|
--- |
|
base_model: BAAI/bge-small-en-v1.5 |
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library_name: setfit |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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tags: |
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- setfit |
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- absa |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: book:I was hooked!! Garth Nix is a awesome writer and though the book is a |
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little babyish - its definetly worth a read! I thought the whole minute - hand |
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- is - a - key part was a real good idea plus the names are so fun! The only thing |
|
I didn't like was that Arthur doesen't take his rightful place as "Monday" |
|
- text: book:The lawyer says he tracked Jack from his book and would like Jack to |
|
investigate the brutal murders of thirty-seven year old Gina Anderson and her |
|
son Joshua in their Seattle home; the house was trashed and the husband a lecturer |
|
at the nearby community college vanished |
|
- text: beings:Arthur with the lesser key to the lower kingdom of the House in hand, |
|
must travel into the House to find a cure for the mysterious plague that is striking |
|
the people of his town and his loved ones and find out why there are beings intent |
|
on getting the key from him, even if it means killing him |
|
- text: figures:But when a fight emerges between the two figures - Mister Monday and |
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Sneezer - they both disappear without any further regard to Arthur |
|
- text: book:I could not put this book down if my life depended on it! I have never |
|
in my life read a book this fast |
|
inference: false |
|
--- |
|
|
|
# SetFit Aspect Model with BAAI/bge-small-en-v1.5 |
|
|
|
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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. In particular, this model is in charge of filtering aspect span candidates. |
|
|
|
The model has been trained using an efficient few-shot learning technique that involves: |
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|
|
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. |
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|
|
This model was trained within the context of a larger system for ABSA, which looks like so: |
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|
|
1. Use a spaCy model to select possible aspect span candidates. |
|
2. **Use this SetFit model to filter these possible aspect span candidates.** |
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3. Use a SetFit model to classify the filtered aspect span candidates. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** SetFit |
|
- **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) |
|
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
|
- **spaCy Model:** en_core_web_lg |
|
- **SetFitABSA Aspect Model:** [omymble/books-full-bge-aspect](https://huggingface.co/omymble/books-full-bge-aspect) |
|
- **SetFitABSA Polarity Model:** [omymble/books-full-bge-polarity](https://huggingface.co/omymble/books-full-bge-polarity) |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 2 classes |
|
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
|
<!-- - **Language:** Unknown --> |
|
<!-- - **License:** Unknown --> |
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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 | |
|
|:----------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| aspect | <ul><li>"younger ones:It might be an entertaining point of discussion with a child 12 or older, but it's too dark for younger ones, unless you're ready to talk about true evil, adult motivations, supernatural forces, and fratricide!"</li><li>'Nix:-enjoy the genre of fantasies, of a unknown world, as Nix weaves a wonderful tale of the things that will open your eyes to a different world'</li><li>'mystery:The mystery is secondary to the rest of the story and is only really approached in the remaining 30 pages of the book'</li></ul> | |
|
| no aspect | <ul><li>"point:It might be an entertaining point of discussion with a child 12 or older, but it's too dark for younger ones, unless you're ready to talk about true evil, adult motivations, supernatural forces, and fratricide!"</li><li>"discussion:It might be an entertaining point of discussion with a child 12 or older, but it's too dark for younger ones, unless you're ready to talk about true evil, adult motivations, supernatural forces, and fratricide!"</li><li>"child:It might be an entertaining point of discussion with a child 12 or older, but it's too dark for younger ones, unless you're ready to talk about true evil, adult motivations, supernatural forces, and fratricide!"</li></ul> | |
|
|
|
## 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 AbsaModel |
|
|
|
# Download from the 🤗 Hub |
|
model = AbsaModel.from_pretrained( |
|
"omymble/books-full-bge-aspect", |
|
"omymble/books-full-bge-polarity", |
|
) |
|
# Run inference |
|
preds = model("The food was great, but the venue is just way too busy.") |
|
``` |
|
|
|
<!-- |
|
### Downstream Use |
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|
|
*List how someone could finetune this model on their own dataset.* |
|
--> |
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|
|
<!-- |
|
### 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 Details |
|
|
|
### Training Set Metrics |
|
| Training set | Min | Median | Max | |
|
|:-------------|:----|:--------|:----| |
|
| Word count | 2 | 25.9648 | 72 | |
|
|
|
| Label | Training Sample Count | |
|
|:----------|:----------------------| |
|
| no aspect | 572 | |
|
| aspect | 167 | |
|
|
|
### Training Hyperparameters |
|
- batch_size: (64, 64) |
|
- num_epochs: (5, 5) |
|
- 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: True |
|
- warmup_proportion: 0.1 |
|
- seed: 42 |
|
- eval_max_steps: -1 |
|
- load_best_model_at_end: True |
|
|
|
### Training Results |
|
| Epoch | Step | Training Loss | Validation Loss | |
|
|:----------:|:--------:|:-------------:|:---------------:| |
|
| 0.0002 | 1 | 0.2687 | - | |
|
| 0.0090 | 50 | 0.2516 | - | |
|
| 0.0180 | 100 | 0.2619 | - | |
|
| 0.0270 | 150 | 0.2499 | - | |
|
| 0.0360 | 200 | 0.2428 | - | |
|
| 0.0450 | 250 | 0.2443 | - | |
|
| 0.0540 | 300 | 0.246 | - | |
|
| 0.0629 | 350 | 0.249 | - | |
|
| 0.0719 | 400 | 0.2354 | - | |
|
| 0.0809 | 450 | 0.2347 | - | |
|
| 0.0899 | 500 | 0.2154 | - | |
|
| 0.0989 | 550 | 0.2285 | - | |
|
| 0.1079 | 600 | 0.1812 | - | |
|
| 0.1169 | 650 | 0.1446 | - | |
|
| 0.1259 | 700 | 0.165 | - | |
|
| 0.1349 | 750 | 0.1125 | - | |
|
| 0.1439 | 800 | 0.0971 | - | |
|
| 0.1529 | 850 | 0.1059 | - | |
|
| 0.1619 | 900 | 0.0866 | - | |
|
| 0.1709 | 950 | 0.0492 | - | |
|
| **0.1799** | **1000** | **0.0546** | **0.274** | |
|
| 0.1888 | 1050 | 0.037 | - | |
|
| 0.1978 | 1100 | 0.0189 | - | |
|
| 0.2068 | 1150 | 0.0279 | - | |
|
| 0.2158 | 1200 | 0.004 | - | |
|
| 0.2248 | 1250 | 0.0309 | - | |
|
| 0.2338 | 1300 | 0.0049 | - | |
|
| 0.2428 | 1350 | 0.0286 | - | |
|
| 0.2518 | 1400 | 0.0234 | - | |
|
| 0.2608 | 1450 | 0.0158 | - | |
|
| 0.2698 | 1500 | 0.0354 | - | |
|
| 0.2788 | 1550 | 0.0062 | - | |
|
| 0.2878 | 1600 | 0.0172 | - | |
|
| 0.2968 | 1650 | 0.0389 | - | |
|
| 0.3058 | 1700 | 0.0221 | - | |
|
| 0.3147 | 1750 | 0.0065 | - | |
|
| 0.3237 | 1800 | 0.0128 | - | |
|
| 0.3327 | 1850 | 0.0225 | - | |
|
| 0.3417 | 1900 | 0.0021 | - | |
|
| 0.3507 | 1950 | 0.0102 | - | |
|
| 0.3597 | 2000 | 0.012 | 0.3429 | |
|
| 0.3687 | 2050 | 0.0249 | - | |
|
| 0.3777 | 2100 | 0.0054 | - | |
|
| 0.3867 | 2150 | 0.0014 | - | |
|
| 0.3957 | 2200 | 0.0014 | - | |
|
| 0.4047 | 2250 | 0.0143 | - | |
|
| 0.4137 | 2300 | 0.0078 | - | |
|
| 0.4227 | 2350 | 0.0195 | - | |
|
| 0.4317 | 2400 | 0.0006 | - | |
|
| 0.4406 | 2450 | 0.0014 | - | |
|
| 0.4496 | 2500 | 0.0083 | - | |
|
| 0.4586 | 2550 | 0.0141 | - | |
|
| 0.4676 | 2600 | 0.0046 | - | |
|
| 0.4766 | 2650 | 0.01 | - | |
|
| 0.4856 | 2700 | 0.0268 | - | |
|
| 0.4946 | 2750 | 0.0008 | - | |
|
| 0.5036 | 2800 | 0.0076 | - | |
|
| 0.5126 | 2850 | 0.0004 | - | |
|
| 0.5216 | 2900 | 0.0037 | - | |
|
| 0.5306 | 2950 | 0.0005 | - | |
|
| 0.5396 | 3000 | 0.0065 | 0.3565 | |
|
| 0.5486 | 3050 | 0.002 | - | |
|
| 0.5576 | 3100 | 0.0072 | - | |
|
| 0.5665 | 3150 | 0.0141 | - | |
|
| 0.5755 | 3200 | 0.0004 | - | |
|
| 0.5845 | 3250 | 0.0086 | - | |
|
| 0.5935 | 3300 | 0.0098 | - | |
|
| 0.6025 | 3350 | 0.0048 | - | |
|
| 0.6115 | 3400 | 0.0013 | - | |
|
| 0.6205 | 3450 | 0.007 | - | |
|
| 0.6295 | 3500 | 0.0059 | - | |
|
| 0.6385 | 3550 | 0.0174 | - | |
|
| 0.6475 | 3600 | 0.0003 | - | |
|
| 0.6565 | 3650 | 0.0004 | - | |
|
| 0.6655 | 3700 | 0.0032 | - | |
|
| 0.6745 | 3750 | 0.0004 | - | |
|
| 0.6835 | 3800 | 0.0035 | - | |
|
| 0.6924 | 3850 | 0.0019 | - | |
|
| 0.7014 | 3900 | 0.015 | - | |
|
| 0.7104 | 3950 | 0.0204 | - | |
|
| 0.7194 | 4000 | 0.0016 | 0.3404 | |
|
| 0.7284 | 4050 | 0.0003 | - | |
|
| 0.7374 | 4100 | 0.0036 | - | |
|
| 0.7464 | 4150 | 0.0016 | - | |
|
| 0.7554 | 4200 | 0.0104 | - | |
|
| 0.7644 | 4250 | 0.003 | - | |
|
| 0.7734 | 4300 | 0.0159 | - | |
|
| 0.7824 | 4350 | 0.0029 | - | |
|
| 0.7914 | 4400 | 0.0068 | - | |
|
| 0.8004 | 4450 | 0.0021 | - | |
|
| 0.8094 | 4500 | 0.006 | - | |
|
| 0.8183 | 4550 | 0.006 | - | |
|
| 0.8273 | 4600 | 0.0038 | - | |
|
| 0.8363 | 4650 | 0.008 | - | |
|
| 0.8453 | 4700 | 0.0003 | - | |
|
| 0.8543 | 4750 | 0.0126 | - | |
|
| 0.8633 | 4800 | 0.0002 | - | |
|
| 0.8723 | 4850 | 0.0041 | - | |
|
| 0.8813 | 4900 | 0.0002 | - | |
|
| 0.8903 | 4950 | 0.0137 | - | |
|
| 0.8993 | 5000 | 0.0041 | 0.3363 | |
|
| 0.9083 | 5050 | 0.0252 | - | |
|
| 0.9173 | 5100 | 0.0023 | - | |
|
| 0.9263 | 5150 | 0.0062 | - | |
|
| 0.9353 | 5200 | 0.0152 | - | |
|
| 0.9442 | 5250 | 0.0014 | - | |
|
| 0.9532 | 5300 | 0.0224 | - | |
|
| 0.9622 | 5350 | 0.0174 | - | |
|
| 0.9712 | 5400 | 0.0066 | - | |
|
| 0.9802 | 5450 | 0.0002 | - | |
|
| 0.9892 | 5500 | 0.0136 | - | |
|
| 0.9982 | 5550 | 0.0036 | - | |
|
| 1.0072 | 5600 | 0.0102 | - | |
|
| 1.0162 | 5650 | 0.011 | - | |
|
| 1.0252 | 5700 | 0.0035 | - | |
|
| 1.0342 | 5750 | 0.0002 | - | |
|
| 1.0432 | 5800 | 0.0002 | - | |
|
| 1.0522 | 5850 | 0.0044 | - | |
|
| 1.0612 | 5900 | 0.0125 | - | |
|
| 1.0701 | 5950 | 0.0061 | - | |
|
| 1.0791 | 6000 | 0.0165 | 0.3591 | |
|
| 1.0881 | 6050 | 0.006 | - | |
|
| 1.0971 | 6100 | 0.0003 | - | |
|
| 1.1061 | 6150 | 0.0074 | - | |
|
| 1.1151 | 6200 | 0.0019 | - | |
|
| 1.1241 | 6250 | 0.0002 | - | |
|
| 1.1331 | 6300 | 0.0064 | - | |
|
| 1.1421 | 6350 | 0.0127 | - | |
|
| 1.1511 | 6400 | 0.0012 | - | |
|
| 1.1601 | 6450 | 0.0003 | - | |
|
| 1.1691 | 6500 | 0.0251 | - | |
|
| 1.1781 | 6550 | 0.0002 | - | |
|
| 1.1871 | 6600 | 0.0003 | - | |
|
| 1.1960 | 6650 | 0.0002 | - | |
|
| 1.2050 | 6700 | 0.0002 | - | |
|
| 1.2140 | 6750 | 0.0123 | - | |
|
| 1.2230 | 6800 | 0.0055 | - | |
|
| 1.2320 | 6850 | 0.0098 | - | |
|
| 1.2410 | 6900 | 0.0028 | - | |
|
| 1.25 | 6950 | 0.0049 | - | |
|
| 1.2590 | 7000 | 0.0021 | 0.3537 | |
|
| 1.2680 | 7050 | 0.0147 | - | |
|
| 1.2770 | 7100 | 0.003 | - | |
|
| 1.2860 | 7150 | 0.0002 | - | |
|
| 1.2950 | 7200 | 0.0049 | - | |
|
| 1.3040 | 7250 | 0.0033 | - | |
|
| 1.3129 | 7300 | 0.0002 | - | |
|
| 1.3219 | 7350 | 0.0065 | - | |
|
| 1.3309 | 7400 | 0.0043 | - | |
|
| 1.3399 | 7450 | 0.0107 | - | |
|
| 1.3489 | 7500 | 0.0184 | - | |
|
| 1.3579 | 7550 | 0.0116 | - | |
|
| 1.3669 | 7600 | 0.0041 | - | |
|
| 1.3759 | 7650 | 0.0001 | - | |
|
| 1.3849 | 7700 | 0.0001 | - | |
|
| 1.3939 | 7750 | 0.0074 | - | |
|
| 1.4029 | 7800 | 0.0002 | - | |
|
| 1.4119 | 7850 | 0.0087 | - | |
|
| 1.4209 | 7900 | 0.0014 | - | |
|
| 1.4299 | 7950 | 0.0045 | - | |
|
| 1.4388 | 8000 | 0.0018 | 0.3439 | |
|
| 1.4478 | 8050 | 0.0039 | - | |
|
| 1.4568 | 8100 | 0.007 | - | |
|
| 1.4658 | 8150 | 0.0066 | - | |
|
| 1.4748 | 8200 | 0.0101 | - | |
|
| 1.4838 | 8250 | 0.0047 | - | |
|
| 1.4928 | 8300 | 0.0021 | - | |
|
| 1.5018 | 8350 | 0.0002 | - | |
|
| 1.5108 | 8400 | 0.0116 | - | |
|
| 1.5198 | 8450 | 0.0017 | - | |
|
| 1.5288 | 8500 | 0.0032 | - | |
|
| 1.5378 | 8550 | 0.0053 | - | |
|
| 1.5468 | 8600 | 0.0038 | - | |
|
| 1.5558 | 8650 | 0.0001 | - | |
|
| 1.5647 | 8700 | 0.002 | - | |
|
| 1.5737 | 8750 | 0.0065 | - | |
|
| 1.5827 | 8800 | 0.0064 | - | |
|
| 1.5917 | 8850 | 0.0001 | - | |
|
| 1.6007 | 8900 | 0.0049 | - | |
|
| 1.6097 | 8950 | 0.0002 | - | |
|
| 1.6187 | 9000 | 0.0083 | 0.3486 | |
|
| 1.6277 | 9050 | 0.0105 | - | |
|
| 1.6367 | 9100 | 0.0019 | - | |
|
| 1.6457 | 9150 | 0.0002 | - | |
|
| 1.6547 | 9200 | 0.0049 | - | |
|
| 1.6637 | 9250 | 0.0001 | - | |
|
| 1.6727 | 9300 | 0.0097 | - | |
|
| 1.6817 | 9350 | 0.0098 | - | |
|
| 1.6906 | 9400 | 0.0022 | - | |
|
| 1.6996 | 9450 | 0.0142 | - | |
|
| 1.7086 | 9500 | 0.0025 | - | |
|
| 1.7176 | 9550 | 0.0147 | - | |
|
| 1.7266 | 9600 | 0.0086 | - | |
|
| 1.7356 | 9650 | 0.0062 | - | |
|
| 1.7446 | 9700 | 0.0002 | - | |
|
| 1.7536 | 9750 | 0.0103 | - | |
|
| 1.7626 | 9800 | 0.0186 | - | |
|
| 1.7716 | 9850 | 0.0112 | - | |
|
| 1.7806 | 9900 | 0.0042 | - | |
|
| 1.7896 | 9950 | 0.0166 | - | |
|
| 1.7986 | 10000 | 0.0002 | 0.3571 | |
|
| 1.8076 | 10050 | 0.0029 | - | |
|
| 1.8165 | 10100 | 0.0055 | - | |
|
| 1.8255 | 10150 | 0.0057 | - | |
|
| 1.8345 | 10200 | 0.0163 | - | |
|
| 1.8435 | 10250 | 0.0093 | - | |
|
| 1.8525 | 10300 | 0.0083 | - | |
|
| 1.8615 | 10350 | 0.0073 | - | |
|
| 1.8705 | 10400 | 0.0089 | - | |
|
| 1.8795 | 10450 | 0.0068 | - | |
|
| 1.8885 | 10500 | 0.0001 | - | |
|
| 1.8975 | 10550 | 0.0232 | - | |
|
| 1.9065 | 10600 | 0.0161 | - | |
|
| 1.9155 | 10650 | 0.0088 | - | |
|
| 1.9245 | 10700 | 0.0002 | - | |
|
| 1.9335 | 10750 | 0.0093 | - | |
|
| 1.9424 | 10800 | 0.0103 | - | |
|
| 1.9514 | 10850 | 0.002 | - | |
|
| 1.9604 | 10900 | 0.0113 | - | |
|
| 1.9694 | 10950 | 0.0055 | - | |
|
| 1.9784 | 11000 | 0.0148 | 0.3461 | |
|
| 1.9874 | 11050 | 0.0001 | - | |
|
| 1.9964 | 11100 | 0.0017 | - | |
|
| 2.0054 | 11150 | 0.0001 | - | |
|
| 2.0144 | 11200 | 0.0204 | - | |
|
| 2.0234 | 11250 | 0.0032 | - | |
|
| 2.0324 | 11300 | 0.0029 | - | |
|
| 2.0414 | 11350 | 0.002 | - | |
|
| 2.0504 | 11400 | 0.0001 | - | |
|
| 2.0594 | 11450 | 0.005 | - | |
|
| 2.0683 | 11500 | 0.0001 | - | |
|
| 2.0773 | 11550 | 0.0051 | - | |
|
| 2.0863 | 11600 | 0.0095 | - | |
|
| 2.0953 | 11650 | 0.0093 | - | |
|
| 2.1043 | 11700 | 0.0171 | - | |
|
| 2.1133 | 11750 | 0.0059 | - | |
|
| 2.1223 | 11800 | 0.0026 | - | |
|
| 2.1313 | 11850 | 0.0092 | - | |
|
| 2.1403 | 11900 | 0.0002 | - | |
|
| 2.1493 | 11950 | 0.0069 | - | |
|
| 2.1583 | 12000 | 0.006 | 0.3572 | |
|
| 2.1673 | 12050 | 0.009 | - | |
|
| 2.1763 | 12100 | 0.008 | - | |
|
| 2.1853 | 12150 | 0.0001 | - | |
|
| 2.1942 | 12200 | 0.0062 | - | |
|
| 2.2032 | 12250 | 0.0086 | - | |
|
| 2.2122 | 12300 | 0.0001 | - | |
|
| 2.2212 | 12350 | 0.0001 | - | |
|
| 2.2302 | 12400 | 0.0001 | - | |
|
| 2.2392 | 12450 | 0.0001 | - | |
|
| 2.2482 | 12500 | 0.0022 | - | |
|
| 2.2572 | 12550 | 0.0014 | - | |
|
| 2.2662 | 12600 | 0.0014 | - | |
|
| 2.2752 | 12650 | 0.009 | - | |
|
| 2.2842 | 12700 | 0.0001 | - | |
|
| 2.2932 | 12750 | 0.0081 | - | |
|
| 2.3022 | 12800 | 0.0127 | - | |
|
| 2.3112 | 12850 | 0.0001 | - | |
|
| 2.3201 | 12900 | 0.0028 | - | |
|
| 2.3291 | 12950 | 0.0016 | - | |
|
| 2.3381 | 13000 | 0.0051 | 0.3587 | |
|
| 2.3471 | 13050 | 0.0044 | - | |
|
| 2.3561 | 13100 | 0.0133 | - | |
|
| 2.3651 | 13150 | 0.0043 | - | |
|
| 2.3741 | 13200 | 0.0001 | - | |
|
| 2.3831 | 13250 | 0.0017 | - | |
|
| 2.3921 | 13300 | 0.0095 | - | |
|
| 2.4011 | 13350 | 0.008 | - | |
|
| 2.4101 | 13400 | 0.0074 | - | |
|
| 2.4191 | 13450 | 0.0181 | - | |
|
| 2.4281 | 13500 | 0.0141 | - | |
|
| 2.4371 | 13550 | 0.0114 | - | |
|
| 2.4460 | 13600 | 0.0046 | - | |
|
| 2.4550 | 13650 | 0.0053 | - | |
|
| 2.4640 | 13700 | 0.0001 | - | |
|
| 2.4730 | 13750 | 0.0001 | - | |
|
| 2.4820 | 13800 | 0.0114 | - | |
|
| 2.4910 | 13850 | 0.0001 | - | |
|
| 2.5 | 13900 | 0.0075 | - | |
|
| 2.5090 | 13950 | 0.0016 | - | |
|
| 2.5180 | 14000 | 0.0014 | 0.3376 | |
|
| 2.5270 | 14050 | 0.0075 | - | |
|
| 2.5360 | 14100 | 0.0001 | - | |
|
| 2.5450 | 14150 | 0.0001 | - | |
|
| 2.5540 | 14200 | 0.0013 | - | |
|
| 2.5629 | 14250 | 0.0001 | - | |
|
| 2.5719 | 14300 | 0.0082 | - | |
|
| 2.5809 | 14350 | 0.0021 | - | |
|
| 2.5899 | 14400 | 0.0001 | - | |
|
| 2.5989 | 14450 | 0.0001 | - | |
|
| 2.6079 | 14500 | 0.0016 | - | |
|
| 2.6169 | 14550 | 0.0001 | - | |
|
| 2.6259 | 14600 | 0.0001 | - | |
|
| 2.6349 | 14650 | 0.0058 | - | |
|
| 2.6439 | 14700 | 0.0223 | - | |
|
| 2.6529 | 14750 | 0.0001 | - | |
|
| 2.6619 | 14800 | 0.0001 | - | |
|
| 2.6709 | 14850 | 0.0249 | - | |
|
| 2.6799 | 14900 | 0.008 | - | |
|
| 2.6888 | 14950 | 0.0071 | - | |
|
| 2.6978 | 15000 | 0.0237 | 0.3769 | |
|
| 2.7068 | 15050 | 0.0001 | - | |
|
| 2.7158 | 15100 | 0.0016 | - | |
|
| 2.7248 | 15150 | 0.0031 | - | |
|
| 2.7338 | 15200 | 0.0063 | - | |
|
| 2.7428 | 15250 | 0.0001 | - | |
|
| 2.7518 | 15300 | 0.0127 | - | |
|
| 2.7608 | 15350 | 0.0001 | - | |
|
| 2.7698 | 15400 | 0.0114 | - | |
|
| 2.7788 | 15450 | 0.0106 | - | |
|
| 2.7878 | 15500 | 0.0086 | - | |
|
| 2.7968 | 15550 | 0.0083 | - | |
|
| 2.8058 | 15600 | 0.0001 | - | |
|
| 2.8147 | 15650 | 0.0001 | - | |
|
| 2.8237 | 15700 | 0.0035 | - | |
|
| 2.8327 | 15750 | 0.0095 | - | |
|
| 2.8417 | 15800 | 0.0041 | - | |
|
| 2.8507 | 15850 | 0.0001 | - | |
|
| 2.8597 | 15900 | 0.0001 | - | |
|
| 2.8687 | 15950 | 0.0001 | - | |
|
| 2.8777 | 16000 | 0.0001 | 0.3509 | |
|
| 2.8867 | 16050 | 0.0001 | - | |
|
| 2.8957 | 16100 | 0.0124 | - | |
|
| 2.9047 | 16150 | 0.0083 | - | |
|
| 2.9137 | 16200 | 0.0017 | - | |
|
| 2.9227 | 16250 | 0.0001 | - | |
|
| 2.9317 | 16300 | 0.0042 | - | |
|
| 2.9406 | 16350 | 0.0058 | - | |
|
| 2.9496 | 16400 | 0.0001 | - | |
|
| 2.9586 | 16450 | 0.0001 | - | |
|
| 2.9676 | 16500 | 0.0021 | - | |
|
| 2.9766 | 16550 | 0.0025 | - | |
|
| 2.9856 | 16600 | 0.0068 | - | |
|
| 2.9946 | 16650 | 0.0099 | - | |
|
| 3.0036 | 16700 | 0.0015 | - | |
|
| 3.0126 | 16750 | 0.0086 | - | |
|
| 3.0216 | 16800 | 0.0162 | - | |
|
| 3.0306 | 16850 | 0.0001 | - | |
|
| 3.0396 | 16900 | 0.0181 | - | |
|
| 3.0486 | 16950 | 0.0083 | - | |
|
| 3.0576 | 17000 | 0.0045 | 0.346 | |
|
| 3.0665 | 17050 | 0.0072 | - | |
|
| 3.0755 | 17100 | 0.0045 | - | |
|
| 3.0845 | 17150 | 0.005 | - | |
|
| 3.0935 | 17200 | 0.003 | - | |
|
| 3.1025 | 17250 | 0.0069 | - | |
|
| 3.1115 | 17300 | 0.0001 | - | |
|
| 3.1205 | 17350 | 0.003 | - | |
|
| 3.1295 | 17400 | 0.0077 | - | |
|
| 3.1385 | 17450 | 0.0001 | - | |
|
| 3.1475 | 17500 | 0.0001 | - | |
|
| 3.1565 | 17550 | 0.0166 | - | |
|
| 3.1655 | 17600 | 0.0001 | - | |
|
| 3.1745 | 17650 | 0.0001 | - | |
|
| 3.1835 | 17700 | 0.0084 | - | |
|
| 3.1924 | 17750 | 0.0106 | - | |
|
| 3.2014 | 17800 | 0.0027 | - | |
|
| 3.2104 | 17850 | 0.0092 | - | |
|
| 3.2194 | 17900 | 0.0001 | - | |
|
| 3.2284 | 17950 | 0.0001 | - | |
|
| 3.2374 | 18000 | 0.0066 | 0.3501 | |
|
| 3.2464 | 18050 | 0.0037 | - | |
|
| 3.2554 | 18100 | 0.0035 | - | |
|
| 3.2644 | 18150 | 0.0029 | - | |
|
| 3.2734 | 18200 | 0.0017 | - | |
|
| 3.2824 | 18250 | 0.0001 | - | |
|
| 3.2914 | 18300 | 0.0034 | - | |
|
| 3.3004 | 18350 | 0.0121 | - | |
|
| 3.3094 | 18400 | 0.0051 | - | |
|
| 3.3183 | 18450 | 0.0024 | - | |
|
| 3.3273 | 18500 | 0.0019 | - | |
|
| 3.3363 | 18550 | 0.0014 | - | |
|
| 3.3453 | 18600 | 0.0167 | - | |
|
| 3.3543 | 18650 | 0.0097 | - | |
|
| 3.3633 | 18700 | 0.0025 | - | |
|
| 3.3723 | 18750 | 0.0065 | - | |
|
| 3.3813 | 18800 | 0.011 | - | |
|
| 3.3903 | 18850 | 0.0001 | - | |
|
| 3.3993 | 18900 | 0.0001 | - | |
|
| 3.4083 | 18950 | 0.0072 | - | |
|
| 3.4173 | 19000 | 0.0132 | 0.3511 | |
|
| 3.4263 | 19050 | 0.0084 | - | |
|
| 3.4353 | 19100 | 0.0015 | - | |
|
| 3.4442 | 19150 | 0.0014 | - | |
|
| 3.4532 | 19200 | 0.011 | - | |
|
| 3.4622 | 19250 | 0.0083 | - | |
|
| 3.4712 | 19300 | 0.0073 | - | |
|
| 3.4802 | 19350 | 0.0024 | - | |
|
| 3.4892 | 19400 | 0.002 | - | |
|
| 3.4982 | 19450 | 0.0155 | - | |
|
| 3.5072 | 19500 | 0.0042 | - | |
|
| 3.5162 | 19550 | 0.0001 | - | |
|
| 3.5252 | 19600 | 0.0043 | - | |
|
| 3.5342 | 19650 | 0.0026 | - | |
|
| 3.5432 | 19700 | 0.0022 | - | |
|
| 3.5522 | 19750 | 0.002 | - | |
|
| 3.5612 | 19800 | 0.0018 | - | |
|
| 3.5701 | 19850 | 0.0001 | - | |
|
| 3.5791 | 19900 | 0.0012 | - | |
|
| 3.5881 | 19950 | 0.002 | - | |
|
| 3.5971 | 20000 | 0.0089 | 0.3516 | |
|
| 3.6061 | 20050 | 0.003 | - | |
|
| 3.6151 | 20100 | 0.0036 | - | |
|
| 3.6241 | 20150 | 0.0001 | - | |
|
| 3.6331 | 20200 | 0.0001 | - | |
|
| 3.6421 | 20250 | 0.0156 | - | |
|
| 3.6511 | 20300 | 0.0001 | - | |
|
| 3.6601 | 20350 | 0.0174 | - | |
|
| 3.6691 | 20400 | 0.0001 | - | |
|
| 3.6781 | 20450 | 0.011 | - | |
|
| 3.6871 | 20500 | 0.0001 | - | |
|
| 3.6960 | 20550 | 0.0047 | - | |
|
| 3.7050 | 20600 | 0.0132 | - | |
|
| 3.7140 | 20650 | 0.007 | - | |
|
| 3.7230 | 20700 | 0.0001 | - | |
|
| 3.7320 | 20750 | 0.0025 | - | |
|
| 3.7410 | 20800 | 0.0049 | - | |
|
| 3.75 | 20850 | 0.0074 | - | |
|
| 3.7590 | 20900 | 0.002 | - | |
|
| 3.7680 | 20950 | 0.0112 | - | |
|
| 3.7770 | 21000 | 0.0001 | 0.3483 | |
|
| 3.7860 | 21050 | 0.0001 | - | |
|
| 3.7950 | 21100 | 0.0064 | - | |
|
| 3.8040 | 21150 | 0.0133 | - | |
|
| 3.8129 | 21200 | 0.0001 | - | |
|
| 3.8219 | 21250 | 0.0112 | - | |
|
| 3.8309 | 21300 | 0.0001 | - | |
|
| 3.8399 | 21350 | 0.0001 | - | |
|
| 3.8489 | 21400 | 0.0001 | - | |
|
| 3.8579 | 21450 | 0.0025 | - | |
|
| 3.8669 | 21500 | 0.0047 | - | |
|
| 3.8759 | 21550 | 0.0001 | - | |
|
| 3.8849 | 21600 | 0.0062 | - | |
|
| 3.8939 | 21650 | 0.0001 | - | |
|
| 3.9029 | 21700 | 0.0315 | - | |
|
| 3.9119 | 21750 | 0.002 | - | |
|
| 3.9209 | 21800 | 0.0034 | - | |
|
| 3.9299 | 21850 | 0.004 | - | |
|
| 3.9388 | 21900 | 0.0046 | - | |
|
| 3.9478 | 21950 | 0.008 | - | |
|
| 3.9568 | 22000 | 0.0103 | 0.3474 | |
|
| 3.9658 | 22050 | 0.0142 | - | |
|
| 3.9748 | 22100 | 0.0207 | - | |
|
| 3.9838 | 22150 | 0.0105 | - | |
|
| 3.9928 | 22200 | 0.0114 | - | |
|
| 4.0018 | 22250 | 0.002 | - | |
|
| 4.0108 | 22300 | 0.0121 | - | |
|
| 4.0198 | 22350 | 0.0001 | - | |
|
| 4.0288 | 22400 | 0.0058 | - | |
|
| 4.0378 | 22450 | 0.0045 | - | |
|
| 4.0468 | 22500 | 0.0001 | - | |
|
| 4.0558 | 22550 | 0.0086 | - | |
|
| 4.0647 | 22600 | 0.0121 | - | |
|
| 4.0737 | 22650 | 0.0045 | - | |
|
| 4.0827 | 22700 | 0.0001 | - | |
|
| 4.0917 | 22750 | 0.0046 | - | |
|
| 4.1007 | 22800 | 0.0076 | - | |
|
| 4.1097 | 22850 | 0.0001 | - | |
|
| 4.1187 | 22900 | 0.0154 | - | |
|
| 4.1277 | 22950 | 0.0108 | - | |
|
| 4.1367 | 23000 | 0.0058 | 0.3575 | |
|
| 4.1457 | 23050 | 0.0088 | - | |
|
| 4.1547 | 23100 | 0.0019 | - | |
|
| 4.1637 | 23150 | 0.0055 | - | |
|
| 4.1727 | 23200 | 0.0299 | - | |
|
| 4.1817 | 23250 | 0.0085 | - | |
|
| 4.1906 | 23300 | 0.0016 | - | |
|
| 4.1996 | 23350 | 0.0001 | - | |
|
| 4.2086 | 23400 | 0.0001 | - | |
|
| 4.2176 | 23450 | 0.0072 | - | |
|
| 4.2266 | 23500 | 0.0092 | - | |
|
| 4.2356 | 23550 | 0.0001 | - | |
|
| 4.2446 | 23600 | 0.0064 | - | |
|
| 4.2536 | 23650 | 0.0065 | - | |
|
| 4.2626 | 23700 | 0.0001 | - | |
|
| 4.2716 | 23750 | 0.0017 | - | |
|
| 4.2806 | 23800 | 0.0083 | - | |
|
| 4.2896 | 23850 | 0.0001 | - | |
|
| 4.2986 | 23900 | 0.0039 | - | |
|
| 4.3076 | 23950 | 0.002 | - | |
|
| 4.3165 | 24000 | 0.0037 | 0.357 | |
|
| 4.3255 | 24050 | 0.0095 | - | |
|
| 4.3345 | 24100 | 0.002 | - | |
|
| 4.3435 | 24150 | 0.017 | - | |
|
| 4.3525 | 24200 | 0.0086 | - | |
|
| 4.3615 | 24250 | 0.007 | - | |
|
| 4.3705 | 24300 | 0.0023 | - | |
|
| 4.3795 | 24350 | 0.0122 | - | |
|
| 4.3885 | 24400 | 0.0097 | - | |
|
| 4.3975 | 24450 | 0.0027 | - | |
|
| 4.4065 | 24500 | 0.0081 | - | |
|
| 4.4155 | 24550 | 0.0043 | - | |
|
| 4.4245 | 24600 | 0.0055 | - | |
|
| 4.4335 | 24650 | 0.0001 | - | |
|
| 4.4424 | 24700 | 0.0014 | - | |
|
| 4.4514 | 24750 | 0.0001 | - | |
|
| 4.4604 | 24800 | 0.0091 | - | |
|
| 4.4694 | 24850 | 0.0087 | - | |
|
| 4.4784 | 24900 | 0.0101 | - | |
|
| 4.4874 | 24950 | 0.0001 | - | |
|
| 4.4964 | 25000 | 0.013 | 0.3566 | |
|
| 4.5054 | 25050 | 0.013 | - | |
|
| 4.5144 | 25100 | 0.0082 | - | |
|
| 4.5234 | 25150 | 0.0063 | - | |
|
| 4.5324 | 25200 | 0.0046 | - | |
|
| 4.5414 | 25250 | 0.0087 | - | |
|
| 4.5504 | 25300 | 0.0063 | - | |
|
| 4.5594 | 25350 | 0.0019 | - | |
|
| 4.5683 | 25400 | 0.0061 | - | |
|
| 4.5773 | 25450 | 0.004 | - | |
|
| 4.5863 | 25500 | 0.0001 | - | |
|
| 4.5953 | 25550 | 0.0001 | - | |
|
| 4.6043 | 25600 | 0.0088 | - | |
|
| 4.6133 | 25650 | 0.0191 | - | |
|
| 4.6223 | 25700 | 0.0124 | - | |
|
| 4.6313 | 25750 | 0.0001 | - | |
|
| 4.6403 | 25800 | 0.0023 | - | |
|
| 4.6493 | 25850 | 0.0001 | - | |
|
| 4.6583 | 25900 | 0.0068 | - | |
|
| 4.6673 | 25950 | 0.0001 | - | |
|
| 4.6763 | 26000 | 0.0034 | 0.3563 | |
|
| 4.6853 | 26050 | 0.0138 | - | |
|
| 4.6942 | 26100 | 0.0001 | - | |
|
| 4.7032 | 26150 | 0.0068 | - | |
|
| 4.7122 | 26200 | 0.0091 | - | |
|
| 4.7212 | 26250 | 0.0001 | - | |
|
| 4.7302 | 26300 | 0.0152 | - | |
|
| 4.7392 | 26350 | 0.0064 | - | |
|
| 4.7482 | 26400 | 0.0021 | - | |
|
| 4.7572 | 26450 | 0.0088 | - | |
|
| 4.7662 | 26500 | 0.0001 | - | |
|
| 4.7752 | 26550 | 0.0042 | - | |
|
| 4.7842 | 26600 | 0.0022 | - | |
|
| 4.7932 | 26650 | 0.0065 | - | |
|
| 4.8022 | 26700 | 0.0039 | - | |
|
| 4.8112 | 26750 | 0.0039 | - | |
|
| 4.8201 | 26800 | 0.0001 | - | |
|
| 4.8291 | 26850 | 0.0155 | - | |
|
| 4.8381 | 26900 | 0.0021 | - | |
|
| 4.8471 | 26950 | 0.0039 | - | |
|
| 4.8561 | 27000 | 0.002 | 0.3555 | |
|
| 4.8651 | 27050 | 0.0092 | - | |
|
| 4.8741 | 27100 | 0.0001 | - | |
|
| 4.8831 | 27150 | 0.0081 | - | |
|
| 4.8921 | 27200 | 0.0081 | - | |
|
| 4.9011 | 27250 | 0.0037 | - | |
|
| 4.9101 | 27300 | 0.0104 | - | |
|
| 4.9191 | 27350 | 0.0022 | - | |
|
| 4.9281 | 27400 | 0.004 | - | |
|
| 4.9371 | 27450 | 0.0076 | - | |
|
| 4.9460 | 27500 | 0.0043 | - | |
|
| 4.9550 | 27550 | 0.0142 | - | |
|
| 4.9640 | 27600 | 0.0126 | - | |
|
| 4.9730 | 27650 | 0.0038 | - | |
|
| 4.9820 | 27700 | 0.0107 | - | |
|
| 4.9910 | 27750 | 0.0019 | - | |
|
| 5.0 | 27800 | 0.0104 | - | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- SetFit: 1.0.3 |
|
- Sentence Transformers: 3.0.1 |
|
- spaCy: 3.7.4 |
|
- Transformers: 4.39.0 |
|
- PyTorch: 2.3.1+cu121 |
|
- Datasets: 2.20.0 |
|
- Tokenizers: 0.15.2 |
|
|
|
## 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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