modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-02 18:52:31
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 533
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-02 18:52:05
| card
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|---|---|---|---|---|---|---|---|---|---|
HaoHu/vit-base-patch16-224-in21k-classify-4scence
|
HaoHu
| 2022-07-24T16:02:55Z | 48 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-24T15:23:48Z |
---
license: other
---
train this model on the Contest
the original dataset is
链接: https://pan.baidu.com/s/1pr094NZ2QMj3nLy12gfa6g 密码: kb7a
|
bigmorning/distilgpt_new3_0030
|
bigmorning
| 2022-07-24T15:59:39Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-24T15:54:12Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: distilgpt_new3_0030
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. -->
# distilgpt_new3_0030
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.5197
- Validation Loss: 2.4026
- Epoch: 29
## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.5407 | 2.4254 | 0 |
| 2.5399 | 2.4247 | 1 |
| 2.5391 | 2.4238 | 2 |
| 2.5383 | 2.4232 | 3 |
| 2.5375 | 2.4210 | 4 |
| 2.5368 | 2.4210 | 5 |
| 2.5361 | 2.4197 | 6 |
| 2.5353 | 2.4193 | 7 |
| 2.5345 | 2.4191 | 8 |
| 2.5339 | 2.4177 | 9 |
| 2.5332 | 2.4188 | 10 |
| 2.5324 | 2.4160 | 11 |
| 2.5317 | 2.4164 | 12 |
| 2.5309 | 2.4145 | 13 |
| 2.5302 | 2.4153 | 14 |
| 2.5295 | 2.4139 | 15 |
| 2.5288 | 2.4134 | 16 |
| 2.5282 | 2.4123 | 17 |
| 2.5274 | 2.4116 | 18 |
| 2.5267 | 2.4110 | 19 |
| 2.5259 | 2.4106 | 20 |
| 2.5251 | 2.4097 | 21 |
| 2.5244 | 2.4074 | 22 |
| 2.5238 | 2.4078 | 23 |
| 2.5232 | 2.4072 | 24 |
| 2.5223 | 2.4062 | 25 |
| 2.5217 | 2.4054 | 26 |
| 2.5211 | 2.4057 | 27 |
| 2.5204 | 2.4044 | 28 |
| 2.5197 | 2.4026 | 29 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
SummerChiam/rust_image_classification_1
|
SummerChiam
| 2022-07-24T14:47:06Z | 48 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-24T14:46:56Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rust_image_classification
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.903797447681427
---
# rust_image_classification
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### nonrust
#### rust
|
bigmorning/distilgpt_new3_0025
|
bigmorning
| 2022-07-24T14:33:32Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-24T14:28:09Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: distilgpt_new3_0025
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. -->
# distilgpt_new3_0025
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.5232
- Validation Loss: 2.4072
- Epoch: 24
## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.5407 | 2.4254 | 0 |
| 2.5399 | 2.4247 | 1 |
| 2.5391 | 2.4238 | 2 |
| 2.5383 | 2.4232 | 3 |
| 2.5375 | 2.4210 | 4 |
| 2.5368 | 2.4210 | 5 |
| 2.5361 | 2.4197 | 6 |
| 2.5353 | 2.4193 | 7 |
| 2.5345 | 2.4191 | 8 |
| 2.5339 | 2.4177 | 9 |
| 2.5332 | 2.4188 | 10 |
| 2.5324 | 2.4160 | 11 |
| 2.5317 | 2.4164 | 12 |
| 2.5309 | 2.4145 | 13 |
| 2.5302 | 2.4153 | 14 |
| 2.5295 | 2.4139 | 15 |
| 2.5288 | 2.4134 | 16 |
| 2.5282 | 2.4123 | 17 |
| 2.5274 | 2.4116 | 18 |
| 2.5267 | 2.4110 | 19 |
| 2.5259 | 2.4106 | 20 |
| 2.5251 | 2.4097 | 21 |
| 2.5244 | 2.4074 | 22 |
| 2.5238 | 2.4078 | 23 |
| 2.5232 | 2.4072 | 24 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
bigmorning/distilgpt_new3_0010
|
bigmorning
| 2022-07-24T10:13:12Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-24T10:07:46Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: distilgpt_new3_0010
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. -->
# distilgpt_new3_0010
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.5339
- Validation Loss: 2.4177
- Epoch: 9
## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.5407 | 2.4254 | 0 |
| 2.5399 | 2.4247 | 1 |
| 2.5391 | 2.4238 | 2 |
| 2.5383 | 2.4232 | 3 |
| 2.5375 | 2.4210 | 4 |
| 2.5368 | 2.4210 | 5 |
| 2.5361 | 2.4197 | 6 |
| 2.5353 | 2.4193 | 7 |
| 2.5345 | 2.4191 | 8 |
| 2.5339 | 2.4177 | 9 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
onon214/transformer-NLP
|
onon214
| 2022-07-24T09:41:22Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-24T09:31:39Z |
---
tags:
- generated_from_trainer
model-index:
- name: transformer-NLP
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. -->
# transformer-NLP
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 8.4503
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 9.8223 | 1.0 | 21 | 9.4635 |
| 9.4003 | 2.0 | 42 | 9.2399 |
| 9.1754 | 3.0 | 63 | 9.0618 |
| 8.9665 | 4.0 | 84 | 8.8478 |
| 8.8297 | 5.0 | 105 | 8.7369 |
| 8.6993 | 6.0 | 126 | 8.6474 |
| 8.6372 | 7.0 | 147 | 8.5848 |
| 8.5375 | 8.0 | 168 | 8.4988 |
| 8.5175 | 9.0 | 189 | 8.4400 |
| 8.4955 | 10.0 | 210 | 8.4503 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
bigmorning/distilgpt_new3_0005
|
bigmorning
| 2022-07-24T08:46:05Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-24T08:41:04Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: distilgpt_new3_0005
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. -->
# distilgpt_new3_0005
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.5375
- Validation Loss: 2.4210
- Epoch: 4
## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.5407 | 2.4254 | 0 |
| 2.5399 | 2.4247 | 1 |
| 2.5391 | 2.4238 | 2 |
| 2.5383 | 2.4232 | 3 |
| 2.5375 | 2.4210 | 4 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
dnouri/ventricular_short_axis_3label
|
dnouri
| 2022-07-24T08:44:02Z | 0 | 0 | null |
[
"MONAI",
"region:us"
] | null | 2022-07-22T11:38:50Z |
---
tags:
- MONAI
---
# 3 Label Ventricular Segmentation
This network segments cardiac ventricle in 2D short axis MR images. The left ventricular pool is class 1, left ventricular myocardium class 2, and right ventricular pool class 3. Full cycle segmentation with this network is possible although much of the training data is composed of segmented end-diastole images. The input to the network is single 2D images thus segmenting whole time-dependent volumes consists of multiple inference operations.
The network and training scheme are essentially identical to that described in:
`Kerfoot E., Clough J., Oksuz I., Lee J., King A.P., Schnabel J.A. (2019) Left-Ventricle Quantification Using Residual U-Net. In: Pop M. et al. (eds) Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science, vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_40`
## Data
The dataset used to train this network unfortunately cannot be made public as it contains unreleased image data from King's College London. Existing public datasets such as the[Sunnybrook Cardiac Dataset](http://www.cardiacatlas.org/studies/sunnybrook-cardiac-data/) and [ACDC Challenge](https://www.creatis.insa-lyon.fr/Challenge/acdc/) set can be used to train a similar network.
The `train.json` configuration assumes all data is stored in a single npz file with keys "images" and "segs" containing respectively the raw image data and their accompanying segmentations. The given network was training with stored volumes with shapes `(9095, 256, 256)` thus other data of differing spatial dimensions must be cropped to `(256, 256)` or zero-padded to that size. For the training data this was done as a preprocessing step but the original pixel values are otherwise unchanged from their original forms.
## Training
The network is trained with this data in conjunction with a series of augmentations for regularisation and robustness. Many of the original images are smaller than the expected size of `(256, 256)` and so were zero-padded, the network can thus be expected to be robust against large amounts of empty space in the inputs. Rotation and zooming is also applied to force the network to learn different sizes and orientations of the heart in the field of view.
Free-form deformation is applied to vary the shape of the heart and its surrounding tissues which mimics to a degree deformation like what would be observed through the cardiac cycle. This of course does not replicate the heart moving through plane during the cycle or represent other observed changes but does provide enough variation that full-cycle segmentation is generally acceptable.
Smooth fields are used to vary contrast and intensity in localised regions to simulate some of the variation in image quality caused by acquisition artefacts. Guassian noise is also added to simulate poor quality acquisition. These together force the network to learn to deal with a wider variation of image quality and partially to account for the difference between scanner vendors.
Training is invoked with the following command line:
```sh
python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf --bundle_root .
```
The dataset file is assumed to be `allimages3label.npz` but can be changed by setting the `dataset_file` value to your own file.
## Inference
An example notebook [visualise.ipynb](./visualise.ipynb) demonstrates using the network directly with input images. Inference of 3D volumes only can be accomplished with the `inference.json` configuration:
```sh
python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf --dataset_dir dataset --output_dir ./output/ --bundle_root .
```
|
WasuratS/q-Taxi-v3
|
WasuratS
| 2022-07-24T06:44:46Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-24T06:28:01Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="WasuratS/q-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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Hairyrice/H
|
Hairyrice
| 2022-07-24T06:34:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-07-24T06:33:50Z |
He was just trying out to be the first time
|
WasuratS/q-FrozenLake-v1-4x4-noSlippery
|
WasuratS
| 2022-07-24T06:22:28Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-24T06:22:21Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="WasuratS/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Sidhanttholenlp/distilbert-finetuned-imdb
|
Sidhanttholenlp
| 2022-07-24T05:39:01Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-24T05:04:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-finetuned-imdb
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. -->
# distilbert-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4667
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7304 | 1.0 | 110 | 2.5467 |
| 2.6068 | 2.0 | 220 | 2.5176 |
| 2.5769 | 3.0 | 330 | 2.4837 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
nishita/results
|
nishita
| 2022-07-24T01:28:03Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-23T15:21:06Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [gagan3012/k2t](https://huggingface.co/gagan3012/k2t) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5481
- Rouge1: 65.0534
- Rouge2: 45.7092
- Rougel: 55.8222
- Rougelsum: 57.1866
- Gen Len: 17.8061
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.5049 | 1.0 | 1101 | 0.5527 | 65.0475 | 45.6298 | 55.8323 | 57.2102 | 17.7929 |
| 0.4994 | 2.0 | 2202 | 0.5490 | 65.0567 | 45.7082 | 55.8808 | 57.2343 | 17.8005 |
| 0.4969 | 3.0 | 3303 | 0.5481 | 65.0534 | 45.7092 | 55.8222 | 57.1866 | 17.8061 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
vamsibanda/sbert-onnx-gtr-t5-xl
|
vamsibanda
| 2022-07-24T00:50:30Z | 4 | 2 |
sentence-transformers
|
[
"sentence-transformers",
"onnx",
"t5",
"sentence-similarity",
"feature-extraction",
"transformers",
"en",
"arxiv:2112.07899",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-07-21T16:02:04Z |
---
language: en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- transformers
- onnx
---
#
This is the ONNX model of sentence-transformers/gtr-t5-xl [Large Dual Encoders Are Generalizable Retrievers](https://arxiv.org/abs/2112.07899). Currently, Hugging Face does not support downloading ONNX files with external format files. I have created a workaround using sbert and optimum together to generate embeddings.
```
pip install onnx
pip install onnxruntime==1.10.0
pip install transformers>4.6.1
pip install sentencepiece
pip install sentence-transformers
pip install optimum
pip install torch==1.9.0
```
Then you can use the model like this:
```python
import os
from sentence_transformers.util import snapshot_download
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForFeatureExtraction
from sentence_transformers.models import Transformer, Pooling, Dense
import torch
from transformers.modeling_outputs import BaseModelOutput
import torch.nn.functional as F
import shutil
model_name = 'vamsibanda/sbert-onnx-gtr-t5-xl'
cache_folder = './'
model_path = os.path.join(cache_folder, model_name.replace("/", "_"))
def generate_embedding(text):
token = tokenizer(text, return_tensors='pt')
embeddings = model(input_ids=token['input_ids'], attention_mask=token['attention_mask'])
sbert_embeddings = mean_pooling(embeddings, token['attention_mask'])
sbert_embeddings = dense_layer.forward({'sentence_embedding':sbert_embeddings})
sbert_embeddings = F.normalize(sbert_embeddings['sentence_embedding'], p=2, dim=1)
return sbert_embeddings.tolist()[0]
def download_onnx_model(model_name, cache_folder, model_path, force_download = False):
if force_download and os.path.exists(model_path):
shutil.rmtree(model_path)
elif os.path.exists(model_path):
return
snapshot_download(model_name,
cache_dir=cache_folder,
library_name='sentence-transformers'
)
return
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
def generate_embedding(text):
token = tokenizer(text, return_tensors='pt')
embedding = model(input_ids=token['input_ids'], attention_mask=token['attention_mask'])
embedding = mean_pooling(embedding, token['attention_mask'])
embedding = dense_layer.forward({'sentence_embedding':embedding})
embedding = F.normalize(embedding['sentence_embedding'], p=2, dim=1)
return embedding.tolist()[0]
_ = download_onnx_model(model_name, cache_folder, model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = ORTModelForFeatureExtraction.from_pretrained(model_path, force_download=False)
pooling_layer = Pooling.load(f"{model_path}/1_Pooling")
dense_layer = Dense.load(f"{model_path}/2_Dense")
generate_embedding('That is a happy person')
```
|
richardbaihe/a3t-vctk
|
richardbaihe
| 2022-07-23T23:00:45Z | 0 | 0 | null |
[
"tensorboard",
"license:apache-2.0",
"region:us"
] | null | 2022-06-27T01:01:01Z |
---
license: apache-2.0
---
There are two folders now:
- conformer: Conformer A3T trained with all VCTK training data.
- unseen_conformer: Conformer A3T trained by excluding some speakers during the training.
|
sudo-s/modeversion1_m7_e4
|
sudo-s
| 2022-07-23T22:44:11Z | 53 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-23T18:20:54Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: modeversion1_m7_e4
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. -->
# modeversion1_m7_e4
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem7 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0902
- Accuracy: 0.9731
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.073 | 0.06 | 100 | 3.9370 | 0.1768 |
| 3.4186 | 0.12 | 200 | 3.2721 | 0.2590 |
| 2.6745 | 0.18 | 300 | 2.6465 | 0.3856 |
| 2.2806 | 0.23 | 400 | 2.2600 | 0.4523 |
| 1.9275 | 0.29 | 500 | 1.9653 | 0.5109 |
| 1.6958 | 0.35 | 600 | 1.6815 | 0.6078 |
| 1.2797 | 0.41 | 700 | 1.4514 | 0.6419 |
| 1.3772 | 0.47 | 800 | 1.3212 | 0.6762 |
| 1.1765 | 0.53 | 900 | 1.1476 | 0.7028 |
| 1.0152 | 0.59 | 1000 | 1.0357 | 0.7313 |
| 0.7861 | 0.64 | 1100 | 1.0230 | 0.7184 |
| 1.0262 | 0.7 | 1200 | 0.9469 | 0.7386 |
| 0.8905 | 0.76 | 1300 | 0.8184 | 0.7756 |
| 0.6919 | 0.82 | 1400 | 0.8083 | 0.7711 |
| 0.7494 | 0.88 | 1500 | 0.7601 | 0.7825 |
| 0.5078 | 0.94 | 1600 | 0.6884 | 0.8056 |
| 0.7134 | 1.0 | 1700 | 0.6311 | 0.8160 |
| 0.4328 | 1.06 | 1800 | 0.5740 | 0.8252 |
| 0.4971 | 1.11 | 1900 | 0.5856 | 0.8290 |
| 0.5207 | 1.17 | 2000 | 0.6219 | 0.8167 |
| 0.4027 | 1.23 | 2100 | 0.5703 | 0.8266 |
| 0.5605 | 1.29 | 2200 | 0.5217 | 0.8372 |
| 0.2723 | 1.35 | 2300 | 0.4805 | 0.8565 |
| 0.401 | 1.41 | 2400 | 0.4811 | 0.8490 |
| 0.3419 | 1.47 | 2500 | 0.4619 | 0.8608 |
| 0.301 | 1.52 | 2600 | 0.4318 | 0.8712 |
| 0.2872 | 1.58 | 2700 | 0.4698 | 0.8573 |
| 0.2451 | 1.64 | 2800 | 0.4210 | 0.8729 |
| 0.2211 | 1.7 | 2900 | 0.3645 | 0.8851 |
| 0.3145 | 1.76 | 3000 | 0.4139 | 0.8715 |
| 0.2001 | 1.82 | 3100 | 0.3605 | 0.8864 |
| 0.3095 | 1.88 | 3200 | 0.4274 | 0.8675 |
| 0.1915 | 1.93 | 3300 | 0.2910 | 0.9101 |
| 0.2465 | 1.99 | 3400 | 0.2726 | 0.9103 |
| 0.1218 | 2.05 | 3500 | 0.2742 | 0.9129 |
| 0.0752 | 2.11 | 3600 | 0.2572 | 0.9183 |
| 0.1067 | 2.17 | 3700 | 0.2584 | 0.9203 |
| 0.0838 | 2.23 | 3800 | 0.2458 | 0.9212 |
| 0.1106 | 2.29 | 3900 | 0.2412 | 0.9237 |
| 0.092 | 2.34 | 4000 | 0.2232 | 0.9277 |
| 0.1056 | 2.4 | 4100 | 0.2817 | 0.9077 |
| 0.0696 | 2.46 | 4200 | 0.2334 | 0.9285 |
| 0.0444 | 2.52 | 4300 | 0.2142 | 0.9363 |
| 0.1046 | 2.58 | 4400 | 0.2036 | 0.9352 |
| 0.066 | 2.64 | 4500 | 0.2115 | 0.9365 |
| 0.0649 | 2.7 | 4600 | 0.1730 | 0.9448 |
| 0.0513 | 2.75 | 4700 | 0.2148 | 0.9339 |
| 0.0917 | 2.81 | 4800 | 0.1810 | 0.9438 |
| 0.0879 | 2.87 | 4900 | 0.1971 | 0.9388 |
| 0.1052 | 2.93 | 5000 | 0.1602 | 0.9508 |
| 0.0362 | 2.99 | 5100 | 0.1475 | 0.9556 |
| 0.041 | 3.05 | 5200 | 0.1328 | 0.9585 |
| 0.0156 | 3.11 | 5300 | 0.1389 | 0.9571 |
| 0.0047 | 3.17 | 5400 | 0.1224 | 0.9638 |
| 0.0174 | 3.22 | 5500 | 0.1193 | 0.9651 |
| 0.0087 | 3.28 | 5600 | 0.1276 | 0.9622 |
| 0.0084 | 3.34 | 5700 | 0.1134 | 0.9662 |
| 0.0141 | 3.4 | 5800 | 0.1239 | 0.9631 |
| 0.0291 | 3.46 | 5900 | 0.1199 | 0.9645 |
| 0.0049 | 3.52 | 6000 | 0.1103 | 0.9679 |
| 0.0055 | 3.58 | 6100 | 0.1120 | 0.9662 |
| 0.0061 | 3.63 | 6200 | 0.1071 | 0.9668 |
| 0.0054 | 3.69 | 6300 | 0.1032 | 0.9697 |
| 0.0041 | 3.75 | 6400 | 0.0961 | 0.9711 |
| 0.0018 | 3.81 | 6500 | 0.0930 | 0.9718 |
| 0.0032 | 3.87 | 6600 | 0.0918 | 0.9730 |
| 0.0048 | 3.93 | 6700 | 0.0906 | 0.9732 |
| 0.002 | 3.99 | 6800 | 0.0902 | 0.9731 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Chris1/a2c-SpaceInvadersNoFrameskip-v4
|
Chris1
| 2022-07-23T22:23:15Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-23T22:22:54Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 532.50 +/- 105.79
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **A2C** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **A2C** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo a2c --env SpaceInvadersNoFrameskip-v4 -orga Chris1 -f logs/
python enjoy.py --algo a2c --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo a2c --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo a2c --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Chris1
```
## Hyperparameters
```python
OrderedDict([('ent_coef', 0.01),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('frame_stack', 4),
('n_envs', 16),
('n_timesteps', 10000000.0),
('policy', 'CnnPolicy'),
('policy_kwargs',
'dict(optimizer_class=RMSpropTFLike, '
'optimizer_kwargs=dict(eps=1e-5))'),
('vf_coef', 0.25),
('normalize', False)])
```
|
Chris1/qrdqn-SpaceInvadersNoFrameskip-v4
|
Chris1
| 2022-07-23T22:20:05Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-23T22:19:33Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: QRDQN
results:
- metrics:
- type: mean_reward
value: 1647.00 +/- 742.05
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -orga Chris1 -f logs/
python enjoy.py --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Chris1
```
## Hyperparameters
```python
OrderedDict([('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_fraction', 0.025),
('frame_stack', 4),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('normalize', False)])
```
|
huggingtweets/bicyclingmag-bike24net-planetcyclery
|
huggingtweets
| 2022-07-23T21:47:24Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-23T21:38:17Z |
---
language: en
thumbnail: http://www.huggingtweets.com/bicyclingmag-bike24net-planetcyclery/1658612826681/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/596705203358801920/mQ6ZGz9R_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/781477479332577280/OOud15hY_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/837440117505585152/kquV327z_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Bicycling Magazine & BIKE24 & Planet Cyclery</div>
<div style="text-align: center; font-size: 14px;">@bicyclingmag-bike24net-planetcyclery</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Bicycling Magazine & BIKE24 & Planet Cyclery.
| Data | Bicycling Magazine | BIKE24 | Planet Cyclery |
| --- | --- | --- | --- |
| Tweets downloaded | 3250 | 3200 | 1636 |
| Retweets | 3 | 42 | 48 |
| Short tweets | 31 | 231 | 22 |
| Tweets kept | 3216 | 2927 | 1566 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/dpmz7fyw/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @bicyclingmag-bike24net-planetcyclery's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/15ynynm2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/15ynynm2/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/bicyclingmag-bike24net-planetcyclery')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Kuro96/dqn-SpaceInvadersNoFrameskip-v4
|
Kuro96
| 2022-07-23T21:21:08Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-23T21:20:36Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 547.00 +/- 194.62
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Kuro96 -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Kuro96
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
huggingtweets/vgdunkey-vgdunkeybot
|
huggingtweets
| 2022-07-23T21:18:37Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-23T08:20:40Z |
---
language: en
thumbnail: http://www.huggingtweets.com/vgdunkey-vgdunkeybot/1658611112335/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/676614171849453568/AZd1Bh-s_400x400.png')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/727879199931944961/vkkeC6d2_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">dunkey & dunkey bot</div>
<div style="text-align: center; font-size: 14px;">@vgdunkey-vgdunkeybot</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from dunkey & dunkey bot.
| Data | dunkey | dunkey bot |
| --- | --- | --- |
| Tweets downloaded | 1282 | 3200 |
| Retweets | 147 | 0 |
| Short tweets | 327 | 526 |
| Tweets kept | 808 | 2674 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/208r9p27/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @vgdunkey-vgdunkeybot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/m3it0jfs) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/m3it0jfs/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/vgdunkey-vgdunkeybot')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
osanseviero/hf_hub_example-023f3150-3eae-45a4-bd3c-7a95639e10e0
|
osanseviero
| 2022-07-23T21:11:54Z | 0 | 0 |
sklearn
|
[
"sklearn",
"region:us"
] | null | 2022-07-23T21:11:49Z |
---
library_name: sklearn
---
# Model description
This is a HistGradientBoostingClassifier model trained on breast cancer dataset. It's trained with Halving Grid Search Cross Validation, with parameter grids on max_leaf_nodes and max_depth.
## Intended uses & limitations
This model is not ready to be used in production.
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameters | Value |
| :-- | :-- |
| aggressive_elimination | False |
| cv | 5 |
| error_score | nan |
| estimator__categorical_features | None |
| estimator__early_stopping | auto |
| estimator__l2_regularization | 0.0 |
| estimator__learning_rate | 0.1 |
| estimator__loss | log_loss |
| estimator__max_bins | 255 |
| estimator__max_depth | None |
| estimator__max_iter | 100 |
| estimator__max_leaf_nodes | 31 |
| estimator__min_samples_leaf | 20 |
| estimator__monotonic_cst | None |
| estimator__n_iter_no_change | 10 |
| estimator__random_state | None |
| estimator__scoring | loss |
| estimator__tol | 1e-07 |
| estimator__validation_fraction | 0.1 |
| estimator__verbose | 0 |
| estimator__warm_start | False |
| estimator | HistGradientBoostingClassifier() |
| factor | 3 |
| max_resources | auto |
| min_resources | exhaust |
| n_jobs | -1 |
| param_grid | {'max_leaf_nodes': [5, 10, 15], 'max_depth': [2, 5, 10]} |
| random_state | 42 |
| refit | True |
| resource | n_samples |
| return_train_score | True |
| scoring | None |
| verbose | 0 |
</details>
### Model Plot
The model plot is below.
<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">HalvingGridSearchCV</label><div class="sk-toggleable__content"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: HistGradientBoostingClassifier</label><div class="sk-toggleable__content"><pre>HistGradientBoostingClassifier()</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">HistGradientBoostingClassifier</label><div class="sk-toggleable__content"><pre>HistGradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div>
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```
import pickle
with open(dtc_pkl_filename, 'rb') as file:
clf = pickle.load(file)
```
</details>
# Model Card Authors
This model card is written by following authors:
skops_user
# Model Card Contact
You can contact the model card authors through following channels:
[More Information Needed]
# Citation
Below you can find information related to citation.
**BibTeX:**
```
[More Information Needed]
```
confusion_matrix
|
huggingtweets/vgdunkey-vgdunkeybot-videobotdunkey
|
huggingtweets
| 2022-07-23T21:11:28Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-23T21:10:35Z |
---
language: en
thumbnail: http://www.huggingtweets.com/vgdunkey-vgdunkeybot-videobotdunkey/1658610683659/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/676614171849453568/AZd1Bh-s_400x400.png')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/727879199931944961/vkkeC6d2_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/889145771760680960/F3g-pbn2_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">dunkey & dunkey bot & dunkey bot</div>
<div style="text-align: center; font-size: 14px;">@vgdunkey-vgdunkeybot-videobotdunkey</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from dunkey & dunkey bot & dunkey bot.
| Data | dunkey | dunkey bot | dunkey bot |
| --- | --- | --- | --- |
| Tweets downloaded | 1282 | 3200 | 911 |
| Retweets | 147 | 0 | 1 |
| Short tweets | 327 | 526 | 33 |
| Tweets kept | 808 | 2674 | 877 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1gs4ik1d/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @vgdunkey-vgdunkeybot-videobotdunkey's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/qqqwy9dp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/qqqwy9dp/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/vgdunkey-vgdunkeybot-videobotdunkey')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
osanseviero/hf_hub_example-821defb0-1482-4d27-884d-4359bfad704f
|
osanseviero
| 2022-07-23T21:08:24Z | 0 | 0 |
sklearn
|
[
"sklearn",
"region:us"
] | null | 2022-07-23T21:08:18Z |
---
library_name: sklearn
---
# Model description
This is a HistGradientBoostingClassifier model trained on breast cancer dataset. It's trained with Halving Grid Search Cross Validation, with parameter grids on max_leaf_nodes and max_depth.
## Intended uses & limitations
This model is not ready to be used in production.
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameters | Value |
| :-- | :-- |
| aggressive_elimination | False |
| cv | 5 |
| error_score | nan |
| estimator__categorical_features | None |
| estimator__early_stopping | auto |
| estimator__l2_regularization | 0.0 |
| estimator__learning_rate | 0.1 |
| estimator__loss | log_loss |
| estimator__max_bins | 255 |
| estimator__max_depth | None |
| estimator__max_iter | 100 |
| estimator__max_leaf_nodes | 31 |
| estimator__min_samples_leaf | 20 |
| estimator__monotonic_cst | None |
| estimator__n_iter_no_change | 10 |
| estimator__random_state | None |
| estimator__scoring | loss |
| estimator__tol | 1e-07 |
| estimator__validation_fraction | 0.1 |
| estimator__verbose | 0 |
| estimator__warm_start | False |
| estimator | HistGradientBoostingClassifier() |
| factor | 3 |
| max_resources | auto |
| min_resources | exhaust |
| n_jobs | -1 |
| param_grid | {'max_leaf_nodes': [5, 10, 15], 'max_depth': [2, 5, 10]} |
| random_state | 42 |
| refit | True |
| resource | n_samples |
| return_train_score | True |
| scoring | None |
| verbose | 0 |
</details>
### Model Plot
The model plot is below.
<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">HalvingGridSearchCV</label><div class="sk-toggleable__content"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: HistGradientBoostingClassifier</label><div class="sk-toggleable__content"><pre>HistGradientBoostingClassifier()</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">HistGradientBoostingClassifier</label><div class="sk-toggleable__content"><pre>HistGradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div>
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```
import pickle
with open(dtc_pkl_filename, 'rb') as file:
clf = pickle.load(file)
```
</details>
# Model Card Authors
This model card is written by following authors:
skops_user
# Model Card Contact
You can contact the model card authors through following channels:
[More Information Needed]
# Citation
Below you can find information related to citation.
**BibTeX:**
```
[More Information Needed]
```
|
osanseviero/hf_hub_example-f7d1d7e5-f207-4eef-99bb-57408d604e2b
|
osanseviero
| 2022-07-23T21:04:42Z | 0 | 0 |
sklearn
|
[
"sklearn",
"region:us"
] | null | 2022-07-23T21:04:37Z |
---
library_name: sklearn
---
# Model description
[More Information Needed]
## Intended uses & limitations
[More Information Needed]
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameters | Value |
| :-- | :-- |
| aggressive_elimination | False |
| cv | 5 |
| error_score | nan |
| estimator__categorical_features | None |
| estimator__early_stopping | auto |
| estimator__l2_regularization | 0.0 |
| estimator__learning_rate | 0.1 |
| estimator__loss | log_loss |
| estimator__max_bins | 255 |
| estimator__max_depth | None |
| estimator__max_iter | 100 |
| estimator__max_leaf_nodes | 31 |
| estimator__min_samples_leaf | 20 |
| estimator__monotonic_cst | None |
| estimator__n_iter_no_change | 10 |
| estimator__random_state | None |
| estimator__scoring | loss |
| estimator__tol | 1e-07 |
| estimator__validation_fraction | 0.1 |
| estimator__verbose | 0 |
| estimator__warm_start | False |
| estimator | HistGradientBoostingClassifier() |
| factor | 3 |
| max_resources | auto |
| min_resources | exhaust |
| n_jobs | -1 |
| param_grid | {'max_leaf_nodes': [5, 10, 15], 'max_depth': [2, 5, 10]} |
| random_state | 42 |
| refit | True |
| resource | n_samples |
| return_train_score | True |
| scoring | None |
| verbose | 0 |
</details>
### Model Plot
The model plot is below.
<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">HalvingGridSearchCV</label><div class="sk-toggleable__content"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: HistGradientBoostingClassifier</label><div class="sk-toggleable__content"><pre>HistGradientBoostingClassifier()</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">HistGradientBoostingClassifier</label><div class="sk-toggleable__content"><pre>HistGradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div>
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```
[More Information Needed]
```
</details>
# Model Card Authors
This model card is written by following authors:
[More Information Needed]
# Model Card Contact
You can contact the model card authors through following channels:
[More Information Needed]
# Citation
Below you can find information related to citation.
**BibTeX:**
```
[More Information Needed]
```
|
jcashmoney123/autotrain-amz-1171143428
|
jcashmoney123
| 2022-07-23T18:31:20Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain",
"unk",
"dataset:jcashmoney123/autotrain-data-amz",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-23T18:27:51Z |
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- jcashmoney123/autotrain-data-amz
co2_eq_emissions: 5.4331208624177245
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1171143428
- CO2 Emissions (in grams): 5.4331208624177245
## Validation Metrics
- Loss: 2.5859596729278564
- Rouge1: 19.3601
- Rouge2: 4.6055
- RougeL: 17.4309
- RougeLsum: 17.4621
- Gen Len: 15.2938
## 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/jcashmoney123/autotrain-amz-1171143428
```
|
oMateos2020/t5-small_adafactor
|
oMateos2020
| 2022-07-23T18:20:11Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-20T11:32:51Z |
---
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small_adafactor
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 32.8631
---
<!-- 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. -->
# t5-small_adafactor
This model is a fine-tuned version of [oMateos2020/t5-small_adafactor](https://huggingface.co/oMateos2020/t5-small_adafactor) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1167
- Rouge1: 32.8631
- Rouge2: 11.658
- Rougel: 26.6192
- Rougelsum: 26.6224
- Gen Len: 18.7663
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.1315 | 0.02 | 200 | 2.1865 | 31.9486 | 10.9605 | 25.7418 | 25.7408 | 18.8466 |
| 2.1297 | 0.05 | 400 | 2.1965 | 31.9598 | 10.9463 | 25.784 | 25.7867 | 18.8525 |
| 2.1284 | 0.07 | 600 | 2.1981 | 32.231 | 11.1003 | 26.0155 | 26.0226 | 18.8466 |
| 2.1315 | 0.09 | 800 | 2.1873 | 31.9161 | 10.8642 | 25.7166 | 25.7273 | 18.8227 |
| 2.1212 | 0.12 | 1000 | 2.1892 | 32.4646 | 11.1852 | 26.2451 | 26.2439 | 18.8259 |
| 2.1028 | 0.14 | 1200 | 2.1978 | 32.2886 | 11.1346 | 26.0795 | 26.0827 | 18.7685 |
| 2.1221 | 0.16 | 1400 | 2.1936 | 32.2901 | 11.0821 | 25.9983 | 26.0024 | 18.7798 |
| 2.1168 | 0.19 | 1600 | 2.1922 | 32.1655 | 11.1451 | 25.986 | 25.9893 | 18.8232 |
| 2.1166 | 0.21 | 1800 | 2.1836 | 32.2611 | 11.174 | 26.0594 | 26.0688 | 18.7633 |
| 2.1053 | 0.24 | 2000 | 2.1929 | 32.3321 | 11.213 | 26.1859 | 26.1903 | 18.7758 |
| 2.1126 | 0.26 | 2200 | 2.1811 | 32.2078 | 11.1792 | 26.0776 | 26.0817 | 18.8197 |
| 2.1038 | 0.28 | 2400 | 2.1836 | 32.2799 | 11.2511 | 26.1191 | 26.1251 | 18.7884 |
| 2.1181 | 0.31 | 2600 | 2.1805 | 32.1197 | 11.1586 | 26.0441 | 26.0441 | 18.8045 |
| 2.1217 | 0.33 | 2800 | 2.1806 | 32.3051 | 11.2638 | 26.1319 | 26.1386 | 18.7886 |
| 2.116 | 0.35 | 3000 | 2.1741 | 32.2799 | 11.1887 | 26.1224 | 26.1363 | 18.7769 |
| 2.1118 | 0.38 | 3200 | 2.1767 | 32.387 | 11.2053 | 26.077 | 26.0845 | 18.8407 |
| 2.1164 | 0.4 | 3400 | 2.1743 | 32.5008 | 11.4021 | 26.3291 | 26.3297 | 18.7731 |
| 2.1068 | 0.42 | 3600 | 2.1673 | 32.2347 | 11.1676 | 26.0657 | 26.0662 | 18.817 |
| 2.1276 | 0.45 | 3800 | 2.1664 | 32.2434 | 11.2862 | 26.094 | 26.0994 | 18.7713 |
| 2.1313 | 0.47 | 4000 | 2.1636 | 32.694 | 11.3724 | 26.4071 | 26.4008 | 18.7709 |
| 2.1229 | 0.49 | 4200 | 2.1633 | 32.456 | 11.4057 | 26.2733 | 26.2689 | 18.7586 |
| 2.129 | 0.52 | 4400 | 2.1641 | 32.309 | 11.2133 | 26.1062 | 26.1121 | 18.7729 |
| 2.1425 | 0.54 | 4600 | 2.1577 | 32.5879 | 11.4001 | 26.3045 | 26.3078 | 18.8104 |
| 2.1536 | 0.56 | 4800 | 2.1507 | 32.5152 | 11.4035 | 26.3054 | 26.3116 | 18.7941 |
| 2.148 | 0.59 | 5000 | 2.1503 | 32.8088 | 11.5641 | 26.5346 | 26.5311 | 18.7602 |
| 2.1541 | 0.61 | 5200 | 2.1491 | 32.8185 | 11.5816 | 26.5261 | 26.527 | 18.7654 |
| 2.155 | 0.64 | 5400 | 2.1466 | 32.7229 | 11.5339 | 26.4363 | 26.442 | 18.8404 |
| 2.1579 | 0.66 | 5600 | 2.1435 | 32.884 | 11.6042 | 26.5862 | 26.5891 | 18.7713 |
| 2.1601 | 0.68 | 5800 | 2.1393 | 32.8027 | 11.5328 | 26.4521 | 26.4567 | 18.7904 |
| 2.1765 | 0.71 | 6000 | 2.1393 | 32.8059 | 11.5751 | 26.5499 | 26.5551 | 18.7768 |
| 2.2176 | 0.73 | 6200 | 2.1345 | 33.0734 | 11.8056 | 26.7546 | 26.7607 | 18.7756 |
| 2.2126 | 0.75 | 6400 | 2.1328 | 32.7478 | 11.5925 | 26.5333 | 26.5359 | 18.7819 |
| 2.1916 | 0.78 | 6600 | 2.1298 | 32.658 | 11.491 | 26.379 | 26.3869 | 18.8101 |
| 2.2162 | 0.8 | 6800 | 2.1297 | 32.7843 | 11.5629 | 26.4736 | 26.4728 | 18.8187 |
| 2.2358 | 0.82 | 7000 | 2.1287 | 32.9181 | 11.6378 | 26.5966 | 26.5987 | 18.8039 |
| 2.2371 | 0.85 | 7200 | 2.1265 | 32.8413 | 11.674 | 26.5905 | 26.5831 | 18.7962 |
| 2.256 | 0.87 | 7400 | 2.1245 | 32.7412 | 11.5627 | 26.4976 | 26.503 | 18.7728 |
| 2.2566 | 0.89 | 7600 | 2.1220 | 32.8165 | 11.6069 | 26.5301 | 26.5295 | 18.7871 |
| 2.2954 | 0.92 | 7800 | 2.1197 | 32.7399 | 11.5417 | 26.4914 | 26.4938 | 18.7752 |
| 2.2766 | 0.94 | 8000 | 2.1187 | 32.853 | 11.6411 | 26.5909 | 26.5938 | 18.7852 |
| 2.3273 | 0.96 | 8200 | 2.1169 | 32.9376 | 11.709 | 26.6665 | 26.6672 | 18.7734 |
| 2.3182 | 0.99 | 8400 | 2.1167 | 32.8631 | 11.658 | 26.6192 | 26.6224 | 18.7663 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Siyong/MC_RN
|
Siyong
| 2022-07-23T16:22:03Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-23T10:22:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Millad_Customer_RN
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. -->
# Millad_Customer_RN
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.5635
- Wer: 0.8113
- Cer: 0.4817
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 4000
- num_epochs: 600
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:-----:|:---------------:|:------:|:------:|
| 1.9257 | 13.33 | 2000 | 2.0606 | 0.9767 | 0.5500 |
| 1.4828 | 26.67 | 4000 | 2.1161 | 0.9019 | 0.4932 |
| 1.2582 | 40.0 | 6000 | 2.0589 | 0.8504 | 0.4942 |
| 0.9804 | 53.33 | 8000 | 2.4633 | 0.8745 | 0.4763 |
| 0.7862 | 66.67 | 10000 | 2.4794 | 0.8861 | 0.4944 |
| 0.6492 | 80.0 | 12000 | 2.8693 | 0.8554 | 0.4928 |
| 0.5375 | 93.33 | 14000 | 2.6125 | 0.8296 | 0.4802 |
| 0.4462 | 106.67 | 16000 | 2.7591 | 0.8770 | 0.4974 |
| 0.3873 | 120.0 | 18000 | 3.0325 | 0.8379 | 0.4800 |
| 0.3445 | 133.33 | 20000 | 2.9965 | 0.8761 | 0.4986 |
| 0.3087 | 146.67 | 22000 | 3.3437 | 0.8221 | 0.4923 |
| 0.2755 | 160.0 | 24000 | 3.3022 | 0.8803 | 0.5211 |
| 0.2467 | 173.33 | 26000 | 3.2348 | 0.8479 | 0.4933 |
| 0.2281 | 186.67 | 28000 | 3.8010 | 0.8695 | 0.5081 |
| 0.2119 | 200.0 | 30000 | 3.0446 | 0.8545 | 0.4902 |
| 0.194 | 213.33 | 32000 | 3.0873 | 0.8454 | 0.4840 |
| 0.1677 | 226.67 | 34000 | 3.6184 | 0.8645 | 0.5019 |
| 0.1642 | 240.0 | 36000 | 3.2480 | 0.8412 | 0.4903 |
| 0.1656 | 253.33 | 38000 | 3.4379 | 0.8362 | 0.4816 |
| 0.1371 | 266.67 | 40000 | 3.5117 | 0.8479 | 0.5040 |
| 0.1301 | 280.0 | 42000 | 3.4360 | 0.8404 | 0.4870 |
| 0.128 | 293.33 | 44000 | 3.6589 | 0.8537 | 0.4977 |
| 0.1152 | 306.67 | 46000 | 4.2359 | 0.8545 | 0.5051 |
| 0.1119 | 320.0 | 48000 | 3.5818 | 0.7980 | 0.4882 |
| 0.1026 | 333.33 | 50000 | 3.7618 | 0.8013 | 0.4865 |
| 0.0945 | 346.67 | 52000 | 4.2197 | 0.8404 | 0.5028 |
| 0.0962 | 360.0 | 54000 | 3.9231 | 0.8653 | 0.5030 |
| 0.088 | 373.33 | 56000 | 3.8400 | 0.8354 | 0.4914 |
| 0.0743 | 386.67 | 58000 | 3.4924 | 0.8088 | 0.4824 |
| 0.0811 | 400.0 | 60000 | 3.8370 | 0.8396 | 0.4861 |
| 0.0696 | 413.33 | 62000 | 4.2808 | 0.8412 | 0.5065 |
| 0.0692 | 426.67 | 64000 | 4.0161 | 0.8088 | 0.4744 |
| 0.0622 | 440.0 | 66000 | 3.9080 | 0.8163 | 0.4910 |
| 0.0591 | 453.33 | 68000 | 3.9838 | 0.8113 | 0.4823 |
| 0.0527 | 466.67 | 70000 | 3.8067 | 0.8329 | 0.4914 |
| 0.056 | 480.0 | 72000 | 4.1415 | 0.8096 | 0.4782 |
| 0.0535 | 493.33 | 74000 | 4.3350 | 0.8229 | 0.4828 |
| 0.0531 | 506.67 | 76000 | 3.9808 | 0.8071 | 0.4807 |
| 0.0451 | 520.0 | 78000 | 4.0301 | 0.7988 | 0.4816 |
| 0.044 | 533.33 | 80000 | 4.4680 | 0.8371 | 0.4921 |
| 0.0389 | 546.67 | 82000 | 4.1380 | 0.8121 | 0.4819 |
| 0.0392 | 560.0 | 84000 | 4.3910 | 0.7930 | 0.4763 |
| 0.0389 | 573.33 | 86000 | 4.5086 | 0.8055 | 0.4802 |
| 0.0355 | 586.67 | 88000 | 4.6259 | 0.8113 | 0.4821 |
| 0.0307 | 600.0 | 90000 | 4.5635 | 0.8113 | 0.4817 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
srini98/a2c-AntBulletEnv-v0
|
srini98
| 2022-07-23T15:42:47Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-23T15:41:36Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 1690.76 +/- 243.94
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
---
# **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
...
```
|
steven123/Check_Gum_Teeth
|
steven123
| 2022-07-23T14:50:43Z | 51 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-23T14:50:33Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: Check_Gum_Teeth
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# Check_Gum_Teeth
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Bad_Gum
#### Good_Gum
|
th1s1s1t/ppo-LunarLander-v2
|
th1s1s1t
| 2022-07-23T14:41:24Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-23T14:41:01Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 290.28 +/- 26.36
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
...
```
|
jonatasgrosman/exp_w2v2r_de_vp-100k_accent_germany-5_austria-5_s3
|
jonatasgrosman
| 2022-07-23T14:28:41Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-23T14:22:07Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_accent_germany-5_austria-5_s3
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Siyong/M_RN
|
Siyong
| 2022-07-23T14:00:34Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-23T10:59:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: MilladRN
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. -->
# MilladRN
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4355
- Wer: 0.4907
- Cer: 0.2802
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 4000
- num_epochs: 750
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:-----:|:---------------:|:------:|:------:|
| 3.3347 | 33.9 | 2000 | 2.2561 | 0.9888 | 0.6087 |
| 1.3337 | 67.8 | 4000 | 1.8137 | 0.6877 | 0.3407 |
| 0.6504 | 101.69 | 6000 | 2.0718 | 0.6245 | 0.3229 |
| 0.404 | 135.59 | 8000 | 2.2246 | 0.6004 | 0.3221 |
| 0.2877 | 169.49 | 10000 | 2.2624 | 0.5836 | 0.3107 |
| 0.2149 | 203.39 | 12000 | 2.3788 | 0.5279 | 0.2802 |
| 0.1693 | 237.29 | 14000 | 1.8928 | 0.5502 | 0.2937 |
| 0.1383 | 271.19 | 16000 | 2.7520 | 0.5725 | 0.3103 |
| 0.1169 | 305.08 | 18000 | 2.2552 | 0.5446 | 0.2968 |
| 0.1011 | 338.98 | 20000 | 2.6794 | 0.5725 | 0.3119 |
| 0.0996 | 372.88 | 22000 | 2.4704 | 0.5595 | 0.3142 |
| 0.0665 | 406.78 | 24000 | 2.9073 | 0.5836 | 0.3194 |
| 0.0538 | 440.68 | 26000 | 3.1357 | 0.5632 | 0.3213 |
| 0.0538 | 474.58 | 28000 | 2.5639 | 0.5613 | 0.3091 |
| 0.0493 | 508.47 | 30000 | 3.3801 | 0.5613 | 0.3119 |
| 0.0451 | 542.37 | 32000 | 3.5469 | 0.5428 | 0.3158 |
| 0.0307 | 576.27 | 34000 | 4.2243 | 0.5390 | 0.3126 |
| 0.0301 | 610.17 | 36000 | 3.6666 | 0.5297 | 0.2929 |
| 0.0269 | 644.07 | 38000 | 3.2164 | 0.5 | 0.2838 |
| 0.0182 | 677.97 | 40000 | 3.0557 | 0.4963 | 0.2779 |
| 0.0191 | 711.86 | 42000 | 3.5190 | 0.5130 | 0.2921 |
| 0.0133 | 745.76 | 44000 | 3.4355 | 0.4907 | 0.2802 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
Someman/pegasus-samsum
|
Someman
| 2022-07-23T13:20:32Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-23T07:30:10Z |
---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4884
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6902 | 0.54 | 500 | 1.4884 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Siyong/M
|
Siyong
| 2022-07-23T10:51:07Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-23T07:38:42Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Millad
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. -->
# Millad
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2265
- Wer: 0.5465
- Cer: 0.3162
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 4000
- num_epochs: 750
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:-----:|:---------------:|:------:|:------:|
| 3.2911 | 33.9 | 2000 | 2.2097 | 0.9963 | 0.6047 |
| 1.3419 | 67.8 | 4000 | 1.9042 | 0.7007 | 0.3565 |
| 0.6542 | 101.69 | 6000 | 1.7195 | 0.5985 | 0.3194 |
| 0.373 | 135.59 | 8000 | 2.2219 | 0.6078 | 0.3241 |
| 0.2805 | 169.49 | 10000 | 2.3114 | 0.6320 | 0.3304 |
| 0.2014 | 203.39 | 12000 | 2.6898 | 0.6338 | 0.3597 |
| 0.1611 | 237.29 | 14000 | 2.7808 | 0.6041 | 0.3379 |
| 0.1265 | 271.19 | 16000 | 2.8304 | 0.5632 | 0.3289 |
| 0.1082 | 305.08 | 18000 | 2.8373 | 0.5874 | 0.3344 |
| 0.103 | 338.98 | 20000 | 2.8580 | 0.5743 | 0.3292 |
| 0.0854 | 372.88 | 22000 | 2.5413 | 0.5539 | 0.3186 |
| 0.0675 | 406.78 | 24000 | 2.5523 | 0.5502 | 0.3229 |
| 0.0531 | 440.68 | 26000 | 2.9369 | 0.5483 | 0.3142 |
| 0.0504 | 474.58 | 28000 | 3.1416 | 0.5595 | 0.3225 |
| 0.0388 | 508.47 | 30000 | 2.5655 | 0.5390 | 0.3111 |
| 0.0396 | 542.37 | 32000 | 3.1923 | 0.5558 | 0.3178 |
| 0.0274 | 576.27 | 34000 | 2.9235 | 0.5520 | 0.3257 |
| 0.0361 | 610.17 | 36000 | 3.3828 | 0.5762 | 0.3312 |
| 0.02 | 644.07 | 38000 | 3.3822 | 0.5874 | 0.3466 |
| 0.0176 | 677.97 | 40000 | 3.1191 | 0.5539 | 0.3209 |
| 0.0181 | 711.86 | 42000 | 3.2022 | 0.5576 | 0.3237 |
| 0.0124 | 745.76 | 44000 | 3.2265 | 0.5465 | 0.3162 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
sudo-s/robot22
|
sudo-s
| 2022-07-23T10:42:11Z | 57 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-23T10:34:24Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: robot22
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. -->
# robot22
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem6 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5674
- Accuracy: 0.5077
## 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: 16
- eval_batch_size: 8
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.9154 | 0.23 | 100 | 3.8417 | 0.2213 |
| 3.1764 | 0.47 | 200 | 3.2243 | 0.3201 |
| 2.8186 | 0.7 | 300 | 2.7973 | 0.4284 |
| 2.632 | 0.93 | 400 | 2.5674 | 0.5077 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
valurank/headline_similarities
|
valurank
| 2022-07-23T10:21:47Z | 4 | 2 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-07-23T10:21:35Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
license: apache-2.0
datasets:
- s2orc
- flax-sentence-embeddings/stackexchange_xml
- MS Marco
- gooaq
- yahoo_answers_topics
- code_search_net
- search_qa
- eli5
- snli
- multi_nli
- wikihow
- natural_questions
- trivia_qa
- embedding-data/sentence-compression
- embedding-data/flickr30k-captions
- embedding-data/altlex
- embedding-data/simple-wiki
- embedding-data/QQP
- embedding-data/SPECTER
- embedding-data/PAQ_pairs
- embedding-data/WikiAnswers
---
# all-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-mpnet-base-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-mpnet-base-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# Normalize embeddings
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-mpnet-base-v2)
------
## Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We used the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model and fine-tuned in on a
1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developped this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developped this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
## Intended uses
Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
By default, input text longer than 384 word pieces is truncated.
## Training procedure
### Pre-training
We use the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. Please refer to the model card for more detailed information about the pre-training procedure.
### Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
#### Hyper parameters
We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
#### Training data
We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
| Dataset | Paper | Number of training tuples |
|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 |
| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| **Total** | | **1,170,060,424** |
|
kmkarakaya/turkishReviews-ds-mini
|
kmkarakaya
| 2022-07-23T09:06:24Z | 6 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-07T13:29:04Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: turkishReviews-ds-mini
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. -->
# turkishReviews-ds-mini
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: 9.1630
- Validation Loss: 9.2431
- 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': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -896, '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 |
|:----------:|:---------------:|:-----:|
| 10.2672 | 9.9647 | 0 |
| 9.6445 | 9.6190 | 1 |
| 9.1630 | 9.2431 | 2 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Chris1/q-FrozenLake-v1-8x8-no_slippery
|
Chris1
| 2022-07-23T08:48:05Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-23T08:47:59Z |
---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-no_slippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Chris1/q-FrozenLake-v1-8x8-no_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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
SummerChiam/pond
|
SummerChiam
| 2022-07-23T07:47:49Z | 51 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-22T18:26:03Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: pond
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9909297227859497
---
# pond
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Algae0
#### Boiling0
#### BoilingNight0
#### Normal0
#### NormalCement0
#### NormalNight0
#### NormalRain0
|
Yuchen/muril-large-cased-hita-qa
|
Yuchen
| 2022-07-23T07:01:06Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
# Question Answering model for Hindi and Tamil
This model is part of the ensemble that ranked 4/943 in the [Hindi and Tamil Question Answering](https://www.kaggle.com/c/chaii-hindi-and-tamil-question-answering) competition held by Google Research India at Kaggle.
```
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("Yuchen/muril-large-cased-hita-qa")
model = AutoModelForQuestionAnswering.from_pretrained("Yuchen/muril-large-cased-hita-qa")
```
|
SushantGautam/SoccerSum-NarSum
|
SushantGautam
| 2022-07-23T06:45:09Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"led",
"text2text-generation",
"generated_from_trainer",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-13T18:51:19Z |
---
language:
- en
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: SportsSum
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. -->
# SportsSum
This model is a fine-tuned version of [allenai/led-base-16384-ms2](https://huggingface.co/allenai/led-base-16384-ms2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2759
- Rouge1: 52.3608
- Rouge2: 27.6526
- Rougel: 31.8509
- Rougelsum: 49.9086
- Gen Len: 248.1199
## 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: 36
- eval_batch_size: 36
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
marice/ppo-LunarLander-v2
|
marice
| 2022-07-23T06:29:26Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-23T06:28:56Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 194.16 +/- 29.74
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
...
```
|
jags/floraldiffusion
|
jags
| 2022-07-23T05:47:56Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-07-19T19:43:01Z |
---
license: mit
---
Floral Diffusion V1
Floral diffusion is a trained model set of 10 K floral sets of 512 kb size images that have been trained on 256 x 256 diffusion model.
custom model settings
model_config.update({
'attention_resolutions': '16',
'class_cond': False,
'diffusion_steps': 1000,
'rescale_timesteps': True,
'timestep_respacing': 'ddim100',
'image_size': 256,
'learn_sigma': True,
'noise_schedule': 'linear',
'num_channels': 128,
'num_head_channels': 64,
'num_res_blocks': 2,
'resblock_updown': True,
'use_checkpoint': use_checkpoint,
'use_fp16': True,
'use_scale_shift_norm': False,
}
FloralDiffusion is a custom diffusion model trained by @jags111.
It can be used to create wonderful floral styled images.
To use it you can use FloralDiffusion as a selection in the DD version.
If you create a fun image with this model, please show your result and <a href= "https://twitter.com/jags111"> [@jags111] </a> #floraldiffusion
Join us in Patreon and extend support <a href="https://www.patreon.com/jags111"> [patreon]</a>
|
bigmorning/distilbert_final_0005
|
bigmorning
| 2022-07-23T05:09:49Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-23T05:03:57Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: distilbert_final_0005
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. -->
# distilbert_final_0005
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9295
- Validation Loss: 0.9157
- Epoch: 4
## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.9319 | 0.9178 | 0 |
| 0.9310 | 0.9167 | 1 |
| 0.9301 | 0.9170 | 2 |
| 0.9300 | 0.9161 | 3 |
| 0.9295 | 0.9157 | 4 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
huggingtweets/fifteenai
|
huggingtweets
| 2022-07-23T04:16:18Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/fifteenai/1658549683215/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1513191641921765388/rToX3RpX_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">15</div>
<div style="text-align: center; font-size: 14px;">@fifteenai</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from 15.
| Data | 15 |
| --- | --- |
| Tweets downloaded | 111 |
| Retweets | 9 |
| Short tweets | 10 |
| Tweets kept | 92 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/169wgrhk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @fifteenai's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/390dyi5s) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/390dyi5s/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/fifteenai')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
shivi/shiftViT-Model
|
shivi
| 2022-07-23T02:00:17Z | 0 | 0 |
keras
|
[
"keras",
"tensorboard",
"tf-keras",
"ShiftVit",
"Image Classification",
"region:us"
] | null | 2022-07-23T01:59:31Z |
---
library_name: keras
tags:
- ShiftVit
- Image Classification
---
## 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 |
| :-- | :-- |
| name | AdamW |
| learning_rate.class_name | WarmUpCosine |
| learning_rate.config.lr_start | 1e-05 |
| learning_rate.config.lr_max | 0.001 |
| learning_rate.config.total_steps | 15625 |
| learning_rate.config.warmup_steps | 2343 |
| decay | 0.0 |
| beta_1 | 0.8999999761581421 |
| beta_2 | 0.9990000128746033 |
| epsilon | 1e-07 |
| amsgrad | False |
| weight_decay | 9.999999747378752e-05 |
| exclude_from_weight_decay | None |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
Chris1/q-FrozenLake-v1-8x8-Slippery
|
Chris1
| 2022-07-23T00:32:56Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-23T00:32:49Z |
---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-Slippery
results:
- metrics:
- type: mean_reward
value: 0.50 +/- 0.50
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8
type: FrozenLake-v1-8x8
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Chris1/q-FrozenLake-v1-8x8-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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
thegenerativegeneration/ukiyoe-diffusion-256
|
thegenerativegeneration
| 2022-07-23T00:28:47Z | 0 | 1 | null |
[
"discodiffusion",
"guideddiffusion",
"dataset:wikiart",
"region:us"
] | null | 2022-07-04T11:56:56Z |
---
tags:
- discodiffusion
- guideddiffusion
thumbnail: https://de.gravatar.com/userimage/52045156/8ab369c1d246e65bda88813ce7c4cb81.jpeg
datasets:
- wikiart
---
# Ukiyo-e Diffusion
If you make something using these models, you're welcome to mention me [@thegenerativegeneration](https://www.instagram.com/thegenerativegeneration/)
Named by dataset used. Current and best version is [models/ukiyoe-all/v1/ema_0.9999_056000.pt](models/ukiyoe-all/v1/ema_0.9999_056000.pt)
# Current Plans
* clean dataset
* remove borders
* remove some of the samples with text in them
# Models
## Ukiyo-e-all
### v1
[models/ukiyoe-all/v1/ema_0.9999_056000.pt](models/ukiyoe-all/v1/ema_0.9999_056000.pt)
Model configuration is:
```python
model_config = {
'attention_resolutions': '32, 16, 8',
'class_cond': False,
'image_size': 256,
'learn_sigma': True,
'rescale_timesteps': True,
'noise_schedule': 'linear',
'num_channels': 128,
'num_heads': 4,
'num_res_blocks': 2,
'resblock_updown': True,
'use_checkpoint': True,
'use_fp16': True,
'use_scale_shift_norm': True,
}
```
#### Tips
- Results closest to original training data are achieved by turning off the secondary model in Disco Diffusion.
- Turning secondary model on can lead to very creative results
- It is not necessary to specify Ukiyo-e as artstyle to get ukiyo-e-like images.
#### Examples
If you make something nice using these models, I would like to link your image.
##### Secondary Off
##### Secondary On
#### About
Trained from scratch on a ~170000 images corpus of [ukiyo-e.org](https://ukiyo-e.org) filtered by [colorfulness](https://pyimagesearch.com/2017/06/05/computing-image-colorfulness-with-opencv-and-python/
) >= 5.
## (Deprecated) Ukiyo-e-few
[models/ukiyoe-few/v1/ukiyoe_diffusion_256_022000.pt](models/ukiyoe-few/v1/ukiyoe_diffusion_256_022000.pt)
Finetuned on 5224 images from Wikiart (1168) and ? ().
Model configuration is
```python
model_config = {
'attention_resolutions': '16',
'class_cond': False,
'diffusion_steps': 1000,
'rescale_timesteps': True,
'timestep_respacing': 'ddim100',
'image_size': 256,
'learn_sigma': True,
'noise_schedule': 'linear',
'num_channels': 128,
'num_heads': 1,
'num_res_blocks': 2,
'use_checkpoint': True,
'use_scale_shift_norm': False
}
```
Trained using a fork of [guided-diffusion-sxela](https://github.com/thegenerativegeneration/guided-diffusion-sxela). Added random crop which did not lead to good results.
|
Chris1/q-FrozenLake-v1-8x8-noSlippery
|
Chris1
| 2022-07-23T00:20:43Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-13T13:35:39Z |
---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Chris1/q-FrozenLake-v1-8x8-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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
huggingtweets/luciengreaves-pontifex
|
huggingtweets
| 2022-07-23T00:00:09Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-22T23:57:54Z |
---
language: en
thumbnail: http://www.huggingtweets.com/luciengreaves-pontifex/1658534403996/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/666311094256971779/rhb7qkCD_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/507818066814590976/KNG-IkT9_400x400.jpeg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Lucien Greaves & Pope Francis</div>
<div style="text-align: center; font-size: 14px;">@luciengreaves-pontifex</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Lucien Greaves & Pope Francis.
| Data | Lucien Greaves | Pope Francis |
| --- | --- | --- |
| Tweets downloaded | 3197 | 3250 |
| Retweets | 536 | 0 |
| Short tweets | 379 | 103 |
| Tweets kept | 2282 | 3147 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/q0nkdf60/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @luciengreaves-pontifex's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2y98dgmx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2y98dgmx/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/luciengreaves-pontifex')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Evelyn18/roberta-base-spanish-squades-modelo1
|
Evelyn18
| 2022-07-22T23:02:37Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-22T22:55:11Z |
---
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: roberta-base-spanish-squades-modelo1
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. -->
# roberta-base-spanish-squades-modelo1
This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://huggingface.co/IIC/roberta-base-spanish-squades) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7001
## 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: 0.0003
- train_batch_size: 11
- eval_batch_size: 11
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 6 | 2.7892 |
| No log | 2.0 | 12 | 3.7037 |
| No log | 3.0 | 18 | 5.1221 |
| No log | 4.0 | 24 | 4.5988 |
| No log | 5.0 | 30 | 5.9202 |
| No log | 6.0 | 36 | 5.0345 |
| No log | 7.0 | 42 | 4.4421 |
| No log | 8.0 | 48 | 4.6969 |
| No log | 9.0 | 54 | 5.2084 |
| No log | 10.0 | 60 | 5.7001 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
tmgondal/bert-finetuned-squad
|
tmgondal
| 2022-07-22T21:13:25Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-22T18:44:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
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. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad 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: 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
vish88/roberta-large-mnli-fer-finetuned
|
vish88
| 2022-07-22T20:30:58Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-15T17:41:22Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large-mnli-fer-finetuned
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. -->
# roberta-large-mnli-fer-finetuned
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6940
- Accuracy: 0.5005
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7049 | 1.0 | 554 | 0.6895 | 0.5750 |
| 0.6981 | 2.0 | 1108 | 0.7054 | 0.5005 |
| 0.7039 | 3.0 | 1662 | 0.6936 | 0.5005 |
| 0.6976 | 4.0 | 2216 | 0.6935 | 0.4995 |
| 0.6991 | 5.0 | 2770 | 0.6940 | 0.5005 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
scottstots/roberta-base-prop-16-train-set
|
scottstots
| 2022-07-22T20:18:31Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-01T18:28:56Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-prop-16-train-set
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. -->
# roberta-base-prop-16-train-set
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown 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: 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
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jeanconstantin/causal_bert_fr
|
jeanconstantin
| 2022-07-22T19:53:23Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-06T20:27:18Z |
Un modèle français entrainé pour reconnaître les relations discursives causales. Le modèle reçoit 2 morceaux de textes et estime la probabilité que leur relation soit une relation de raison, résultat ou non causale.
Ce modèle a été entrainé avec la Penn Discourse Tree Bank 2 (PDTB2), base de données anglaise de référence pour les relations discursive. PDTB2 a été automatiquement traduite en Français afin de fine-tuner le modèle pré-entrainé CamemBERT-large.
Le modèle peut être chargé via la librairie CamemBERT: CamembertForSequenceClassification. Avant d'être traité, le texte doit être tokenisé via le tokenizer CamemBERT: CamembertTokenizer.
|
masterdezign/ppo-CarRacing-v0-10M
|
masterdezign
| 2022-07-22T19:28:05Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-22T19:27:11Z |
---
library_name: stable-baselines3
tags:
- CarRacing-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 65.27 +/- 147.53
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CarRacing-v0
type: CarRacing-v0
---
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-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
...
```
|
huggingtweets/deepleffen-falco-tsm_leffen
|
huggingtweets
| 2022-07-22T19:10:49Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-22T19:09:51Z |
---
language: en
thumbnail: http://www.huggingtweets.com/deepleffen-falco-tsm_leffen/1658517045179/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1241879678455078914/e2EdZIrr_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1527824997388935168/-Ohf5n-I_400x400.png')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1547974425718300675/wvQuPBGR_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Deep Leffen Bot & nick & TSM FTX Leffen</div>
<div style="text-align: center; font-size: 14px;">@deepleffen-falco-tsm_leffen</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Deep Leffen Bot & nick & TSM FTX Leffen.
| Data | Deep Leffen Bot | nick | TSM FTX Leffen |
| --- | --- | --- | --- |
| Tweets downloaded | 591 | 3249 | 3221 |
| Retweets | 14 | 180 | 285 |
| Short tweets | 27 | 582 | 282 |
| Tweets kept | 550 | 2487 | 2654 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/13ch35ln/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @deepleffen-falco-tsm_leffen's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1pw6etfi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1pw6etfi/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/deepleffen-falco-tsm_leffen')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
osanseviero/platzi-test
|
osanseviero
| 2022-07-22T18:15:02Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-22T18:11:18Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: platzi-test
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. -->
# platzi-test
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
## 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': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 9375, '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-07, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
huggingtweets/deepleffen-tsm_leffen
|
huggingtweets
| 2022-07-22T17:50:36Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-22T17:49:13Z |
---
language: en
thumbnail: http://www.huggingtweets.com/deepleffen-tsm_leffen/1658512231427/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1241879678455078914/e2EdZIrr_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1547974425718300675/wvQuPBGR_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Deep Leffen Bot & TSM FTX Leffen</div>
<div style="text-align: center; font-size: 14px;">@deepleffen-tsm_leffen</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Deep Leffen Bot & TSM FTX Leffen.
| Data | Deep Leffen Bot | TSM FTX Leffen |
| --- | --- | --- |
| Tweets downloaded | 591 | 3249 |
| Retweets | 14 | 291 |
| Short tweets | 27 | 283 |
| Tweets kept | 550 | 2675 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3lq4lpvp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @deepleffen-tsm_leffen's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1v9tktg9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1v9tktg9/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/deepleffen-tsm_leffen')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
heriosousa/testpyramidsrnd
|
heriosousa
| 2022-07-22T17:50:31Z | 7 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2022-07-22T17:50:26Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: heriosousa/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DL4NLP-Group105/xtremedistil-l12-h384-uncased-hotpot_qa
|
DL4NLP-Group105
| 2022-07-22T16:57:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-07-22T16:55:10Z |
Language model:
xtremedistil-l12-h384-uncased
Language: English
Downstream-task: xtremedistil-l12-h384-uncased
Training data: hotpot_qa
Eval data: hotpot_qa
EM:
F1:
GroupID:105
|
llei/xtremedistil-l12-h384-uncased-HotpotQA
|
llei
| 2022-07-22T16:52:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-07-22T16:44:04Z |
Language model:xtremedistil-l12-h384-uncased
Language: English
Training data: hotpot_qa
Eval data: hotpot_qa
Code: See an example QA pipeline on Haystack
EM:46.4
F1:64.6
GroupId:105
|
FabioDataGeek/distilbert-base-uncased-finetuned-emotion
|
FabioDataGeek
| 2022-07-22T16:02:35Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.926
- name: F1
type: f1
value: 0.9258450981645597
---
<!-- 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: 0.2196
- Accuracy: 0.926
- F1: 0.9258
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8279 | 1.0 | 250 | 0.3208 | 0.9025 | 0.8979 |
| 0.2538 | 2.0 | 500 | 0.2196 | 0.926 | 0.9258 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Eleven/distilbert-base-uncased-finetuned-emotion
|
Eleven
| 2022-07-22T15:05:00Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-27T17:59:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2263
- Accuracy: 0.9225
- F1: 0.9221
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8571 | 1.0 | 250 | 0.3333 | 0.902 | 0.8982 |
| 0.2507 | 2.0 | 500 | 0.2263 | 0.9225 | 0.9221 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Tokenizers 0.12.1
|
sudo-s/exper7_mesum5
|
sudo-s
| 2022-07-22T14:31:45Z | 58 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-22T13:42:11Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper7_mesum5
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. -->
# exper7_mesum5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5889
- Accuracy: 0.8538
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2072 | 0.23 | 100 | 4.1532 | 0.1923 |
| 3.5433 | 0.47 | 200 | 3.5680 | 0.2888 |
| 3.1388 | 0.7 | 300 | 3.1202 | 0.3911 |
| 2.7924 | 0.93 | 400 | 2.7434 | 0.4787 |
| 2.1269 | 1.16 | 500 | 2.3262 | 0.5781 |
| 1.8589 | 1.4 | 600 | 1.9754 | 0.6272 |
| 1.7155 | 1.63 | 700 | 1.7627 | 0.6840 |
| 1.4689 | 1.86 | 800 | 1.5937 | 0.6994 |
| 1.0149 | 2.09 | 900 | 1.3168 | 0.7497 |
| 0.8148 | 2.33 | 1000 | 1.1630 | 0.7615 |
| 0.7159 | 2.56 | 1100 | 1.0869 | 0.7675 |
| 0.7257 | 2.79 | 1200 | 0.9607 | 0.7893 |
| 0.4171 | 3.02 | 1300 | 0.8835 | 0.7935 |
| 0.2969 | 3.26 | 1400 | 0.8259 | 0.8130 |
| 0.2405 | 3.49 | 1500 | 0.7711 | 0.8142 |
| 0.2948 | 3.72 | 1600 | 0.7629 | 0.8112 |
| 0.1765 | 3.95 | 1700 | 0.7117 | 0.8124 |
| 0.1603 | 4.19 | 1800 | 0.6946 | 0.8237 |
| 0.0955 | 4.42 | 1900 | 0.6597 | 0.8349 |
| 0.0769 | 4.65 | 2000 | 0.6531 | 0.8266 |
| 0.0816 | 4.88 | 2100 | 0.6335 | 0.8337 |
| 0.0315 | 5.12 | 2200 | 0.6087 | 0.8402 |
| 0.0368 | 5.35 | 2300 | 0.6026 | 0.8444 |
| 0.0377 | 5.58 | 2400 | 0.6450 | 0.8278 |
| 0.0603 | 5.81 | 2500 | 0.6564 | 0.8343 |
| 0.0205 | 6.05 | 2600 | 0.6119 | 0.8467 |
| 0.019 | 6.28 | 2700 | 0.6070 | 0.8479 |
| 0.0249 | 6.51 | 2800 | 0.6002 | 0.8538 |
| 0.0145 | 6.74 | 2900 | 0.6012 | 0.8497 |
| 0.0134 | 6.98 | 3000 | 0.5991 | 0.8521 |
| 0.0271 | 7.21 | 3100 | 0.5972 | 0.8503 |
| 0.0128 | 7.44 | 3200 | 0.5911 | 0.8521 |
| 0.0123 | 7.67 | 3300 | 0.5889 | 0.8538 |
| 0.0278 | 7.91 | 3400 | 0.6135 | 0.8491 |
| 0.0106 | 8.14 | 3500 | 0.5934 | 0.8533 |
| 0.0109 | 8.37 | 3600 | 0.5929 | 0.8533 |
| 0.0095 | 8.6 | 3700 | 0.5953 | 0.8550 |
| 0.009 | 8.84 | 3800 | 0.5933 | 0.8574 |
| 0.009 | 9.07 | 3900 | 0.5948 | 0.8550 |
| 0.0089 | 9.3 | 4000 | 0.5953 | 0.8556 |
| 0.0086 | 9.53 | 4100 | 0.5956 | 0.8544 |
| 0.0085 | 9.77 | 4200 | 0.5955 | 0.8556 |
| 0.0087 | 10.0 | 4300 | 0.5954 | 0.8538 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
tsrivatsav/wav2vec2-large-xls-r-300m-en-colab
|
tsrivatsav
| 2022-07-22T14:20:41Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-20T02:32:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- librispeech_asr
model-index:
- name: wav2vec2-large-xls-r-300m-en-colab
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. -->
# wav2vec2-large-xls-r-300m-en-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7541
- Wer: 1.0
- Cer: 0.9877
## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:---:|:------:|
| No log | 1.94 | 33 | 2.9905 | 1.0 | 1.0 |
| No log | 3.88 | 66 | 2.9023 | 1.0 | 1.0 |
| No log | 5.82 | 99 | 2.8788 | 1.0 | 1.0 |
| 3.7488 | 7.76 | 132 | 2.8624 | 1.0 | 1.0 |
| 3.7488 | 9.71 | 165 | 2.7541 | 1.0 | 0.9877 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cpu
- Datasets 1.18.3
- Tokenizers 0.12.1
|
Desh/SOTA
|
Desh
| 2022-07-22T14:00:38Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-07-22T13:55:47Z |
---
title: 🙋NLP QA Text Context Gradio👩⚕️
emoji: 👩⚕️🙋📑
colorFrom: purple
colorTo: green
sdk: gradio
sdk_version: 3.0.5
app_file: app.py
pinned: false
license: mit
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
|
bigmorning/distilbert_new2_0060
|
bigmorning
| 2022-07-22T13:36:26Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-22T13:17:27Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: distilbert_new2_0060
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. -->
# distilbert_new2_0060
This model is a fine-tuned version of [/content/drive/MyDrive/Colab Notebooks/oscar/trybackup_distilbert/new_backup_0105105](https://huggingface.co//content/drive/MyDrive/Colab Notebooks/oscar/trybackup_distilbert/new_backup_0105105) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9522
- Validation Loss: 0.9345
- Epoch: 59
## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.0180 | 0.9873 | 0 |
| 1.0163 | 0.9878 | 1 |
| 1.0145 | 0.9856 | 2 |
| 1.0139 | 0.9830 | 3 |
| 1.0122 | 0.9831 | 4 |
| 1.0118 | 0.9830 | 5 |
| 1.0094 | 0.9800 | 6 |
| 1.0075 | 0.9809 | 7 |
| 1.0066 | 0.9784 | 8 |
| 1.0062 | 0.9768 | 9 |
| 1.0032 | 0.9751 | 10 |
| 1.0023 | 0.9764 | 11 |
| 1.0008 | 0.9735 | 12 |
| 0.9994 | 0.9730 | 13 |
| 0.9986 | 0.9761 | 14 |
| 0.9975 | 0.9714 | 15 |
| 0.9953 | 0.9708 | 16 |
| 0.9941 | 0.9683 | 17 |
| 0.9933 | 0.9681 | 18 |
| 0.9920 | 0.9688 | 19 |
| 0.9907 | 0.9648 | 20 |
| 0.9897 | 0.9625 | 21 |
| 0.9890 | 0.9642 | 22 |
| 0.9873 | 0.9633 | 23 |
| 0.9867 | 0.9618 | 24 |
| 0.9857 | 0.9600 | 25 |
| 0.9839 | 0.9598 | 26 |
| 0.9827 | 0.9585 | 27 |
| 0.9821 | 0.9607 | 28 |
| 0.9809 | 0.9579 | 29 |
| 0.9803 | 0.9561 | 30 |
| 0.9786 | 0.9563 | 31 |
| 0.9774 | 0.9536 | 32 |
| 0.9766 | 0.9542 | 33 |
| 0.9756 | 0.9523 | 34 |
| 0.9743 | 0.9525 | 35 |
| 0.9730 | 0.9513 | 36 |
| 0.9721 | 0.9507 | 37 |
| 0.9715 | 0.9506 | 38 |
| 0.9702 | 0.9482 | 39 |
| 0.9694 | 0.9493 | 40 |
| 0.9689 | 0.9462 | 41 |
| 0.9673 | 0.9463 | 42 |
| 0.9669 | 0.9444 | 43 |
| 0.9659 | 0.9450 | 44 |
| 0.9643 | 0.9429 | 45 |
| 0.9625 | 0.9432 | 46 |
| 0.9625 | 0.9428 | 47 |
| 0.9609 | 0.9408 | 48 |
| 0.9598 | 0.9399 | 49 |
| 0.9596 | 0.9407 | 50 |
| 0.9590 | 0.9393 | 51 |
| 0.9580 | 0.9380 | 52 |
| 0.9562 | 0.9383 | 53 |
| 0.9558 | 0.9369 | 54 |
| 0.9543 | 0.9379 | 55 |
| 0.9545 | 0.9362 | 56 |
| 0.9534 | 0.9349 | 57 |
| 0.9523 | 0.9338 | 58 |
| 0.9522 | 0.9345 | 59 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
danhsf/m2m100_418M-finetuned-kde4-en-to-pt_BR
|
danhsf
| 2022-07-22T12:47:59Z | 71 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"m2m_100",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-07-22T01:46:42Z |
---
license: mit
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: m2m100_418M-finetuned-kde4-en-to-pt_BR
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
args: en-pt_BR
metrics:
- name: Bleu
type: bleu
value: 58.31959113813223
---
<!-- 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. -->
# m2m100_418M-finetuned-kde4-en-to-pt_BR
This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5150
- Bleu: 58.3196
## 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
sudo-s/exper3_mesum5
|
sudo-s
| 2022-07-22T12:10:49Z | 58 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-22T11:30:55Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper3_mesum5
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. -->
# exper3_mesum5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6366
- Accuracy: 0.8367
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.895 | 0.23 | 100 | 3.8276 | 0.1935 |
| 3.1174 | 0.47 | 200 | 3.1217 | 0.3107 |
| 2.6 | 0.7 | 300 | 2.5399 | 0.4207 |
| 2.256 | 0.93 | 400 | 2.1767 | 0.5160 |
| 1.5441 | 1.16 | 500 | 1.8086 | 0.5852 |
| 1.3834 | 1.4 | 600 | 1.5565 | 0.6325 |
| 1.1995 | 1.63 | 700 | 1.3339 | 0.6763 |
| 1.0845 | 1.86 | 800 | 1.3299 | 0.6533 |
| 0.6472 | 2.09 | 900 | 1.0679 | 0.7219 |
| 0.5948 | 2.33 | 1000 | 1.0286 | 0.7124 |
| 0.5565 | 2.56 | 1100 | 0.9595 | 0.7284 |
| 0.4879 | 2.79 | 1200 | 0.8915 | 0.7420 |
| 0.2816 | 3.02 | 1300 | 0.8159 | 0.7763 |
| 0.2412 | 3.26 | 1400 | 0.7766 | 0.7911 |
| 0.2015 | 3.49 | 1500 | 0.7850 | 0.7828 |
| 0.274 | 3.72 | 1600 | 0.7361 | 0.7935 |
| 0.1244 | 3.95 | 1700 | 0.7299 | 0.7911 |
| 0.0794 | 4.19 | 1800 | 0.7441 | 0.7846 |
| 0.0915 | 4.42 | 1900 | 0.7614 | 0.7941 |
| 0.0817 | 4.65 | 2000 | 0.7310 | 0.8012 |
| 0.0561 | 4.88 | 2100 | 0.7222 | 0.8065 |
| 0.0165 | 5.12 | 2200 | 0.7515 | 0.8059 |
| 0.0168 | 5.35 | 2300 | 0.6687 | 0.8213 |
| 0.0212 | 5.58 | 2400 | 0.6671 | 0.8249 |
| 0.0389 | 5.81 | 2500 | 0.6893 | 0.8278 |
| 0.0087 | 6.05 | 2600 | 0.6839 | 0.8260 |
| 0.0087 | 6.28 | 2700 | 0.6412 | 0.8320 |
| 0.0077 | 6.51 | 2800 | 0.6366 | 0.8367 |
| 0.0065 | 6.74 | 2900 | 0.6697 | 0.8272 |
| 0.0061 | 6.98 | 3000 | 0.6510 | 0.8349 |
| 0.0185 | 7.21 | 3100 | 0.6452 | 0.8367 |
| 0.0059 | 7.44 | 3200 | 0.6426 | 0.8379 |
| 0.0062 | 7.67 | 3300 | 0.6398 | 0.8379 |
| 0.0315 | 7.91 | 3400 | 0.6397 | 0.8385 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ameerazam08/autotrain-imdb-1166543171
|
ameerazam08
| 2022-07-22T11:56:54Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain",
"en",
"dataset:ameerazam08/autotrain-data-imdb",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-22T11:46:52Z |
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ameerazam08/autotrain-data-imdb
co2_eq_emissions: 0.07308302140406821
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1166543171
- CO2 Emissions (in grams): 0.07308302140406821
## Validation Metrics
- Loss: 0.2211569994688034
- Accuracy: 0.9138
- Precision: 0.9020598523124758
- Recall: 0.9284
- AUC: 0.9711116000000001
- F1: 0.9150404100137985
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/ameerazam08/autotrain-imdb-1166543171
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ameerazam08/autotrain-imdb-1166543171", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("ameerazam08/autotrain-imdb-1166543171", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
sudo-s/exper2_mesum5
|
sudo-s
| 2022-07-22T11:39:11Z | 55 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-22T11:15:01Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper2_mesum5
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. -->
# exper2_mesum5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem5 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4589
- Accuracy: 0.1308
## 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: 0.002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.4265 | 0.23 | 100 | 4.3676 | 0.0296 |
| 4.1144 | 0.47 | 200 | 4.1606 | 0.0544 |
| 4.0912 | 0.7 | 300 | 4.1071 | 0.0509 |
| 4.0361 | 0.93 | 400 | 4.0625 | 0.0669 |
| 4.0257 | 1.16 | 500 | 3.9682 | 0.0822 |
| 3.8846 | 1.4 | 600 | 3.9311 | 0.0834 |
| 3.9504 | 1.63 | 700 | 3.9255 | 0.0698 |
| 3.9884 | 1.86 | 800 | 3.9404 | 0.0722 |
| 3.7191 | 2.09 | 900 | 3.8262 | 0.0935 |
| 3.7952 | 2.33 | 1000 | 3.8236 | 0.0734 |
| 3.8085 | 2.56 | 1100 | 3.7694 | 0.0964 |
| 3.7535 | 2.79 | 1200 | 3.6757 | 0.1059 |
| 3.4218 | 3.02 | 1300 | 3.6474 | 0.1095 |
| 3.5172 | 3.26 | 1400 | 3.5621 | 0.1166 |
| 3.5173 | 3.49 | 1500 | 3.5579 | 0.1207 |
| 3.4346 | 3.72 | 1600 | 3.4817 | 0.1249 |
| 3.3995 | 3.95 | 1700 | 3.4589 | 0.1308 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
sudo-s/exper1_mesum5
|
sudo-s
| 2022-07-22T11:23:22Z | 59 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-22T11:00:05Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper1_mesum5
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. -->
# exper1_mesum5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6401
- Accuracy: 0.8278
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.9352 | 0.23 | 100 | 3.8550 | 0.1959 |
| 3.1536 | 0.47 | 200 | 3.1755 | 0.2888 |
| 2.6937 | 0.7 | 300 | 2.6332 | 0.4272 |
| 2.3748 | 0.93 | 400 | 2.2833 | 0.4970 |
| 1.5575 | 1.16 | 500 | 1.8712 | 0.5888 |
| 1.4063 | 1.4 | 600 | 1.6048 | 0.6314 |
| 1.1841 | 1.63 | 700 | 1.4109 | 0.6621 |
| 1.0857 | 1.86 | 800 | 1.1832 | 0.7112 |
| 0.582 | 2.09 | 900 | 1.0371 | 0.7479 |
| 0.5971 | 2.33 | 1000 | 0.9839 | 0.7462 |
| 0.4617 | 2.56 | 1100 | 0.9233 | 0.7657 |
| 0.4621 | 2.79 | 1200 | 0.8417 | 0.7828 |
| 0.2128 | 3.02 | 1300 | 0.7644 | 0.7970 |
| 0.1883 | 3.26 | 1400 | 0.7001 | 0.8183 |
| 0.1501 | 3.49 | 1500 | 0.6826 | 0.8201 |
| 0.1626 | 3.72 | 1600 | 0.6568 | 0.8254 |
| 0.1053 | 3.95 | 1700 | 0.6401 | 0.8278 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
FAICAM/distilled-finetuned-imdb
|
FAICAM
| 2022-07-22T10:59:52Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-22T10:53:28Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: FAICAM/distilled-finetuned-imdb
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. -->
# FAICAM/distilled-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.8612
- Validation Loss: 2.5836
- 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': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -687, '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 |
|:----------:|:---------------:|:-----:|
| 2.8612 | 2.5836 | 0 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Tokenizers 0.12.1
|
huggingtweets/thenextweb
|
huggingtweets
| 2022-07-22T10:35:30Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-22T10:35:23Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1306571874000830464/AZtkNMd-_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">TNW</div>
<div style="text-align: center; font-size: 14px;">@thenextweb</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from TNW.
| Data | TNW |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 39 |
| Short tweets | 44 |
| Tweets kept | 3167 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3egcwo6t/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @thenextweb's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1s2bu9ha) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1s2bu9ha/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/thenextweb')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
ghpkishore/distilbert-base-uncased-finetuned-emotion
|
ghpkishore
| 2022-07-22T10:09:57Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-10T11:51:59Z |
---
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
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9285
- name: F1
type: f1
value: 0.9285439912301902
---
<!-- 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: 0.2183
- Accuracy: 0.9285
- F1: 0.9285
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8381 | 1.0 | 250 | 0.3165 | 0.9075 | 0.9040 |
| 0.2524 | 2.0 | 500 | 0.2183 | 0.9285 | 0.9285 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
igpaub/a2c-AntBulletEnv-v0
|
igpaub
| 2022-07-22T09:36:59Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-22T07:19:05Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 1596.61 +/- 177.30
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
---
# **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
...
```
|
hlopez/ViT_waste_classifier
|
hlopez
| 2022-07-22T09:11:40Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2022-07-22T08:57:04Z |
---
language: en
---
## Description
This model is a ViT trained to classify waste images into 6 categories:
- Organic
- Carton
- Glass
- General
- Plastics
- Dangerous.
The repository related to this model is: https://github.com/hectorLop/Waste-Detector
Also, the code related to this model can be found here https://github.com/hectorLop/Waste-Detector/blob/main/waste_detector/classifier/sagemaker/model.py
### Requirements
- Works with RGB images of size 224x224
|
bigmorning/distilbert_new2_0040
|
bigmorning
| 2022-07-22T08:50:47Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-22T08:50:33Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: distilbert_new2_0040
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. -->
# distilbert_new2_0040
This model is a fine-tuned version of [/content/drive/MyDrive/Colab Notebooks/oscar/trybackup_distilbert/new_backup_0105105](https://huggingface.co//content/drive/MyDrive/Colab Notebooks/oscar/trybackup_distilbert/new_backup_0105105) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9702
- Validation Loss: 0.9482
- Epoch: 39
## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.0180 | 0.9873 | 0 |
| 1.0163 | 0.9878 | 1 |
| 1.0145 | 0.9856 | 2 |
| 1.0139 | 0.9830 | 3 |
| 1.0122 | 0.9831 | 4 |
| 1.0118 | 0.9830 | 5 |
| 1.0094 | 0.9800 | 6 |
| 1.0075 | 0.9809 | 7 |
| 1.0066 | 0.9784 | 8 |
| 1.0062 | 0.9768 | 9 |
| 1.0032 | 0.9751 | 10 |
| 1.0023 | 0.9764 | 11 |
| 1.0008 | 0.9735 | 12 |
| 0.9994 | 0.9730 | 13 |
| 0.9986 | 0.9761 | 14 |
| 0.9975 | 0.9714 | 15 |
| 0.9953 | 0.9708 | 16 |
| 0.9941 | 0.9683 | 17 |
| 0.9933 | 0.9681 | 18 |
| 0.9920 | 0.9688 | 19 |
| 0.9907 | 0.9648 | 20 |
| 0.9897 | 0.9625 | 21 |
| 0.9890 | 0.9642 | 22 |
| 0.9873 | 0.9633 | 23 |
| 0.9867 | 0.9618 | 24 |
| 0.9857 | 0.9600 | 25 |
| 0.9839 | 0.9598 | 26 |
| 0.9827 | 0.9585 | 27 |
| 0.9821 | 0.9607 | 28 |
| 0.9809 | 0.9579 | 29 |
| 0.9803 | 0.9561 | 30 |
| 0.9786 | 0.9563 | 31 |
| 0.9774 | 0.9536 | 32 |
| 0.9766 | 0.9542 | 33 |
| 0.9756 | 0.9523 | 34 |
| 0.9743 | 0.9525 | 35 |
| 0.9730 | 0.9513 | 36 |
| 0.9721 | 0.9507 | 37 |
| 0.9715 | 0.9506 | 38 |
| 0.9702 | 0.9482 | 39 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ptrsxu/bert-base-chinese
|
ptrsxu
| 2022-07-22T08:09:06Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-10T06:48:33Z |
---
language: zh
---
# Bert-base-chinese
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
- **Model Description:**
This model has been pre-trained for Chinese, training and random input masking has been applied independently to word pieces (as in the original BERT paper).
- **Developed by:** HuggingFace team
- **Model Type:** Fill-Mask
- **Language(s):** Chinese
- **License:** [More Information needed]
- **Parent Model:** See the [BERT base uncased model](https://huggingface.co/bert-base-uncased) for more information about the BERT base model.
## Uses
#### Direct Use
This model can be used for masked language modeling
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## Training
#### Training Procedure
* **type_vocab_size:** 2
* **vocab_size:** 21128
* **num_hidden_layers:** 12
#### Training Data
[More Information Needed]
## Evaluation
#### Results
[More Information Needed]
## How to Get Started With the Model
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese")
model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese")
```
|
semy/hf-model-full-0
|
semy
| 2022-07-22T07:02:08Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-19T10:07:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: hf-model-full-0
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. -->
# hf-model-full-0
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4295
- Accuracy: 0.802
- F1: 0.802
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|
| 0.9446 | 1.0 | 563 | 0.4208 | 0.793 | 0.793 |
| 0.1259 | 2.0 | 1126 | 0.4295 | 0.802 | 0.802 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
RupE/xlm-roberta-base-finetuned-panx-en
|
RupE
| 2022-07-22T05:50:25Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-22T05:47:33Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.en
split: train
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.5541666666666666
---
<!-- 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-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6380
- F1: 0.5542
## 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: 96
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 13 | 1.0388 | 0.1801 |
| No log | 2.0 | 26 | 0.7545 | 0.5053 |
| No log | 3.0 | 39 | 0.6380 | 0.5542 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
okho0653/Bio_ClinicalBERT-zero-shot-finetuned-50cad
|
okho0653
| 2022-07-22T05:42:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-22T05:29:59Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: Bio_ClinicalBERT-zero-shot-finetuned-50cad
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. -->
# Bio_ClinicalBERT-zero-shot-finetuned-50cad
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1475
- Accuracy: 0.5
- F1: 0.6667
## 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: 2
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jaeyeon/korean-aihub-learning-3
|
jaeyeon
| 2022-07-22T05:35:44Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-20T10:44:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: korean-aihub-learning-3
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. -->
# korean-aihub-learning-3
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2854
- Wer: 0.7921
## 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: 0.0003
- 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: 500
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.99 | 35 | 45.5713 | 1.0 |
| No log | 1.99 | 70 | 24.4376 | 1.0 |
| 35.4145 | 2.99 | 105 | 18.3030 | 1.0 |
| 35.4145 | 3.99 | 140 | 12.6702 | 1.0 |
| 35.4145 | 4.99 | 175 | 7.4939 | 1.0 |
| 11.687 | 5.99 | 210 | 4.9592 | 1.0 |
| 11.687 | 6.99 | 245 | 4.6777 | 1.0 |
| 11.687 | 7.99 | 280 | 4.6597 | 1.0 |
| 4.8003 | 8.99 | 315 | 4.6777 | 1.0 |
| 4.8003 | 9.99 | 350 | 4.7003 | 1.0 |
| 4.8003 | 10.99 | 385 | 4.6129 | 1.0 |
| 4.6383 | 11.99 | 420 | 4.6209 | 1.0 |
| 4.6383 | 12.99 | 455 | 4.6035 | 1.0 |
| 4.6383 | 13.99 | 490 | 4.6166 | 1.0 |
| 4.577 | 14.99 | 525 | 4.6026 | 1.0 |
| 4.577 | 15.99 | 560 | 4.5337 | 1.0 |
| 4.577 | 16.99 | 595 | 4.5284 | 1.0 |
| 4.5124 | 17.99 | 630 | 4.5710 | 1.0 |
| 4.5124 | 18.99 | 665 | 4.5223 | 1.0 |
| 4.3818 | 19.99 | 700 | 4.4472 | 1.0 |
| 4.3818 | 20.99 | 735 | 4.4272 | 0.9977 |
| 4.3818 | 21.99 | 770 | 4.4160 | 0.9977 |
| 4.2796 | 22.99 | 805 | 4.3741 | 0.9988 |
| 4.2796 | 23.99 | 840 | 4.3087 | 1.0 |
| 4.2796 | 24.99 | 875 | 4.2336 | 1.0 |
| 4.0489 | 25.99 | 910 | 4.1352 | 0.9988 |
| 4.0489 | 26.99 | 945 | 4.0669 | 1.0 |
| 4.0489 | 27.99 | 980 | 3.8551 | 0.9988 |
| 3.6122 | 28.99 | 1015 | 3.6699 | 0.9919 |
| 3.6122 | 29.99 | 1050 | 3.4580 | 0.9781 |
| 3.6122 | 30.99 | 1085 | 3.1899 | 0.9434 |
| 2.8886 | 31.99 | 1120 | 3.0746 | 0.9550 |
| 2.8886 | 32.99 | 1155 | 2.8143 | 0.9353 |
| 2.8886 | 33.99 | 1190 | 2.7004 | 0.9122 |
| 2.0277 | 34.99 | 1225 | 2.5284 | 0.9076 |
| 2.0277 | 35.99 | 1260 | 2.4677 | 0.8972 |
| 2.0277 | 36.99 | 1295 | 2.3426 | 0.8568 |
| 1.2486 | 37.99 | 1330 | 2.2456 | 0.8822 |
| 1.2486 | 38.99 | 1365 | 2.3250 | 0.9238 |
| 0.7572 | 39.99 | 1400 | 2.2832 | 0.8557 |
| 0.7572 | 40.99 | 1435 | 2.2671 | 0.8406 |
| 0.7572 | 41.99 | 1470 | 2.3070 | 0.8857 |
| 0.4768 | 42.99 | 1505 | 2.2138 | 0.8476 |
| 0.4768 | 43.99 | 1540 | 2.2034 | 0.8799 |
| 0.4768 | 44.99 | 1575 | 2.2215 | 0.8487 |
| 0.3362 | 45.99 | 1610 | 2.3416 | 0.8834 |
| 0.3362 | 46.99 | 1645 | 2.3452 | 0.8383 |
| 0.3362 | 47.99 | 1680 | 2.2449 | 0.8360 |
| 0.257 | 48.99 | 1715 | 2.2249 | 0.8199 |
| 0.257 | 49.99 | 1750 | 2.3455 | 0.8106 |
| 0.257 | 50.99 | 1785 | 2.2537 | 0.8233 |
| 0.2116 | 51.99 | 1820 | 2.2501 | 0.8025 |
| 0.2116 | 52.99 | 1855 | 2.3180 | 0.8649 |
| 0.2116 | 53.99 | 1890 | 2.1855 | 0.8106 |
| 0.1787 | 54.99 | 1925 | 2.2140 | 0.8014 |
| 0.1787 | 55.99 | 1960 | 2.3140 | 0.8453 |
| 0.1787 | 56.99 | 1995 | 2.2140 | 0.8025 |
| 0.1498 | 57.99 | 2030 | 2.3381 | 0.8314 |
| 0.1498 | 58.99 | 2065 | 2.2591 | 0.8256 |
| 0.1372 | 59.99 | 2100 | 2.2538 | 0.7979 |
| 0.1372 | 60.99 | 2135 | 2.2052 | 0.7933 |
| 0.1372 | 61.99 | 2170 | 2.2370 | 0.8233 |
| 0.129 | 62.99 | 2205 | 2.2331 | 0.7898 |
| 0.129 | 63.99 | 2240 | 2.3022 | 0.8002 |
| 0.129 | 64.99 | 2275 | 2.3514 | 0.7956 |
| 0.1075 | 65.99 | 2310 | 2.3303 | 0.8279 |
| 0.1075 | 66.99 | 2345 | 2.2747 | 0.8025 |
| 0.1075 | 67.99 | 2380 | 2.2899 | 0.8152 |
| 0.0979 | 68.99 | 2415 | 2.3299 | 0.8164 |
| 0.0979 | 69.99 | 2450 | 2.1819 | 0.7945 |
| 0.0979 | 70.99 | 2485 | 2.2141 | 0.8222 |
| 0.0973 | 71.99 | 2520 | 2.3683 | 0.8395 |
| 0.0973 | 72.99 | 2555 | 2.2235 | 0.8199 |
| 0.0973 | 73.99 | 2590 | 2.2474 | 0.8048 |
| 0.0814 | 74.99 | 2625 | 2.3116 | 0.7968 |
| 0.0814 | 75.99 | 2660 | 2.2494 | 0.7945 |
| 0.0814 | 76.99 | 2695 | 2.2441 | 0.7968 |
| 0.0745 | 77.99 | 2730 | 2.2489 | 0.7864 |
| 0.0745 | 78.99 | 2765 | 2.2568 | 0.7921 |
| 0.0741 | 79.99 | 2800 | 2.2598 | 0.7875 |
| 0.0741 | 80.99 | 2835 | 2.3131 | 0.8002 |
| 0.0741 | 81.99 | 2870 | 2.2719 | 0.7898 |
| 0.0662 | 82.99 | 2905 | 2.2901 | 0.7875 |
| 0.0662 | 83.99 | 2940 | 2.3092 | 0.7979 |
| 0.0662 | 84.99 | 2975 | 2.3361 | 0.8048 |
| 0.0556 | 85.99 | 3010 | 2.3308 | 0.8152 |
| 0.0556 | 86.99 | 3045 | 2.3106 | 0.8164 |
| 0.0556 | 87.99 | 3080 | 2.3363 | 0.8002 |
| 0.0504 | 88.99 | 3115 | 2.3588 | 0.7910 |
| 0.0504 | 89.99 | 3150 | 2.3528 | 0.7956 |
| 0.0504 | 90.99 | 3185 | 2.3201 | 0.7794 |
| 0.0496 | 91.99 | 3220 | 2.3386 | 0.7991 |
| 0.0496 | 92.99 | 3255 | 2.3423 | 0.7956 |
| 0.0496 | 93.99 | 3290 | 2.3312 | 0.7956 |
| 0.0468 | 94.99 | 3325 | 2.3362 | 0.7968 |
| 0.0468 | 95.99 | 3360 | 2.2962 | 0.7887 |
| 0.0468 | 96.99 | 3395 | 2.2864 | 0.7841 |
| 0.0475 | 97.99 | 3430 | 2.2870 | 0.7898 |
| 0.0475 | 98.99 | 3465 | 2.2866 | 0.7898 |
| 0.0411 | 99.99 | 3500 | 2.2854 | 0.7921 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
RupE/xlm-roberta-base-finetuned-panx-de
|
RupE
| 2022-07-22T05:15:36Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-22T04:35:55Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: train
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8503293209175562
---
<!-- 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-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1354
- F1: 0.8503
## 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: 96
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 132 | 0.1757 | 0.8055 |
| No log | 2.0 | 264 | 0.1372 | 0.8424 |
| No log | 3.0 | 396 | 0.1354 | 0.8503 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Shunichiro/distilbert-base-uncased-finetuned-squad
|
Shunichiro
| 2022-07-22T05:11:33Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-06T06:58:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-squad
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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.0244
## 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: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 30 | 3.5643 |
| No log | 2.0 | 60 | 2.4546 |
| No log | 3.0 | 90 | 2.3018 |
| No log | 4.0 | 120 | 2.4636 |
| No log | 5.0 | 150 | 2.4736 |
| No log | 6.0 | 180 | 2.5580 |
| No log | 7.0 | 210 | 2.6686 |
| No log | 8.0 | 240 | 2.7249 |
| No log | 9.0 | 270 | 3.2596 |
| No log | 10.0 | 300 | 3.5904 |
| No log | 11.0 | 330 | 3.6709 |
| No log | 12.0 | 360 | 3.6431 |
| No log | 13.0 | 390 | 3.6343 |
| No log | 14.0 | 420 | 3.8316 |
| No log | 15.0 | 450 | 3.6363 |
| No log | 16.0 | 480 | 3.8468 |
| 0.8931 | 17.0 | 510 | 3.7114 |
| 0.8931 | 18.0 | 540 | 3.8719 |
| 0.8931 | 19.0 | 570 | 4.0872 |
| 0.8931 | 20.0 | 600 | 4.2989 |
| 0.8931 | 21.0 | 630 | 4.5494 |
| 0.8931 | 22.0 | 660 | 4.2565 |
| 0.8931 | 23.0 | 690 | 4.3009 |
| 0.8931 | 24.0 | 720 | 4.1816 |
| 0.8931 | 25.0 | 750 | 4.2583 |
| 0.8931 | 26.0 | 780 | 4.2276 |
| 0.8931 | 27.0 | 810 | 4.3481 |
| 0.8931 | 28.0 | 840 | 4.4369 |
| 0.8931 | 29.0 | 870 | 4.4891 |
| 0.8931 | 30.0 | 900 | 4.5521 |
| 0.8931 | 31.0 | 930 | 4.5201 |
| 0.8931 | 32.0 | 960 | 4.6323 |
| 0.8931 | 33.0 | 990 | 4.4766 |
| 0.0297 | 34.0 | 1020 | 4.7612 |
| 0.0297 | 35.0 | 1050 | 4.9057 |
| 0.0297 | 36.0 | 1080 | 4.7580 |
| 0.0297 | 37.0 | 1110 | 4.6351 |
| 0.0297 | 38.0 | 1140 | 4.6495 |
| 0.0297 | 39.0 | 1170 | 4.5980 |
| 0.0297 | 40.0 | 1200 | 4.6370 |
| 0.0297 | 41.0 | 1230 | 4.6523 |
| 0.0297 | 42.0 | 1260 | 4.5802 |
| 0.0297 | 43.0 | 1290 | 4.6304 |
| 0.0297 | 44.0 | 1320 | 4.7111 |
| 0.0297 | 45.0 | 1350 | 4.7219 |
| 0.0297 | 46.0 | 1380 | 4.7323 |
| 0.0297 | 47.0 | 1410 | 4.9115 |
| 0.0297 | 48.0 | 1440 | 4.7873 |
| 0.0297 | 49.0 | 1470 | 4.9340 |
| 0.0023 | 50.0 | 1500 | 5.0638 |
| 0.0023 | 51.0 | 1530 | 5.0750 |
| 0.0023 | 52.0 | 1560 | 4.9338 |
| 0.0023 | 53.0 | 1590 | 4.9197 |
| 0.0023 | 54.0 | 1620 | 4.9282 |
| 0.0023 | 55.0 | 1650 | 5.0038 |
| 0.0023 | 56.0 | 1680 | 4.9848 |
| 0.0023 | 57.0 | 1710 | 4.9932 |
| 0.0023 | 58.0 | 1740 | 5.0134 |
| 0.0023 | 59.0 | 1770 | 5.0303 |
| 0.0023 | 60.0 | 1800 | 5.0244 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Tokenizers 0.12.1
|
huggingtweets/hotwingsuk
|
huggingtweets
| 2022-07-22T03:26:48Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-22T03:25:34Z |
---
language: en
thumbnail: http://www.huggingtweets.com/hotwingsuk/1658460403599/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1280474754214957056/GKqk3gAm_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">HotWings</div>
<div style="text-align: center; font-size: 14px;">@hotwingsuk</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from HotWings.
| Data | HotWings |
| --- | --- |
| Tweets downloaded | 2057 |
| Retweets | 69 |
| Short tweets | 258 |
| Tweets kept | 1730 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3opu8h6o/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @hotwingsuk's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/bzf76pmf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/bzf76pmf/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/hotwingsuk')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
jsalvatier/Reinforce-cartpole1
|
jsalvatier
| 2022-07-22T00:41:21Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-22T00:41:06Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole1
results:
- metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **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 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
Gianni33/q-Taxi-v3
|
Gianni33
| 2022-07-21T22:38:49Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-21T22:38:44Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.54 +/- 2.70
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="/q-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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Gianni33/q-FrozenLake-v1-4x4-noSlippery
|
Gianni33
| 2022-07-21T22:30:59Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-21T22:30:53Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Gianni33/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
TheJarmanitor/rl-class-1
|
TheJarmanitor
| 2022-07-21T21:00:51Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-21T19:57:23Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 277.12 +/- 19.47
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
...
```
|
AdoubleLen/q-Taxi-v3
|
AdoubleLen
| 2022-07-21T20:40:57Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-21T20:40:13Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.52 +/- 2.73
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="AdoubleLen/q-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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Willaim/AI
|
Willaim
| 2022-07-21T20:26:26Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2022-07-21T20:26:26Z |
---
license: bigscience-bloom-rail-1.0
---
|
trevorj/BART_reddit_other
|
trevorj
| 2022-07-21T18:56:10Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-21T16:49:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: BART_reddit_other
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. -->
# BART_reddit_other
This model is a fine-tuned version of [sshleifer/distilbart-xsum-6-6](https://huggingface.co/sshleifer/distilbart-xsum-6-6) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5792
- Rouge1: 18.5705
- Rouge2: 5.0107
- Rougel: 15.2581
- Rougelsum: 16.082
- Gen Len: 19.402
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 3.7887 | 1.0 | 1875 | 3.6044 | 18.4668 | 5.182 | 15.359 | 16.169 | 19.341 |
| 3.3816 | 2.0 | 3750 | 3.5628 | 18.0998 | 4.8937 | 15.0179 | 15.7615 | 17.789 |
| 3.134 | 3.0 | 5625 | 3.5792 | 18.5705 | 5.0107 | 15.2581 | 16.082 | 19.402 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
enoriega/rule_learning_margin_1mm_many_negatives_spanpred_attention
|
enoriega
| 2022-07-21T18:09:20Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:enoriega/odinsynth_dataset",
"endpoints_compatible",
"region:us"
] | null | 2022-07-20T06:09:21Z |
---
tags:
- generated_from_trainer
datasets:
- enoriega/odinsynth_dataset
model-index:
- name: rule_learning_margin_1mm_many_negatives_spanpred_attention
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. -->
# rule_learning_margin_1mm_many_negatives_spanpred_attention
This model is a fine-tuned version of [enoriega/rule_softmatching](https://huggingface.co/enoriega/rule_softmatching) on the enoriega/odinsynth_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2369
- Margin Accuracy: 0.8923
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2000
- total_train_batch_size: 8000
- 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 | Margin Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|
| 0.3814 | 0.16 | 20 | 0.3909 | 0.8317 |
| 0.349 | 0.32 | 40 | 0.3335 | 0.8463 |
| 0.3196 | 0.48 | 60 | 0.3101 | 0.8587 |
| 0.3083 | 0.64 | 80 | 0.3010 | 0.8645 |
| 0.2828 | 0.8 | 100 | 0.2871 | 0.8686 |
| 0.294 | 0.96 | 120 | 0.2800 | 0.8715 |
| 0.2711 | 1.12 | 140 | 0.2708 | 0.8741 |
| 0.2663 | 1.28 | 160 | 0.2671 | 0.8767 |
| 0.2656 | 1.44 | 180 | 0.2612 | 0.8822 |
| 0.2645 | 1.6 | 200 | 0.2537 | 0.8851 |
| 0.2625 | 1.76 | 220 | 0.2483 | 0.8878 |
| 0.2651 | 1.92 | 240 | 0.2471 | 0.8898 |
| 0.2407 | 2.08 | 260 | 0.2438 | 0.8905 |
| 0.2315 | 2.24 | 280 | 0.2408 | 0.8909 |
| 0.2461 | 2.4 | 300 | 0.2390 | 0.8918 |
| 0.2491 | 2.56 | 320 | 0.2390 | 0.8921 |
| 0.2511 | 2.72 | 340 | 0.2369 | 0.8918 |
| 0.2341 | 2.88 | 360 | 0.2363 | 0.8921 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
iuihgisgsd/jhgifgdsg
|
iuihgisgsd
| 2022-07-21T18:01:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-07-21T18:01:13Z |
oghdogspsdfughuisdfhgsudfigdfg
https://www.xing.com/events/new
|
rbiswas4/distilbert-base-uncased-finetuned-squad
|
rbiswas4
| 2022-07-21T17:48:26Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-21T11:24:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1542
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2137 | 1.0 | 5533 | 1.1516 |
| 0.9463 | 2.0 | 11066 | 1.1115 |
| 0.7665 | 3.0 | 16599 | 1.1542 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
bigmorning/distilbert_new_0100
|
bigmorning
| 2022-07-21T17:14:28Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-21T16:14:42Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: distilgpt_new_0100
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. -->
# distilgpt_new_0100
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.0286
- Validation Loss: 0.9952
- Epoch: 99
## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.5889 | 2.6197 | 0 |
| 2.4784 | 2.2040 | 1 |
| 2.1855 | 1.9980 | 2 |
| 2.0181 | 1.8643 | 3 |
| 1.9031 | 1.7652 | 4 |
| 1.8166 | 1.6924 | 5 |
| 1.7467 | 1.6360 | 6 |
| 1.6904 | 1.5843 | 7 |
| 1.6430 | 1.5421 | 8 |
| 1.6021 | 1.5059 | 9 |
| 1.5668 | 1.4761 | 10 |
| 1.5359 | 1.4481 | 11 |
| 1.5071 | 1.4220 | 12 |
| 1.4841 | 1.4020 | 13 |
| 1.4608 | 1.3797 | 14 |
| 1.4399 | 1.3595 | 15 |
| 1.4213 | 1.3426 | 16 |
| 1.4031 | 1.3266 | 17 |
| 1.3875 | 1.3113 | 18 |
| 1.3735 | 1.3024 | 19 |
| 1.3600 | 1.2871 | 20 |
| 1.3456 | 1.2753 | 21 |
| 1.3336 | 1.2648 | 22 |
| 1.3214 | 1.2539 | 23 |
| 1.3103 | 1.2451 | 24 |
| 1.3005 | 1.2335 | 25 |
| 1.2905 | 1.2258 | 26 |
| 1.2815 | 1.2179 | 27 |
| 1.2728 | 1.2123 | 28 |
| 1.2643 | 1.2029 | 29 |
| 1.2564 | 1.1980 | 30 |
| 1.2494 | 1.1877 | 31 |
| 1.2414 | 1.1806 | 32 |
| 1.2348 | 1.1788 | 33 |
| 1.2290 | 1.1699 | 34 |
| 1.2209 | 1.1654 | 35 |
| 1.2156 | 1.1575 | 36 |
| 1.2110 | 1.1537 | 37 |
| 1.2046 | 1.1499 | 38 |
| 1.1986 | 1.1436 | 39 |
| 1.1940 | 1.1408 | 40 |
| 1.1877 | 1.1356 | 41 |
| 1.1830 | 1.1314 | 42 |
| 1.1779 | 1.1278 | 43 |
| 1.1737 | 1.1211 | 44 |
| 1.1692 | 1.1192 | 45 |
| 1.1647 | 1.1163 | 46 |
| 1.1611 | 1.1107 | 47 |
| 1.1560 | 1.1066 | 48 |
| 1.1521 | 1.1060 | 49 |
| 1.1489 | 1.1002 | 50 |
| 1.1440 | 1.0960 | 51 |
| 1.1406 | 1.0931 | 52 |
| 1.1373 | 1.0897 | 53 |
| 1.1329 | 1.0855 | 54 |
| 1.1302 | 1.0842 | 55 |
| 1.1265 | 1.0818 | 56 |
| 1.1237 | 1.0784 | 57 |
| 1.1204 | 1.0737 | 58 |
| 1.1173 | 1.0714 | 59 |
| 1.1140 | 1.0694 | 60 |
| 1.1112 | 1.0691 | 61 |
| 1.1083 | 1.0668 | 62 |
| 1.1044 | 1.0611 | 63 |
| 1.1027 | 1.0607 | 64 |
| 1.0990 | 1.0586 | 65 |
| 1.0969 | 1.0545 | 66 |
| 1.0944 | 1.0522 | 67 |
| 1.0921 | 1.0517 | 68 |
| 1.0891 | 1.0496 | 69 |
| 1.0862 | 1.0457 | 70 |
| 1.0828 | 1.0448 | 71 |
| 1.0824 | 1.0439 | 72 |
| 1.0793 | 1.0389 | 73 |
| 1.0769 | 1.0375 | 74 |
| 1.0740 | 1.0362 | 75 |
| 1.0717 | 1.0358 | 76 |
| 1.0700 | 1.0299 | 77 |
| 1.0675 | 1.0312 | 78 |
| 1.0639 | 1.0288 | 79 |
| 1.0643 | 1.0270 | 80 |
| 1.0607 | 1.0258 | 81 |
| 1.0602 | 1.0233 | 82 |
| 1.0568 | 1.0225 | 83 |
| 1.0557 | 1.0198 | 84 |
| 1.0534 | 1.0179 | 85 |
| 1.0512 | 1.0165 | 86 |
| 1.0495 | 1.0170 | 87 |
| 1.0478 | 1.0124 | 88 |
| 1.0458 | 1.0134 | 89 |
| 1.0439 | 1.0104 | 90 |
| 1.0418 | 1.0092 | 91 |
| 1.0401 | 1.0057 | 92 |
| 1.0377 | 1.0035 | 93 |
| 1.0370 | 1.0037 | 94 |
| 1.0345 | 1.0029 | 95 |
| 1.0339 | 1.0014 | 96 |
| 1.0322 | 1.0016 | 97 |
| 1.0296 | 0.9986 | 98 |
| 1.0286 | 0.9952 | 99 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
trevorj/BART_reddit_gaming
|
trevorj
| 2022-07-21T16:51:59Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-21T15:20:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: BART_reddit_gaming
results: []
---
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# BART_reddit_gaming
This model is a fine-tuned version of [sshleifer/distilbart-xsum-6-6](https://huggingface.co/sshleifer/distilbart-xsum-6-6) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7373
- Rouge1: 18.1202
- Rouge2: 4.6045
- Rougel: 15.1273
- Rougelsum: 15.7601
- Gen Len: 18.208
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 3.864 | 1.0 | 1875 | 3.7752 | 17.3754 | 4.51 | 14.6763 | 15.22 | 16.944 |
| 3.4755 | 2.0 | 3750 | 3.7265 | 17.8066 | 4.4188 | 14.9432 | 15.5396 | 18.104 |
| 3.2629 | 3.0 | 5625 | 3.7373 | 18.1202 | 4.6045 | 15.1273 | 15.7601 | 18.208 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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