Downloads · 30 days
23
34% of all-time downloads
ardi555/setfit_reuters21578_reducedto15
setfit_reuters21578_reducedto15 is a text classification model from ardi555. Use it when you need a label for a piece of text. It is set up for setfit.
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A OneVsRestClassifier instance is use…
Downloads · 30 days
23
34% of all-time downloads
All-time downloads
68
Public
Parameters
109M
438 MB on disk
Likes
0
Public
Click a slice to open those files.
.safetensors438 MB · 100%
From the Hugging Face model README
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
| Label | Accuracy |
|---|---|
| all | 0.7852 |
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("ardi555/setfit_reuters21578_reducedto15")
# Run inference
preds = model("Oper shr 69 cts vs 83 cts
Oper net 35.9 mln vs 42.4 mln
Revs 798.9 mln vs 659.2 mln
Avg shrs 52.0 mln vs 50.9 mln
Nine mths
Oper shr 2.38 dlrs vs 2.75 dlrs
Oper net 123.3 mln vs 135.6 mln
Revs 2.31 billion vs 1.86 billion
Avg shrs 51.8 mln vs 49.3 mln
NOTE: Net excludes losses from discontinued operations of
nil vs 16.1 mln dlrs in quarter and 227.5 mln dlrs vs 42.7 mln
dlrs in nine mths.
Quarter net includes gains from sale of aircraft of two mln
dlrs vs 6,200,000 dlrs.
Reuter
")
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 1 | 181.1067 | 788 |
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0013 | 1 | 0.4971 | - |
| 0.0667 | 50 | 0.1826 | - |
| 0.1333 | 100 | 0.1223 | - |
| 0.2 | 150 | 0.0699 | - |
| 0.2667 | 200 | 0.0712 | - |
| 0.3333 | 250 | 0.0646 | - |
| 0.4 | 300 | 0.055 | - |
| 0.4667 | 350 | 0.0611 | - |
| 0.5333 | 400 | 0.053 | - |
| 0.6 | 450 | 0.0555 | - |
| 0.6667 | 500 | 0.0475 | - |
| 0.7333 | 550 | 0.0716 | - |
| 0.8 | 600 | 0.0587 | - |
| 0.8667 | 650 | 0.0571 | - |
| 0.9333 | 700 | 0.0436 | - |
| 1.0 | 750 | 0.0505 | - |
@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}
}
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->