metadata
language: en
license: apache-2.0
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
metrics:
- accuracy
widget:
- text: >-
great movie, so close to perfection let me get this straight. this is a
brilliant brilliant refreshingly brutal movie.i'm glad they didn't soften
the general malevolence, but i feel they missed out on what i consider the
most pivotal point of the book.paul drinks the water of life. with it his
genetic memory is unlocked, he can foresee the actions of people in the
future. the golden path is laid out. and so pursues the mind altering
awakening, leaving him a husk; trapped in one path of fate - trapped
between his own ego and the true path needed for humanity. in the movie,
paul drink bad, paul wake up. paul president with superpower!i understand
that it's a very hard thing to portray for an audience but i think i was
just really hoping for villeneuve to emphasise the importance of that part
and it felt quite rushed in that regard.but i doubt they'll make a movie
about a big virgin worm so prescience might not matter too much.
- text: >-
absolutely breathtaking the movie is the complete cinematic experience. i
loved every single line every moment every little thing that makes this
movie.the only thing that is bothering me is the thirst so bad for the
next part.i felt like i was in the movie riding a sand worm, i was a
fremen. i felt the pain the wonder the joy the anger. this felt like
reading the book and you just can't stop. the excellence of this movie is
not only the cast or the story it is the very making of it. i loved every
dialogue that was uttered. its just a masterpiece.though there is a
stagnant pace in between it doesn't seem to matter. because most of the
second part of the movie is such a cliff hanger. 6 out of 10 found this
helpful. was this review helpful? sign in to vote. permalink
- text: >-
let's be serious, guys.. appreciate that everyone is entitled to their
opinion, so here's mine: anyone giving this less than a solid 9 needs to
re-evaluate themselves as a person. because you either have no imagination
or are just generally a negative human. this film has everything and is a
modern day great. easily the best cinematic experience i've ever had,
comparable to films like the dark knight trilogy and the original star
wars films.for a nearly three hour long film, basically nobody got up to
go for a toilet break and the entire time i felt totally present, gripped
by it.don't listen to anyone on here leaving poor reviews. go and watch
the film and see the magic for yourself. 8 out of 13 found this helpful.
was this review helpful? sign in to vote. permalink
- text: >-
phenomenal this movie was particularly gorgeous and exciting giving all
the key moments and suspense that anybody of the sort would love, this
movie brings the suspense and excitement to keep you engaged and always
cautious of what's next, a truly wonderful story that is acted so
perfectly and well, this adaptation has brung the story alive and in the
spotlight proving there is not only a lot to it but also that it has a lot
more to come and personally i want to see it all. i left the theater
thoroughly wanting even more for the story and continuing on that i can't
wait for what is to come of this movie. it is truly a must watch
masterpiece. 4 out of 6 found this helpful. was this review helpful? sign
in to vote. permalink
- text: >-
film of the decade i've always wished to watch films like lord of the
rings and star wars in theaters, but i was simply born too late. dune 2
made me feel like i was watching those movies in theaters, the epic
sweaping shots, the massive amount of extras, the attention to detail, the
costumes, every single fight looked like they spent days choreographing
it. the soundtrack was the best i heard since interstellar, and it matched
the mood at every point. honestly i thought film was going down, disney is
losing it and they own almost everything. but dune 2 restored my hope in
movies and actually made me want to pursue a career in film. overall, this
movie was epic and easily deserves a 10 star rating. 1 out of 1 found this
helpful. was this review helpful? sign in to vote. permalink
pipeline_tag: text-classification
inference: true
SetFit with sentence-transformers/paraphrase-mpnet-base-v2 on data/raw/15239678.jsonl
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 SetFitHead instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
- Model Type: SetFit
- Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2
- Classification head: a SetFitHead instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 2 classes
- Language: en
- License: apache-2.0
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
Label | Examples |
---|---|
positive |
|
negative |
|
Uses
Direct Use for Inference
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("carlesoctav/SentimentClassifierDune")
# Run inference
preds = model("phenomenal this movie was particularly gorgeous and exciting giving all the key moments and suspense that anybody of the sort would love, this movie brings the suspense and excitement to keep you engaged and always cautious of what's next, a truly wonderful story that is acted so perfectly and well, this adaptation has brung the story alive and in the spotlight proving there is not only a lot to it but also that it has a lot more to come and personally i want to see it all. i left the theater thoroughly wanting even more for the story and continuing on that i can't wait for what is to come of this movie. it is truly a must watch masterpiece. 4 out of 6 found this helpful. was this review helpful? sign in to vote. permalink")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 107 | 215.2273 | 972 |
Label | Training Sample Count |
---|---|
negative | 99 |
positive | 99 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- 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: True
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0008 | 1 | 0.2606 | - |
0.0404 | 50 | 0.1578 | - |
0.0808 | 100 | 0.0066 | - |
0.1212 | 150 | 0.0004 | - |
0.1616 | 200 | 0.0003 | - |
0.2019 | 250 | 0.0005 | - |
0.2423 | 300 | 0.0002 | - |
0.2827 | 350 | 0.0003 | - |
0.3231 | 400 | 0.0001 | - |
0.3635 | 450 | 0.0001 | - |
0.4039 | 500 | 0.0001 | - |
0.4443 | 550 | 0.0001 | - |
0.4847 | 600 | 0.0 | - |
0.5250 | 650 | 0.0 | - |
0.5654 | 700 | 0.0 | - |
0.6058 | 750 | 0.0 | - |
0.6462 | 800 | 0.0 | - |
0.6866 | 850 | 0.0 | - |
0.7270 | 900 | 0.0 | - |
0.7674 | 950 | 0.0 | - |
0.8078 | 1000 | 0.0 | - |
0.8481 | 1050 | 0.0 | - |
0.8885 | 1100 | 0.0 | - |
0.9289 | 1150 | 0.0 | - |
0.9693 | 1200 | 0.0 | - |
1.0 | 1238 | - | 0.1555 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.11
- SetFit: 1.0.3
- Sentence Transformers: 2.5.1
- Transformers: 4.38.2
- PyTorch: 2.0.1
- Datasets: 2.18.0
- Tokenizers: 0.15.2
Citation
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}
}