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AnonymousSub/rule_based_only_classfn_epochs_1_shard_10
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feature-extraction
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---
tags:
- image-classification
- timm
library_tag: timm
license: apache-2.0
datasets:
- imagenet-1k
- laion-2b
---
# Model card for convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320
A ConvNeXt image classification model. CLIP image tower weights pretrained in [OpenCLIP](https://github.com/mlfoundations/open_clip) on LAION and fine-tuned on ImageNet-12k followed by ImageNet-1k in `timm` bby Ross Wightman.
Please see related OpenCLIP model cards for more details on pretrain:
* https://huggingface.co/laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup
* https://huggingface.co/laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg
* https://huggingface.co/laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg
* https://huggingface.co/laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 200.1
- GMACs: 70.2
- Activations (M): 88.0
- Image size: 320 x 320
- **Papers:**
- LAION-5B: An open large-scale dataset for training next generation image-text models: https://arxiv.org/abs/2210.08402
- A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
- Learning Transferable Visual Models From Natural Language Supervision: https://arxiv.org/abs/2103.00020
- **Original:** https://github.com/mlfoundations/open_clip
- **Pretrain Dataset:** LAION-2B
- **Dataset:** ImageNet-1k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 192, 80, 80])
# torch.Size([1, 384, 40, 40])
# torch.Size([1, 768, 20, 20])
# torch.Size([1, 1536, 10, 10])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1536, 10, 10) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.
| model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size|
|------------------------------------------------------------------------------------------------------------------------------|------|------|--------|-----------|------|------|---------------|----------|
| [convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512) |88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 |
| [convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384) |88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 |
| [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) |88.612|98.704|256 |846.47 |198.09|124.45|122.45 |256 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384) |88.312|98.578|384 |200.13 |101.11|126.74|196.84 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384) |88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320) |87.968|98.47 |320 |200.13 |70.21 |88.02 |283.42 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384) |87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 |
| [convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384) |87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 |
| [convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384) |87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 |
| [convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k) |87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k) |87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384) |87.138|98.212|384 |88.59 |45.21 |84.49 |365.47 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k) |87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 |
| [convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384) |86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 |
| [convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k) |86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 |
| [convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k) |86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 |
| [convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384) |86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k) |86.344|97.97 |256 |88.59 |20.09 |37.55 |816.14 |256 |
| [convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k) |86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 |
| [convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384) |86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k) |86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 |
| [convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k) |85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 |
| [convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384) |85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 |
| [convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k) |85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 |
| [convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k) |85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 |
| [convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384) |85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384) |85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 |
| [convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k) |84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 |
| [convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k) |84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 |
| [convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k) |84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 |
| [convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k) |84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 |
| [convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384) |84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k) |83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 |
| [convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k) |83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384) |83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 |
| [convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k) |83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 |
| [convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k) |82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 |
| [convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k) |82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 |
| [convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k) |82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 |
| [convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k) |82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 |
| [convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k) |82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) |82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 |
| [convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k) |81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 |
| [convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k) |80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 |
| [convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k) |80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 |
| [convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k) |80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 |
| [convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k) |79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 |
| [convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k) |79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 |
| [convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k) |78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 |
| [convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k) |77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 |
| [convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k) |77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 |
| [convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k) |76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 |
| [convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k) |75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 |
| [convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k) |75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 |
## Citation
```bibtex
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
```
```bibtex
@inproceedings{schuhmann2022laionb,
title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
author={Christoph Schuhmann and
Romain Beaumont and
Richard Vencu and
Cade W Gordon and
Ross Wightman and
Mehdi Cherti and
Theo Coombes and
Aarush Katta and
Clayton Mullis and
Mitchell Wortsman and
Patrick Schramowski and
Srivatsa R Kundurthy and
Katherine Crowson and
Ludwig Schmidt and
Robert Kaczmarczyk and
Jenia Jitsev},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=M3Y74vmsMcY}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
```bibtex
@inproceedings{Radford2021LearningTV,
title={Learning Transferable Visual Models From Natural Language Supervision},
author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
booktitle={ICML},
year={2021}
}
```
```bibtex
@article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}
```
|
AnonymousSub/rule_based_roberta_hier_triplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 4
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Variome_5e-05_250
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Variome_5e-05_250
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0679
- Precision: 0.6097
- Recall: 0.5389
- F1: 0.5721
- Accuracy: 0.9860
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5834 | 0.35 | 25 | 0.1849 | 0.0 | 0.0 | 0.0 | 0.9760 |
| 0.1856 | 0.69 | 50 | 0.1791 | 0.0 | 0.0 | 0.0 | 0.9760 |
| 0.1611 | 1.04 | 75 | 0.1698 | 0.0 | 0.0 | 0.0 | 0.9760 |
| 0.1471 | 1.39 | 100 | 0.1219 | 0.1478 | 0.0290 | 0.0485 | 0.9764 |
| 0.1117 | 1.74 | 125 | 0.1133 | 0.1784 | 0.1426 | 0.1585 | 0.9767 |
| 0.1071 | 2.08 | 150 | 0.1030 | 0.2899 | 0.2220 | 0.2515 | 0.9789 |
| 0.0844 | 2.43 | 175 | 0.0977 | 0.3838 | 0.2750 | 0.3204 | 0.9805 |
| 0.087 | 2.78 | 200 | 0.0884 | 0.4084 | 0.3903 | 0.3991 | 0.9815 |
| 0.0785 | 3.12 | 225 | 0.0803 | 0.4895 | 0.4176 | 0.4507 | 0.9833 |
| 0.0647 | 3.47 | 250 | 0.0784 | 0.5545 | 0.4518 | 0.4979 | 0.9842 |
| 0.0592 | 3.82 | 275 | 0.0740 | 0.5655 | 0.5013 | 0.5315 | 0.9847 |
| 0.0525 | 4.17 | 300 | 0.0725 | 0.5916 | 0.5158 | 0.5511 | 0.9854 |
| 0.0515 | 4.51 | 325 | 0.0698 | 0.5861 | 0.5115 | 0.5463 | 0.9853 |
| 0.0483 | 4.86 | 350 | 0.0691 | 0.5994 | 0.5201 | 0.5569 | 0.9855 |
| 0.047 | 5.21 | 375 | 0.0702 | 0.5905 | 0.5209 | 0.5535 | 0.9855 |
| 0.0429 | 5.56 | 400 | 0.0693 | 0.5986 | 0.5286 | 0.5615 | 0.9858 |
| 0.0435 | 5.9 | 425 | 0.0673 | 0.5951 | 0.5397 | 0.5661 | 0.9858 |
| 0.0418 | 6.25 | 450 | 0.0676 | 0.5949 | 0.5329 | 0.5622 | 0.9858 |
| 0.038 | 6.6 | 475 | 0.0679 | 0.6013 | 0.5397 | 0.5689 | 0.9860 |
| 0.0355 | 6.94 | 500 | 0.0679 | 0.6097 | 0.5389 | 0.5721 | 0.9860 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_hier_triplet_epochs_1_shard_1_wikiqa_copy
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
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}
| 2
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Yepes_0.0001_250
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Yepes_0.0001_250
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1555
- Precision: 0.5922
- Recall: 0.4552
- F1: 0.5148
- Accuracy: 0.9768
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4065 | 1.39 | 25 | 0.2115 | 0.0 | 0.0 | 0.0 | 0.9672 |
| 0.1995 | 2.78 | 50 | 0.2120 | 0.0 | 0.0 | 0.0 | 0.9672 |
| 0.1995 | 4.17 | 75 | 0.2108 | 0.0 | 0.0 | 0.0 | 0.9672 |
| 0.1694 | 5.56 | 100 | 0.1646 | 0.0 | 0.0 | 0.0 | 0.9672 |
| 0.1493 | 6.94 | 125 | 0.1513 | 0.0 | 0.0 | 0.0 | 0.9672 |
| 0.1266 | 8.33 | 150 | 0.1446 | 0.0 | 0.0 | 0.0 | 0.9672 |
| 0.106 | 9.72 | 175 | 0.1396 | 0.4019 | 0.2139 | 0.2792 | 0.9704 |
| 0.086 | 11.11 | 200 | 0.1162 | 0.5037 | 0.3408 | 0.4065 | 0.9740 |
| 0.0613 | 12.5 | 225 | 0.1230 | 0.5015 | 0.4104 | 0.4514 | 0.9740 |
| 0.047 | 13.89 | 250 | 0.1306 | 0.5333 | 0.4378 | 0.4809 | 0.9753 |
| 0.0351 | 15.28 | 275 | 0.1351 | 0.5629 | 0.4453 | 0.4972 | 0.9757 |
| 0.0266 | 16.67 | 300 | 0.1453 | 0.5617 | 0.4303 | 0.4873 | 0.9765 |
| 0.02 | 18.06 | 325 | 0.1441 | 0.5573 | 0.4478 | 0.4966 | 0.9757 |
| 0.0153 | 19.44 | 350 | 0.1555 | 0.5922 | 0.4552 | 0.5148 | 0.9768 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_only_classfn_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
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"translation_en_to_ro": {
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}
| 5
| null |
# `vocabtrimmer/xlm-v-base-trimmed-ar-tweet-sentiment-ar`
This model is a fine-tuned version of [/home/asahi/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-ar](https://huggingface.co//home/asahi/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-ar) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (arabic).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(arabic).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 65.4 | 65.4 | 65.4 | 64.72 | 65.4 | 65.15 | 65.4 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-trimmed-ar-tweet-sentiment-ar/raw/main/eval.json).
|
AnonymousSub/rule_based_roberta_only_classfn_twostage_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 2
| 2023-03-31T23:38:50Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: SETH_2e-05_250
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SETH_2e-05_250
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0676
- Precision: 0.7820
- Recall: 0.7891
- F1: 0.7855
- Accuracy: 0.9837
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4635 | 0.76 | 25 | 0.1662 | 0.0 | 0.0 | 0.0 | 0.9625 |
| 0.0991 | 1.52 | 50 | 0.0805 | 0.7425 | 0.6291 | 0.6811 | 0.9770 |
| 0.0585 | 2.27 | 75 | 0.0616 | 0.6952 | 0.7836 | 0.7368 | 0.9801 |
| 0.0495 | 3.03 | 100 | 0.0564 | 0.7129 | 0.7945 | 0.7515 | 0.9819 |
| 0.0413 | 3.79 | 125 | 0.0531 | 0.7188 | 0.8273 | 0.7692 | 0.9824 |
| 0.0393 | 4.55 | 150 | 0.0512 | 0.7350 | 0.8218 | 0.7760 | 0.9827 |
| 0.0317 | 5.3 | 175 | 0.0490 | 0.7543 | 0.7927 | 0.7730 | 0.9832 |
| 0.0283 | 6.06 | 200 | 0.0546 | 0.7780 | 0.7836 | 0.7808 | 0.9833 |
| 0.0255 | 6.82 | 225 | 0.0524 | 0.7504 | 0.7818 | 0.7658 | 0.9829 |
| 0.022 | 7.58 | 250 | 0.0567 | 0.7613 | 0.7945 | 0.7776 | 0.9835 |
| 0.0183 | 8.33 | 275 | 0.0566 | 0.7730 | 0.7927 | 0.7828 | 0.9842 |
| 0.0179 | 9.09 | 300 | 0.0592 | 0.7668 | 0.7655 | 0.7662 | 0.9830 |
| 0.016 | 9.85 | 325 | 0.0648 | 0.7855 | 0.7855 | 0.7855 | 0.9841 |
| 0.0135 | 10.61 | 350 | 0.0639 | 0.7732 | 0.7873 | 0.7802 | 0.9832 |
| 0.0121 | 11.36 | 375 | 0.0676 | 0.7820 | 0.7891 | 0.7855 | 0.9837 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_only_classfn_twostage_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
}
| 2
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Variome_0.0001_250
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Variome_0.0001_250
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0638
- Precision: 0.6586
- Recall: 0.5816
- F1: 0.6177
- Accuracy: 0.9867
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3778 | 0.35 | 25 | 0.1802 | 0.0 | 0.0 | 0.0 | 0.9760 |
| 0.1563 | 0.69 | 50 | 0.1200 | 0.4524 | 0.0162 | 0.0313 | 0.9763 |
| 0.1061 | 1.04 | 75 | 0.1041 | 0.3604 | 0.2767 | 0.3130 | 0.9799 |
| 0.0981 | 1.39 | 100 | 0.0902 | 0.4585 | 0.3826 | 0.4171 | 0.9814 |
| 0.0807 | 1.74 | 125 | 0.0783 | 0.5129 | 0.4244 | 0.4645 | 0.9835 |
| 0.0731 | 2.08 | 150 | 0.0727 | 0.5513 | 0.5047 | 0.5270 | 0.9844 |
| 0.0526 | 2.43 | 175 | 0.0720 | 0.6368 | 0.5167 | 0.5705 | 0.9856 |
| 0.0604 | 2.78 | 200 | 0.0686 | 0.589 | 0.5030 | 0.5426 | 0.9849 |
| 0.0542 | 3.12 | 225 | 0.0671 | 0.6131 | 0.5371 | 0.5726 | 0.9856 |
| 0.0441 | 3.47 | 250 | 0.0669 | 0.6635 | 0.5389 | 0.5947 | 0.9860 |
| 0.0438 | 3.82 | 275 | 0.0667 | 0.625 | 0.5423 | 0.5807 | 0.9859 |
| 0.0381 | 4.17 | 300 | 0.0658 | 0.6562 | 0.5525 | 0.5999 | 0.9858 |
| 0.0404 | 4.51 | 325 | 0.0648 | 0.6578 | 0.5713 | 0.6115 | 0.9862 |
| 0.0341 | 4.86 | 350 | 0.0625 | 0.6637 | 0.5679 | 0.6121 | 0.9865 |
| 0.0298 | 5.21 | 375 | 0.0646 | 0.6727 | 0.5739 | 0.6194 | 0.9868 |
| 0.029 | 5.56 | 400 | 0.0643 | 0.6569 | 0.5739 | 0.6126 | 0.9861 |
| 0.0287 | 5.9 | 425 | 0.0637 | 0.6713 | 0.5739 | 0.6188 | 0.9869 |
| 0.027 | 6.25 | 450 | 0.0637 | 0.6660 | 0.5739 | 0.6165 | 0.9868 |
| 0.0236 | 6.6 | 475 | 0.0639 | 0.6644 | 0.5833 | 0.6212 | 0.9869 |
| 0.0233 | 6.94 | 500 | 0.0638 | 0.6586 | 0.5816 | 0.6177 | 0.9867 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
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},
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},
"translation_en_to_fr": {
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}
}
| 4
| null |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 42.11917291581875
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8559
- Bleu: 42.1192
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
}
}
| 24
| null |
# `vocabtrimmer/xlm-v-base-trimmed-ar-5000-tweet-sentiment-ar`
This model is a fine-tuned version of [/home/asahi/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-ar-5000](https://huggingface.co//home/asahi/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-ar-5000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (arabic).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(arabic).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 46.55 | 46.55 | 46.55 | 37.81 | 46.55 | 41.09 | 46.55 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-trimmed-ar-5000-tweet-sentiment-ar/raw/main/eval.json).
|
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
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}
| 2
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: SETH_5e-05_250
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SETH_5e-05_250
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0716
- Precision: 0.7964
- Recall: 0.8036
- F1: 0.8000
- Accuracy: 0.9849
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3757 | 0.76 | 25 | 0.1924 | 0.0 | 0.0 | 0.0 | 0.9625 |
| 0.1119 | 1.52 | 50 | 0.0723 | 0.6237 | 0.7473 | 0.6799 | 0.9775 |
| 0.0565 | 2.27 | 75 | 0.0614 | 0.6569 | 0.7727 | 0.7101 | 0.9794 |
| 0.048 | 3.03 | 100 | 0.0586 | 0.6667 | 0.8655 | 0.7532 | 0.9801 |
| 0.0355 | 3.79 | 125 | 0.0519 | 0.7206 | 0.8345 | 0.7734 | 0.9835 |
| 0.0328 | 4.55 | 150 | 0.0532 | 0.7165 | 0.8455 | 0.7756 | 0.9831 |
| 0.0258 | 5.3 | 175 | 0.0539 | 0.7460 | 0.8382 | 0.7894 | 0.9835 |
| 0.022 | 6.06 | 200 | 0.0561 | 0.7612 | 0.7709 | 0.7660 | 0.9836 |
| 0.0189 | 6.82 | 225 | 0.0564 | 0.7636 | 0.74 | 0.7516 | 0.9828 |
| 0.0166 | 7.58 | 250 | 0.0597 | 0.7274 | 0.8491 | 0.7836 | 0.9836 |
| 0.0128 | 8.33 | 275 | 0.0626 | 0.8251 | 0.7636 | 0.7932 | 0.9854 |
| 0.0113 | 9.09 | 300 | 0.0603 | 0.8029 | 0.8 | 0.8015 | 0.9854 |
| 0.009 | 9.85 | 325 | 0.0687 | 0.8026 | 0.7909 | 0.7967 | 0.9857 |
| 0.0075 | 10.61 | 350 | 0.0716 | 0.7964 | 0.8036 | 0.8000 | 0.9849 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
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},
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},
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},
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},
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},
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}
}
}
| 2
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: SETH_0.0001_250
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SETH_0.0001_250
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0681
- Precision: 0.7818
- Recall: 0.7945
- F1: 0.7881
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2912 | 0.76 | 25 | 0.1275 | 0.8475 | 0.0909 | 0.1642 | 0.9647 |
| 0.0752 | 1.52 | 50 | 0.0588 | 0.6884 | 0.7873 | 0.7345 | 0.9799 |
| 0.0433 | 2.27 | 75 | 0.0603 | 0.6623 | 0.8309 | 0.7371 | 0.9803 |
| 0.0394 | 3.03 | 100 | 0.0516 | 0.6761 | 0.8727 | 0.7619 | 0.9822 |
| 0.0292 | 3.79 | 125 | 0.0534 | 0.7430 | 0.8145 | 0.7771 | 0.9836 |
| 0.0249 | 4.55 | 150 | 0.0520 | 0.7384 | 0.8109 | 0.7730 | 0.9828 |
| 0.0196 | 5.3 | 175 | 0.0618 | 0.7442 | 0.8145 | 0.7778 | 0.9833 |
| 0.0165 | 6.06 | 200 | 0.0604 | 0.7538 | 0.8182 | 0.7847 | 0.9846 |
| 0.0131 | 6.82 | 225 | 0.0613 | 0.7788 | 0.7745 | 0.7767 | 0.9843 |
| 0.0095 | 7.58 | 250 | 0.0681 | 0.7818 | 0.7945 | 0.7881 | 0.9850 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
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},
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},
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},
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},
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}
}
}
| 1
| null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
- question answering
widget:
- text: "question: What is a person called is practicing heresy?, context: Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things."
example_title: "Question Answering Example 1"
- text: "question: who created the post as we know it today?, context: 'So much of The Post is Ben,' Mrs. Graham said in 1994, three years after Bradlee retired as editor. 'He created it as we know it today.'— Ed O'Keefe (@edatpost) October 21, 2014"
example_title: "Question Answering Example 2"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-en-90000-squad-qa
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squad
type: default
args: default
metrics:
- name: BLEU4 (Question Answering)
type: bleu4_question_answering
value: 33.47
- name: ROUGE-L (Question Answering)
type: rouge_l_question_answering
value: 67.38
- name: METEOR (Question Answering)
type: meteor_question_answering
value: 39.13
- name: BERTScore (Question Answering)
type: bertscore_question_answering
value: 91.86
- name: MoverScore (Question Answering)
type: moverscore_question_answering
value: 81.36
- name: AnswerF1Score (Question Answering)
type: answer_f1_score__question_answering
value: 68.65
- name: AnswerExactMatch (Question Answering)
type: answer_exact_match_question_answering
value: 54.26
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-en-90000-squad-qa`
This model is fine-tuned version of [ckpts/mt5-small-trimmed-en-90000](https://huggingface.co/ckpts/mt5-small-trimmed-en-90000) for question answering task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [ckpts/mt5-small-trimmed-en-90000](https://huggingface.co/ckpts/mt5-small-trimmed-en-90000)
- **Language:** en
- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="en", model="vocabtrimmer/mt5-small-trimmed-en-90000-squad-qa")
# model prediction
answers = model.answer_q(list_question="What is a person called is practicing heresy?", list_context=" Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-en-90000-squad-qa")
output = pipe("question: What is a person called is practicing heresy?, context: Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.")
```
## Evaluation
- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-en-90000-squad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:---------------------------------------------------------------|
| AnswerExactMatch | 54.26 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| AnswerF1Score | 68.65 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| BERTScore | 91.86 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1 | 49.27 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2 | 43.25 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3 | 37.89 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4 | 33.47 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR | 39.13 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore | 81.36 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L | 67.38 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_squad
- dataset_name: default
- input_types: ['paragraph_question']
- output_types: ['answer']
- prefix_types: None
- model: ckpts/mt5-small-trimmed-en-90000
- max_length: 512
- max_length_output: 32
- epoch: 10
- batch: 32
- lr: 0.0005
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 2
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-en-90000-squad-qa/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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},
"translation_en_to_fr": {
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"translation_en_to_ro": {
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}
}
}
| 24
| null |
# `vocabtrimmer/xlm-v-base-trimmed-ar-10000-tweet-sentiment-ar`
This model is a fine-tuned version of [/home/asahi/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-ar-10000](https://huggingface.co//home/asahi/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-ar-10000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (arabic).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(arabic).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 61.38 | 61.38 | 61.38 | 60.99 | 61.38 | 60.95 | 61.38 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-trimmed-ar-10000-tweet-sentiment-ar/raw/main/eval.json).
|
AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 6
| null |
# Vocabulary Trimmed [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base): `vocabtrimmer/xlm-v-base-trimmed-ar-15000`
This model is a trimmed version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | facebook/xlm-v-base | vocabtrimmer/xlm-v-base-trimmed-ar-15000 |
|:---------------------------|:----------------------|:-------------------------------------------|
| parameter_size_full | 779,396,349 | 97,580,186 |
| parameter_size_embedding | 692,451,072 | 11,521,536 |
| vocab_size | 901,629 | 15,002 |
| compression_rate_full | 100.0 | 12.52 |
| compression_rate_embedding | 100.0 | 1.66 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ar | vocabtrimmer/mc4_validation | text | ar | validation | 15000 | 2 |
|
AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 4
| null |
# Vocabulary Trimmed [cardiffnlp/xlm-v-base-tweet-sentiment-fr](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-fr): `vocabtrimmer/xlm-v-base-tweet-sentiment-fr-trimmed-fr`
This model is a trimmed version of [cardiffnlp/xlm-v-base-tweet-sentiment-fr](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-fr) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-v-base-tweet-sentiment-fr | vocabtrimmer/xlm-v-base-tweet-sentiment-fr-trimmed-fr |
|:---------------------------|:-------------------------------------------|:--------------------------------------------------------|
| parameter_size_full | 778,495,491 | 253,812,483 |
| parameter_size_embedding | 692,451,072 | 167,768,064 |
| vocab_size | 901,629 | 218,448 |
| compression_rate_full | 100.0 | 32.6 |
| compression_rate_embedding | 100.0 | 24.23 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|:--------------------|----------------:|
| fr | vocabtrimmer/mc4_validation | text | fr | validation | | 2 |
|
AnonymousSub/rule_based_twostage_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
"translation_en_to_ro": {
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}
}
}
| 6
| null |
# Vocabulary Trimmed [cardiffnlp/xlm-v-base-tweet-sentiment-fr](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-fr): `vocabtrimmer/xlm-v-base-tweet-sentiment-fr-trimmed-fr-5000`
This model is a trimmed version of [cardiffnlp/xlm-v-base-tweet-sentiment-fr](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-fr) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-v-base-tweet-sentiment-fr | vocabtrimmer/xlm-v-base-tweet-sentiment-fr-trimmed-fr-5000 |
|:---------------------------|:-------------------------------------------|:-------------------------------------------------------------|
| parameter_size_full | 778,495,491 | 89,885,955 |
| parameter_size_embedding | 692,451,072 | 3,841,536 |
| vocab_size | 901,629 | 5,002 |
| compression_rate_full | 100.0 | 11.55 |
| compression_rate_embedding | 100.0 | 0.55 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| fr | vocabtrimmer/mc4_validation | text | fr | validation | 5000 | 2 |
|
AnonymousSub/rule_based_twostage_quadruplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 30
| null |
# `vocabtrimmer/xlm-v-base-trimmed-ar-15000-tweet-sentiment-ar`
This model is a fine-tuned version of [/home/asahi/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-ar-15000](https://huggingface.co//home/asahi/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-ar-15000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (arabic).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(arabic).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 52.87 | 52.87 | 52.87 | 46.59 | 52.87 | 50.88 | 52.87 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-trimmed-ar-15000-tweet-sentiment-ar/raw/main/eval.json).
|
AnonymousSub/rule_based_twostagequadruplet_hier_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 1
| null |
# Vocabulary Trimmed [cardiffnlp/xlm-v-base-tweet-sentiment-fr](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-fr): `vocabtrimmer/xlm-v-base-tweet-sentiment-fr-trimmed-fr-10000`
This model is a trimmed version of [cardiffnlp/xlm-v-base-tweet-sentiment-fr](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-fr) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-v-base-tweet-sentiment-fr | vocabtrimmer/xlm-v-base-tweet-sentiment-fr-trimmed-fr-10000 |
|:---------------------------|:-------------------------------------------|:--------------------------------------------------------------|
| parameter_size_full | 778,495,491 | 93,725,955 |
| parameter_size_embedding | 692,451,072 | 7,681,536 |
| vocab_size | 901,629 | 10,002 |
| compression_rate_full | 100.0 | 12.04 |
| compression_rate_embedding | 100.0 | 1.11 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| fr | vocabtrimmer/mc4_validation | text | fr | validation | 10000 | 2 |
|
AnonymousSub/rule_based_twostagequadruplet_hier_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
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"prefix": null
}
}
}
| 28
| null |
# Vocabulary Trimmed [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base): `vocabtrimmer/xlm-v-base-trimmed-ar-30000`
This model is a trimmed version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | facebook/xlm-v-base | vocabtrimmer/xlm-v-base-trimmed-ar-30000 |
|:---------------------------|:----------------------|:-------------------------------------------|
| parameter_size_full | 779,396,349 | 109,115,186 |
| parameter_size_embedding | 692,451,072 | 23,041,536 |
| vocab_size | 901,629 | 30,002 |
| compression_rate_full | 100.0 | 14.0 |
| compression_rate_embedding | 100.0 | 3.33 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ar | vocabtrimmer/mc4_validation | text | ar | validation | 30000 | 2 |
|
AnonymousSub/rule_based_twostagetriplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
}
| 10
| null |
# Vocabulary Trimmed [lmqg/mbart-large-cc25-squad-qa](https://huggingface.co/lmqg/mbart-large-cc25-squad-qa): `vocabtrimmer/mbart-large-cc25-squad-qa-trimmed-en`
This model is a trimmed version of [lmqg/mbart-large-cc25-squad-qa](https://huggingface.co/lmqg/mbart-large-cc25-squad-qa) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mbart-large-cc25-squad-qa | vocabtrimmer/mbart-large-cc25-squad-qa-trimmed-en |
|:---------------------------|:---------------------------------|:----------------------------------------------------|
| parameter_size_full | 610,852,864 | 532,235,264 |
| parameter_size_embedding | 256,028,672 | 177,411,072 |
| vocab_size | 250,028 | 173,253 |
| compression_rate_full | 100.0 | 87.13 |
| compression_rate_embedding | 100.0 | 69.29 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|:--------------------|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | | 2 |
|
AnonymousSub/rule_based_twostagetriplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
}
| 27
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Variome_0.0005_250
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Variome_0.0005_250
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1812
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.9760
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| 0.3356 | 0.35 | 25 | 0.1809 | 0.0 | 0.0 | 0.0 | 0.9760 |
| 0.1851 | 0.69 | 50 | 0.1807 | 0.0 | 0.0 | 0.0 | 0.9760 |
| 0.1635 | 1.04 | 75 | 0.1863 | 0.0 | 0.0 | 0.0 | 0.9760 |
| 0.1848 | 1.39 | 100 | 0.1810 | 0.0 | 0.0 | 0.0 | 0.9760 |
| 0.1697 | 1.74 | 125 | 0.1817 | 0.0 | 0.0 | 0.0 | 0.9760 |
| 0.1735 | 2.08 | 150 | 0.1802 | 0.0 | 0.0 | 0.0 | 0.9760 |
| 0.1576 | 2.43 | 175 | 0.1833 | 0.0 | 0.0 | 0.0 | 0.9760 |
| 0.178 | 2.78 | 200 | 0.1811 | 0.0 | 0.0 | 0.0 | 0.9760 |
| 0.18 | 3.12 | 225 | 0.1815 | 0.0 | 0.0 | 0.0 | 0.9760 |
| 0.1809 | 3.47 | 250 | 0.1825 | 0.0 | 0.0 | 0.0 | 0.9760 |
| 0.1616 | 3.82 | 275 | 0.1828 | 0.0 | 0.0 | 0.0 | 0.9760 |
| 0.1682 | 4.17 | 300 | 0.1812 | 0.0 | 0.0 | 0.0 | 0.9760 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_twostagetriplet_hier_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
}
| 5
| null |
# Vocabulary Trimmed [cardiffnlp/xlm-v-base-tweet-sentiment-fr](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-fr): `vocabtrimmer/xlm-v-base-tweet-sentiment-fr-trimmed-fr-15000`
This model is a trimmed version of [cardiffnlp/xlm-v-base-tweet-sentiment-fr](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-fr) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-v-base-tweet-sentiment-fr | vocabtrimmer/xlm-v-base-tweet-sentiment-fr-trimmed-fr-15000 |
|:---------------------------|:-------------------------------------------|:--------------------------------------------------------------|
| parameter_size_full | 778,495,491 | 97,565,955 |
| parameter_size_embedding | 692,451,072 | 11,521,536 |
| vocab_size | 901,629 | 15,002 |
| compression_rate_full | 100.0 | 12.53 |
| compression_rate_embedding | 100.0 | 1.66 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| fr | vocabtrimmer/mc4_validation | text | fr | validation | 15000 | 2 |
|
AnonymousSub/rule_based_twostagetriplet_hier_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 27
| null |
---
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: opus-mt-tc-big-en-tr-finetuned-en-to-tr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wmt16
type: wmt16
config: tr-en
split: validation
args: tr-en
metrics:
- name: Bleu
type: bleu
value: 19.8227
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-tc-big-en-tr-finetuned-en-to-tr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-tc-big-en-tr](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-tr) on the wmt16 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6141
- Bleu: 19.8227
- Gen Len: 23.0539
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.981 | 1.0 | 12860 | 1.6141 | 19.8227 | 23.0539 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
AnonymousSub/specter-bert-model_copy
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 2
| null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: sjadhav3/hallucination_free_dialogue
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# sjadhav3/hallucination_free_dialogue
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 10.9520
- Validation Loss: 10.9647
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -994, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 10.9520 | 10.9647 | 0 |
### Framework versions
- Transformers 4.27.4
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.2
|
AnonymousSub/specter-bert-model_copy_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
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},
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}
}
}
| 26
| null |
# Vocabulary Trimmed [cardiffnlp/xlm-v-base-tweet-sentiment-fr](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-fr): `vocabtrimmer/xlm-v-base-tweet-sentiment-fr-trimmed-fr-30000`
This model is a trimmed version of [cardiffnlp/xlm-v-base-tweet-sentiment-fr](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-fr) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-v-base-tweet-sentiment-fr | vocabtrimmer/xlm-v-base-tweet-sentiment-fr-trimmed-fr-30000 |
|:---------------------------|:-------------------------------------------|:--------------------------------------------------------------|
| parameter_size_full | 778,495,491 | 109,085,955 |
| parameter_size_embedding | 692,451,072 | 23,041,536 |
| vocab_size | 901,629 | 30,002 |
| compression_rate_full | 100.0 | 14.01 |
| compression_rate_embedding | 100.0 | 3.33 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| fr | vocabtrimmer/mc4_validation | text | fr | validation | 30000 | 2 |
|
AnonymousSub/specter-bert-model_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
}
| 1
| null |
# `vocabtrimmer/xlm-v-base-trimmed-ar-30000-tweet-sentiment-ar`
This model is a fine-tuned version of [/home/asahi/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-ar-30000](https://huggingface.co//home/asahi/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-ar-30000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (arabic).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(arabic).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 60.69 | 60.69 | 60.69 | 59.86 | 60.69 | 59.88 | 60.69 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-trimmed-ar-30000-tweet-sentiment-ar/raw/main/eval.json).
|
AnonymousSub/unsup-consert-base_copy
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
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},
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}
| 6
| null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### olisyoyoliso Dreambooth model trained by trappy with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
AnonymousSub/unsup-consert-base_copy_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 26
| null |
Access to model AlekseyCalvin/Neurealistarot is restricted and you are not in the authorized list. Visit https://huggingface.co/AlekseyCalvin/Neurealistarot to ask for access.
|
AnonymousSub/unsup-consert-emanuals
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 2
| null |
# Vocabulary Trimmed [cardiffnlp/xlm-v-base-tweet-sentiment-fr](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-fr): `vocabtrimmer/xlm-v-base-tweet-sentiment-fr-trimmed-fr-60000`
This model is a trimmed version of [cardiffnlp/xlm-v-base-tweet-sentiment-fr](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-fr) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-v-base-tweet-sentiment-fr | vocabtrimmer/xlm-v-base-tweet-sentiment-fr-trimmed-fr-60000 |
|:---------------------------|:-------------------------------------------|:--------------------------------------------------------------|
| parameter_size_full | 778,495,491 | 132,125,955 |
| parameter_size_embedding | 692,451,072 | 46,081,536 |
| vocab_size | 901,629 | 60,002 |
| compression_rate_full | 100.0 | 16.97 |
| compression_rate_embedding | 100.0 | 6.65 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| fr | vocabtrimmer/mc4_validation | text | fr | validation | 60000 | 2 |
|
AnonymousSub/unsup-consert-papers-bert
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 9
| null |
Update (4/1): Added ggml for Cuda model
Dataset is here (instruct): https://github.com/teknium1/GPTeacher
Okay... Two different models now. One generated in the Triton branch, one generated in Cuda. Use the Cuda one for now unless the Triton branch becomes widely used.
Cuda info (use this one):
Command:
CUDA_VISIBLE_DEVICES=0 python llama.py ./models/chavinlo-gpt4-x-alpaca --wbits 4 --true-sequential --groupsize 128 --save gpt-x-alpaca-13b-native-4bit-128g-cuda.pt
Prev. info
Quantized on GPTQ-for-LLaMa commit 5955e9c67d9bfe8a8144ffbe853c2769f1e87cdd
GPTQ 4bit quantization of: https://huggingface.co/chavinlo/gpt4-x-alpaca
Note: This was quantized with this branch of GPTQ-for-LLaMA: https://github.com/qwopqwop200/GPTQ-for-LLaMa/tree/triton
Because of this, it appears to be incompatible with Oobabooga at the moment. Stay tuned?
Command:
CUDA_VISIBLE_DEVICES=0 python llama.py ./models/chavinlo-gpt4-x-alpaca --wbits 4 --true-sequential --act-order --groupsize 128 --save gpt-x-alpaca-13b-native-4bit-128g.pt
|
Anonymreign/savagebeta
|
[] | null |
{
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},
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},
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},
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}
}
}
| 0
| 2023-04-01T01:09:07Z
|
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
- question answering
widget:
- text: "question: What is a person called is practicing heresy?, context: Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things."
example_title: "Question Answering Example 1"
- text: "question: who created the post as we know it today?, context: 'So much of The Post is Ben,' Mrs. Graham said in 1994, three years after Bradlee retired as editor. 'He created it as we know it today.'— Ed O'Keefe (@edatpost) October 21, 2014"
example_title: "Question Answering Example 2"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-en-10000-squad-qa
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squad
type: default
args: default
metrics:
- name: BLEU4 (Question Answering)
type: bleu4_question_answering
value: 37.47
- name: ROUGE-L (Question Answering)
type: rouge_l_question_answering
value: 70.1
- name: METEOR (Question Answering)
type: meteor_question_answering
value: 41.78
- name: BERTScore (Question Answering)
type: bertscore_question_answering
value: 91.95
- name: MoverScore (Question Answering)
type: moverscore_question_answering
value: 82.22
- name: AnswerF1Score (Question Answering)
type: answer_f1_score__question_answering
value: 71.82
- name: AnswerExactMatch (Question Answering)
type: answer_exact_match_question_answering
value: 59.11
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-en-10000-squad-qa`
This model is fine-tuned version of [ckpts/mt5-small-trimmed-en-10000](https://huggingface.co/ckpts/mt5-small-trimmed-en-10000) for question answering task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [ckpts/mt5-small-trimmed-en-10000](https://huggingface.co/ckpts/mt5-small-trimmed-en-10000)
- **Language:** en
- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="en", model="vocabtrimmer/mt5-small-trimmed-en-10000-squad-qa")
# model prediction
answers = model.answer_q(list_question="What is a person called is practicing heresy?", list_context=" Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-en-10000-squad-qa")
output = pipe("question: What is a person called is practicing heresy?, context: Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.")
```
## Evaluation
- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-en-10000-squad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:---------------------------------------------------------------|
| AnswerExactMatch | 59.11 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| AnswerF1Score | 71.82 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| BERTScore | 91.95 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1 | 52.64 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2 | 47.08 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3 | 41.84 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4 | 37.47 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR | 41.78 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore | 82.22 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L | 70.1 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_squad
- dataset_name: default
- input_types: ['paragraph_question']
- output_types: ['answer']
- prefix_types: None
- model: ckpts/mt5-small-trimmed-en-10000
- max_length: 512
- max_length_output: 32
- epoch: 12
- batch: 32
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-en-10000-squad-qa/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
Anorak/nirvana
|
[
"pytorch",
"pegasus",
"text2text-generation",
"unk",
"dataset:Anorak/autonlp-data-Niravana-test2",
"transformers",
"autonlp",
"co2_eq_emissions",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
"task_specific_params": {
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},
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},
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},
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}
}
}
| 7
| null |
# Vocabulary Trimmed [lmqg/mbart-large-cc25-squad-qa](https://huggingface.co/lmqg/mbart-large-cc25-squad-qa): `vocabtrimmer/mbart-large-cc25-squad-qa-trimmed-en-5000`
This model is a trimmed version of [lmqg/mbart-large-cc25-squad-qa](https://huggingface.co/lmqg/mbart-large-cc25-squad-qa) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mbart-large-cc25-squad-qa | vocabtrimmer/mbart-large-cc25-squad-qa-trimmed-en-5000 |
|:---------------------------|:---------------------------------|:---------------------------------------------------------|
| parameter_size_full | 610,852,864 | 359,948,288 |
| parameter_size_embedding | 512,057,344 | 10,248,192 |
| vocab_size | 250,028 | 5,004 |
| compression_rate_full | 100.0 | 58.93 |
| compression_rate_embedding | 100.0 | 2.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | 5000 | 2 |
|
Anthos23/distilbert-base-uncased-finetuned-sst2
|
[
"tf",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_keras_callback",
"license:apache-2.0"
] |
text-classification
|
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}
| 21
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-large-finetuned-augument-visquad2-1-4-2023-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-large-finetuned-augument-visquad2-1-4-2023-1
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Best F1: 77.2917
- Loss: 1.3981
- Exact: 39.8063
- F1: 57.2058
- Total: 3821
- Hasans Exact: 56.6152
- Hasans F1: 81.6749
- Hasans Total: 2653
- Noans Exact: 1.6267
- Noans F1: 1.6267
- Noans Total: 1168
- Best Exact: 61.4499
- Best Exact Thresh: 0.8562
- Best F1 Thresh: 0.8563
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Best F1 | Validation Loss | Exact | F1 | Total | Hasans Exact | Hasans F1 | Hasans Total | Noans Exact | Noans F1 | Noans Total | Best Exact | Best Exact Thresh | Best F1 Thresh |
|:-------------:|:-----:|:-----:|:-------:|:---------------:|:-------:|:-------:|:-----:|:------------:|:---------:|:------------:|:-----------:|:--------:|:-----------:|:----------:|:-----------------:|:--------------:|
| 1.2156 | 1.0 | 2110 | 72.5368 | 1.1646 | 37.6341 | 55.4110 | 3821 | 54.1651 | 79.7684 | 2653 | 0.0856 | 0.0856 | 1168 | 57.3672 | 0.8787 | 0.9480 |
| 0.442 | 2.0 | 4221 | 75.8888 | 1.0343 | 38.7857 | 56.3275 | 3821 | 55.8613 | 81.1261 | 2653 | 0.0 | 0.0 | 1168 | 60.6386 | 0.8815 | 0.8837 |
| 0.3067 | 3.0 | 6332 | 76.3203 | 1.1121 | 39.7540 | 56.6409 | 3821 | 57.2559 | 81.5774 | 2653 | 0.0 | 0.0 | 1168 | 61.3975 | 0.7946 | 0.9089 |
| 0.2223 | 4.0 | 8443 | 76.6216 | 1.2653 | 39.6493 | 56.7375 | 3821 | 57.0675 | 81.6789 | 2653 | 0.0856 | 0.0856 | 1168 | 61.3190 | 0.7363 | 0.9385 |
| 0.1674 | 5.0 | 10550 | 77.2917 | 1.3981 | 39.8063 | 57.2058 | 3821 | 56.6152 | 81.6749 | 2653 | 1.6267 | 1.6267 | 1168 | 61.4499 | 0.8562 | 0.8563 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Anthos23/my-awesome-model
|
[
"pytorch",
"tf",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
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| 30
| null |
---
datasets:
- gsdf/EasyNegative
language:
- hr
tags:
- chemistry
- music
- art
- text-generation-inference
---
|
Anthos23/sentiment-roberta-large-english-finetuned-sentiment-analysis
|
[] | null |
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| 0
| null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
- question answering
widget:
- text: "question: What is a person called is practicing heresy?, context: Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things."
example_title: "Question Answering Example 1"
- text: "question: who created the post as we know it today?, context: 'So much of The Post is Ben,' Mrs. Graham said in 1994, three years after Bradlee retired as editor. 'He created it as we know it today.'— Ed O'Keefe (@edatpost) October 21, 2014"
example_title: "Question Answering Example 2"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-en-5000-squad-qa
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squad
type: default
args: default
metrics:
- name: BLEU4 (Question Answering)
type: bleu4_question_answering
value: 45.47
- name: ROUGE-L (Question Answering)
type: rouge_l_question_answering
value: 72.24
- name: METEOR (Question Answering)
type: meteor_question_answering
value: 43.2
- name: BERTScore (Question Answering)
type: bertscore_question_answering
value: 92.31
- name: MoverScore (Question Answering)
type: moverscore_question_answering
value: 83.24
- name: AnswerF1Score (Question Answering)
type: answer_f1_score__question_answering
value: 74.01
- name: AnswerExactMatch (Question Answering)
type: answer_exact_match_question_answering
value: 61.29
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-en-5000-squad-qa`
This model is fine-tuned version of [ckpts/mt5-small-trimmed-en-5000](https://huggingface.co/ckpts/mt5-small-trimmed-en-5000) for question answering task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [ckpts/mt5-small-trimmed-en-5000](https://huggingface.co/ckpts/mt5-small-trimmed-en-5000)
- **Language:** en
- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="en", model="vocabtrimmer/mt5-small-trimmed-en-5000-squad-qa")
# model prediction
answers = model.answer_q(list_question="What is a person called is practicing heresy?", list_context=" Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-en-5000-squad-qa")
output = pipe("question: What is a person called is practicing heresy?, context: Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.")
```
## Evaluation
- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-en-5000-squad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:---------------------------------------------------------------|
| AnswerExactMatch | 61.29 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| AnswerF1Score | 74.01 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| BERTScore | 92.31 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1 | 59.87 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2 | 54.66 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3 | 49.7 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4 | 45.47 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR | 43.2 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore | 83.24 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L | 72.24 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_squad
- dataset_name: default
- input_types: ['paragraph_question']
- output_types: ['answer']
- prefix_types: None
- model: ckpts/mt5-small-trimmed-en-5000
- max_length: 512
- max_length_output: 32
- epoch: 13
- batch: 32
- lr: 0.0005
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 2
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-en-5000-squad-qa/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
AntonClaesson/movie-plot-generator
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPT2LMHeadModel"
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}
}
}
| 9
| null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.97 +/- 1.73
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Anubhav23/IndianlegalBert
|
[] | null |
{
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}
}
}
| 0
| null |
# Vocabulary Trimmed [lmqg/mbart-large-cc25-squad-qa](https://huggingface.co/lmqg/mbart-large-cc25-squad-qa): `vocabtrimmer/mbart-large-cc25-squad-qa-trimmed-en-10000`
This model is a trimmed version of [lmqg/mbart-large-cc25-squad-qa](https://huggingface.co/lmqg/mbart-large-cc25-squad-qa) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mbart-large-cc25-squad-qa | vocabtrimmer/mbart-large-cc25-squad-qa-trimmed-en-10000 |
|:---------------------------|:---------------------------------|:----------------------------------------------------------|
| parameter_size_full | 610,852,864 | 365,068,288 |
| parameter_size_embedding | 512,057,344 | 20,488,192 |
| vocab_size | 250,028 | 10,004 |
| compression_rate_full | 100.0 | 59.76 |
| compression_rate_embedding | 100.0 | 4.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | 10000 | 2 |
|
Anubhav23/model_name
|
[] | null |
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}
| 0
| null |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
- imagenet-21k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# Vision Transformer (base-sized model)
|
Anupam/QuestionClassifier
|
[] | null |
{
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}
| 0
| null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="wjmm/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Aplinxy9plin/toxic-detection-rus
|
[] | null |
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}
| 0
| 2023-04-01T01:58:19Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.78
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="wjmm/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Apoorva/k2t-test
|
[
"pytorch",
"t5",
"text2text-generation",
"en",
"transformers",
"keytotext",
"k2t",
"Keywords to Sentences",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
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"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 7
| 2023-04-01T02:00:30Z
|
---
license: bigscience-openrail-m
base_model: riffusion/riffusion-model-v1
datasets:
- rxk/MC_caption
language:
- en
tags:
- riffusion
---
|
Appolo/TestModel
|
[] | null |
{
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},
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}
| 0
| 2023-04-01T02:02:56Z
|
# Vocabulary Trimmed [cardiffnlp/xlm-v-base-tweet-sentiment-pt](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-pt): `vocabtrimmer/xlm-v-base-tweet-sentiment-pt-trimmed-pt`
This model is a trimmed version of [cardiffnlp/xlm-v-base-tweet-sentiment-pt](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-pt) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-v-base-tweet-sentiment-pt | vocabtrimmer/xlm-v-base-tweet-sentiment-pt-trimmed-pt |
|:---------------------------|:-------------------------------------------|:--------------------------------------------------------|
| parameter_size_full | 778,495,491 | 225,338,883 |
| parameter_size_embedding | 692,451,072 | 139,294,464 |
| vocab_size | 901,629 | 181,373 |
| compression_rate_full | 100.0 | 28.95 |
| compression_rate_embedding | 100.0 | 20.12 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|:--------------------|----------------:|
| pt | vocabtrimmer/mc4_validation | text | pt | validation | | 2 |
|
ArBert/albert-base-v2-finetuned-ner-agglo-twitter
|
[
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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},
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"max_length": null,
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}
}
}
| 27
| 2023-04-01T02:06:23Z
|
# Vocabulary Trimmed [lmqg/mbart-large-cc25-squad-qa](https://huggingface.co/lmqg/mbart-large-cc25-squad-qa): `vocabtrimmer/mbart-large-cc25-squad-qa-trimmed-en-15000`
This model is a trimmed version of [lmqg/mbart-large-cc25-squad-qa](https://huggingface.co/lmqg/mbart-large-cc25-squad-qa) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mbart-large-cc25-squad-qa | vocabtrimmer/mbart-large-cc25-squad-qa-trimmed-en-15000 |
|:---------------------------|:---------------------------------|:----------------------------------------------------------|
| parameter_size_full | 610,852,864 | 370,188,288 |
| parameter_size_embedding | 512,057,344 | 30,728,192 |
| vocab_size | 250,028 | 15,004 |
| compression_rate_full | 100.0 | 60.6 |
| compression_rate_embedding | 100.0 | 6.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | 15000 | 2 |
|
ArashEsk95/bert-base-uncased-finetuned-stsb
|
[] | null |
{
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}
| 0
| null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 257.81 +/- 19.06
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Arnold/wav2vec2-large-xlsr-hausa2-demo-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
}
| 5
| null |
---
tags:
- generated_from_trainer
model-index:
- name: long_legal_test_sm
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# long_legal_test_sm
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 20
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Ayham/bertgpt2_cnn
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"prefix": null
},
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"prefix": null
}
}
}
| 4
| null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
- question answering
widget:
- text: "question: What is a person called is practicing heresy?, context: Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things."
example_title: "Question Answering Example 1"
- text: "question: who created the post as we know it today?, context: 'So much of The Post is Ben,' Mrs. Graham said in 1994, three years after Bradlee retired as editor. 'He created it as we know it today.'— Ed O'Keefe (@edatpost) October 21, 2014"
example_title: "Question Answering Example 2"
model-index:
- name: vocabtrimmer/mt5-small-trimmed-en-120000-squad-qa
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squad
type: default
args: default
metrics:
- name: BLEU4 (Question Answering)
type: bleu4_question_answering
value: 43.61
- name: ROUGE-L (Question Answering)
type: rouge_l_question_answering
value: 64.92
- name: METEOR (Question Answering)
type: meteor_question_answering
value: 36.74
- name: BERTScore (Question Answering)
type: bertscore_question_answering
value: 91.84
- name: MoverScore (Question Answering)
type: moverscore_question_answering
value: 80.82
- name: AnswerF1Score (Question Answering)
type: answer_f1_score__question_answering
value: 66.19
- name: AnswerExactMatch (Question Answering)
type: answer_exact_match_question_answering
value: 52.14
---
# Model Card of `vocabtrimmer/mt5-small-trimmed-en-120000-squad-qa`
This model is fine-tuned version of [ckpts/mt5-small-trimmed-en-120000](https://huggingface.co/ckpts/mt5-small-trimmed-en-120000) for question answering task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [ckpts/mt5-small-trimmed-en-120000](https://huggingface.co/ckpts/mt5-small-trimmed-en-120000)
- **Language:** en
- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="en", model="vocabtrimmer/mt5-small-trimmed-en-120000-squad-qa")
# model prediction
answers = model.answer_q(list_question="What is a person called is practicing heresy?", list_context=" Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-en-120000-squad-qa")
output = pipe("question: What is a person called is practicing heresy?, context: Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.")
```
## Evaluation
- ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-en-120000-squad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:---------------------------------------------------------------|
| AnswerExactMatch | 52.14 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| AnswerF1Score | 66.19 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| BERTScore | 91.84 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1 | 57.95 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2 | 52.49 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3 | 47.69 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4 | 43.61 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR | 36.74 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore | 80.82 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L | 64.92 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_squad
- dataset_name: default
- input_types: ['paragraph_question']
- output_types: ['answer']
- prefix_types: None
- model: ckpts/mt5-small-trimmed-en-120000
- max_length: 512
- max_length_output: 32
- epoch: 18
- batch: 32
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 2
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-en-120000-squad-qa/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
Ayham/distilbert_bert_summarization_cnn_dailymail
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
}
}
| 11
| 2023-04-01T07:44:23Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 256.67 +/- 15.02
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Ayham/distilbert_roberta_summarization_cnn_dailymail
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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},
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},
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}
}
| 14
| null |
---
license: apache-2.0
---
# ChatGLM-Med: 基于中文医学知识的ChatGLM模型微调
[](https://github.com/SCIR-HI/Med-ChatGLM/blob/main/LICENSE)
[](https://www.python.org/downloads/release/python-390/)
本项目开源了经过中文医学指令精调/指令微调(Instruct-tuning) 的ChatGLM-6B模型。我们通过医学知识图谱和GPT3.5 API构建了中文医学指令数据集，并在此基础上对ChatGLM-6B进行了指令微调，提高了ChatGLM在医疗领域的问答效果。
基于相同的数据，我们还训练了医疗版本的LLaMA模型: [华驼](https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese)
## A Quick Start
首先安装依赖包，python环境建议3.9+
```
pip install -r requirements.txt
```
## 模型下载
训练好的模型参数可以通过如下方式下载：
| 模型名称 | 大小 | 模型下载地址 |
| :----------------- | :------: |:----------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| ChatGLM-6B-Med | 约13.4GB | [[百度网盘]](https://pan.baidu.com/s/1Sfi1bRwV741GIChIEOUW0A?pwd=i73e)<br>[[GoogleDrive]](https://drive.google.com/drive/folders/1ZQSN56DloRGQ-Qj7IwzY4jV3ZHKMe9Bc) |
## 交互式测试
在安装好环境后，即可进行交互式测试：
```
python infer.py
```
## 数据集构建
我们采用了公开和自建的中文医学知识库，主要参考了[cMeKG](https://github.com/king-yyf/CMeKG_tools)。
医学知识库围绕疾病、药物、检查指标等构建，字段包括并发症，高危因素，组织学检查，临床症状，药物治疗，辅助治疗等。知识库示例如下:
```
{"中心词": "偏头痛", "相关疾病": ["妊娠合并偏头痛", "恶寒发热"], "相关症状": ["皮肤变硬", "头部及眼后部疼痛并能听到连续不断的隆隆声", "晨起头痛加重"], "所属科室": ["中西医结合科", "内科"], "发病部位": ["头部"]}
```
我们利用GPT3.5接口围绕医学知识库构建问答数据，设置了多种Prompt形式来充分利用知识。
指令微调的训练集数据示例如下：
```
"问题：一位年轻男性长期使用可卡因，突然出现胸痛、呕吐、出汗等症状，经检查发现心电图反映心肌急性损伤，请问可能患的是什么疾病？治疗方式是什么？"
回答: 可能患的是心肌梗塞，需要进行维拉帕米、依普利酮、硝酸甘油、ß阻滞剂、吗啡等药物治疗，并进行溶栓治疗、低分子量肝素、钙通道阻滞剂等辅助治疗。此外需要及时停用可卡因等药物，以防止病情加重。"
```
指令微调数据集质量仍有限，后续将进行不断迭代，同时医学知识库和数据集构建代码还在整理中，整理完成将会发布。
## Instruct-tuning 指令微调
### 下载ChatGLM-6B原始参数
请参照[https://github.com/THUDM/ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B)，并将下载的模型放入`model`目录下。
### 指令微调
```
sh script/sft_medchat.sh
```
请根据实际情况调整batch size等超参，默认为我们使用的参数。
### 新数据指令微调
如果想用自己的数据集继续指令微调/指令精调（Instruct-tuning）ChatGLM，可以构建与`data`目录中训练集类似的文件格式进行。或参照[ChatGLM-Instruct-Tuning](https://github.com/thinksoso/ChatGLM-Instruct-Tuning/issues)仓库。
### 计算资源需求
我们在一张A100-SXM-80GB显卡上进行了训练，根据经验，训练显存建议选择32G及以上。
## 模型效果对比
| 测试输入 | ChatGLM-6B输出 | ChatGLM-6B-Med输出 |
|:------------- |:---------------| :-------------|
| 谷氨酰转肽酶水平会因吸毒或饮酒而升高吗? | 吸毒和饮酒都会对身体的健康产生负面影响,包括对肝脏和心脏的影响。<br>饮酒可能导致肝脏损伤和心脏疾病,因为酒精会破坏肝脏和心脏的功能。长期饮酒还可能导致脂肪肝、肝硬化、心脏病等健康问题。<br>吸毒会对肝脏和心脏产生严重的损伤,导致肝脏纤维化、肝硬化、心脏血管疾病等。吸毒还可能导致精神障碍、社会退缩、暴力和犯罪等问题。<br>因此,无论是饮酒还是吸毒,都会对健康产生负面影响,应该避免过度使用。 | 谷氨酰转肽酶升高的原因有很多,包括感染、消化性溃疡、感染、药物副作用等,而吸毒或饮酒等不良行为可能会导致谷氨酰转肽酶的升高。 |
| 小李最近出现了心动过速的症状，伴有轻度胸痛。体检发现P-R间期延长，伴有T波低平和ST段异常 | 非常抱歉,我不理解您的问题。请提供更多背景信息或上下文,以便我更好地回答您的问题。谢谢! | 小李出现了心动过速的症状,并且伴有胸痛,需要考虑是否有心肌病、冠状动脉粥样硬化等心血管疾病,建议进行心电图检查、血液检查、心脏超声检查等 |
| ...... | ...... |......|
## 项目参与者
本项目由哈尔滨工业大学社会计算与信息检索研究中心健康智能组[王昊淳](https://github.com/s65b40) 、[刘驰](https://github.com/thinksoso)完成，指导教师为赵森栋副教授，秦兵教授以及刘挺教授。
## 致谢
本项目参考了以下开源项目，在此对相关项目和研究开发人员表示感谢。
- ChatGLM: [https://github.com/THUDM/ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B)
- ChatGLM-Instruct-Tuning: [https://github.com/thinksoso/ChatGLM-Instruct-Tuning/issues](https://github.com/thinksoso/ChatGLM-Instruct-Tuning/issues)
- CMeKG: [https://github.com/king-yyf/CMeKG_tools](https://github.com/king-yyf/CMeKG_tools)
## 免责声明
本项目相关资源仅供学术研究之用，严禁用于商业用途。使用涉及第三方代码的部分时，请严格遵循相应的开源协议。模型生成的内容受模型计算、随机性和量化精度损失等因素影响，本项目无法对其准确性作出保证。本项目数据集绝大部分由模型生成，即使符合某些医学事实，也不能被用作实际医学诊断的依据。对于模型输出的任何内容，本项目不承担任何法律责任，亦不对因使用相关资源和输出结果而可能产生的任何损失承担责任。
## Citation
如果你使用了本项目的数据或者代码，请声明引用
```
@misc{ChatGLM-Med,
author={Haochun Wang, Chi Liu, Sendong Zhao, Bing Qin, Ting Liu},
title = {ChatGLM-Med: 基于中文医学知识的ChatGLM模型微调},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/SCIR-HI/Med-ChatGLM}},
}
```
|
Ayham/ernie_gpt2_summarization_cnn_dailymail
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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},
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}
}
| 13
| null |
# Vocabulary Trimmed [cardiffnlp/xlm-v-base-tweet-sentiment-es](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-es): `vocabtrimmer/xlm-v-base-tweet-sentiment-es-trimmed-es-15000`
This model is a trimmed version of [cardiffnlp/xlm-v-base-tweet-sentiment-es](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-es) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-v-base-tweet-sentiment-es | vocabtrimmer/xlm-v-base-tweet-sentiment-es-trimmed-es-15000 |
|:---------------------------|:-------------------------------------------|:--------------------------------------------------------------|
| parameter_size_full | 778,495,491 | 97,565,955 |
| parameter_size_embedding | 692,451,072 | 11,521,536 |
| vocab_size | 901,629 | 15,002 |
| compression_rate_full | 100.0 | 12.53 |
| compression_rate_embedding | 100.0 | 1.66 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| es | vocabtrimmer/mc4_validation | text | es | validation | 15000 | 2 |
|
Ayham/robertagpt2_xsum2
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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},
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},
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},
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},
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}
}
}
| 6
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.929
- name: F1
type: f1
value: 0.9289897994289955
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2202
- Accuracy: 0.929
- F1: 0.9290
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8318 | 1.0 | 250 | 0.3208 | 0.9065 | 0.9032 |
| 0.2543 | 2.0 | 500 | 0.2202 | 0.929 | 0.9290 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Ayran/DialoGPT-small-harry-potter-1-through-3
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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},
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}
}
| 12
| null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: lm-003-20230401-001
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lm-003-20230401-001
This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3313
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.907 | 1.0 | 651 | 3.6066 |
| 3.6162 | 2.0 | 1302 | 3.0848 |
| 3.2119 | 3.0 | 1953 | 2.7986 |
| 2.7763 | 4.0 | 2604 | 2.6678 |
| 2.7219 | 5.0 | 3255 | 2.5311 |
| 2.588 | 6.0 | 3906 | 2.4662 |
| 2.4796 | 7.0 | 4557 | 2.4072 |
| 2.4286 | 8.0 | 5208 | 2.3815 |
| 2.4069 | 9.0 | 5859 | 2.3650 |
| 2.3557 | 10.0 | 6510 | 2.3676 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Ayu/Shiriro
|
[] | null |
{
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"model_type": null,
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},
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}
| 0
| null |
---
license: openrail
---
```
import torch
from transformers import AutoTokenizer, MobileBertForSequenceClassification
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Load the saved model
model_name = 'harshith20/Emotion_predictor'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = MobileBertForSequenceClassification.from_pretrained(model_name)
# Tokenize input text
input_text = "I am feeling happy today"
input_ids = tokenizer.encode(input_text, add_special_tokens=True, truncation=True, max_length=128)
input_tensor = torch.tensor([input_ids]).to(device)
# Predict emotion
with torch.no_grad():
outputs = model(input_tensor)
logits = outputs[0]
# Get the predicted label
predicted_emotion = torch.argmax(logits, dim=1).item()
emotion_labels = {0:'sadness',1:'joy',2:'love',3:'anger',4:'fear',5:'surprise'}
predicted_emotion_label = emotion_labels[predicted_emotion]
print(f"Input text: {input_text}")
print(f"Predicted emotion: {predicted_emotion_label}")```
|
Ayumi/Jovana
|
[] | null |
{
"architectures": null,
"model_type": null,
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},
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},
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},
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}
}
| 0
| 2023-04-01T09:27:12Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -127.70 +/- 23.59
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AyushPJ/ai-club-inductions-21-nlp-XLNet
|
[
"pytorch",
"xlnet",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"XLNetForQuestionAnsweringSimple"
],
"model_type": "xlnet",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 250
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 9
| null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ShadeEngine/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
BSC-LT/roberta-large-bne-sqac
|
[
"pytorch",
"roberta",
"question-answering",
"es",
"dataset:BSC-TeMU/SQAC",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"qa",
"question answering",
"license:apache-2.0",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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}
| 15
| null |
# Vocabulary Trimmed [cardiffnlp/xlm-v-base-tweet-sentiment-de](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-de): `vocabtrimmer/xlm-v-base-tweet-sentiment-de-trimmed-de-30000`
This model is a trimmed version of [cardiffnlp/xlm-v-base-tweet-sentiment-de](https://huggingface.co/cardiffnlp/xlm-v-base-tweet-sentiment-de) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | cardiffnlp/xlm-v-base-tweet-sentiment-de | vocabtrimmer/xlm-v-base-tweet-sentiment-de-trimmed-de-30000 |
|:---------------------------|:-------------------------------------------|:--------------------------------------------------------------|
| parameter_size_full | 778,495,491 | 109,085,955 |
| parameter_size_embedding | 692,451,072 | 23,041,536 |
| vocab_size | 901,629 | 30,002 |
| compression_rate_full | 100.0 | 14.01 |
| compression_rate_embedding | 100.0 | 3.33 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| de | vocabtrimmer/mc4_validation | text | de | validation | 30000 | 2 |
|
BSen/wav2vec2-base-timit-demo-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
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| 4
| 2023-04-01T10:35:42Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: dal-bert-finetuned-medical-v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dal-bert-finetuned-medical-v3
This model is a fine-tuned version of [sharif-dal/dal-bert](https://huggingface.co/sharif-dal/dal-bert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7144
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0146 | 1.0 | 398 | 1.8037 |
| 1.8739 | 2.0 | 796 | 1.7564 |
| 1.8234 | 3.0 | 1194 | 1.7405 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
BSen/wav2vec2-large-xls-r-300m-turkish-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
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| 6
| 2023-04-01T10:35:49Z
|
Embending : colorz estilo de color dragon ball z del capitulo de cuando raditz llego a la tierra 10000 epochs para colorear tus imagenes estilo dragon ball z
Embending : colorz dragon ball z coloring style from the chapter when raditz came to earth 10000 epochs to color your dragon ball z coloring style pictures
|
Badr/model1
|
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}
| 0
| null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: RL-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Bagus/SER-LSSED
|
[] | null |
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| 0
| null |
---
datasets:
- relbert/conceptnet_relational_similarity
model-index:
- name: relbert/relbert-roberta-base-nce-a-conceptnet
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8482936507936508
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4144385026737968
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.41543026706231456
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5453029460811561
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.702
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.41228070175438597
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4027777777777778
- task:
name: Analogy Questions (ConceptNet Analogy)
type: multiple-choice-qa
dataset:
name: ConceptNet Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.1686241610738255
- task:
name: Analogy Questions (TREX Analogy)
type: multiple-choice-qa
dataset:
name: TREX Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4207650273224044
- task:
name: Analogy Questions (NELL-ONE Analogy)
type: multiple-choice-qa
dataset:
name: NELL-ONE Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5383333333333333
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9029682085279493
- name: F1 (macro)
type: f1_macro
value: 0.8994189513480091
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8265258215962441
- name: F1 (macro)
type: f1_macro
value: 0.6121687792528943
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.605092091007584
- name: F1 (macro)
type: f1_macro
value: 0.5927492718889079
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9595186756625165
- name: F1 (macro)
type: f1_macro
value: 0.8760942019541216
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8868693199623943
- name: F1 (macro)
type: f1_macro
value: 0.8821614248132884
---
# relbert/relbert-roberta-base-nce-a-conceptnet
RelBERT based on [roberta-base](https://huggingface.co/roberta-base) fine-tuned on [relbert/conceptnet_relational_similarity](https://huggingface.co/datasets/relbert/conceptnet_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-a-conceptnet/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.4144385026737968
- Accuracy on SAT: 0.41543026706231456
- Accuracy on BATS: 0.5453029460811561
- Accuracy on U2: 0.41228070175438597
- Accuracy on U4: 0.4027777777777778
- Accuracy on Google: 0.702
- Accuracy on ConceptNet Analogy: 0.1686241610738255
- Accuracy on T-Rex Analogy: 0.4207650273224044
- Accuracy on NELL-ONE Analogy: 0.5383333333333333
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-a-conceptnet/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9029682085279493
- Micro F1 score on CogALexV: 0.8265258215962441
- Micro F1 score on EVALution: 0.605092091007584
- Micro F1 score on K&H+N: 0.9595186756625165
- Micro F1 score on ROOT09: 0.8868693199623943
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-a-conceptnet/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8482936507936508
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-nce-a-conceptnet")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-base
- max_length: 64
- epoch: 5
- batch: 16
- random_seed: 0
- lr: 5e-06
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/conceptnet_relational_similarity
- data_name: None
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: nce
- classification_loss: False
- loss_function_config: {'temperature': 0.05, 'num_negative': 300, 'num_positive': 30}
- augment_negative_by_positive: True
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-base-nce-a-conceptnet/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
Bala/model_name
|
[] | null |
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}
| 0
| 2023-04-01T11:11:39Z
|
---
datasets:
- relbert/conceptnet_relational_similarity
model-index:
- name: relbert/relbert-roberta-base-nce-b-conceptnet
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8640476190476191
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.39572192513368987
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3916913946587537
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5352973874374652
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.654
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.41228070175438597
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3958333333333333
- task:
name: Analogy Questions (ConceptNet Analogy)
type: multiple-choice-qa
dataset:
name: ConceptNet Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.17030201342281878
- task:
name: Analogy Questions (TREX Analogy)
type: multiple-choice-qa
dataset:
name: TREX Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3224043715846995
- task:
name: Analogy Questions (NELL-ONE Analogy)
type: multiple-choice-qa
dataset:
name: NELL-ONE Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.555
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9064336296519512
- name: F1 (macro)
type: f1_macro
value: 0.8976080161341962
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.82981220657277
- name: F1 (macro)
type: f1_macro
value: 0.6245251777136291
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6180931744312026
- name: F1 (macro)
type: f1_macro
value: 0.6115811882584634
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9563191208179731
- name: F1 (macro)
type: f1_macro
value: 0.8680991698722992
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8793481667188969
- name: F1 (macro)
type: f1_macro
value: 0.8736244537702125
---
# relbert/relbert-roberta-base-nce-b-conceptnet
RelBERT based on [roberta-base](https://huggingface.co/roberta-base) fine-tuned on [relbert/conceptnet_relational_similarity](https://huggingface.co/datasets/relbert/conceptnet_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-b-conceptnet/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.39572192513368987
- Accuracy on SAT: 0.3916913946587537
- Accuracy on BATS: 0.5352973874374652
- Accuracy on U2: 0.41228070175438597
- Accuracy on U4: 0.3958333333333333
- Accuracy on Google: 0.654
- Accuracy on ConceptNet Analogy: 0.17030201342281878
- Accuracy on T-Rex Analogy: 0.3224043715846995
- Accuracy on NELL-ONE Analogy: 0.555
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-b-conceptnet/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9064336296519512
- Micro F1 score on CogALexV: 0.82981220657277
- Micro F1 score on EVALution: 0.6180931744312026
- Micro F1 score on K&H+N: 0.9563191208179731
- Micro F1 score on ROOT09: 0.8793481667188969
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-b-conceptnet/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8640476190476191
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-nce-b-conceptnet")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-base
- max_length: 64
- epoch: 5
- batch: 16
- random_seed: 0
- lr: 5e-06
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/conceptnet_relational_similarity
- data_name: None
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: nce
- classification_loss: False
- loss_function_config: {'temperature': 0.05, 'num_negative': 300, 'num_positive': 30}
- augment_negative_by_positive: True
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-base-nce-b-conceptnet/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
Banshee/dialoGPT-luke-small
|
[] | null |
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}
| 0
| null |
---
language:
- en
library_name: diffusers
license: creativeml-openrail-m
pipeline_tag: text-to-image
---
Model mix aims to create the most realistic and natural images possible. It's currently in the testing process, so please comment.
Available on Sinkin.ai with GPU acceleration.
MY MODELS WILL ALWAYS BE FREE.
https://sinkin.ai/m/DreamFul
https://www.mage.space/u/hius
Guide:
For the settings or parameters, I recommend using these settings.
Sampler: DPM++ SDE Karras or Ruler a
Steps: 30-50
CFG Scale: 7.5
How to use:
Structure: render for a `+ <subject> ++ <details> + <lights> + <color> + <resolution> + <option> `
For example:
render for a girl, beautiful face, autumn lights,pastel colors, high quality, trending on ArtStation, trending on CGSociety,(extremely detailed CG unity 8k wallpaper)
Negative Prompt:
illustration, painting, cartoons, sketch, (worst quality:2), (low quality:2), (normal quality:2), lowres, bad anatomy, bad hands, ((monochrome)), ((grayscale)), collapsed eyeshadow, multiple eyeblows, vaginas in breasts, (cropped), oversaturated, extra limb, missing limbs, deformed hands, long neck, long body, imperfect, (bad hands), signature, watermark, username, artist name, conjoined fingers, deformed fingers, ugly eyes, imperfect eyes, skewed eyes, unnatural face, unnatural body, error
Can you customize it all to your liking and show me it? Thank you!!!
LORA is not added yet
|
Banshee/dialoGPT-small-luke
|
[] | null |
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}
}
}
| 0
| null |
---
datasets:
- relbert/conceptnet_relational_similarity
model-index:
- name: relbert/relbert-roberta-base-nce-c-conceptnet
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7699404761904762
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.44385026737967914
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.44510385756676557
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5630906058921623
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.726
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.36403508771929827
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4166666666666667
- task:
name: Analogy Questions (ConceptNet Analogy)
type: multiple-choice-qa
dataset:
name: ConceptNet Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.1610738255033557
- task:
name: Analogy Questions (TREX Analogy)
type: multiple-choice-qa
dataset:
name: TREX Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.34972677595628415
- task:
name: Analogy Questions (NELL-ONE Analogy)
type: multiple-choice-qa
dataset:
name: NELL-ONE Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.415
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8963387072472503
- name: F1 (macro)
type: f1_macro
value: 0.8876100098828134
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8204225352112676
- name: F1 (macro)
type: f1_macro
value: 0.6005350014814115
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6164680390032503
- name: F1 (macro)
type: f1_macro
value: 0.5977855053854718
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9603533421437018
- name: F1 (macro)
type: f1_macro
value: 0.8749710467707641
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8815418364149169
- name: F1 (macro)
type: f1_macro
value: 0.8753272553056995
---
# relbert/relbert-roberta-base-nce-c-conceptnet
RelBERT based on [roberta-base](https://huggingface.co/roberta-base) fine-tuned on [relbert/conceptnet_relational_similarity](https://huggingface.co/datasets/relbert/conceptnet_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-c-conceptnet/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.44385026737967914
- Accuracy on SAT: 0.44510385756676557
- Accuracy on BATS: 0.5630906058921623
- Accuracy on U2: 0.36403508771929827
- Accuracy on U4: 0.4166666666666667
- Accuracy on Google: 0.726
- Accuracy on ConceptNet Analogy: 0.1610738255033557
- Accuracy on T-Rex Analogy: 0.34972677595628415
- Accuracy on NELL-ONE Analogy: 0.415
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-c-conceptnet/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8963387072472503
- Micro F1 score on CogALexV: 0.8204225352112676
- Micro F1 score on EVALution: 0.6164680390032503
- Micro F1 score on K&H+N: 0.9603533421437018
- Micro F1 score on ROOT09: 0.8815418364149169
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-c-conceptnet/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7699404761904762
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-nce-c-conceptnet")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-base
- max_length: 64
- epoch: 5
- batch: 16
- random_seed: 0
- lr: 5e-06
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/conceptnet_relational_similarity
- data_name: None
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: nce
- classification_loss: False
- loss_function_config: {'temperature': 0.05, 'num_negative': 300, 'num_positive': 30}
- augment_negative_by_positive: True
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-base-nce-c-conceptnet/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
BaptisteDoyen/camembert-base-xnli
|
[
"pytorch",
"tf",
"camembert",
"text-classification",
"fr",
"dataset:xnli",
"transformers",
"zero-shot-classification",
"xnli",
"nli",
"license:mit",
"has_space"
] |
zero-shot-classification
|
{
"architectures": [
"CamembertForSequenceClassification"
],
"model_type": "camembert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 405,474
| null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 32.90 +/- 16.97
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Barbarameerr/Barbara
|
[] | null |
{
"architectures": null,
"model_type": null,
"task_specific_params": {
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"prefix": null
},
"translation_en_to_fr": {
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"prefix": null
},
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"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
}
| 0
| null |
---
license: openrail
library_name: diffusers
pipeline_tag: text-to-image
---
|
Barkavi/totto-t5-base-bert-score-121K
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 51
| 2023-04-01T11:37:40Z
|
# danseibosyu
## Trigger Word
```
danseibosyu
```
## Sample
```prompt example
masterpiece, best quality, ultra-detailed, 1girl,danseibosyu<lora:danseibosyu:1>
```
<img src="https://huggingface.co/ymmttks/danseibosyu/resolve/main/samples/00151-2557695287.png" width="600">
<img src="https://huggingface.co/ymmttks/danseibosyu/resolve/main/samples/00020-3194616569.png" width="600">
## omake
<img src="https://huggingface.co/ymmttks/danseibosyu/resolve/main/samples/00015-3046191811.png" width="600">
<img src="https://huggingface.co/ymmttks/danseibosyu/resolve/main/samples/00006-1918596583.png" width="600">
|
Barleysack/AERoberta2
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 2
| null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.76 +/- 0.26
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Barytes/hellohf
|
[
"tf",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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}
}
}
| 2
| 2023-04-01T11:44:35Z
|
---
datasets:
- relbert/conceptnet_relational_similarity
model-index:
- name: relbert/relbert-roberta-base-nce-conceptnet
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8411507936507936
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.44919786096256686
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4421364985163205
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6197887715397443
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.81
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.42543859649122806
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4097222222222222
- task:
name: Analogy Questions (ConceptNet Analogy)
type: multiple-choice-qa
dataset:
name: ConceptNet Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.2273489932885906
- task:
name: Analogy Questions (TREX Analogy)
type: multiple-choice-qa
dataset:
name: TREX Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.44808743169398907
- task:
name: Analogy Questions (NELL-ONE Analogy)
type: multiple-choice-qa
dataset:
name: NELL-ONE Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6616666666666666
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8880518306463764
- name: F1 (macro)
type: f1_macro
value: 0.8803268244708621
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.828169014084507
- name: F1 (macro)
type: f1_macro
value: 0.6164214086385178
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.628385698808234
- name: F1 (macro)
type: f1_macro
value: 0.6170915381058782
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9501982332892815
- name: F1 (macro)
type: f1_macro
value: 0.8664191428414516
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8884362268881228
- name: F1 (macro)
type: f1_macro
value: 0.8877705847530848
---
# relbert/relbert-roberta-base-nce-conceptnet
RelBERT based on [roberta-base](https://huggingface.co/roberta-base) fine-tuned on [relbert/conceptnet_relational_similarity](https://huggingface.co/datasets/relbert/conceptnet_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-conceptnet/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.44919786096256686
- Accuracy on SAT: 0.4421364985163205
- Accuracy on BATS: 0.6197887715397443
- Accuracy on U2: 0.42543859649122806
- Accuracy on U4: 0.4097222222222222
- Accuracy on Google: 0.81
- Accuracy on ConceptNet Analogy: 0.2273489932885906
- Accuracy on T-Rex Analogy: 0.44808743169398907
- Accuracy on NELL-ONE Analogy: 0.6616666666666666
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-conceptnet/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8880518306463764
- Micro F1 score on CogALexV: 0.828169014084507
- Micro F1 score on EVALution: 0.628385698808234
- Micro F1 score on K&H+N: 0.9501982332892815
- Micro F1 score on ROOT09: 0.8884362268881228
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-conceptnet/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8411507936507936
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-nce-conceptnet")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-base
- max_length: 64
- epoch: 5
- batch: 16
- random_seed: 0
- lr: 5e-06
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/conceptnet_relational_similarity
- data_name: None
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: nce
- classification_loss: False
- loss_function_config: {'temperature': 0.05, 'num_negative': 300, 'num_positive': 30}
- augment_negative_by_positive: True
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-base-nce-conceptnet/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
Batsy24/DialoGPT-small-Twilight_EdBot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 6
| null |
---
tags:
- autotrain
- summarization
language:
- en
widget:
- text: Hello Pikachu🤗
datasets:
- altafalam3/autotrain-data-synopsize
co2_eq_emissions:
emissions: 0.0014579314567438177
license: apache-2.0
metrics:
- accuracy
- code_eval
pipeline_tag: summarization
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 45723114297
- CO2 Emissions (in grams): 0.0015
## Validation Metrics
- Loss: 0.767
- Rouge1: 84.624
- Rouge2: 57.742
- RougeL: 84.631
- RougeLsum: 84.946
- Gen Len: 6.419
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/altafalam3/autotrain-synopsize-45723114297
```
|
BatuhanYilmaz/distilbert-base-uncased-finetuned-squad-d5716d28
|
[
"pytorch",
"distilbert",
"fill-mask",
"en",
"dataset:squad",
"arxiv:1910.01108",
"transformers",
"question-answering",
"license:apache-2.0",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 18
| null |
---
license: openrail
---
# 本仓库为备份仓库，模型来源于网络
# 命令下载格式：
git lfs clone https://huggingface.co/用户名/项目
(下载全部)
aria2c https://huggingface.co/用户名/项目/resolve/main/目录/文件名
(下载单个文件)
|
BatuhanYilmaz/dummy-model
|
[
"tf",
"camembert",
"fill-mask",
"transformers",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"CamembertForMaskedLM"
],
"model_type": "camembert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 6
| null |
---
datasets:
- relbert/conceptnet_relational_similarity
model-index:
- name: relbert/relbert-roberta-base-nce-e-conceptnet
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.719484126984127
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3716577540106952
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3887240356083086
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5514174541411896
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.688
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.36403508771929827
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3958333333333333
- task:
name: Analogy Questions (ConceptNet Analogy)
type: multiple-choice-qa
dataset:
name: ConceptNet Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.17030201342281878
- task:
name: Analogy Questions (TREX Analogy)
type: multiple-choice-qa
dataset:
name: TREX Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3770491803278688
- task:
name: Analogy Questions (NELL-ONE Analogy)
type: multiple-choice-qa
dataset:
name: NELL-ONE Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6283333333333333
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8844357390387223
- name: F1 (macro)
type: f1_macro
value: 0.8761554028932904
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8199530516431925
- name: F1 (macro)
type: f1_macro
value: 0.5979228940177277
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6175514626218852
- name: F1 (macro)
type: f1_macro
value: 0.6085488125663312
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9567364540585658
- name: F1 (macro)
type: f1_macro
value: 0.8732584259146792
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8708868693199624
- name: F1 (macro)
type: f1_macro
value: 0.8742440668757364
---
# relbert/relbert-roberta-base-nce-e-conceptnet
RelBERT based on [roberta-base](https://huggingface.co/roberta-base) fine-tuned on [relbert/conceptnet_relational_similarity](https://huggingface.co/datasets/relbert/conceptnet_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-e-conceptnet/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.3716577540106952
- Accuracy on SAT: 0.3887240356083086
- Accuracy on BATS: 0.5514174541411896
- Accuracy on U2: 0.36403508771929827
- Accuracy on U4: 0.3958333333333333
- Accuracy on Google: 0.688
- Accuracy on ConceptNet Analogy: 0.17030201342281878
- Accuracy on T-Rex Analogy: 0.3770491803278688
- Accuracy on NELL-ONE Analogy: 0.6283333333333333
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-e-conceptnet/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8844357390387223
- Micro F1 score on CogALexV: 0.8199530516431925
- Micro F1 score on EVALution: 0.6175514626218852
- Micro F1 score on K&H+N: 0.9567364540585658
- Micro F1 score on ROOT09: 0.8708868693199624
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-e-conceptnet/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.719484126984127
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-nce-e-conceptnet")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-base
- max_length: 64
- epoch: 5
- batch: 16
- random_seed: 0
- lr: 5e-06
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/conceptnet_relational_similarity
- data_name: None
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: nce
- classification_loss: False
- loss_function_config: {'temperature': 0.05, 'num_negative': 300, 'num_positive': 30}
- augment_negative_by_positive: True
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-base-nce-e-conceptnet/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
BatuhanYilmaz/marian-finetuned-kde4-en-to-fr
|
[] | null |
{
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 0
| null |
# Vocabulary Trimmed [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base): `vocabtrimmer/xlm-v-base-trimmed-es`
This model is a trimmed version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | facebook/xlm-v-base | vocabtrimmer/xlm-v-base-trimmed-es |
|:---------------------------|:----------------------|:-------------------------------------|
| parameter_size_full | 779,396,349 | 273,192,102 |
| parameter_size_embedding | 692,451,072 | 186,905,088 |
| vocab_size | 901,629 | 243,366 |
| compression_rate_full | 100.0 | 35.05 |
| compression_rate_embedding | 100.0 | 26.99 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|:--------------------|----------------:|
| es | vocabtrimmer/mc4_validation | text | es | validation | | 2 |
|
BatuhanYilmaz/mlm-finetuned-imdb
|
[] | null |
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| 0
| null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1448.43 +/- 75.28
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Baybars/debateGPT
|
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}
| 0
| 2023-04-01T12:11:33Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-chemprot
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-chemprot
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5437
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 383 | 3.5809 |
| 3.7069 | 2.0 | 766 | 3.5498 |
| 3.5543 | 3.0 | 1149 | 3.5437 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Baybars/wav2vec2-xls-r-1b-turkish
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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}
| 13
| null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 264.37 +/- 13.55
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Baybars/wav2vec2-xls-r-300m-cv8-turkish
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"license:apache-2.0"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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}
| 5
| null |
---
datasets:
- relbert/nell_relational_similarity
model-index:
- name: relbert/relbert-roberta-base-nce-a-nell
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7451388888888889
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3983957219251337
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4065281899109792
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3924402445803224
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.632
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.39473684210526316
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4212962962962963
- task:
name: Analogy Questions (ConceptNet Analogy)
type: multiple-choice-qa
dataset:
name: ConceptNet Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.10318791946308725
- task:
name: Analogy Questions (TREX Analogy)
type: multiple-choice-qa
dataset:
name: TREX Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.639344262295082
- task:
name: Analogy Questions (NELL-ONE Analogy)
type: multiple-choice-qa
dataset:
name: NELL-ONE Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.8
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8987494349856864
- name: F1 (macro)
type: f1_macro
value: 0.890050406980326
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8011737089201879
- name: F1 (macro)
type: f1_macro
value: 0.5692353291338583
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6105092091007583
- name: F1 (macro)
type: f1_macro
value: 0.5995431523479036
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9476942338457258
- name: F1 (macro)
type: f1_macro
value: 0.856425065439868
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8824819805703541
- name: F1 (macro)
type: f1_macro
value: 0.8824104543652189
---
# relbert/relbert-roberta-base-nce-a-nell
RelBERT based on [roberta-base](https://huggingface.co/roberta-base) fine-tuned on [relbert/nell_relational_similarity](https://huggingface.co/datasets/relbert/nell_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-a-nell/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.3983957219251337
- Accuracy on SAT: 0.4065281899109792
- Accuracy on BATS: 0.3924402445803224
- Accuracy on U2: 0.39473684210526316
- Accuracy on U4: 0.4212962962962963
- Accuracy on Google: 0.632
- Accuracy on ConceptNet Analogy: 0.10318791946308725
- Accuracy on T-Rex Analogy: 0.639344262295082
- Accuracy on NELL-ONE Analogy: 0.8
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-a-nell/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8987494349856864
- Micro F1 score on CogALexV: 0.8011737089201879
- Micro F1 score on EVALution: 0.6105092091007583
- Micro F1 score on K&H+N: 0.9476942338457258
- Micro F1 score on ROOT09: 0.8824819805703541
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-a-nell/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7451388888888889
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-nce-a-nell")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-base
- max_length: 64
- epoch: 5
- batch: 16
- random_seed: 0
- lr: 5e-06
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/nell_relational_similarity
- data_name: None
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: nce
- classification_loss: False
- loss_function_config: {'temperature': 0.05, 'num_negative': 300, 'num_positive': 30}
- augment_negative_by_positive: True
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-base-nce-a-nell/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
BeIR/query-gen-msmarco-t5-base-v1
|
[
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
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},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 1,816
| 2023-04-01T12:19:54Z
|
tokenizer="roberta-base"
model="roberta-base"
epochs=5
|
BearThreat/distilbert-base-uncased-finetuned-cola
|
[
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] |
text-classification
|
{
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"DistilBertForSequenceClassification"
],
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}
}
}
| 30
| null |
---
license: cc-by-nc-4.0
---
---
license: cc-by-nc-4.0
---
<p align="center">
<img width="500px" alt="Project Baize" src="https://user-images.githubusercontent.com/22514219/229195563-0cddfa74-e52f-4413-b4b4-e4ba489c4b3d.png">
</p>
<hr>
## What's Baize?
Baize is an open-source chat model fine-tuned with [LoRA](https://github.com/microsoft/LoRA). It uses 100k dialogs generated by letting ChatGPT chat with itself. We also use Alpaca's data to improve its performance. This repo contains 30B model.
## Why it's called Baize?
Baize (白泽) is a mythical creature in Chinese folklore, who speaks human languages and knows everything. This is exactly what we expect from a chat model.
## Training Parameters
- Base Model: [LLaMA-30B](https://arxiv.org/pdf/2302.13971.pdf)
- Training Epoch: 1
- Batch Size: 64
- Maximum Input Length: 512
- Learning Rate: 5e-5
- LoRA Rank: 8
- Updated Modules: All Linears
## Training Dataset
- [Standford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) (51,942)
- [Quora Dialogs](https://github.com/project-baize/baize) (54,456):
- [StackOverflow Dialogs](https://github.com/project-baize/baize) (57,046)
More details can be found in the Baize [GitHub]((https://github.com/project-baize/baize))
|
Beatriz/model_name
|
[] | null |
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}
| 0
| null |
Access to model prows12/asd is restricted and you are not in the authorized list. Visit https://huggingface.co/prows12/asd to ask for access.
|
Bee-Garbs/DialoGPT-cartman-small
|
[] | null |
{
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}
| 0
| 2023-04-01T12:31:40Z
|
---
datasets:
- relbert/nell_relational_similarity
model-index:
- name: relbert/relbert-roberta-base-nce-b-nell
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7011904761904761
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3770053475935829
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3827893175074184
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3802112284602557
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.584
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.3991228070175439
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.41435185185185186
- task:
name: Analogy Questions (ConceptNet Analogy)
type: multiple-choice-qa
dataset:
name: ConceptNet Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.08053691275167785
- task:
name: Analogy Questions (TREX Analogy)
type: multiple-choice-qa
dataset:
name: TREX Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.639344262295082
- task:
name: Analogy Questions (NELL-ONE Analogy)
type: multiple-choice-qa
dataset:
name: NELL-ONE Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7916666666666666
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8912159108030737
- name: F1 (macro)
type: f1_macro
value: 0.882832586768981
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7922535211267606
- name: F1 (macro)
type: f1_macro
value: 0.5399502573058529
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6045503791982665
- name: F1 (macro)
type: f1_macro
value: 0.6027670935592911
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9501286777491827
- name: F1 (macro)
type: f1_macro
value: 0.8672983510521417
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8749608273268568
- name: F1 (macro)
type: f1_macro
value: 0.8713638706052124
---
# relbert/relbert-roberta-base-nce-b-nell
RelBERT based on [roberta-base](https://huggingface.co/roberta-base) fine-tuned on [relbert/nell_relational_similarity](https://huggingface.co/datasets/relbert/nell_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-b-nell/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.3770053475935829
- Accuracy on SAT: 0.3827893175074184
- Accuracy on BATS: 0.3802112284602557
- Accuracy on U2: 0.3991228070175439
- Accuracy on U4: 0.41435185185185186
- Accuracy on Google: 0.584
- Accuracy on ConceptNet Analogy: 0.08053691275167785
- Accuracy on T-Rex Analogy: 0.639344262295082
- Accuracy on NELL-ONE Analogy: 0.7916666666666666
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-b-nell/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8912159108030737
- Micro F1 score on CogALexV: 0.7922535211267606
- Micro F1 score on EVALution: 0.6045503791982665
- Micro F1 score on K&H+N: 0.9501286777491827
- Micro F1 score on ROOT09: 0.8749608273268568
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-b-nell/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7011904761904761
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-nce-b-nell")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-base
- max_length: 64
- epoch: 5
- batch: 16
- random_seed: 0
- lr: 5e-06
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/nell_relational_similarity
- data_name: None
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: nce
- classification_loss: False
- loss_function_config: {'temperature': 0.05, 'num_negative': 300, 'num_positive': 30}
- augment_negative_by_positive: True
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-base-nce-b-nell/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
Beelow/wav2vec2-ukrainian-model-large
|
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| 0
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-large-finetuned-augument-visquad2-1-4-2023-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-large-finetuned-augument-visquad2-1-4-2023-2
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Best F1: 76.7211
- Loss: 2.6463
- Exact: 38.4193
- F1: 56.9010
- Total: 3821
- Hasans Exact: 54.6551
- Hasans F1: 81.2735
- Hasans Total: 2653
- Noans Exact: 1.5411
- Noans F1: 1.5411
- Noans Total: 1168
- Best Exact: 60.2722
- Best Exact Thresh: 0.5938
- Best F1 Thresh: 0.9029
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- lr_scheduler_warmup_steps: 2000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Best F1 | Validation Loss | Exact | F1 | Total | Hasans Exact | Hasans F1 | Hasans Total | Noans Exact | Noans F1 | Noans Total | Best Exact | Best Exact Thresh | Best F1 Thresh |
|:-------------:|:-----:|:-----:|:-------:|:---------------:|:-------:|:-------:|:-----:|:------------:|:---------:|:------------:|:-----------:|:--------:|:-----------:|:----------:|:-----------------:|:--------------:|
| 1.5748 | 1.0 | 2110 | 62.9530 | 1.3457 | 34.5983 | 52.8434 | 3821 | 49.8304 | 76.1080 | 2653 | 0.0 | 0.0 | 1168 | 48.4952 | 0.7967 | 0.9001 |
| 0.5259 | 2.0 | 4221 | 73.5147 | 1.0489 | 37.9482 | 55.5987 | 3821 | 54.6551 | 80.0763 | 2653 | 0.0 | 0.0 | 1168 | 58.5972 | 0.8404 | 0.8855 |
| 0.3605 | 3.0 | 6332 | 75.9336 | 1.0857 | 39.4399 | 56.4203 | 3821 | 56.8036 | 81.2597 | 2653 | 0.0 | 0.0 | 1168 | 61.0311 | 0.8271 | 0.9440 |
| 0.2592 | 4.0 | 8443 | 75.9761 | 1.2592 | 39.2044 | 56.3936 | 3821 | 56.1628 | 80.9197 | 2653 | 0.6849 | 0.6849 | 1168 | 60.5339 | 0.8107 | 0.8616 |
| 0.1932 | 5.0 | 10553 | 76.2008 | 1.3238 | 39.3614 | 56.8951 | 3821 | 55.9367 | 81.1897 | 2653 | 1.7123 | 1.7123 | 1168 | 60.5862 | 0.7110 | 0.8943 |
| 0.1421 | 6.0 | 12664 | 76.3034 | 1.5313 | 38.1837 | 56.2592 | 3821 | 54.7305 | 80.7638 | 2653 | 0.5993 | 0.5993 | 1168 | 60.1675 | 0.7653 | 0.9318 |
| 0.1052 | 7.0 | 14775 | 76.1684 | 1.7621 | 38.2884 | 56.6387 | 3821 | 54.6928 | 81.1219 | 2653 | 1.0274 | 1.0274 | 1168 | 59.9320 | 0.8190 | 0.8802 |
| 0.0776 | 8.0 | 16886 | 76.3481 | 2.2045 | 38.1837 | 56.8339 | 3821 | 54.3159 | 81.1769 | 2653 | 1.5411 | 1.5411 | 1168 | 59.9320 | 0.7125 | 0.9312 |
| 0.0572 | 9.0 | 18996 | 76.5232 | 2.4641 | 38.5239 | 56.9164 | 3821 | 54.9190 | 81.4088 | 2653 | 1.2842 | 1.2842 | 1168 | 60.1413 | 0.6647 | 0.9936 |
| 0.0456 | 10.0 | 21100 | 76.7211 | 2.6463 | 38.4193 | 56.9010 | 3821 | 54.6551 | 81.2735 | 2653 | 1.5411 | 1.5411 | 1168 | 60.2722 | 0.5938 | 0.9029 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Begimay/Task
|
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| 0
| 2023-04-01T12:38:08Z
|
---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| inner_optimizer.class_name | Custom>RMSprop |
| inner_optimizer.config.name | RMSprop |
| inner_optimizer.config.weight_decay | None |
| inner_optimizer.config.clipnorm | None |
| inner_optimizer.config.global_clipnorm | None |
| inner_optimizer.config.clipvalue | None |
| inner_optimizer.config.use_ema | False |
| inner_optimizer.config.ema_momentum | 0.99 |
| inner_optimizer.config.ema_overwrite_frequency | 100 |
| inner_optimizer.config.jit_compile | True |
| inner_optimizer.config.is_legacy_optimizer | False |
| inner_optimizer.config.learning_rate | 0.0010000000474974513 |
| inner_optimizer.config.rho | 0.9 |
| inner_optimizer.config.momentum | 0.0 |
| inner_optimizer.config.epsilon | 1e-07 |
| inner_optimizer.config.centered | False |
| dynamic | True |
| initial_scale | 32768.0 |
| dynamic_growth_steps | 2000 |
| training_precision | mixed_float16 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
BenGeorge/MyModel
|
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| 0
| null |
# `vocabtrimmer/xlm-v-base-trimmed-es-tweet-sentiment-es`
This model is a fine-tuned version of [/home/c.c2042013/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-es](https://huggingface.co//home/c.c2042013/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-es) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (spanish).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(spanish).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 67.24 | 67.24 | 67.24 | 66.58 | 67.24 | 66.81 | 67.24 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-trimmed-es-tweet-sentiment-es/raw/main/eval.json).
|
Beri/legal-qa
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
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| 10
| null |
---
duplicated_from: chavinlo/gpt4-x-alpaca
---
# GPT4 x Alpaca
As a base model we used: https://huggingface.co/chavinlo/alpaca-13b
Finetuned on GPT4's responses, for 3 epochs.
NO LORA
|
BertChristiaens/EmojiPredictor
|
[
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
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}
| 6
| null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.69 +/- 0.59
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BigDaddyNe1L/Hhaa
|
[] | null |
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}
| 0
| null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Mihara-bot/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
BigSalmon/BestMask2
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
| 10
| 2023-04-01T13:50:19Z
|
# `vocabtrimmer/xlm-v-base-trimmed-es-10000-tweet-sentiment-es`
This model is a fine-tuned version of [/home/c.c2042013/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-es-10000](https://huggingface.co//home/c.c2042013/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-es-10000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (spanish).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(spanish).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 57.01 | 57.01 | 57.01 | 55.37 | 57.01 | 56.87 | 57.01 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-trimmed-es-10000-tweet-sentiment-es/raw/main/eval.json).
|
BigSalmon/FroBurta
|
[] | null |
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| 0
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: student-offense-distilled_ok_ok_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# student-offense-distilled_ok_ok_2
This model is a fine-tuned version of [racai/distilbert-base-romanian-cased](https://huggingface.co/racai/distilbert-base-romanian-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4055
- Accuracy: 0.7814
- F1: 0.7819
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.4798 | 1.0 | 9956 | 0.4859 | 0.7645 | 0.7591 |
| 0.3181 | 2.0 | 19912 | 0.4299 | 0.7814 | 0.7813 |
| 0.2428 | 3.0 | 29868 | 0.4059 | 0.7878 | 0.7874 |
| 0.2142 | 4.0 | 39824 | 0.4059 | 0.7822 | 0.7847 |
| 0.2271 | 5.0 | 49780 | 0.4055 | 0.7814 | 0.7819 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
BigSalmon/InfillFormalLincoln
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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| 8
| null |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.61 +/- 0.49
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Iggg0r/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
BigSalmon/InformalToFormalLincoln15
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
],
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},
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},
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}
| 11
| null |
---
datasets:
- relbert/t_rex_relational_similarity
model-index:
- name: relbert/relbert-roberta-base-nce-t-rex
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8037103174603175
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4679144385026738
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4836795252225519
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5102834908282379
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.75
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.42543859649122806
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4398148148148148
- task:
name: Analogy Questions (ConceptNet Analogy)
type: multiple-choice-qa
dataset:
name: ConceptNet Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.18791946308724833
- task:
name: Analogy Questions (TREX Analogy)
type: multiple-choice-qa
dataset:
name: TREX Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.8360655737704918
- task:
name: Analogy Questions (NELL-ONE Analogy)
type: multiple-choice-qa
dataset:
name: NELL-ONE Analogy
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7216666666666667
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.893174627090553
- name: F1 (macro)
type: f1_macro
value: 0.8930103376872522
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.828169014084507
- name: F1 (macro)
type: f1_macro
value: 0.635622257984698
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6473456121343445
- name: F1 (macro)
type: f1_macro
value: 0.6272978919061384
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9520762328719482
- name: F1 (macro)
type: f1_macro
value: 0.8665439837723805
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8824819805703541
- name: F1 (macro)
type: f1_macro
value: 0.8753300511142968
---
# relbert/relbert-roberta-base-nce-t-rex
RelBERT based on [roberta-base](https://huggingface.co/roberta-base) fine-tuned on [relbert/t_rex_relational_similarity](https://huggingface.co/datasets/relbert/t_rex_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
This model achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-t-rex/raw/main/analogy.forward.json)):
- Accuracy on SAT (full): 0.4679144385026738
- Accuracy on SAT: 0.4836795252225519
- Accuracy on BATS: 0.5102834908282379
- Accuracy on U2: 0.42543859649122806
- Accuracy on U4: 0.4398148148148148
- Accuracy on Google: 0.75
- Accuracy on ConceptNet Analogy: 0.18791946308724833
- Accuracy on T-Rex Analogy: 0.8360655737704918
- Accuracy on NELL-ONE Analogy: 0.7216666666666667
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-t-rex/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.893174627090553
- Micro F1 score on CogALexV: 0.828169014084507
- Micro F1 score on EVALution: 0.6473456121343445
- Micro F1 score on K&H+N: 0.9520762328719482
- Micro F1 score on ROOT09: 0.8824819805703541
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-t-rex/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8037103174603175
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/relbert-roberta-base-nce-t-rex")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
```
### Training hyperparameters
- model: roberta-base
- max_length: 64
- epoch: 5
- batch: 32
- random_seed: 0
- lr: 5e-06
- lr_warmup: 10
- aggregation_mode: average_no_mask
- data: relbert/t_rex_relational_similarity
- data_name: None
- exclude_relation: None
- split: train
- split_valid: validation
- loss_function: nce
- classification_loss: False
- loss_function_config: {'temperature': 0.05, 'num_negative': 300, 'num_positive': 30}
- augment_negative_by_positive: True
See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-base-nce-t-rex/raw/main/finetuning_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
```
@inproceedings{ushio-etal-2021-distilling,
title = "Distilling Relation Embeddings from Pretrained Language Models",
author = "Ushio, Asahi and
Camacho-Collados, Jose and
Schockaert, Steven",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.712",
doi = "10.18653/v1/2021.emnlp-main.712",
pages = "9044--9062",
abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
}
```
|
BigSalmon/MrLincolnBerta
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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}
| 8
| null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: HASAN55/bert-finetuned-squadd
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# HASAN55/bert-finetuned-squadd
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3206
- Train End Logits Accuracy: 0.9105
- Train Start Logits Accuracy: 0.9007
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 765, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:-----:|
| 0.7458 | 0.7968 | 0.7738 | 0 |
| 0.4481 | 0.8745 | 0.8627 | 1 |
| 0.3206 | 0.9105 | 0.9007 | 2 |
### Framework versions
- Transformers 4.27.4
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.2
|
BigSalmon/NEO125InformalToFormalLincoln
|
[
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
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}
| 8
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: news-summarization
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# news-summarization
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
BigSalmon/Rowerta
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
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}
| 4
| null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="vcncolin/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
BigTooth/DialoGPT-Megumin
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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| 16
| null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 221.09 +/- 18.84
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BillelBenoudjit/jplu-wikiann
|
[
"fr",
"dataset:wikiann",
"model-index"
] | null |
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| 0
| null |
---
tags:
- stable_diffusion
- checkpoint
---
The source of the models is listed below. Please check the original licenses from the source.
https://civitai.com/models/6424
|
Blackmist786/DialoGPt-small-transformers4
|
[
"pytorch"
] | null |
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| 4
| null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 258.90 +/- 16.24
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Blaine-Mason/hackMIT-finetuned-sst2
|
[
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer"
] |
text-classification
|
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}
| 36
| null |
---
license: bigscience-bloom-rail-1.0
tags:
- generated_from_trainer
model-index:
- name: bloomz-7b1-mt-ft-ask2democracy-cqa-salud-esco
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bloomz-7b1-mt-ft-ask2democracy-cqa-salud-esco
This model is a fine-tuned version of [bigscience/bloomz-7b1-mt](https://huggingface.co/bigscience/bloomz-7b1-mt) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
|
BonjinKim/dst_kor_bert
|
[
"pytorch",
"jax",
"bert",
"pretraining",
"transformers"
] | null |
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| 5
| null |
---
license: mit
---
Ran for 7 epoch on Squad dataset.
|
Boondong/Wandee
|
[] | null |
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}
| 0
| null |
---
license: mit
pipeline_tag: text-classification
---
# Roberta-Fact-Check Model
The Roberta-Fact-Check Model is a deep learning model that uses the Roberta architecture for text classification. It is designed to classify claims as either supported or refuted based on the provided evidence.
## Model Training
The model was trained using the Adam optimizer with a learning rate of 2-4e, epsilon of 1-8, and weight decay of 2-8e. The training dataset mainly consisted of the FEVER and Hover datasets, along with a small sample of manually created data.
## Input and Output
The model takes a claim and corresponding evidence as input and returns a label indicating whether the evidence supports or refutes the claim. The two possible labels are:
- 0: Supports
- 1: Refutes
## Usage
To use the Roberta-Fact-Check Model, you can simply pass in a claim and evidence as input to the model and receive a label indicating whether the evidence supports or refutes the claim. The model can be integrated into various applications for fact-checking and misinformation detection.
```python
import torch
from transformers import RobertaTokenizer, RobertaForSequenceClassification
# Load the tokenizer and model
tokenizer = RobertaTokenizer.from_pretrained('Dzeniks/roberta-fact-check')
model = RobertaForSequenceClassification.from_pretrained('Dzeniks/roberta-fact-check')
# Define the claim with evidence to classify
claim = "Albert Einstein work in the field of computer science"
evidence = "Albert Einstein was a German-born theoretical physicist, widely acknowledged to be one of the greatest and most influential physicists of all time."
# Tokenize the claim with evidence
x = tokenizer.encode_plus(claim, evidence, return_tensors="pt")
model.eval()
with torch.no_grad():
prediction = model(**x)
label = torch.argmax(outputs[0]).item()
print(f"Label: {label}")
```
## Acknowledgements
This model was developed using the Hugging Face transformers library and trained on the FEVER and Hover datasets. We would like to thank the developers of these datasets for their contributions to the community.
## Disclaimer
While the Roberta-Fact-Check Model has been trained on a large dataset and can provide accurate results in many cases, it may not always provide correct results. Users should always exercise caution when making decisions based on the output of any machine learning model.
|
Bosio/full-sentence-distillroberta3-finetuned-wikitext2
|
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| 0
| null |
---
license: creativeml-openrail-m
tags:
- keras
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- keras-sprint
- keras-dreambooth
- scifi
inference: true
widget:
- text: a drawing of drawbayc monkey as a turtle
---
# KerasCV Stable Diffusion in Diffusers 🧨🤗
DreamBooth model for the `drawbayc monkey` concept trained by nielsgl on the `nielsgl/bayc-tiny` dataset, images from this [Kaggle dataset](https://www.kaggle.com/datasets/stanleyjzheng/bored-apes-yacht-club).
It can be used by modifying the `instance_prompt`: **a drawing of drawbayc monkey**
## Description
The pipeline contained in this repository was created using a modified version of [this Space](https://huggingface.co/spaces/sayakpaul/convert-kerascv-sd-diffusers) for StableDiffusionV2 from KerasCV. The purpose is to convert the KerasCV Stable Diffusion weights in a way that is compatible with [Diffusers](https://github.com/huggingface/diffusers). This allows users to fine-tune using KerasCV and use the fine-tuned weights in Diffusers taking advantage of its nifty features (like [schedulers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/schedulers), [fast attention](https://huggingface.co/docs/diffusers/optimization/fp16), etc.).
This model was created as part of the Keras DreamBooth Sprint 🔥. Visit the [organisation page](https://huggingface.co/keras-dreambooth) for instructions on how to take part!
## Examples
> A drawing of drawbayc monkey dressed as an astronaut
> A drawing of drawbayc monkey dressed as the pope
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('nielsgl/dreambooth-bored-ape')
image = pipeline().images[0]
image
```
## Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | RMSprop |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | 100 |
| jit_compile | True |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| rho | 0.9 |
| momentum | 0.0 |
| epsilon | 1e-07 |
| centered | False |
| training_precision | float32 |
|
Botjallu/DialoGPT-small-harrypotter
|
[] | null |
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| 0
| null |
---
datasets:
- IlyaGusev/ru_turbo_alpaca
language:
- ru
inference: false
pipeline_tag: text2text-generation
---
Llama.cpp compatible version of an original [7B model](https://huggingface.co/IlyaGusev/llama_7b_ru_turbo_alpaca_lora).
How to run:
```
sudo apt-get install git-lfs
git clone https://huggingface.co/IlyaGusev/llama_7b_ru_turbo_alpaca_lora_llamacpp
cd llama_7b_ru_turbo_alpaca_lora_llamacpp && git lfs install && git lfs pull && cd ..
git clone https://github.com/ggerganov/llama.cpp
cp -R llama_7b_ru_turbo_alpaca_lora_llamacpp/* llama.cpp/models/
cd llama.cpp
make
./main -m ./models/7B/ggml-model-q4_0.bin -p "Вопрос: Почему трава зеленая? Ответ:" -n 512 --temp 0.1
```
|
Brayan/CNN_Brain_Tumor
|
[] | null |
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}
| 0
| null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -167.18 +/- 67.49
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo_google_colab'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'Musha-the-Yusha/ppo-LunarLander-v1'
'batch_size': 512
'minibatch_size': 128}
```
|
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