Add new CrossEncoder model
Browse files- README.md +133 -92
- config.json +1 -1
- model.safetensors +2 -2
README.md
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- sentence-transformers
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- cross-encoder
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- generated_from_trainer
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- dataset_size:
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- loss:BinaryCrossEntropyLoss
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base_model: cross-encoder/ms-marco-MiniLM-
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datasets:
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- CharlesPing/climate-cross-encoder-mixed-neg-
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pipeline_tag: text-ranking
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library_name: sentence-transformers
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metrics:
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- map
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- mrr@
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- ndcg@
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model-index:
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- name: CrossEncoder based on cross-encoder/ms-marco-MiniLM-
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results:
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- task:
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type: cross-encoder-reranking
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type: climate-rerank-multineg
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metrics:
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- type: map
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value: 0.
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name: Map
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- type: mrr@
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value: 0.
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name: Mrr@
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- type: ndcg@
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value: 0.
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name: Ndcg@
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---
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# CrossEncoder based on cross-encoder/ms-marco-MiniLM-
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This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/ms-marco-MiniLM-
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## Model Details
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### Model Description
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- **Model Type:** Cross Encoder
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- **Base model:** [cross-encoder/ms-marco-MiniLM-
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- **Maximum Sequence Length:** 512 tokens
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- **Number of Output Labels:** 1 label
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- **Training Dataset:**
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- [climate-cross-encoder-mixed-neg-
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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model = CrossEncoder("CharlesPing/finetuned-ce-climate-multineg-v1")
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# Get scores for pairs of texts
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pairs = [
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scores = model.predict(pairs)
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print(scores.shape)
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# Or rank different texts based on similarity to a single text
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ranks = model.rank(
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# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
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* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
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```json
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{
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"at_k":
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"always_rerank_positives": false
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}
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```
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| Metric | Value |
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|:-----------|:---------------------|
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| map | 0.
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| mrr@
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| **ndcg@
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<!--
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## Bias, Risks and Limitations
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### Training Dataset
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#### climate-cross-encoder-mixed-neg-
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* Dataset: [climate-cross-encoder-mixed-neg-
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* Size:
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* Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | query | doc | label
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| type | string | string | float
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| details | <ul><li>min:
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* Samples:
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| query
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| <code
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| <code
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| <code
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* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
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```json
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{
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### Evaluation Dataset
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#### climate-cross-encoder-mixed-neg-
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* Dataset: [climate-cross-encoder-mixed-neg-
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* Size:
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* Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| type | string | string
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| details | <ul><li>min:
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* Samples:
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| query
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| <code
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| <code
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| <code
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* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
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```json
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{
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</details>
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### Training Logs
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| Epoch | Step | Training Loss | Validation Loss | climate-rerank-multineg_ndcg@
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|:----------:|:--------:|:-------------:|:---------------:|:------------------------------:|
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* The bold row denotes the saved checkpoint.
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- sentence-transformers
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- cross-encoder
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- generated_from_trainer
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- dataset_size:41052
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- loss:BinaryCrossEntropyLoss
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base_model: cross-encoder/ms-marco-MiniLM-L12-v2
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datasets:
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- CharlesPing/climate-cross-encoder-mixed-neg-v3
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pipeline_tag: text-ranking
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library_name: sentence-transformers
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metrics:
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- map
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+
- mrr@1
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+
- ndcg@1
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model-index:
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+
- name: CrossEncoder based on cross-encoder/ms-marco-MiniLM-L12-v2
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results:
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- task:
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type: cross-encoder-reranking
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type: climate-rerank-multineg
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metrics:
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- type: map
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value: 0.6809
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name: Map
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- type: mrr@1
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value: 0.6748
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name: Mrr@1
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- type: ndcg@1
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value: 0.6748
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name: Ndcg@1
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---
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# CrossEncoder based on cross-encoder/ms-marco-MiniLM-L12-v2
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This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/ms-marco-MiniLM-L12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L12-v2) on the [climate-cross-encoder-mixed-neg-v3](https://huggingface.co/datasets/CharlesPing/climate-cross-encoder-mixed-neg-v3) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
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## Model Details
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### Model Description
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- **Model Type:** Cross Encoder
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+
- **Base model:** [cross-encoder/ms-marco-MiniLM-L12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L12-v2) <!-- at revision a34da8fab3ad458d48778dea3276ce729857efaf -->
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- **Maximum Sequence Length:** 512 tokens
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- **Number of Output Labels:** 1 label
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- **Training Dataset:**
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+
- [climate-cross-encoder-mixed-neg-v3](https://huggingface.co/datasets/CharlesPing/climate-cross-encoder-mixed-neg-v3)
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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model = CrossEncoder("CharlesPing/finetuned-ce-climate-multineg-v1")
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# Get scores for pairs of texts
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pairs = [
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+
['Scientific analysis of past climates\xa0shows that greenhouse gasses, principally CO2,\xa0have controlled most ancient\xa0climate changes.', 'Greenhouse gases, in particular carbon dioxide and methane, played a significant role during the Eocene in controlling the surface temperature.'],
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['Scientific analysis of past climates\xa0shows that greenhouse gasses, principally CO2,\xa0have controlled most ancient\xa0climate changes.', 'Climatic geomorphology is of limited use to study recent (Quaternary, Holocene) large climate changes since there are seldom discernible in the geomorphological record.'],
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['Scientific analysis of past climates\xa0shows that greenhouse gasses, principally CO2,\xa0have controlled most ancient\xa0climate changes.', 'There is also a close correlation between CO2 and temperature, where CO2 has a strong control over global temperatures in Earth history.'],
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['Scientific analysis of past climates\xa0shows that greenhouse gasses, principally CO2,\xa0have controlled most ancient\xa0climate changes.', 'While scientists knew of past climate change such as the ice ages, the concept of climate as unchanging was useful in the development of a general theory of what determines climate.'],
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['Scientific analysis of past climates\xa0shows that greenhouse gasses, principally CO2,\xa0have controlled most ancient\xa0climate changes.', 'Some long term modifications along the history of the planet have been significant, such as the incorporation of oxygen to the atmosphere.'],
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]
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scores = model.predict(pairs)
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print(scores.shape)
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# Or rank different texts based on similarity to a single text
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ranks = model.rank(
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'Scientific analysis of past climates\xa0shows that greenhouse gasses, principally CO2,\xa0have controlled most ancient\xa0climate changes.',
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[
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'Greenhouse gases, in particular carbon dioxide and methane, played a significant role during the Eocene in controlling the surface temperature.',
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'Climatic geomorphology is of limited use to study recent (Quaternary, Holocene) large climate changes since there are seldom discernible in the geomorphological record.',
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'There is also a close correlation between CO2 and temperature, where CO2 has a strong control over global temperatures in Earth history.',
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+
'While scientists knew of past climate change such as the ice ages, the concept of climate as unchanging was useful in the development of a general theory of what determines climate.',
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'Some long term modifications along the history of the planet have been significant, such as the incorporation of oxygen to the atmosphere.',
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]
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)
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# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
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* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
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```json
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{
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"at_k": 1,
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"always_rerank_positives": false
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}
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```
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| Metric | Value |
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|:-----------|:---------------------|
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| map | 0.6809 (-0.3191) |
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| mrr@1 | 0.6748 (-0.3252) |
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| **ndcg@1** | **0.6748 (-0.3252)** |
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<!--
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## Bias, Risks and Limitations
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### Training Dataset
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#### climate-cross-encoder-mixed-neg-v3
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+
* Dataset: [climate-cross-encoder-mixed-neg-v3](https://huggingface.co/datasets/CharlesPing/climate-cross-encoder-mixed-neg-v3) at [cd49b57](https://huggingface.co/datasets/CharlesPing/climate-cross-encoder-mixed-neg-v3/tree/cd49b575d132b8a377614b398f1a30378ce0c563)
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* Size: 41,052 training samples
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* Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | query | doc | label |
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|:--------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
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| type | string | string | float |
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| details | <ul><li>min: 49 characters</li><li>mean: 140.03 characters</li><li>max: 306 characters</li></ul> | <ul><li>min: 4 characters</li><li>mean: 136.03 characters</li><li>max: 731 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.09</li><li>max: 1.0</li></ul> |
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* Samples:
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| query | doc | label |
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|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
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| <code>“A leading Canadian authority on polar bears, Mitch Taylor, said: ‘We’re seeing an increase in bears that’s really unprecedented, and in places where we’re seeing a decrease in the population</code> | <code>Warnings about the future of the polar bear are often contrasted with the fact that worldwide population estimates have increased over the past 50 years and are relatively stable today.</code> | <code>1.0</code> |
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| <code>“A leading Canadian authority on polar bears, Mitch Taylor, said: ‘We’re seeing an increase in bears that’s really unprecedented, and in places where we’re seeing a decrease in the population</code> | <code>Species distribution models of recent years indicate that the deer tick, known as "I. scapularis," is pushing its distribution to higher latitudes of the Northeastern United States and Canada, as well as pushing and maintaining populations in the South Central and Northern Midwest regions of the United States.</code> | <code>0.0</code> |
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| <code>“A leading Canadian authority on polar bears, Mitch Taylor, said: ‘We’re seeing an increase in bears that’s really unprecedented, and in places where we’re seeing a decrease in the population</code> | <code>Bear and deer are among the animals present.</code> | <code>0.0</code> |
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* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
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```json
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{
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### Evaluation Dataset
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#### climate-cross-encoder-mixed-neg-v3
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* Dataset: [climate-cross-encoder-mixed-neg-v3](https://huggingface.co/datasets/CharlesPing/climate-cross-encoder-mixed-neg-v3) at [cd49b57](https://huggingface.co/datasets/CharlesPing/climate-cross-encoder-mixed-neg-v3/tree/cd49b575d132b8a377614b398f1a30378ce0c563)
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* Size: 4,290 evaluation samples
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* Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | query | doc | label |
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+
|:--------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
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| type | string | string | float |
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| details | <ul><li>min: 39 characters</li><li>mean: 116.67 characters</li><li>max: 240 characters</li></ul> | <ul><li>min: 18 characters</li><li>mean: 132.92 characters</li><li>max: 731 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.09</li><li>max: 1.0</li></ul> |
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* Samples:
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| query | doc | label |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
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| <code>Scientific analysis of past climates shows that greenhouse gasses, principally CO2, have controlled most ancient climate changes.</code> | <code>Greenhouse gases, in particular carbon dioxide and methane, played a significant role during the Eocene in controlling the surface temperature.</code> | <code>1.0</code> |
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+
| <code>Scientific analysis of past climates shows that greenhouse gasses, principally CO2, have controlled most ancient climate changes.</code> | <code>Climatic geomorphology is of limited use to study recent (Quaternary, Holocene) large climate changes since there are seldom discernible in the geomorphological record.</code> | <code>0.0</code> |
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+
| <code>Scientific analysis of past climates shows that greenhouse gasses, principally CO2, have controlled most ancient climate changes.</code> | <code>There is also a close correlation between CO2 and temperature, where CO2 has a strong control over global temperatures in Earth history.</code> | <code>0.0</code> |
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* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
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```json
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{
|
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</details>
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### Training Logs
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| Epoch | Step | Training Loss | Validation Loss | climate-rerank-multineg_ndcg@1 |
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|:----------:|:--------:|:-------------:|:---------------:|:------------------------------:|
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| 0.0390 | 100 | 0.5097 | - | - |
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| 0.0779 | 200 | 0.3662 | - | - |
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| 0.1169 | 300 | 0.3034 | - | - |
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| 0.1559 | 400 | 0.2655 | - | - |
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| 0.1949 | 500 | 0.2651 | 0.2262 | 0.6585 (-0.3415) |
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| 0.2338 | 600 | 0.2161 | - | - |
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| 0.2728 | 700 | 0.227 | - | - |
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| 355 |
+
| 0.3118 | 800 | 0.235 | - | - |
|
| 356 |
+
| 0.3507 | 900 | 0.2243 | - | - |
|
| 357 |
+
| 0.3897 | 1000 | 0.2081 | 0.2174 | 0.6992 (-0.3008) |
|
| 358 |
+
| 0.4287 | 1100 | 0.1961 | - | - |
|
| 359 |
+
| 0.4677 | 1200 | 0.207 | - | - |
|
| 360 |
+
| 0.5066 | 1300 | 0.2375 | - | - |
|
| 361 |
+
| 0.5456 | 1400 | 0.2117 | - | - |
|
| 362 |
+
| 0.5846 | 1500 | 0.2058 | 0.2253 | 0.6748 (-0.3252) |
|
| 363 |
+
| 0.6235 | 1600 | 0.2163 | - | - |
|
| 364 |
+
| 0.6625 | 1700 | 0.2235 | - | - |
|
| 365 |
+
| 0.7015 | 1800 | 0.2193 | - | - |
|
| 366 |
+
| 0.7405 | 1900 | 0.1924 | - | - |
|
| 367 |
+
| 0.7794 | 2000 | 0.2084 | 0.2095 | 0.6748 (-0.3252) |
|
| 368 |
+
| 0.8184 | 2100 | 0.2113 | - | - |
|
| 369 |
+
| 0.8574 | 2200 | 0.2276 | - | - |
|
| 370 |
+
| 0.8963 | 2300 | 0.2071 | - | - |
|
| 371 |
+
| 0.9353 | 2400 | 0.2374 | - | - |
|
| 372 |
+
| 0.9743 | 2500 | 0.2173 | 0.2172 | 0.6667 (-0.3333) |
|
| 373 |
+
| 1.0133 | 2600 | 0.2011 | - | - |
|
| 374 |
+
| 1.0522 | 2700 | 0.1634 | - | - |
|
| 375 |
+
| 1.0912 | 2800 | 0.1807 | - | - |
|
| 376 |
+
| 1.1302 | 2900 | 0.1878 | - | - |
|
| 377 |
+
| 1.1691 | 3000 | 0.2037 | 0.2147 | 0.6911 (-0.3089) |
|
| 378 |
+
| 1.2081 | 3100 | 0.1904 | - | - |
|
| 379 |
+
| 1.2471 | 3200 | 0.1911 | - | - |
|
| 380 |
+
| 1.2860 | 3300 | 0.1828 | - | - |
|
| 381 |
+
| 1.3250 | 3400 | 0.1686 | - | - |
|
| 382 |
+
| 1.3640 | 3500 | 0.1892 | 0.2179 | 0.6992 (-0.3008) |
|
| 383 |
+
| 1.4030 | 3600 | 0.188 | - | - |
|
| 384 |
+
| 1.4419 | 3700 | 0.1691 | - | - |
|
| 385 |
+
| 1.4809 | 3800 | 0.1946 | - | - |
|
| 386 |
+
| 1.5199 | 3900 | 0.1938 | - | - |
|
| 387 |
+
| **1.5588** | **4000** | **0.211** | **0.2088** | **0.6992 (-0.3008)** |
|
| 388 |
+
| 1.5978 | 4100 | 0.1826 | - | - |
|
| 389 |
+
| 1.6368 | 4200 | 0.1608 | - | - |
|
| 390 |
+
| 1.6758 | 4300 | 0.1782 | - | - |
|
| 391 |
+
| 1.7147 | 4400 | 0.1803 | - | - |
|
| 392 |
+
| 1.7537 | 4500 | 0.1804 | 0.2160 | 0.6911 (-0.3089) |
|
| 393 |
+
| 1.7927 | 4600 | 0.1823 | - | - |
|
| 394 |
+
| 1.8316 | 4700 | 0.1844 | - | - |
|
| 395 |
+
| 1.8706 | 4800 | 0.1727 | - | - |
|
| 396 |
+
| 1.9096 | 4900 | 0.1937 | - | - |
|
| 397 |
+
| 1.9486 | 5000 | 0.1662 | 0.2219 | 0.6829 (-0.3171) |
|
| 398 |
+
| 1.9875 | 5100 | 0.1653 | - | - |
|
| 399 |
+
| 2.0265 | 5200 | 0.1658 | - | - |
|
| 400 |
+
| 2.0655 | 5300 | 0.1316 | - | - |
|
| 401 |
+
| 2.1044 | 5400 | 0.1379 | - | - |
|
| 402 |
+
| 2.1434 | 5500 | 0.152 | 0.2513 | 0.6504 (-0.3496) |
|
| 403 |
+
| 2.1824 | 5600 | 0.1848 | - | - |
|
| 404 |
+
| 2.2214 | 5700 | 0.1507 | - | - |
|
| 405 |
+
| 2.2603 | 5800 | 0.1495 | - | - |
|
| 406 |
+
| 2.2993 | 5900 | 0.1469 | - | - |
|
| 407 |
+
| 2.3383 | 6000 | 0.1596 | 0.2407 | 0.6585 (-0.3415) |
|
| 408 |
+
| 2.3772 | 6100 | 0.1518 | - | - |
|
| 409 |
+
| 2.4162 | 6200 | 0.1351 | - | - |
|
| 410 |
+
| 2.4552 | 6300 | 0.1706 | - | - |
|
| 411 |
+
| 2.4942 | 6400 | 0.1538 | - | - |
|
| 412 |
+
| 2.5331 | 6500 | 0.1329 | 0.2505 | 0.6911 (-0.3089) |
|
| 413 |
+
| 2.5721 | 6600 | 0.147 | - | - |
|
| 414 |
+
| 2.6111 | 6700 | 0.1289 | - | - |
|
| 415 |
+
| 2.6500 | 6800 | 0.1698 | - | - |
|
| 416 |
+
| 2.6890 | 6900 | 0.1456 | - | - |
|
| 417 |
+
| 2.7280 | 7000 | 0.141 | 0.2618 | 0.6748 (-0.3252) |
|
| 418 |
+
| 2.7670 | 7100 | 0.1413 | - | - |
|
| 419 |
+
| 2.8059 | 7200 | 0.1474 | - | - |
|
| 420 |
+
| 2.8449 | 7300 | 0.1381 | - | - |
|
| 421 |
+
| 2.8839 | 7400 | 0.1252 | - | - |
|
| 422 |
+
| 2.9228 | 7500 | 0.1384 | 0.2608 | 0.6748 (-0.3252) |
|
| 423 |
+
| 2.9618 | 7600 | 0.1826 | - | - |
|
| 424 |
|
| 425 |
* The bold row denotes the saved checkpoint.
|
| 426 |
|
config.json
CHANGED
|
@@ -20,7 +20,7 @@
|
|
| 20 |
"max_position_embeddings": 512,
|
| 21 |
"model_type": "bert",
|
| 22 |
"num_attention_heads": 12,
|
| 23 |
-
"num_hidden_layers":
|
| 24 |
"pad_token_id": 0,
|
| 25 |
"position_embedding_type": "absolute",
|
| 26 |
"sentence_transformers": {
|
|
|
|
| 20 |
"max_position_embeddings": 512,
|
| 21 |
"model_type": "bert",
|
| 22 |
"num_attention_heads": 12,
|
| 23 |
+
"num_hidden_layers": 12,
|
| 24 |
"pad_token_id": 0,
|
| 25 |
"position_embedding_type": "absolute",
|
| 26 |
"sentence_transformers": {
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:2fcb49a81642309eb64539d1e4bf527bea3c3d4f93f63a50a2b0f7cd2b58ccac
|
| 3 |
+
size 133464836
|