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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: considering the use of so-called “fractional citations” in which one divides
the number of citations associated with a given paper by the number of authors
on that paper [33–38];
- text: Indeed, this is only one of a number of such practical inconsistencies inherent
in the traditional h-index; other similar inconsistencies are discussed in Refs.
[3, 4].
- text: One of the referees recommends mentioning Quesada (2008) as another characterization
of the Hirsch index relying as well on monotonicity.
- text: considering the use of so-called “fractional citations” in which one divides
the number of citations associated with a given paper by the number of authors
on that paper [33–38];
- text: increasing the weighting of very highly-cited papers, either through the introduction
of intrinsic weighting factors or the development of entirely new indices which
mix the h-index with other more traditional indices (such as total citation count)
[3, 4, 7, 8, 26–32];
pipeline_tag: text-classification
inference: true
base_model: jinaai/jina-embeddings-v2-base-en
model-index:
- name: SetFit with jinaai/jina-embeddings-v2-base-en
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.6666666666666666
name: Accuracy
---
# SetFit with jinaai/jina-embeddings-v2-base-en
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 8192 tokens
- **Number of Classes:** 9 classes
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### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:-----------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| ccro:BasedOn | <ul><li>'The axiomatizations presented in Quesada (2010, 2011) also dispense with strong monotonicity.'</li></ul> |
| ccro:Basedon | <ul><li>'A formal mathematical description of the h-index introduced by Hirsch (2005)'</li><li>'Woeginger (2008a, b) and Quesada (2009, 2010) have already suggested characterizations of the Hirsch index'</li><li>'Woeginger (2008a, b) and Quesada (2009, 2010) have already suggested characterizations of the Hirsch index'</li></ul> |
| ccro:Compare | <ul><li>'Instead, a variety of studies [8, 9] have shown that the h index by and large agrees with other objective and subjective measures of scientific quality in a variety of different disciplines (10–15),'</li><li>'Instead, a variety of studies [8, 9] have shown that the h index by and large agrees with other objective and subjective measures of scientific quality in a variety of different disciplines (10–15),'</li><li>'Instead, a variety of studies [8, 9] have shown that the h index by and large agrees with other objective and subjective measures of scientific quality in a variety of different disciplines (10–15),'</li></ul> |
| ccro:Contrast | <ul><li>'Hirsch (2005) argues that two individuals with similar Hirsch-index are comparable in terms of their overall scientific impact, even if their total number of papers or their total number of citations is very different.'</li><li>'The three differ from Woeginger’s (2008a) characterization in requiring fewer axioms (three instead of five)'</li><li>'Marchant (2009), instead of characterizing the index itself, characterizes the ranking that the Hirsch index induces on outputs.'</li></ul> |
| ccro:Criticize | <ul><li>'The h-index does not take into account that some papers may have extraordinarily many citations, and the g-index tries to compensate for this; see also Egghe (2006b) and Tol (2008).'</li><li>'The h-index does not take into account that some papers may have extraordinarily many citations, and the g-index tries to compensate for this; see also Egghe (2006b) and Tol (2008).'</li><li>'Woeginger (2008a, p. 227) stresses that his axioms should be interpreted within the context of MON.'</li></ul> |
| ccro:Discuss | <ul><li>'The relation between N and h will depend on the detailed form of the particular distribution (HI0501-01)'</li><li>'As discussed by Redner (HI0501-03), most papers earn their citations over a limited period of popularity and then they are no longer cited.'</li><li>'It is also possible that papers "drop out" and then later come back into the h count, as would occur for the kind of papers termed "sleeping beauties" (HI0501-04).'</li></ul> |
| ccro:Extend | <ul><li>'In [3] the analogous formula for the g-index has been proved'</li></ul> |
| ccro:Incorporate | <ul><li>'In this paper, we provide an axiomatic characterization of the Hirsch-index, in very much the same spirit as Arrow (1950, 1951), May (1952), and Moulin (1988) did for numerous other problems in mathematical decision making.'</li><li>'In this paper, we provide an axiomatic characterization of the Hirsch-index, in very much the same spirit as Arrow (1950, 1951), May (1952), and Moulin (1988) did for numerous other problems in mathematical decision making.'</li><li>'In this paper, we provide an axiomatic characterization of the Hirsch-index, in very much the same spirit as Arrow (1950, 1951), May (1952), and Moulin (1988) did for numerous other problems in mathematical decision making.'</li></ul> |
| ccro:Negate | <ul><li>'Recently, Lehmann et al. (2, 3) have argued that the mean number of citations per paper (nc = Nc/Np) is a superior indicator.'</li><li>'If one chose instead to use as indicator of scientific achievement the mean number of citations per paper [following Lehmann et al. (2, 3)], our results suggest that (as in the stock market) ‘‘past performance is not predictive of future performance.’’'</li><li>'It has been argued in the literature that one drawback of the h index is that it does not give enough ‘‘credit’’ to very highly cited papers, and various modifications have been proposed to correct this, in particular, Egghe’s g index (4), Jin et al.’s AR index (5), and Komulski’s H(2) index (6).'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.6667 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("Corran/CCRO2")
# Run inference
preds = model("One of the referees recommends mentioning Quesada (2008) as another characterization of the Hirsch index relying as well on monotonicity.")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 6 | 25.7812 | 53 |
| Label | Training Sample Count |
|:-----------------|:----------------------|
| ccro:BasedOn | 1 |
| ccro:Basedon | 11 |
| ccro:Compare | 21 |
| ccro:Contrast | 3 |
| ccro:Criticize | 4 |
| ccro:Discuss | 37 |
| ccro:Extend | 1 |
| ccro:Incorporate | 14 |
| ccro:Negate | 4 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 100
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0017 | 1 | 0.311 | - |
| 0.0833 | 50 | 0.1338 | - |
| 0.1667 | 100 | 0.0054 | - |
| 0.25 | 150 | 0.0017 | - |
| 0.3333 | 200 | 0.0065 | - |
| 0.4167 | 250 | 0.0003 | - |
| 0.5 | 300 | 0.0003 | - |
| 0.5833 | 350 | 0.0005 | - |
| 0.6667 | 400 | 0.0004 | - |
| 0.75 | 450 | 0.0002 | - |
| 0.8333 | 500 | 0.0002 | - |
| 0.9167 | 550 | 0.0002 | - |
| 1.0 | 600 | 0.0002 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.0
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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