Finetuned Ce Climate Multineg V1

🚀 CrossEncoder based on cross-encoder/ms-marco-MiniLM-L12-v2

This is a Cross Encoder model. It's finetuned from cross-encoder/ms-marco-MiniLM-L12-v2 on the climate-cross-encoder-mixed-neg-v3 dataset using the sentence-transformers library. It can compute scores for text pairs, which are useful for text reranking and semantic search.

✨ Features

• Text Scoring: Computes scores for pairs of texts.
• Reranking and Search: Can be used for text reranking and semantic search.

📦 Installation

First, you need to install the Sentence Transformers library:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("CharlesPing/finetuned-ce-climate-multineg-v1")
# Get scores for pairs of texts
pairs = [
    ['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.'],
    ['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.'],
    ['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.'],
    ['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.'],
    ['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.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    '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.',
        'Climatic geomorphology is of limited use to study recent (Quaternary, Holocene) large climate changes since there are seldom discernible in the geomorphological record.',
        'There is also a close correlation between CO2 and temperature, where CO2 has a strong control over global temperatures in Earth history.',
        '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.',
        'Some long term modifications along the history of the planet have been significant, such as the incorporation of oxygen to the atmosphere.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

📚 Documentation

Model Details

Model Sources

• Documentation: Sentence Transformers Documentation
• Documentation: Cross Encoder Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Cross Encoders on Hugging Face

🔧 Technical Details

Evaluation

Cross Encoder Reranking

• Dataset: climate-rerank-multineg
• Evaluated with CrossEncoderRerankingEvaluator with these parameters:

  {
      "at_k": 1,
      "always_rerank_positives": false
  }
  

Training Details

Training Dataset - climate-cross-encoder-mixed-neg-v3

• Dataset: climate-cross-encoder-mixed-neg-v3 at cd49b57
• Size: 41,052 training samples
• Columns: query, doc, and label
• Approximate statistics based on the first 1000 samples:

  	query	doc	label
  type	string	string	float
  details	min: 49 characters mean: 140.03 characters max: 306 characters	min: 4 characters mean: 136.03 characters max: 731 characters	min: 0.0 mean: 0.09 max: 1.0

• Samples:

  query	doc	label
  “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	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.	1.0
  “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	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.	0.0
  “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	Bear and deer are among the animals present.	0.0

• Loss: BinaryCrossEntropyLoss with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }
  

Evaluation Dataset - climate-cross-encoder-mixed-neg-v3

• Dataset: climate-cross-encoder-mixed-neg-v3 at cd49b57
• Size: 4,290 evaluation samples
• Columns: query, doc, and label
• Approximate statistics based on the first 1000 samples:

  	query	doc	label
  type	string	string	float
  details	min: 39 characters mean: 116.67 characters max: 240 characters	min: 18 characters mean: ... characters max: ... characters	...