
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value: 504.3712158203125
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name: Max F1 Threshold
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- type: max_precision
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value: 1.0
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name: Max Precision
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- type: max_recall
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value: 0.8
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name: Max Recall
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- type: max_ap
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value: 0.9666666666666666
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name: Max Ap
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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
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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 -->
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- **Maximum Sequence Length:** 512 tokens
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- **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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- **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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SentenceTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
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(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})
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)
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```
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pip install -U sentence-transformers
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```
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("sentence_transformers_model_id")
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# Run inference
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sentences = [
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'The weather is lovely today.',
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"It's so sunny outside!",
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'He drove to the stadium.',
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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# [3, 768]
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similarities = model.similarity(embeddings, embeddings)
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print(similarities.shape)
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# [3, 3]
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```
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### Direct Usage (Transformers)
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-->
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### Downstream Usage (Sentence Transformers)
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-->
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<!--
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### Out-of-Scope Use
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-->
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## Evaluation
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### Metrics
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#### Binary Classification
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* Dataset: `custom-arc-semantics-data-jp`
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* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
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| Metric | Value |
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|:-----------------------------|:-----------|
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| cosine_accuracy | 0.6667 |
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| cosine_accuracy_threshold | 0.9126 |
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| cosine_f1 | 0.8 |
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| cosine_f1_threshold | 0.878 |
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| cosine_precision | 0.8 |
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| cosine_recall | 0.8 |
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| cosine_ap | 0.9267 |
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| dot_accuracy | 0.8333 |
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| dot_accuracy_threshold | 504.3712 |
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| dot_f1 | 0.8889 |
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| dot_f1_threshold | 504.3712 |
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| dot_precision | 1.0 |
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| dot_recall | 0.8 |
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| dot_ap | 0.9667 |
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| manhattan_accuracy | 0.6667 |
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| manhattan_accuracy_threshold | 217.2082 |
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| manhattan_f1 | 0.8 |
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| manhattan_f1_threshold | 259.5026 |
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| manhattan_precision | 0.8 |
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| manhattan_recall | 0.8 |
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| manhattan_ap | 0.9267 |
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| euclidean_accuracy | 0.6667 |
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| euclidean_accuracy_threshold | 9.8741 |
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| euclidean_f1 | 0.8 |
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| euclidean_f1_threshold | 11.742 |
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| euclidean_precision | 0.8 |
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| euclidean_recall | 0.8 |
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| euclidean_ap | 0.9267 |
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| max_accuracy | 0.8333 |
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| max_accuracy_threshold | 504.3712 |
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| max_f1 | 0.8889 |
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| max_f1_threshold | 504.3712 |
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| max_precision | 1.0 |
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| max_recall | 0.8 |
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| **max_ap** | **0.9667** |
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<!--
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## Bias, Risks and Limitations
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-->
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<!--
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### Recommendations
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## Training Details
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### Training
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- `optim`: adamw_torch
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- `optim_args`: None
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- `adafactor`: False
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- `group_by_length`: False
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- `length_column_name`: length
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- `dataloader_pin_memory`: True
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- `skip_memory_metrics`: True
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- `use_legacy_prediction_loop`: False
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- `push_to_hub`: False
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- `hub_model_id`: None
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- `hub_strategy`: every_save
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- `gradient_checkpointing`: False
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- `gradient_checkpointing_kwargs`: None
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- `include_inputs_for_metrics`: False
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- `eval_do_concat_batches`: True
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- `fp16_backend`: auto
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- `mp_parameters`:
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- `auto_find_batch_size`: False
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- `full_determinism`: False
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- `torchdynamo`: None
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- `ray_scope`: last
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- `ddp_timeout`: 1800
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- `torch_compile`: False
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- `split_batches`: None
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- `include_tokens_per_second`: False
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- `include_num_input_tokens_seen`: False
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- `neftune_noise_alpha`: None
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- `batch_sampler`: no_duplicates
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- `multi_dataset_batch_sampler`: proportional
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</details>
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### Training Logs
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| Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
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|:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
|
483 |
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| 1.0 | 6 | 0.9921 | 0.4063 | 0.8767 |
|
484 |
-
| 2.0 | 12 | 0.7955 | 0.3550 | 0.8767 |
|
485 |
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| 3.0 | 18 | 0.5309 | 0.2716 | 0.8767 |
|
486 |
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| 4.0 | 24 | 0.2716 | 0.2080 | 0.9267 |
|
487 |
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| 5.0 | 30 | 0.2395 | 0.1971 | 0.9667 |
|
488 |
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| 6.0 | 36 | 0.0401 | 0.1826 | 0.9667 |
|
489 |
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| 7.0 | 42 | 0.0403 | 0.0186 | 1.0 |
|
490 |
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| 8.0 | 48 | 0.0386 | 0.1842 | 0.9667 |
|
491 |
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| 9.0 | 54 | 0.0682 | 0.1904 | 0.9667 |
|
492 |
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| 10.0 | 60 | 0.001 | 0.1942 | 0.9667 |
|
493 |
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| 11.0 | 66 | 0.0013 | 0.1962 | 0.9667 |
|
494 |
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| 12.0 | 72 | 0.0016 | 0.1974 | 0.9667 |
|
495 |
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| 13.0 | 78 | 0.0013 | 0.1973 | 0.9667 |
|
496 |
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| 14.0 | 84 | 0.0009 | 0.1972 | 0.9667 |
|
497 |
-
|
498 |
-
|
499 |
-
### Framework Versions
|
500 |
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- Python: 3.10.14
|
501 |
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- Sentence Transformers: 3.1.0
|
502 |
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- Transformers: 4.44.2
|
503 |
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- PyTorch: 2.4.1+cu121
|
504 |
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- Accelerate: 0.34.2
|
505 |
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- Datasets: 2.20.0
|
506 |
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- Tokenizers: 0.19.1
|
507 |
-
|
508 |
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## Citation
|
509 |
-
|
510 |
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### BibTeX
|
511 |
-
|
512 |
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#### Sentence Transformers
|
513 |
-
```bibtex
|
514 |
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@inproceedings{reimers-2019-sentence-bert,
|
515 |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
516 |
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author = "Reimers, Nils and Gurevych, Iryna",
|
517 |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
518 |
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month = "11",
|
519 |
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year = "2019",
|
520 |
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publisher = "Association for Computational Linguistics",
|
521 |
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url = "https://arxiv.org/abs/1908.10084",
|
522 |
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}
|
523 |
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```
|
524 |
-
|
525 |
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<!--
|
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## Glossary
|
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|
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*Clearly define terms in order to be accessible across audiences.*
|
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-->
|
530 |
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|
531 |
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<!--
|
532 |
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## Model Card Authors
|
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|
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
535 |
-
-->
|
536 |
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|
537 |
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<!--
|
538 |
## Model Card Contact
|
539 |
|
540 |
-
|
541 |
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-->
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---
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base_model: colorfulscoop/sbert-base-ja
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+
language: ja
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+
license: cc-by-sa-4.0
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+
model_name: LeoChiuu/sbert-base-ja-arc
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6 |
---
|
7 |
|
8 |
+
# Model Card for LeoChiuu/sbert-base-ja-arc
|
9 |
+
|
10 |
+
<!-- Provide a quick summary of what the model is/does. -->
|
11 |
+
|
12 |
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13 |
|
14 |
## Model Details
|
15 |
|
16 |
### Model Description
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17 |
|
18 |
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<!-- Provide a longer summary of what this model is. -->
|
19 |
|
20 |
+
Generates similarity embeddings
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|
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]
|
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|
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. -->
|
|
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|
41 |
|
42 |
+
### Direct Use
|
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|
43 |
|
44 |
+
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
|
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|
45 |
|
46 |
+
[More Information Needed]
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|
47 |
|
48 |
+
### Downstream Use [optional]
|
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|
49 |
|
50 |
+
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
51 |
|
52 |
+
[More Information Needed]
|
|
|
53 |
|
54 |
+
### Out-of-Scope Use
|
|
|
55 |
|
56 |
+
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
57 |
|
58 |
+
[More Information Needed]
|
59 |
|
60 |
+
## Bias, Risks, and Limitations
|
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|
61 |
|
62 |
+
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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|
63 |
|
64 |
+
[More Information Needed]
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|
65 |
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|
66 |
### Recommendations
|
67 |
|
68 |
+
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
69 |
+
|
70 |
+
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
|
71 |
+
|
72 |
+
## How to Get Started with the Model
|
73 |
+
|
74 |
+
Use the code below to get started with the model.
|
75 |
+
|
76 |
+
[More Information Needed]
|
77 |
|
78 |
## Training Details
|
79 |
|
80 |
+
### Training Data
|
81 |
+
|
82 |
+
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
|
83 |
+
|
84 |
+
[More Information Needed]
|
85 |
+
|
86 |
+
### Training Procedure
|
87 |
+
|
88 |
+
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
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 |
+
[More Information Needed]
|
104 |
+
|
105 |
+
## Evaluation
|
106 |
+
|
107 |
+
<!-- This section describes the evaluation protocols and provides the results. -->
|
108 |
+
|
109 |
+
### Testing Data, Factors & Metrics
|
110 |
+
|
111 |
+
#### Testing Data
|
112 |
+
|
113 |
+
<!-- This should link to a Dataset Card if possible. -->
|
114 |
+
|
115 |
+
[More Information Needed]
|
116 |
+
|
117 |
+
#### Factors
|
118 |
+
|
119 |
+
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
|
120 |
+
|
121 |
+
[More Information Needed]
|
122 |
+
|
123 |
+
#### Metrics
|
124 |
+
|
125 |
+
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
126 |
+
|
127 |
+
[More Information Needed]
|
128 |
+
|
129 |
+
### Results
|
130 |
+
|
131 |
+
[More Information Needed]
|
132 |
+
|
133 |
+
#### Summary
|
134 |
+
|
135 |
+
|
136 |
+
|
137 |
+
## Model Examination [optional]
|
138 |
+
|
139 |
+
<!-- Relevant interpretability work for the model goes here -->
|
140 |
+
|
141 |
+
[More Information Needed]
|
142 |
+
|
143 |
+
## Environmental Impact
|
144 |
+
|
145 |
+
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
146 |
+
|
147 |
+
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
|
148 |
+
|
149 |
+
- **Hardware Type:** [More Information Needed]
|
150 |
+
- **Hours used:** [More Information Needed]
|
151 |
+
- **Cloud Provider:** [More Information Needed]
|
152 |
+
- **Compute Region:** [More Information Needed]
|
153 |
+
- **Carbon Emitted:** [More Information Needed]
|
154 |
+
|
155 |
+
## Technical Specifications [optional]
|
156 |
+
|
157 |
+
### Model Architecture and Objective
|
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 |
+
## Model Card Authors [optional]
|
196 |
+
|
197 |
+
[More Information Needed]
|
198 |
+
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|
199 |
## Model Card Contact
|
200 |
|
201 |
+
[More Information Needed]
|
|