modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-04 18:27:43
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 539
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-04 18:27:26
| card
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1.01M
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|---|---|---|---|---|---|---|---|---|---|
sd-concepts-library/nomad
|
sd-concepts-library
| 2022-10-01T21:08:36Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-10-01T21:08:32Z |
---
license: mit
---
### Nomad on Stable Diffusion
This is the `<nomad>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
IIIT-L/roberta-large-finetuned-TRAC-DS
|
IIIT-L
| 2022-10-01T20:45:23Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-01T17:17:37Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-large-finetuned-TRAC-DS
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-finetuned-TRAC-DS
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8198
- Accuracy: 0.7190
- Precision: 0.6955
- Recall: 0.6979
- F1: 0.6963
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9538 | 1.0 | 612 | 0.8083 | 0.6111 | 0.6192 | 0.6164 | 0.5994 |
| 0.7924 | 2.0 | 1224 | 0.7594 | 0.6601 | 0.6688 | 0.6751 | 0.6424 |
| 0.6844 | 3.0 | 1836 | 0.6986 | 0.7042 | 0.6860 | 0.6969 | 0.6858 |
| 0.5715 | 3.99 | 2448 | 0.7216 | 0.7075 | 0.6957 | 0.6978 | 0.6925 |
| 0.45 | 4.99 | 3060 | 0.7963 | 0.7288 | 0.7126 | 0.7074 | 0.7073 |
| 0.352 | 5.99 | 3672 | 1.0824 | 0.7141 | 0.6999 | 0.6774 | 0.6818 |
| 0.2546 | 6.99 | 4284 | 1.0884 | 0.7230 | 0.7006 | 0.7083 | 0.7028 |
| 0.1975 | 7.99 | 4896 | 1.5338 | 0.7337 | 0.7090 | 0.7063 | 0.7074 |
| 0.1656 | 8.99 | 5508 | 1.8182 | 0.7100 | 0.6882 | 0.6989 | 0.6896 |
| 0.1358 | 9.98 | 6120 | 2.1623 | 0.7173 | 0.6917 | 0.6959 | 0.6934 |
| 0.1235 | 10.98 | 6732 | 2.3249 | 0.7141 | 0.6881 | 0.6914 | 0.6888 |
| 0.1003 | 11.98 | 7344 | 2.3474 | 0.7124 | 0.6866 | 0.6920 | 0.6887 |
| 0.0826 | 12.98 | 7956 | 2.3574 | 0.7083 | 0.6853 | 0.6959 | 0.6874 |
| 0.0727 | 13.98 | 8568 | 2.4989 | 0.7116 | 0.6858 | 0.6934 | 0.6883 |
| 0.0553 | 14.98 | 9180 | 2.8090 | 0.7026 | 0.6747 | 0.6710 | 0.6725 |
| 0.0433 | 15.97 | 9792 | 2.6647 | 0.7255 | 0.7010 | 0.7028 | 0.7018 |
| 0.0449 | 16.97 | 10404 | 2.6568 | 0.7247 | 0.7053 | 0.6997 | 0.7010 |
| 0.0373 | 17.97 | 11016 | 2.7632 | 0.7149 | 0.6888 | 0.6938 | 0.6909 |
| 0.0278 | 18.97 | 11628 | 2.8245 | 0.7124 | 0.6866 | 0.6930 | 0.6889 |
| 0.0288 | 19.97 | 12240 | 2.8198 | 0.7190 | 0.6955 | 0.6979 | 0.6963 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
MoososCap/SpongeBob-SquarePants-Diffusion
|
MoososCap
| 2022-10-01T19:15:12Z | 0 | 2 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-10-01T18:20:25Z |
---
license: creativeml-openrail-m
---
Modified from original model：CompVis/stable-diffusion-v-1-4-original
training using images：
https://i.imgur.com/D76R0eV.jpg
https://i.imgur.com/7zQ6f72.jpg
https://i.imgur.com/T2vcv5K.jpg
https://i.imgur.com/T4RsGHU.jpg
https://i.imgur.com/CRrskPZ.jpg
https://i.imgur.com/HG9Ba3q.jpg
https://i.imgur.com/X0XV8sG.jpg
https://i.imgur.com/RTnZIMr.jpg
https://i.imgur.com/4QVQodx.jpg
https://i.imgur.com/VTsdYj8.jpg
https://i.imgur.com/MM4ng1M.jpg
If you will not import the model, feel free to use the COLAB below
https://colab.research.google.com/drive/1MJ96yoU5J8h1fBWzabBNYBmK_MvNtx71?usp=sharing
|
LunNova/sd1.4-pony-finetune
|
LunNova
| 2022-10-01T18:35:14Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-10-01T18:35:14Z |
---
license: creativeml-openrail-m
---
|
gabrielgmendonca/bert-base-portuguese-cased-finetuned-chico-xavier-finetuned-chico-xavier
|
gabrielgmendonca
| 2022-10-01T18:10:15Z | 75 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-28T11:15:15Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: gabrielgmendonca/bert-base-portuguese-cased-finetuned-chico-xavier-finetuned-chico-xavier
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. -->
# gabrielgmendonca/bert-base-portuguese-cased-finetuned-chico-xavier-finetuned-chico-xavier
This model is a fine-tuned version of [gabrielgmendonca/bert-base-portuguese-cased-finetuned-chico-xavier](https://huggingface.co/gabrielgmendonca/bert-base-portuguese-cased-finetuned-chico-xavier) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.8630
- Validation Loss: 1.7215
- 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': 3430, '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: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.8630 | 1.7215 | 0 |
### Framework versions
- Transformers 4.22.2
- TensorFlow 2.8.2
- Datasets 2.5.1
- Tokenizers 0.12.1
|
IIIT-L/roberta-large-finetuned-ours-DS
|
IIIT-L
| 2022-10-01T17:03:49Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-01T15:57:22Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-large-finetuned-ours-DS
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-finetuned-ours-DS
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3369
- Accuracy: 0.75
- Precision: 0.7054
- Recall: 0.6949
- F1: 0.6974
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0561 | 0.99 | 99 | 0.8773 | 0.615 | 0.4054 | 0.5584 | 0.4591 |
| 0.762 | 1.98 | 198 | 0.6514 | 0.715 | 0.6735 | 0.6672 | 0.6588 |
| 0.5661 | 2.97 | 297 | 0.6806 | 0.71 | 0.6764 | 0.6608 | 0.6435 |
| 0.3699 | 3.96 | 396 | 0.8358 | 0.71 | 0.6611 | 0.6691 | 0.6570 |
| 0.2184 | 4.95 | 495 | 1.1627 | 0.7 | 0.6597 | 0.6337 | 0.6414 |
| 0.1743 | 5.94 | 594 | 1.0544 | 0.725 | 0.6831 | 0.6949 | 0.6831 |
| 0.098 | 6.93 | 693 | 1.4757 | 0.73 | 0.6885 | 0.6902 | 0.6892 |
| 0.0813 | 7.92 | 792 | 1.8146 | 0.73 | 0.6840 | 0.6772 | 0.6800 |
| 0.0435 | 8.91 | 891 | 1.6697 | 0.755 | 0.7141 | 0.7127 | 0.7132 |
| 0.0209 | 9.9 | 990 | 1.8931 | 0.755 | 0.7102 | 0.7070 | 0.7082 |
| 0.0201 | 10.89 | 1089 | 2.1934 | 0.74 | 0.6971 | 0.6866 | 0.6907 |
| 0.0095 | 11.88 | 1188 | 2.1389 | 0.75 | 0.7014 | 0.6915 | 0.6932 |
| 0.0141 | 12.87 | 1287 | 2.1902 | 0.74 | 0.6942 | 0.6943 | 0.6936 |
| 0.0112 | 13.86 | 1386 | 2.5021 | 0.73 | 0.6889 | 0.6669 | 0.6741 |
| 0.0054 | 14.85 | 1485 | 2.3840 | 0.73 | 0.6819 | 0.6715 | 0.6746 |
| 0.0088 | 15.84 | 1584 | 2.3224 | 0.74 | 0.6909 | 0.6825 | 0.6787 |
| 0.003 | 16.83 | 1683 | 2.2641 | 0.75 | 0.7054 | 0.6949 | 0.6974 |
| 0.0017 | 17.82 | 1782 | 2.3361 | 0.75 | 0.7077 | 0.6968 | 0.7012 |
| 0.0014 | 18.81 | 1881 | 2.3041 | 0.755 | 0.7131 | 0.7009 | 0.7051 |
| 0.0083 | 19.8 | 1980 | 2.3369 | 0.75 | 0.7054 | 0.6949 | 0.6974 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
grantsl/distilbert-base-uncased-finetuned-emotion
|
grantsl
| 2022-10-01T15:19:20Z | 103 | 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
| 2022-10-01T15:02:22Z |
---
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: 0.9415
- name: F1
type: f1
value: 0.9414702638466222
---
<!-- 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: 0.1764
- Accuracy: 0.9415
- F1: 0.9415
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.436 | 1.0 | 2000 | 0.2178 | 0.93 | 0.9305 |
| 0.1615 | 2.0 | 4000 | 0.1764 | 0.9415 | 0.9415 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
lv416e/distilbert-base-uncased-finetuned-emotion
|
lv416e
| 2022-10-01T13:07:04Z | 108 | 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
| 2022-09-30T17:24:23Z |
---
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: 0.9265
- name: F1
type: f1
value: 0.9264561231665573
---
<!-- 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: 0.2194
- Accuracy: 0.9265
- F1: 0.9265
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8297 | 1.0 | 250 | 0.3140 | 0.9055 | 0.9031 |
| 0.2499 | 2.0 | 500 | 0.2194 | 0.9265 | 0.9265 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
JJRohan/LunarLander-v2
|
JJRohan
| 2022-10-01T12:49:19Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-10-01T12:29:39Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 281.85 +/- 21.67
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
...
```
|
sd-concepts-library/alex-thumbnail-object-2000-steps
|
sd-concepts-library
| 2022-10-01T11:51:21Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-10-01T11:51:15Z |
---
license: mit
---
### Alex Thumbnail Object 2000 Steps on Stable Diffusion
This is the `<alex>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
Betka/finetuning-sentiment-model-3000-samples
|
Betka
| 2022-10-01T10:17:53Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-01T10:06:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8733333333333333
- name: F1
type: f1
value: 0.87248322147651
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
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: 0.2850
- Accuracy: 0.8733
- F1: 0.8725
## 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 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
bambooNerdFreeDownloads/Kizuna_Akali_tr
|
bambooNerdFreeDownloads
| 2022-10-01T09:38:23Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-30T19:16:21Z |
---
license: mit
---
# Description
Trainer: bambooNerdKagu8
# akali_waifu1.3ckpt
Kizuna Akali from Voiceroid
ref: https://www.ah-soft.com/voiceroid/akari/index.html
## Dataset
>Training: 40 images
>Regularization: 599 images
## Info
>Model Used: Waifu Diffusion 1.3 beta(4epoch)
>Steps: 500
>Keyword: kizuna_akali_tr
>Class Phrase: kizuna_akali_class
# yuzuki_yukari_waifu1_3_ep4.ckpt
Yuzuki Yukari from Voiceroid
ref: https://www.ah-soft.com/yukari/
## Dataset
>Training: 40 images
>Regularization: 233 images
## Info
>Model Used: Waifu Diffusion 1.3 beta(4epoch)
>Steps: 1000
>Keyword: yuzuki_yukari_tr
>Class Phrase: yuzuki_yukari_class
|
philschmid/distilbert-onnx-banking77
|
philschmid
| 2022-10-01T07:40:53Z | 27 | 5 |
generic
|
[
"generic",
"onnx",
"text-classification",
"endpoints-template",
"optimum",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-24T19:53:29Z |
---
tags:
- text-classification
- endpoints-template
- optimum
library_name: generic
---
# Optimized and Quantized DistilBERT with a custom pipeline with handler.py
> NOTE: Blog post coming soon
This is a template repository for Text Classification using Optimum and onnxruntime to support generic inference with Hugging Face Hub generic Inference API. There are two required steps:
1. Specify the requirements by defining a `requirements.txt` file.
2. Implement the `handler.py` `__init__` and `__call__` methods. These methods are called by the Inference API. The `__init__` method should load the model and preload the optimum model and tokenizers as well as the `text-classification` pipeline needed for inference. This is only called once. The `__call__` method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.
add
```
library_name: generic
```
to the readme.
_note: the `generic` community image currently only support `inputs` as parameter and no parameter._
|
FIT17/q-Taxi-v3
|
FIT17
| 2022-10-01T06:05:37Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-10-01T06:05:28Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.76
name: mean_reward
verified: false
---
# **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="FIT17/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"])
```
|
FIT17/q-FrozenLake-v1-4x4-noSlippery
|
FIT17
| 2022-10-01T06:02:34Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-10-01T06:02:26Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="FIT17/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"])
```
|
Bistolero/german_dutchall_mixed2ep
|
Bistolero
| 2022-10-01T03:53:57Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-01T03:34:34Z |
---
tags:
- generated_from_trainer
model-index:
- name: german_dutchall_mixed2ep
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. -->
# german_dutchall_mixed2ep
This model is a fine-tuned version of [Bistolero/nl_ge_alltr](https://huggingface.co/Bistolero/nl_ge_alltr) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 30
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
tuananh18/VietnamesePunctuation
|
tuananh18
| 2022-10-01T02:39:08Z | 109 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-09-24T15:16:42Z |
This is a test model, so the results are not really good.
The team is continuing to grow.
If you like it, Click like above to support the author. 🤗
|
jamesesguerra/mt5-small-finetuned-1.0.3
|
jamesesguerra
| 2022-09-30T23:20:08Z | 85 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-30T11:37:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-1.0.3
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. -->
# mt5-small-finetuned-1.0.3
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1389
- Rouge1: 48.1276
- Rouge2: 45.1735
- Rougel: 47.9444
- Rougelsum: 47.9803
## 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: 0.0004
- 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 0.8385 | 1.0 | 1161 | 0.1389 | 48.1276 | 45.1735 | 47.9444 | 47.9803 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
sd-concepts-library/ohisashiburi-style
|
sd-concepts-library
| 2022-09-30T22:39:01Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-30T22:38:57Z |
---
license: mit
---
### ohisashiburi-style on Stable Diffusion
This is the `<ohishashiburi-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
huggingtweets/dominasnow-kinkyfetishviv-mistresslhush
|
huggingtweets
| 2022-09-30T22:27:43Z | 49 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-30T22:27:15Z |
---
language: en
thumbnail: http://www.huggingtweets.com/dominasnow-kinkyfetishviv-mistresslhush/1664576858505/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/1569863287310802945/0rB4kb-c_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/1384245697755582474/vPcYIiXA_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/1528340382509154311/FamGl7eU_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">Mistress Vivienne l’Amour - Serve me on Onlyfans! & Goddess Alexandra Snow 🌟 V4M Creator of the Year & 𝐌𝐢𝐬𝐭𝐫𝐞𝐬𝐬 𝐋𝐨𝐥𝐢𝐭𝐚 𝐇𝐮𝐬𝐡</div>
<div style="text-align: center; font-size: 14px;">@dominasnow-kinkyfetishviv-mistresslhush</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 Mistress Vivienne l’Amour - Serve me on Onlyfans! & Goddess Alexandra Snow 🌟 V4M Creator of the Year & 𝐌𝐢𝐬𝐭𝐫𝐞𝐬𝐬 𝐋𝐨𝐥𝐢𝐭𝐚 𝐇𝐮𝐬𝐡.
| Data | Mistress Vivienne l’Amour - Serve me on Onlyfans! | Goddess Alexandra Snow 🌟 V4M Creator of the Year | 𝐌𝐢𝐬𝐭𝐫𝐞𝐬𝐬 𝐋𝐨𝐥𝐢𝐭𝐚 𝐇𝐮𝐬𝐡 |
| --- | --- | --- | --- |
| Tweets downloaded | 3207 | 3223 | 2186 |
| Retweets | 781 | 435 | 301 |
| Short tweets | 268 | 206 | 426 |
| Tweets kept | 2158 | 2582 | 1459 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/lwew59sn/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 @dominasnow-kinkyfetishviv-mistresslhush's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1l6o456x) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1l6o456x/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/dominasnow-kinkyfetishviv-mistresslhush')
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)
|
lucaordronneau/finbert-finetuned-FG-SINGLE_SENTENCE-NEWS-WEIGHTED
|
lucaordronneau
| 2022-09-30T18:45:49Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-11T12:45:07Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finbert-finetuned-FG-SINGLE_SENTENCE-NEWS-WEIGHTED
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. -->
# finbert-finetuned-FG-SINGLE_SENTENCE-NEWS-WEIGHTED
This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2741
- Accuracy: 0.7475
- F1: 0.7253
## 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: 6e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 249 | 0.9150 | 0.7346 | 0.6484 |
| No log | 2.0 | 498 | 0.8837 | 0.6210 | 0.6317 |
| 1.033 | 3.0 | 747 | 0.8460 | 0.6485 | 0.6666 |
| 1.033 | 4.0 | 996 | 1.0089 | 0.6831 | 0.6909 |
| 0.5642 | 5.0 | 1245 | 1.2507 | 0.7352 | 0.7152 |
| 0.5642 | 6.0 | 1494 | 1.3241 | 0.7129 | 0.7042 |
| 0.2078 | 7.0 | 1743 | 1.5163 | 0.7528 | 0.7230 |
| 0.2078 | 8.0 | 1992 | 1.5818 | 0.7352 | 0.7236 |
| 0.1108 | 9.0 | 2241 | 1.7930 | 0.7012 | 0.7046 |
| 0.1108 | 10.0 | 2490 | 1.8262 | 0.7305 | 0.7211 |
| 0.07 | 11.0 | 2739 | 2.0415 | 0.7440 | 0.7192 |
| 0.07 | 12.0 | 2988 | 2.1260 | 0.7563 | 0.7230 |
| 0.0392 | 13.0 | 3237 | 2.1502 | 0.7528 | 0.7323 |
| 0.0392 | 14.0 | 3486 | 2.2117 | 0.7516 | 0.7270 |
| 0.0174 | 15.0 | 3735 | 2.2657 | 0.7405 | 0.7236 |
| 0.0174 | 16.0 | 3984 | 2.2741 | 0.7475 | 0.7253 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
|
stevhliu/my_awesome_wnut_model
|
stevhliu
| 2022-09-30T18:27:37Z | 176 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-30T17:31:41Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: stevhliu/my_awesome_wnut_model
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. -->
# stevhliu/my_awesome_wnut_model
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: 0.1210
- Validation Loss: 0.2698
- Train Precision: 0.5099
- Train Recall: 0.3995
- Train F1: 0.4480
- Train Accuracy: 0.9444
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.3233 | 0.3099 | 0.4155 | 0.2117 | 0.2805 | 0.9333 | 0 |
| 0.1600 | 0.2743 | 0.5111 | 0.3589 | 0.4216 | 0.9416 | 1 |
| 0.1210 | 0.2698 | 0.5099 | 0.3995 | 0.4480 | 0.9444 | 2 |
### Framework versions
- Transformers 4.22.2
- TensorFlow 2.8.2
- Datasets 2.5.1
- Tokenizers 0.12.1
|
espnet/talromur2_xvector_tacotron2
|
espnet
| 2022-09-30T17:56:29Z | 4 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"is",
"dataset:talromur2",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-09-30T17:14:14Z |
---
tags:
- espnet
- audio
- text-to-speech
language: is
datasets:
- talromur2
license: cc-by-4.0
---
## ESPnet2 TTS model
### ``
This model was trained by Gunnar Thor using talromur2 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd talromur2/tts1/talromur2_xvector_tacotron2
./run.sh --skip_data_prep false --skip_train true --download_model
```
## TTS config
<details><summary>expand</summary>
```
config: ./conf/tuning/train_xvector_tacotron2.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/tts_train_xvector_tacotron2_raw_phn_none
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 500
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- loss
- min
- - train
- loss
- min
keep_nbest_models: 5
nbest_averaging_interval: 0
grad_clip: 1.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: 500
batch_size: 20
valid_batch_size: null
batch_bins: 3750000
valid_batch_bins: null
train_shape_file:
- exp/tts_stats_raw_phn_none/train/text_shape.phn
- exp/tts_stats_raw_phn_none/train/speech_shape
valid_shape_file:
- exp/tts_stats_raw_phn_none/valid/text_shape.phn
- exp/tts_stats_raw_phn_none/valid/speech_shape
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 204800
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_phn/text
- text
- text
- - dump/raw/train_phn/wav.scp
- speech
- sound
- - dump/xvector/train_phn/xvector.scp
- spembs
- kaldi_ark
valid_data_path_and_name_and_type:
- - dump/raw/dev_phn/text
- text
- text
- - dump/raw/dev_phn/wav.scp
- speech
- sound
- - dump/xvector/dev_phn/xvector.scp
- spembs
- kaldi_ark
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.001
eps: 1.0e-06
weight_decay: 0.0
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- r
- a
- t
- I
- n
- s
- D
- Y
- E
- l
- v
- m
- h
- k
- 'a:'
- j
- 'E:'
- T
- f
- G
- p
- 'i:'
- 'au:'
- c
- 'O:'
- i
- r_0
- 'I:'
- t_h
- ei
- O
- k_h
- ou
- '9'
- 'u:'
- ai
- au
- 'ou:'
- u
- 'ei:'
- l_0
- N
- n_0
- '9:'
- p_h
- 'ai:'
- c_h
- 9i
- C
- '9i:'
- x
- 'Y:'
- N_0
- J
- m_0
- Yi
- Oi
- J_0
- <sos/eos>
odim: null
model_conf: {}
use_preprocessor: true
token_type: phn
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
feats_extract: fbank
feats_extract_conf:
n_fft: 1024
hop_length: 256
win_length: null
fs: 22050
fmin: 80
fmax: 7600
n_mels: 80
normalize: global_mvn
normalize_conf:
stats_file: exp/tts_stats_raw_phn_none/train/feats_stats.npz
tts: tacotron2
tts_conf:
embed_dim: 512
elayers: 1
eunits: 512
econv_layers: 3
econv_chans: 512
econv_filts: 5
atype: location
adim: 512
aconv_chans: 32
aconv_filts: 15
cumulate_att_w: true
dlayers: 2
dunits: 1024
prenet_layers: 2
prenet_units: 256
postnet_layers: 5
postnet_chans: 512
postnet_filts: 5
output_activation: null
use_batch_norm: true
use_concate: true
use_residual: false
spk_embed_dim: 512
spk_embed_integration_type: add
dropout_rate: 0.5
zoneout_rate: 0.1
reduction_factor: 1
use_masking: true
bce_pos_weight: 10.0
use_guided_attn_loss: true
guided_attn_loss_sigma: 0.4
guided_attn_loss_lambda: 1.0
pitch_extract: null
pitch_extract_conf: {}
pitch_normalize: null
pitch_normalize_conf: {}
energy_extract: null
energy_extract_conf: {}
energy_normalize: null
energy_normalize_conf: {}
required:
- output_dir
- token_list
version: '202204'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
cardiffnlp/roberta-base-tweet-topic-single-2020
|
cardiffnlp
| 2022-09-30T17:45:15Z | 54 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"dataset:cardiffnlp/tweet_topic_single",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-30T07:32:09Z |
---
datasets:
- cardiffnlp/tweet_topic_single
metrics:
- f1
- accuracy
model-index:
- name: cardiffnlp/roberta-base-tweet-topic-single-2020
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: cardiffnlp/tweet_topic_single
type: cardiffnlp/tweet_topic_single
args: cardiffnlp/tweet_topic_single
split: test_2021
metrics:
- name: F1
type: f1
value: 0.8682811577082102
- name: F1 (macro)
type: f1_macro
value: 0.7296667105332716
- name: Accuracy
type: accuracy
value: 0.8682811577082102
pipeline_tag: text-classification
widget:
- text: "I'm sure the {@Tampa Bay Lightning@} would’ve rather faced the Flyers but man does their experience versus the Blue Jackets this year and last help them a lot versus this Islanders team. Another meat grinder upcoming for the good guys"
example_title: "Example 1"
- text: "Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US."
example_title: "Example 2"
---
# cardiffnlp/roberta-base-tweet-topic-single-2020
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the [tweet_topic_single](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single). This model is fine-tuned on `train_2020` split and validated on `test_2021` split of tweet_topic.
Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single/blob/main/lm_finetuning.py). It achieves the following results on the test_2021 set:
- F1 (micro): 0.8682811577082102
- F1 (macro): 0.7296667105332716
- Accuracy: 0.8682811577082102
### Usage
```python
from transformers import pipeline
pipe = pipeline("text-classification", "cardiffnlp/roberta-base-tweet-topic-single-2020")
topic = pipe("Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US.")
print(topic)
```
### Reference
```
@inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
}
```
|
abu2sid/my-awesome-model
|
abu2sid
| 2022-09-30T17:41:53Z | 26 | 0 |
transformers
|
[
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-30T17:38:09Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: my-awesome-model
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. -->
# my-awesome-model
This model is a fine-tuned version of [Rocketknight1/t5-small-finetuned-xsum](https://huggingface.co/Rocketknight1/t5-small-finetuned-xsum) 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.22.2
- TensorFlow 2.8.2
- Datasets 2.5.1
- Tokenizers 0.12.1
|
cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-2020
|
cardiffnlp
| 2022-09-30T17:34:59Z | 52 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"dataset:cardiffnlp/tweet_topic_single",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-30T07:19:52Z |
---
datasets:
- cardiffnlp/tweet_topic_single
metrics:
- f1
- accuracy
model-index:
- name: cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-2020
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: cardiffnlp/tweet_topic_single
type: cardiffnlp/tweet_topic_single
args: cardiffnlp/tweet_topic_single
split: test_2021
metrics:
- name: F1
type: f1
value: 0.8824571766095688
- name: F1 (macro)
type: f1_macro
value: 0.7401873227149222
- name: Accuracy
type: accuracy
value: 0.8824571766095688
pipeline_tag: text-classification
widget:
- text: "I'm sure the {@Tampa Bay Lightning@} would’ve rather faced the Flyers but man does their experience versus the Blue Jackets this year and last help them a lot versus this Islanders team. Another meat grinder upcoming for the good guys"
example_title: "Example 1"
- text: "Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US."
example_title: "Example 2"
---
# cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-2020
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2019-90m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) on the [tweet_topic_single](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single). This model is fine-tuned on `train_2020` split and validated on `test_2021` split of tweet_topic.
Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single/blob/main/lm_finetuning.py). It achieves the following results on the test_2021 set:
- F1 (micro): 0.8824571766095688
- F1 (macro): 0.7401873227149222
- Accuracy: 0.8824571766095688
### Usage
```python
from transformers import pipeline
pipe = pipeline("text-classification", "cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-2020")
topic = pipe("Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US.")
print(topic)
```
### Reference
```
@inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
}
```
|
luminolblue/HomunculusGPT-testbot
|
luminolblue
| 2022-09-30T17:23:59Z | 50 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-30T17:18:30Z |
---
tags:
- conversational
---
# Purely made as a joke, it's bad, don't expect anything ty.
|
ioanfr/distilbert-base-uncased-finetuned-cola
|
ioanfr
| 2022-09-30T16:28:22Z | 44 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-30T14:09:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5340667882909217
---
<!-- 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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8124
- Matthews Correlation: 0.5341
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5227 | 1.0 | 535 | 0.5222 | 0.4210 |
| 0.3467 | 2.0 | 1070 | 0.5046 | 0.4855 |
| 0.2335 | 3.0 | 1605 | 0.5637 | 0.5173 |
| 0.1813 | 4.0 | 2140 | 0.7634 | 0.5200 |
| 0.1334 | 5.0 | 2675 | 0.8124 | 0.5341 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.5.1
- Tokenizers 0.13.0
|
ner4archives/fr_ner4archives_default_test
|
ner4archives
| 2022-09-30T16:24:27Z | 4 | 0 |
spacy
|
[
"spacy",
"token-classification",
"fr",
"model-index",
"region:us"
] |
token-classification
| 2022-07-28T14:55:57Z |
---
widget:
- text: "415 Lyon Lettres de rémission accordées à Denis Fromant, marinier, pour meurtre commis à Saint-Haon 1, au pays de Roannais, sur la personne de Driet Cantin qui l'accusait d'avoir maltraité un de ses pages et de l'avoir dépouillé d'une jument (Fol 145 v°, n° 415) Septembre 1501."
example_title: "FRAN_IR_000061"
- text: "BB/29/988 page 143 Penne (Lot-et-Garronne) 14 décembre 1822. BB/29/988 page 145 Billom (Puy-de-Dôme) 11 janvier 1823."
example_title: "FRAN_IR_050370"
tags:
- spacy
- token-classification
language:
- fr
model-index:
- name: fr_ner4archives_default_test
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8
- name: NER Recall
type: recall
value: 0.741301059
- name: NER F Score
type: f_score
value: 0.7695327837
---
NER4Archives pipeline optimized for CPU and specialized on French National Archives findings aids (XML-EAD) - Corpus V2. Components: tok2vec, ner. Base default CNN architecture.
| Feature | Description |
| --- | --- |
| **Name** | `fr_ner4archives_default_test` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.3.1,<3.4.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | French corpus for the NER task composed of finding aids in XML-EAD from the National Archives of France (v. 2.0) - [Check corpus version on GitHub](https://github.com/NER4Archives-project/Corpus_TrainingData) |
| **License** | CC-BY-4.0 license |
| **Author** | [Archives nationales]() / [Inria-Almanach]() |
### Label Scheme
<details>
<summary>View label scheme (5 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `EVENT`, `LOCATION`, `ORGANISATION`, `PERSON`, `TITLE` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 76.95 |
| `ENTS_P` | 80.00 |
| `ENTS_R` | 74.13 |
| `TOK2VEC_LOSS` | 76044.50 |
| `NER_LOSS` | 75529.77 |
|
anas-awadalla/bart-large-finetuned-squad-seq2seq
|
anas-awadalla
| 2022-09-30T16:02:03Z | 51 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-29T19:23:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-large-finetuned-squad-seq2seq
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-large-finetuned-squad-seq2seq
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) 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: 3e-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: 2.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-2020
|
cardiffnlp
| 2022-09-30T14:43:17Z | 55 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"dataset:cardiffnlp/tweet_topic_single",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-30T05:07:45Z |
---
datasets:
- cardiffnlp/tweet_topic_single
metrics:
- f1
- accuracy
model-index:
- name: cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-2020
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: cardiffnlp/tweet_topic_single
type: cardiffnlp/tweet_topic_single
args: cardiffnlp/tweet_topic_single
split: test_2021
metrics:
- name: F1
type: f1
value: 0.8759598346131128
- name: F1 (macro)
type: f1_macro
value: 0.7462751206081605
- name: Accuracy
type: accuracy
value: 0.8759598346131128
pipeline_tag: text-classification
widget:
- text: "I'm sure the {@Tampa Bay Lightning@} would’ve rather faced the Flyers but man does their experience versus the Blue Jackets this year and last help them a lot versus this Islanders team. Another meat grinder upcoming for the good guys"
example_title: "Example 1"
- text: "Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US."
example_title: "Example 2"
---
# cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-2020
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) on the [tweet_topic_single](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single). This model is fine-tuned on `train_2020` split and validated on `test_2021` split of tweet_topic.
Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single/blob/main/lm_finetuning.py). It achieves the following results on the test_2021 set:
- F1 (micro): 0.8759598346131128
- F1 (macro): 0.7462751206081605
- Accuracy: 0.8759598346131128
### Usage
```python
from transformers import pipeline
pipe = pipeline("text-classification", "cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-2020")
topic = pipe("Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US.")
print(topic)
```
### Reference
```
@inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
}
```
|
mfreihaut/refinement-finetuned-mnli-2
|
mfreihaut
| 2022-09-30T13:55:53Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-29T16:36:24Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: refinement-finetuned-mnli-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# refinement-finetuned-mnli-2
This model is a fine-tuned version of [mfreihaut/refinement-finetuned-mnli-1](https://huggingface.co/mfreihaut/refinement-finetuned-mnli-1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0242
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| No log | 1.0 | 303 | 0.3730 |
| 1.1146 | 2.0 | 606 | 0.9860 |
| 1.1146 | 3.0 | 909 | 0.7304 |
| 1.0018 | 4.0 | 1212 | 0.6386 |
| 1.0045 | 5.0 | 1515 | 0.4228 |
| 1.0045 | 6.0 | 1818 | 0.6769 |
| 0.9618 | 7.0 | 2121 | 0.3008 |
| 0.9618 | 8.0 | 2424 | 0.4496 |
| 0.964 | 9.0 | 2727 | 0.1826 |
| 0.9586 | 10.0 | 3030 | 0.0367 |
| 0.9586 | 11.0 | 3333 | 0.1811 |
| 1.0467 | 12.0 | 3636 | 0.1352 |
| 1.0467 | 13.0 | 3939 | 0.0612 |
| 1.0047 | 14.0 | 4242 | 0.1702 |
| 1.0012 | 15.0 | 4545 | 0.0622 |
| 1.0012 | 16.0 | 4848 | 0.7077 |
| 1.0514 | 17.0 | 5151 | 0.2146 |
| 1.0514 | 18.0 | 5454 | 0.5531 |
| 0.9389 | 19.0 | 5757 | 1.2304 |
| 0.9229 | 20.0 | 6060 | 0.6252 |
| 0.9229 | 21.0 | 6363 | 0.6844 |
| 0.9334 | 22.0 | 6666 | 0.5663 |
| 0.9334 | 23.0 | 6969 | 0.9912 |
| 0.9312 | 24.0 | 7272 | 0.3112 |
| 0.8971 | 25.0 | 7575 | 0.4511 |
| 0.8971 | 26.0 | 7878 | 0.3860 |
| 0.9022 | 27.0 | 8181 | 0.5904 |
| 0.9022 | 28.0 | 8484 | 0.4710 |
| 0.7568 | 29.0 | 8787 | 0.8233 |
| 0.6753 | 30.0 | 9090 | 0.6951 |
| 0.6753 | 31.0 | 9393 | 0.6363 |
| 0.5802 | 32.0 | 9696 | 0.8018 |
| 0.5802 | 33.0 | 9999 | 0.9381 |
| 0.5323 | 34.0 | 10302 | 0.9941 |
| 0.5218 | 35.0 | 10605 | 0.9418 |
| 0.5218 | 36.0 | 10908 | 0.9236 |
| 0.4558 | 37.0 | 11211 | 0.4542 |
| 0.4247 | 38.0 | 11514 | 0.9279 |
| 0.4247 | 39.0 | 11817 | 0.9567 |
| 0.43 | 40.0 | 12120 | 0.8077 |
| 0.43 | 41.0 | 12423 | 0.9595 |
| 0.352 | 42.0 | 12726 | 0.9189 |
| 0.3393 | 43.0 | 13029 | 0.8762 |
| 0.3393 | 44.0 | 13332 | 1.0505 |
| 0.316 | 45.0 | 13635 | 0.9273 |
| 0.316 | 46.0 | 13938 | 1.0716 |
| 0.2983 | 47.0 | 14241 | 1.0084 |
| 0.2503 | 48.0 | 14544 | 1.1027 |
| 0.2503 | 49.0 | 14847 | 1.0478 |
| 0.2462 | 50.0 | 15150 | 1.0242 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.10.0
- Datasets 2.5.1
- Tokenizers 0.12.1
|
cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all
|
cardiffnlp
| 2022-09-30T13:23:43Z | 116 | 2 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"dataset:cardiffnlp/tweet_topic_single",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-30T03:27:04Z |
---
datasets:
- cardiffnlp/tweet_topic_single
metrics:
- f1
- accuracy
model-index:
- name: cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: cardiffnlp/tweet_topic_single
type: cardiffnlp/tweet_topic_single
args: cardiffnlp/tweet_topic_single
split: test_2021
metrics:
- name: F1
type: f1
value: 0.8948611931482575
- name: F1 (macro)
type: f1_macro
value: 0.800952410284692
- name: Accuracy
type: accuracy
value: 0.8948611931482575
pipeline_tag: text-classification
widget:
- text: "I'm sure the {@Tampa Bay Lightning@} would’ve rather faced the Flyers but man does their experience versus the Blue Jackets this year and last help them a lot versus this Islanders team. Another meat grinder upcoming for the good guys"
example_title: "Example 1"
- text: "Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US."
example_title: "Example 2"
---
# cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the [tweet_topic_single](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single). This model is fine-tuned on `train_all` split and validated on `test_2021` split of tweet_topic.
Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single/blob/main/lm_finetuning.py). It achieves the following results on the test_2021 set:
- F1 (micro): 0.8948611931482575
- F1 (macro): 0.800952410284692
- Accuracy: 0.8948611931482575
### Usage
```python
from transformers import pipeline
pipe = pipeline("text-classification", "cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all")
topic = pipe("Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US.")
print(topic)
```
### Reference
```
@inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
}
```
|
facebook/vit-msn-base
|
facebook
| 2022-09-30T13:22:24Z | 1,246 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit_msn",
"image-feature-extraction",
"vision",
"dataset:imagenet-1k",
"arxiv:2204.07141",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-feature-extraction
| 2022-09-09T06:08:37Z |
---
license: apache-2.0
tags:
- vision
datasets:
- imagenet-1k
---
# Vision Transformer (base-sized model) pre-trained with MSN
Vision Transformer (ViT) model pre-trained using the MSN method. It was introduced in the paper [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas and first released in [this repository](https://github.com/facebookresearch/msn).
Disclaimer: The team releasing MSN did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like). Images are presented to the model as a sequence of fixed-size patches.
MSN presents a joint-embedding architecture to match the prototypes of masked patches with that of the unmasked patches. With this setup, their method yields excellent performance in the low-shot and extreme low-shot regimes.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder.
## Intended uses & limitations
You can use the raw model for downstream tasks like image classification. See the [model hub](https://huggingface.co/models?filter=vit_msn) to look for different versions of MSN pre-trained models that interest you. The model is particularly beneficial when you have a few labeled samples in your training set.
### How to use
Here is how to use this backbone encoder:
```python
from transformers import AutoFeatureExtractor, ViTMSNModel
import torch
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/vit-msn-base")
model = ViTMSNModel.from_pretrained("facebook/vit-msn-base")
inputs = feature_extractor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
For fine-tuning on image classification use the `ViTMSNForImageClassification` class:
```python
from transformers import AutoFeatureExtractor, ViTMSNForImageClassification
import torch
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/vit-msn-base")
model = ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-base")
...
```
### Citation
```bibtex
@article{assran2022masked,
title={Masked Siamese Networks for Label-Efficient Learning},
author={Assran, Mahmoud, and Caron, Mathilde, and Misra, Ishan, and Bojanowski, Piotr, and Bordes, Florian and Vincent, Pascal, and Joulin, Armand, and Rabbat, Michael, and Ballas, Nicolas},
journal={arXiv preprint arXiv:2204.07141},
year={2022}
}
```
|
anas-awadalla/bart-base-few-shot-k-1024-finetuned-squad-seed-2
|
anas-awadalla
| 2022-09-30T12:57:50Z | 54 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-30T12:50:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-1024-finetuned-squad-seed-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-few-shot-k-1024-finetuned-squad-seed-2
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
FIT17/ppo-LunarLander-v2
|
FIT17
| 2022-09-30T12:30:36Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-30T12:29:57Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 123.84 +/- 87.24
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
...
```
|
anas-awadalla/bart-base-few-shot-k-512-finetuned-squad-seed-2
|
anas-awadalla
| 2022-09-30T12:30:21Z | 49 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-30T12:26:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-512-finetuned-squad-seed-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-few-shot-k-512-finetuned-squad-seed-2
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/bart-base-few-shot-k-256-finetuned-squad-seed-4
|
anas-awadalla
| 2022-09-30T12:17:35Z | 47 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-30T12:15:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-256-finetuned-squad-seed-4
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-base-few-shot-k-256-finetuned-squad-seed-4
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
n2ziaei/q-Taxi-v3
|
n2ziaei
| 2022-09-30T11:59:04Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-30T11:58:58Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **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="n2ziaei/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"])
```
|
anas-awadalla/bart-base-few-shot-k-128-finetuned-squad-seed-0
|
anas-awadalla
| 2022-09-30T11:55:31Z | 54 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-30T11:53:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-128-finetuned-squad-seed-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. -->
# bart-base-few-shot-k-128-finetuned-squad-seed-0
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/bart-base-few-shot-k-64-finetuned-squad-seed-4
|
anas-awadalla
| 2022-09-30T11:51:42Z | 57 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-30T11:50:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-64-finetuned-squad-seed-4
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-base-few-shot-k-64-finetuned-squad-seed-4
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
n2ziaei/q-FrozenLake-v1-4x4-noSlippery
|
n2ziaei
| 2022-09-30T11:40:07Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-30T11:40:02Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="n2ziaei/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"])
```
|
anas-awadalla/bart-base-few-shot-k-32-finetuned-squad-seed-4
|
anas-awadalla
| 2022-09-30T11:40:04Z | 72 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-30T11:38:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-32-finetuned-squad-seed-4
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-base-few-shot-k-32-finetuned-squad-seed-4
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/bart-base-few-shot-k-32-finetuned-squad-seed-0
|
anas-awadalla
| 2022-09-30T11:32:03Z | 52 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-30T11:30:25Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-32-finetuned-squad-seed-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. -->
# bart-base-few-shot-k-32-finetuned-squad-seed-0
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/bart-base-few-shot-k-16-finetuned-squad-seed-4
|
anas-awadalla
| 2022-09-30T11:28:08Z | 55 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-30T11:26:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-16-finetuned-squad-seed-4
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-base-few-shot-k-16-finetuned-squad-seed-4
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/bart-base-few-shot-k-16-finetuned-squad-seed-2
|
anas-awadalla
| 2022-09-30T11:24:05Z | 58 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-29T19:53:45Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-16-finetuned-squad-seed-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-few-shot-k-16-finetuned-squad-seed-2
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/bart-base-few-shot-k-16-finetuned-squad-seed-0
|
anas-awadalla
| 2022-09-30T11:19:55Z | 68 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-29T19:46:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-few-shot-k-16-finetuned-squad-seed-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. -->
# bart-base-few-shot-k-16-finetuned-squad-seed-0
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
Jardenna/opus-mt-en-nl-finetuned-en-to-af
|
Jardenna
| 2022-09-30T10:16:54Z | 74 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-28T17:36:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: opus-mt-en-nl-finetuned-en-to-af
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. -->
# opus-mt-en-nl-finetuned-en-to-af
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-nl](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 241 | 4.1990 | 5.0127 | 21.973 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
susnato/distilbert-base-uncased-finetuned-emotion
|
susnato
| 2022-09-30T10:09:58Z | 65 | 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
| 2022-09-30T09:32:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9325
- name: F1
type: f1
value: 0.9325489261096217
---
<!-- 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: 0.1553
- Accuracy: 0.9325
- F1: 0.9325
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2091 | 1.0 | 250 | 0.1686 | 0.9275 | 0.9267 |
| 0.1379 | 2.0 | 500 | 0.1553 | 0.9325 | 0.9325 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.12.1
|
rebolforces/dectrans-half-cheetah
|
rebolforces
| 2022-09-30T09:40:18Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"dataset:decision_transformer_gym_replay",
"endpoints_compatible",
"region:us"
] | null | 2022-09-30T09:18:54Z |
---
tags:
- generated_from_trainer
datasets:
- decision_transformer_gym_replay
model-index:
- name: output
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. -->
# output
This model is a fine-tuned version of [](https://huggingface.co/) on the decision_transformer_gym_replay dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 128
- 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_ratio: 0.1
- num_epochs: 220
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1.post200
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Hoax0930/kyoto_marian_test
|
Hoax0930
| 2022-09-30T09:16:48Z | 65 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-09-30T07:09:27Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: kyoto_marian_test
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. -->
# kyoto_marian_test
This model is a fine-tuned version of [Hoax0930/kyoto_marian_mod_2_2_1](https://huggingface.co/Hoax0930/kyoto_marian_mod_2_2_1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4758
- Bleu: 47.0794
## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
microsoft/markuplm-large-finetuned-websrc
|
microsoft
| 2022-09-30T08:58:02Z | 93 | 10 |
transformers
|
[
"transformers",
"pytorch",
"markuplm",
"question-answering",
"en",
"dataset:websrc",
"arxiv:2110.08518",
"region:us"
] |
question-answering
| 2022-06-14T13:38:07Z |
---
language:
- en
datasets:
- websrc
inference: false
---
# MarkupLM, fine-tuned on WebSRC
**Multimodal (text +markup language) pre-training for [Document AI](https://www.microsoft.com/en-us/research/project/document-ai/)**
## Introduction
MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. MarkupLM archives the SOTA results on multiple datasets. For more details, please refer to our paper:
[MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://arxiv.org/abs/2110.08518) Junlong Li, Yiheng Xu, Lei Cui, Furu Wei
## Usage
We refer to the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/markuplm) and [demo notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/MarkupLM).
|
microsoft/markuplm-base-finetuned-websrc
|
microsoft
| 2022-09-30T08:57:47Z | 548 | 10 |
transformers
|
[
"transformers",
"pytorch",
"markuplm",
"question-answering",
"en",
"dataset:websrc",
"arxiv:2110.08518",
"region:us"
] |
question-answering
| 2022-06-14T13:08:06Z |
---
language:
- en
datasets:
- websrc
inference: false
---
# MarkupLM, fine-tuned on WebSRC
**Multimodal (text +markup language) pre-training for [Document AI](https://www.microsoft.com/en-us/research/project/document-ai/)**
## Introduction
MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. MarkupLM archives the SOTA results on multiple datasets. For more details, please refer to our paper:
[MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://arxiv.org/abs/2110.08518) Junlong Li, Yiheng Xu, Lei Cui, Furu Wei
## Usage
We refer to the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/markuplm) and [demo notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/MarkupLM).
|
microsoft/markuplm-large
|
microsoft
| 2022-09-30T08:56:38Z | 760 | 20 |
transformers
|
[
"transformers",
"pytorch",
"markuplm",
"en",
"arxiv:2110.08518",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language:
- en
---
# MarkupLM
**Multimodal (text +markup language) pre-training for [Document AI](https://www.microsoft.com/en-us/research/project/document-ai/)**
## Introduction
MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. MarkupLM archives the SOTA results on multiple datasets. For more details, please refer to our paper:
[MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://arxiv.org/abs/2110.08518) Junlong Li, Yiheng Xu, Lei Cui, Furu Wei
## Usage
We refer to the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/markuplm) and [demo notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/MarkupLM).
|
floodpark/test
|
floodpark
| 2022-09-30T08:42:33Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-09-30T08:42:33Z |
---
license: creativeml-openrail-m
---
|
Ahmedshabana/distilbert-base-uncased-finetuned-mnli
|
Ahmedshabana
| 2022-09-30T08:19:48Z | 65 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-26T20:34:53Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mnli
split: train
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.42
---
<!-- 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-mnli
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1091
- Accuracy: 0.42
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 32 | 1.1005 | 0.28 |
| No log | 2.0 | 64 | 1.1038 | 0.3 |
| No log | 3.0 | 96 | 1.1074 | 0.32 |
| No log | 4.0 | 128 | 1.1088 | 0.42 |
| No log | 5.0 | 160 | 1.1091 | 0.42 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
sobamchan/bart-large-scitldr
|
sobamchan
| 2022-09-30T07:38:58Z | 174 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-30T07:32:46Z |
```py
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sobamchan/bart-large-scitldr")
model = AutoModelForSeq2SeqLM.from_pretrained("sobamchan/bart-large-scitldr")
text = "Abstract of a paper."
batch = tok(text, return_tensors="pt")
generated_ids = model.generate(batch["input_ids"])
print(tok.batch_decode(generated_ids, skip_special_tokens=True))
```
|
sd-concepts-library/80s-anime-ai-being
|
sd-concepts-library
| 2022-09-30T05:08:50Z | 0 | 3 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-30T05:08:44Z |
---
license: mit
---
### 80s Anime AI Being on Stable Diffusion
This is the `<anime-AI-being>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
Assadullah1467/donut-base-sroie2
|
Assadullah1467
| 2022-09-30T02:28:54Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2022-09-29T05:31:06Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-sroie2
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. -->
# donut-base-sroie2
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder 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: 2
- eval_batch_size: 16
- 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
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.8.1+cu102
- Datasets 2.5.1
- Tokenizers 0.13.0
|
anas-awadalla/t5-base-few-shot-k-1024-finetuned-squad-seed-4
|
anas-awadalla
| 2022-09-30T02:13:17Z | 74 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-27T21:25:45Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-1024-finetuned-squad-seed-4
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. -->
# t5-base-few-shot-k-1024-finetuned-squad-seed-4
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) 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: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
bigdino/bart-large-finetuned-cnn-dailymail
|
bigdino
| 2022-09-30T02:03:05Z | 52 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-30T01:36:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: bart-large-finetuned-cnn-dailymail
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-large-finetuned-cnn-dailymail
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the cnn_dailymail dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
anas-awadalla/t5-base-few-shot-k-1024-finetuned-squad-seed-0
|
anas-awadalla
| 2022-09-30T01:23:02Z | 60 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-27T19:58:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-1024-finetuned-squad-seed-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. -->
# t5-base-few-shot-k-1024-finetuned-squad-seed-0
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) 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: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
waifu-research-department/Holo
|
waifu-research-department
| 2022-09-30T01:16:44Z | 0 | 3 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-30T00:50:36Z |
---
license: mit
---
# Description
Trainer: Hank
Holo from Spice and Wolf
# Dataset
>Training: 7 images
>Regularization: 14 images
# Info
>Model Used: Waifu Diffusion 1.2
>Steps: 3000
>Keyword: Holo (Use this in the prompt)
>Class Phrase: wolf_girl
|
gary109/ai-light-dance_singing6_ft_wav2vec2-large-xlsr-53-5gram-v4-2
|
gary109
| 2022-09-30T01:15:03Z | 44 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-26T09:40:25Z |
---
tags:
- automatic-speech-recognition
- gary109/AI_Light_Dance
- generated_from_trainer
model-index:
- name: ai-light-dance_singing6_ft_wav2vec2-large-xlsr-53-5gram-v4-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-light-dance_singing6_ft_wav2vec2-large-xlsr-53-5gram-v4-2
This model is a fine-tuned version of [gary109/ai-light-dance_singing6_ft_wav2vec2-large-xlsr-53-5gram-v4-2](https://huggingface.co/gary109/ai-light-dance_singing6_ft_wav2vec2-large-xlsr-53-5gram-v4-2) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING6 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1731
- Wer: 0.0949
## 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: 4e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4688 | 1.0 | 171 | 0.1822 | 0.0986 |
| 0.4505 | 2.0 | 342 | 0.1822 | 0.0995 |
| 0.4418 | 3.0 | 513 | 0.1819 | 0.0996 |
| 0.4505 | 4.0 | 684 | 0.1842 | 0.1009 |
| 0.4403 | 5.0 | 855 | 0.1817 | 0.0983 |
| 0.4362 | 6.0 | 1026 | 0.1820 | 0.1001 |
| 0.4546 | 7.0 | 1197 | 0.1795 | 0.0979 |
| 0.4283 | 8.0 | 1368 | 0.1841 | 0.1004 |
| 0.4289 | 9.0 | 1539 | 0.1783 | 0.0970 |
| 0.445 | 10.0 | 1710 | 0.1785 | 0.0969 |
| 0.4483 | 11.0 | 1881 | 0.1781 | 0.0990 |
| 0.4319 | 12.0 | 2052 | 0.1811 | 0.0995 |
| 0.4175 | 13.0 | 2223 | 0.1781 | 0.0979 |
| 0.4298 | 14.0 | 2394 | 0.1761 | 0.0969 |
| 0.4511 | 15.0 | 2565 | 0.1741 | 0.0950 |
| 0.4287 | 16.0 | 2736 | 0.1771 | 0.0976 |
| 0.4417 | 17.0 | 2907 | 0.1780 | 0.0979 |
| 0.4257 | 18.0 | 3078 | 0.1762 | 0.0963 |
| 0.4588 | 19.0 | 3249 | 0.1750 | 0.0957 |
| 0.4439 | 20.0 | 3420 | 0.1758 | 0.0968 |
| 0.4515 | 21.0 | 3591 | 0.1782 | 0.0966 |
| 0.4311 | 22.0 | 3762 | 0.1774 | 0.0975 |
| 0.403 | 23.0 | 3933 | 0.1758 | 0.0963 |
| 0.4168 | 24.0 | 4104 | 0.1775 | 0.0972 |
| 0.425 | 25.0 | 4275 | 0.1742 | 0.0952 |
| 0.4493 | 26.0 | 4446 | 0.1749 | 0.0963 |
| 0.4232 | 27.0 | 4617 | 0.1749 | 0.0966 |
| 0.4331 | 28.0 | 4788 | 0.1754 | 0.0964 |
| 0.4306 | 29.0 | 4959 | 0.1756 | 0.0967 |
| 0.4261 | 30.0 | 5130 | 0.1753 | 0.0969 |
| 0.4284 | 31.0 | 5301 | 0.1749 | 0.0958 |
| 0.4322 | 32.0 | 5472 | 0.1748 | 0.0952 |
| 0.4225 | 33.0 | 5643 | 0.1747 | 0.0952 |
| 0.4179 | 34.0 | 5814 | 0.1749 | 0.0955 |
| 0.4264 | 35.0 | 5985 | 0.1757 | 0.0966 |
| 0.4217 | 36.0 | 6156 | 0.1753 | 0.0955 |
| 0.4556 | 37.0 | 6327 | 0.1749 | 0.0957 |
| 0.4181 | 38.0 | 6498 | 0.1756 | 0.0972 |
| 0.4286 | 39.0 | 6669 | 0.1747 | 0.0956 |
| 0.4427 | 40.0 | 6840 | 0.1747 | 0.0965 |
| 0.4292 | 41.0 | 7011 | 0.1742 | 0.0955 |
| 0.424 | 42.0 | 7182 | 0.1740 | 0.0952 |
| 0.4314 | 43.0 | 7353 | 0.1743 | 0.0963 |
| 0.4359 | 44.0 | 7524 | 0.1744 | 0.0952 |
| 0.4195 | 45.0 | 7695 | 0.1736 | 0.0949 |
| 0.4214 | 46.0 | 7866 | 0.1731 | 0.0949 |
| 0.4358 | 47.0 | 8037 | 0.1738 | 0.0952 |
| 0.4347 | 48.0 | 8208 | 0.1742 | 0.0956 |
| 0.4032 | 49.0 | 8379 | 0.1739 | 0.0956 |
| 0.441 | 50.0 | 8550 | 0.1737 | 0.0957 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
anas-awadalla/t5-base-few-shot-k-512-finetuned-squad-seed-2
|
anas-awadalla
| 2022-09-30T00:42:57Z | 64 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-27T19:11:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-512-finetuned-squad-seed-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-few-shot-k-512-finetuned-squad-seed-2
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) 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: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-all
|
cardiffnlp
| 2022-09-30T00:31:32Z | 65 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"dataset:cardiffnlp/tweet_topic_multi",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-27T16:53:48Z |
---
datasets:
- cardiffnlp/tweet_topic_multi
metrics:
- f1
- accuracy
model-index:
- name: cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-all
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: cardiffnlp/tweet_topic_multi
type: cardiffnlp/tweet_topic_multi
args: cardiffnlp/tweet_topic_multi
split: test_2021
metrics:
- name: F1
type: f1
value: 0.7599173553719007
- name: F1 (macro)
type: f1_macro
value: 0.5990098728991452
- name: Accuracy
type: accuracy
value: 0.5360333531864205
pipeline_tag: text-classification
widget:
- text: "I'm sure the {@Tampa Bay Lightning@} would’ve rather faced the Flyers but man does their experience versus the Blue Jackets this year and last help them a lot versus this Islanders team. Another meat grinder upcoming for the good guys"
example_title: "Example 1"
- text: "Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US."
example_title: "Example 2"
---
# cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-all
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) on the [tweet_topic_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). This model is fine-tuned on `train_all` split and validated on `test_2021` split of tweet_topic.
Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi/blob/main/lm_finetuning.py). It achieves the following results on the test_2021 set:
- F1 (micro): 0.7599173553719007
- F1 (macro): 0.5990098728991452
- Accuracy: 0.5360333531864205
### Usage
```python
import math
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
def sigmoid(x):
return 1 / (1 + math.exp(-x))
tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-all")
model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-all", problem_type="multi_label_classification")
model.eval()
class_mapping = model.config.id2label
with torch.no_grad():
text = #NewVideo Cray Dollas- Water- Ft. Charlie Rose- (Official Music Video)- {{URL}} via {@YouTube@} #watchandlearn {{USERNAME}}
tokens = tokenizer(text, return_tensors='pt')
output = model(**tokens)
flags = [sigmoid(s) > 0.5 for s in output[0][0].detach().tolist()]
topic = [class_mapping[n] for n, i in enumerate(flags) if i]
print(topic)
```
### Reference
```
@inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
}
```
|
huggingtweets/pukicho
|
huggingtweets
| 2022-09-30T00:31:10Z | 62 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-30T00:29:29Z |
---
language: en
thumbnail: http://www.huggingtweets.com/pukicho/1664497866027/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/866045441942487041/xRAnnstd_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">Pukicho</div>
<div style="text-align: center; font-size: 14px;">@pukicho</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 Pukicho.
| Data | Pukicho |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 60 |
| Short tweets | 301 |
| Tweets kept | 2886 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1tuqgf1r/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 @pukicho's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3f17ip6z) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3f17ip6z/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/pukicho')
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)
|
cardiffnlp/roberta-large-tweet-topic-multi-2020
|
cardiffnlp
| 2022-09-30T00:30:48Z | 60 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"dataset:cardiffnlp/tweet_topic_multi",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-29T14:23:19Z |
---
datasets:
- cardiffnlp/tweet_topic_multi
metrics:
- f1
- accuracy
model-index:
- name: cardiffnlp/roberta-large-tweet-topic-multi-2020
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: cardiffnlp/tweet_topic_multi
type: cardiffnlp/tweet_topic_multi
args: cardiffnlp/tweet_topic_multi
split: test_2021
metrics:
- name: F1
type: f1
value: 0.7323655694132079
- name: F1 (macro)
type: f1_macro
value: 0.5794562917377284
- name: Accuracy
type: accuracy
value: 0.4937462775461584
pipeline_tag: text-classification
widget:
- text: "I'm sure the {@Tampa Bay Lightning@} would’ve rather faced the Flyers but man does their experience versus the Blue Jackets this year and last help them a lot versus this Islanders team. Another meat grinder upcoming for the good guys"
example_title: "Example 1"
- text: "Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US."
example_title: "Example 2"
---
# cardiffnlp/roberta-large-tweet-topic-multi-2020
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the [tweet_topic_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). This model is fine-tuned on `train_2020` split and validated on `test_2021` split of tweet_topic.
Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi/blob/main/lm_finetuning.py). It achieves the following results on the test_2021 set:
- F1 (micro): 0.7323655694132079
- F1 (macro): 0.5794562917377284
- Accuracy: 0.4937462775461584
### Usage
```python
import math
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
def sigmoid(x):
return 1 / (1 + math.exp(-x))
tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/roberta-large-tweet-topic-multi-2020")
model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/roberta-large-tweet-topic-multi-2020", problem_type="multi_label_classification")
model.eval()
class_mapping = model.config.id2label
with torch.no_grad():
text = #NewVideo Cray Dollas- Water- Ft. Charlie Rose- (Official Music Video)- {{URL}} via {@YouTube@} #watchandlearn {{USERNAME}}
tokens = tokenizer(text, return_tensors='pt')
output = model(**tokens)
flags = [sigmoid(s) > 0.5 for s in output[0][0].detach().tolist()]
topic = [class_mapping[n] for n, i in enumerate(flags) if i]
print(topic)
```
### Reference
```
@inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
}
```
|
cardiffnlp/roberta-large-tweet-topic-multi-all
|
cardiffnlp
| 2022-09-30T00:29:30Z | 78 | 6 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"dataset:cardiffnlp/tweet_topic_multi",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-29T17:02:02Z |
---
datasets:
- cardiffnlp/tweet_topic_multi
metrics:
- f1
- accuracy
model-index:
- name: cardiffnlp/roberta-large-tweet-topic-multi-all
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: cardiffnlp/tweet_topic_multi
type: cardiffnlp/tweet_topic_multi
args: cardiffnlp/tweet_topic_multi
split: test_2021
metrics:
- name: F1
type: f1
value: 0.7631035905901775
- name: F1 (macro)
type: f1_macro
value: 0.6202570779365779
- name: Accuracy
type: accuracy
value: 0.5366289458010721
pipeline_tag: text-classification
widget:
- text: "I'm sure the {@Tampa Bay Lightning@} would’ve rather faced the Flyers but man does their experience versus the Blue Jackets this year and last help them a lot versus this Islanders team. Another meat grinder upcoming for the good guys"
example_title: "Example 1"
- text: "Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US."
example_title: "Example 2"
---
# cardiffnlp/roberta-large-tweet-topic-multi-all
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the [tweet_topic_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). This model is fine-tuned on `train_all` split and validated on `test_2021` split of tweet_topic.
Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi/blob/main/lm_finetuning.py). It achieves the following results on the test_2021 set:
- F1 (micro): 0.7631035905901775
- F1 (macro): 0.6202570779365779
- Accuracy: 0.5366289458010721
### Usage
```python
import math
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
def sigmoid(x):
return 1 / (1 + math.exp(-x))
tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/roberta-large-tweet-topic-multi-all")
model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/roberta-large-tweet-topic-multi-all", problem_type="multi_label_classification")
model.eval()
class_mapping = model.config.id2label
with torch.no_grad():
text = #NewVideo Cray Dollas- Water- Ft. Charlie Rose- (Official Music Video)- {{URL}} via {@YouTube@} #watchandlearn {{USERNAME}}
tokens = tokenizer(text, return_tensors='pt')
output = model(**tokens)
flags = [sigmoid(s) > 0.5 for s in output[0][0].detach().tolist()]
topic = [class_mapping[n] for n, i in enumerate(flags) if i]
print(topic)
```
### Reference
```
@inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
}
```
|
anas-awadalla/t5-base-few-shot-k-512-finetuned-squad-seed-0
|
anas-awadalla
| 2022-09-30T00:28:20Z | 63 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-27T18:47:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-512-finetuned-squad-seed-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. -->
# t5-base-few-shot-k-512-finetuned-squad-seed-0
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) 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: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/t5-base-few-shot-k-256-finetuned-squad-seed-4
|
anas-awadalla
| 2022-09-30T00:12:43Z | 67 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-27T18:33:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-256-finetuned-squad-seed-4
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. -->
# t5-base-few-shot-k-256-finetuned-squad-seed-4
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) 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: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
huggingtweets/lovely_lads
|
huggingtweets
| 2022-09-30T00:05:18Z | 92 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-30T00:03:46Z |
---
language: en
thumbnail: http://www.huggingtweets.com/lovely_lads/1664496313498/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/3342017159/2260e8f14ecdf5fd391b3a371e706820_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
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">Beatles' lyrics bot</div>
<div style="text-align: center; font-size: 14px;">@lovely_lads</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 Beatles' lyrics bot.
| Data | Beatles' lyrics bot |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 0 |
| Short tweets | 0 |
| Tweets kept | 3250 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/38xe0fqm/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 @lovely_lads's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1bgsb3aq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1bgsb3aq/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/lovely_lads')
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)
|
huggingtweets/tally_lyrics
|
huggingtweets
| 2022-09-29T23:57:17Z | 119 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-29T23:55:56Z |
---
language: en
thumbnail: http://www.huggingtweets.com/tally_lyrics/1664495833031/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/1280523258597707776/YLMt_BC-_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">tally hall lyrics</div>
<div style="text-align: center; font-size: 14px;">@tally_lyrics</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 tally hall lyrics.
| Data | tally hall lyrics |
| --- | --- |
| Tweets downloaded | 3200 |
| Retweets | 1 |
| Short tweets | 215 |
| Tweets kept | 2984 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2m1lt2gw/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 @tally_lyrics's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/jcv42uh2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/jcv42uh2/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/tally_lyrics')
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)
|
anas-awadalla/t5-base-few-shot-k-128-finetuned-squad-seed-4
|
anas-awadalla
| 2022-09-29T23:44:21Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-27T17:57:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-128-finetuned-squad-seed-4
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. -->
# t5-base-few-shot-k-128-finetuned-squad-seed-4
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) 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: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/t5-base-few-shot-k-128-finetuned-squad-seed-2
|
anas-awadalla
| 2022-09-29T23:35:20Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-27T17:47:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-base-few-shot-k-128-finetuned-squad-seed-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-few-shot-k-128-finetuned-squad-seed-2
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) 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: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
IIIT-L/muril-base-cased-finetuned-TRAC-DS
|
IIIT-L
| 2022-09-29T23:14:48Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-29T22:08:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: muril-base-cased-finetuned-TRAC-DS
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. -->
# muril-base-cased-finetuned-TRAC-DS
This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1894
- Accuracy: 0.6838
- Precision: 0.6534
- Recall: 0.6513
- F1: 0.6522
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0109 | 1.99 | 612 | 0.9284 | 0.5948 | 0.4327 | 0.5193 | 0.4509 |
| 0.8635 | 3.99 | 1224 | 0.8556 | 0.6291 | 0.6012 | 0.5865 | 0.5888 |
| 0.764 | 5.98 | 1836 | 0.8585 | 0.6609 | 0.6249 | 0.6275 | 0.6260 |
| 0.6744 | 7.97 | 2448 | 0.8469 | 0.6732 | 0.6391 | 0.6408 | 0.6398 |
| 0.5865 | 9.97 | 3060 | 0.8438 | 0.6667 | 0.6424 | 0.6395 | 0.6395 |
| 0.4978 | 11.96 | 3672 | 0.9269 | 0.6855 | 0.6532 | 0.6582 | 0.6542 |
| 0.4245 | 13.95 | 4284 | 0.9934 | 0.6699 | 0.6397 | 0.6482 | 0.6396 |
| 0.378 | 15.95 | 4896 | 1.0488 | 0.6830 | 0.6530 | 0.6446 | 0.6474 |
| 0.3349 | 17.94 | 5508 | 1.0548 | 0.6806 | 0.6505 | 0.6536 | 0.6518 |
| 0.3019 | 19.93 | 6120 | 1.1092 | 0.6757 | 0.6476 | 0.6497 | 0.6482 |
| 0.2869 | 21.93 | 6732 | 1.1515 | 0.6814 | 0.6507 | 0.6514 | 0.6510 |
| 0.2575 | 23.92 | 7344 | 1.1894 | 0.6838 | 0.6534 | 0.6513 | 0.6522 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Bistolero/nlge24mixed
|
Bistolero
| 2022-09-29T23:11:33Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-29T23:08:39Z |
---
tags:
- generated_from_trainer
model-index:
- name: nlge24mixed
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. -->
# nlge24mixed
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 30
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
nbroad/fix_punct_uncased_t5_small
|
nbroad
| 2022-09-29T22:50:22Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-29T22:18:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: fix_punct_uncased_t5_small
results: []
datasets:
- https://huggingface.co/datasets/nbroad/fix_punctuation
widget:
- text: this is, a sentence. with odd punctuation to show off what, the model. can do
- text: what, should the proper. punctuation. in. this sentence be?
---
# fix_punct_uncased_t5_small
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the [NPR utterances dataset](https://www.kaggle.com/datasets/shuyangli94/interview-npr-media-dialog-transcripts?select=utterances.csv).
## Dataset
The model was trained on 80k rows from the above dataset consisting of NPR radio transcripts. Commans, periods, and semicolons were removed from the text and then random commas, periods, and semicolons were added. The model was trained to place those three punctuation marks in the correct location. All texts were lowercase during training.
It achieves the following results on the evaluation set:
- Loss: 0.3050
- Rouge1: 92.8762
- Rouge2: 90.4805
- Rougel: 92.8662
- Rougelsum: 92.7068
- Gen Len: 48.6130
## Model description
The purpose of this model is to correct the punctuation in a sentence. For example, the phrase "this is, a sentence. with odd punctuation to show off what, the model. can do" gets changed to "this is a sentence with odd punctuation to show off what the model can do."
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 128
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.3066 | 1.0 | 600 | 0.4347 | 59.0002 | 54.7692 | 58.7112 | 58.7856 | 16.3808 |
| 0.8192 | 2.0 | 1200 | 0.3154 | 62.4672 | 59.0199 | 62.4096 | 62.3667 | 16.5158 |
| 0.7208 | 3.0 | 1800 | 0.3050 | 62.701 | 59.3201 | 62.6739 | 62.6165 | 16.5471 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.11.0a0+17540c5
- Datasets 2.5.1
- Tokenizers 0.12.1
|
aware-ai/wav2vec2-xls-r-300m-german
|
aware-ai
| 2022-09-29T22:20:41Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_10_0",
"generated_from_trainer",
"de",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-08-25T04:37:30Z |
---
language:
- de
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_10_0
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-300m-german
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-xls-r-300m-german
This model is a fine-tuned version of [wav2vec2-xls-r-300m-german](https://huggingface.co/wav2vec2-xls-r-300m-german) on the MOZILLA-FOUNDATION/COMMON_VOICE_10_0 - DE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4842
- Wer: 0.3940
## 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: 3e-07
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2759 | 1.0 | 3612 | 0.4813 | 0.3922 |
| 0.2672 | 2.0 | 7224 | 0.4796 | 0.3925 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/lphr-style
|
sd-concepts-library
| 2022-09-29T21:48:37Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-29T16:46:23Z |
---
license: mit
---
### LPHR Style on Stable Diffusion
This is the `<lphr-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Images from here: https://www.luispabloherrera.com/
Note: Images are copyrighted
Here is the new concept you will be able to use as a `style`:
|
anas-awadalla/bart-base-finetuned-squad-seq2seq
|
anas-awadalla
| 2022-09-29T21:34:29Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-29T19:29:33Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bart-base-finetuned-squad-seq2seq
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-base-finetuned-squad-seq2seq
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
waifu-research-department/Blaze
|
waifu-research-department
| 2022-09-29T21:04:14Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-29T00:53:41Z |
---
license: mit
---
# Description
Trainer: naotsue
Blaze from Arknights
# Dataset
>Training: 29 images
>Regularization: (~300)
# Info
>Model Used: Waifu Diffusion 1.2
>Steps: 3000
>Keyword: BLAZE (Use this in the prompt)
>Class Phrase: 1girl_dark_hair_red_headband_fox_ears
|
anas-awadalla/t5-small-few-shot-k-16-finetuned-squad-seed-0
|
anas-awadalla
| 2022-09-29T20:45:16Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-27T12:09:47Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-16-finetuned-squad-seed-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. -->
# t5-small-few-shot-k-16-finetuned-squad-seed-0
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
matemato/Reinforce-cartpole_2
|
matemato
| 2022-09-29T19:59:02Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-29T19:58:46Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole_2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
matemato/Reinforce-cartpole
|
matemato
| 2022-09-29T19:53:51Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-29T19:52:10Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 96.80 +/- 19.03
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
n2ziaei/ppo-LunarLander-v2
|
n2ziaei
| 2022-09-29T18:52:23Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-29T18:52:03Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -276.53 +/- 169.07
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
...
```
|
sd-concepts-library/trust-support
|
sd-concepts-library
| 2022-09-29T18:38:52Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-29T18:38:40Z |
---
license: mit
---
### trust_support on Stable Diffusion
This is the `<trust>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
TingChenChang/cMedQA2-multi-qa-mpnet-zh
|
TingChenChang
| 2022-09-29T18:22:33Z | 6 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-29T18:22:15Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
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.
<!--- Describe your model here -->
## 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('{MODEL_NAME}')
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
#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('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# 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. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 11781 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 1178,
"evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 1e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 5891,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
TingChenChang/lcqmc-ocnli-cnsd-multi-MiniLM-v2
|
TingChenChang
| 2022-09-29T18:21:36Z | 5 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-22T18:18:52Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## 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('{MODEL_NAME}')
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
#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('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# 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. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 2166 with parameters:
```
{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 216,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 1e-06
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 2166,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
TingChenChang/multi-qa-mpnet-zh
|
TingChenChang
| 2022-09-29T18:17:21Z | 6 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-29T18:17:06Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
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.
<!--- Describe your model here -->
## 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('{MODEL_NAME}')
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
#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('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# 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. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 11898 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Tritkoman/GermantoNorthFrisian
|
Tritkoman
| 2022-09-29T17:33:29Z | 99 | 2 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"translation",
"de",
"en",
"dataset:Tritkoman/autotrain-data-ttreddsd",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-09-29T17:21:11Z |
---
tags:
- autotrain
- translation
language:
- de
- en
datasets:
- Tritkoman/autotrain-data-ttreddsd
co2_eq_emissions:
emissions: 21.087082943674986
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1599456406
- CO2 Emissions (in grams): 21.0871
## Validation Metrics
- Loss: 1.347
- SacreBLEU: 40.859
- Gen len: 13.513
|
sd-concepts-library/concept-art
|
sd-concepts-library
| 2022-09-29T17:25:34Z | 0 | 37 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-29T17:25:23Z |
---
license: mit
---
### Concept Art on Stable Diffusion
This is the `<concept-art>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
Hoax0930/kyoto_marian_mod_5_1
|
Hoax0930
| 2022-09-29T17:13:22Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-09-29T15:23:46Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: kyoto_marian_mod_5_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# kyoto_marian_mod_5_1
This model is a fine-tuned version of [Hoax0930/kyoto_marian_mod_5_0](https://huggingface.co/Hoax0930/kyoto_marian_mod_5_0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7105
- Bleu: 20.5324
## 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: 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: 8
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
eloi/ibaillanosmodel
|
eloi
| 2022-09-29T16:18:00Z | 32 | 0 |
diffusers
|
[
"diffusers",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2022-09-29T16:16:03Z |
---
license: mit
---
### ibaillanosmodel on Stable Diffusion via Dreambooth
#### model by eloi
This your the Stable Diffusion model fine-tuned the ibaillanosmodel concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **ibaillanos**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
huggingtweets/apandahvevo-apandeez
|
huggingtweets
| 2022-09-29T15:59:29Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-29T15:59:21Z |
---
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/1572592902672470016/kAEvgyZL_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/1487225505573183490/b3iFm538_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">apandah & big poo</div>
<div style="text-align: center; font-size: 14px;">@apandahvevo-apandeez</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 apandah & big poo.
| Data | apandah | big poo |
| --- | --- | --- |
| Tweets downloaded | 3229 | 657 |
| Retweets | 53 | 22 |
| Short tweets | 1470 | 341 |
| Tweets kept | 1706 | 294 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/36gnlq3h/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 @apandahvevo-apandeez's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3gv7a5fr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3gv7a5fr/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/apandahvevo-apandeez')
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)
|
DrishtiSharma/finetuned-ConvNext-Indian-food
|
DrishtiSharma
| 2022-09-29T15:50:06Z | 200 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"convnext",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-09-29T14:54:46Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: finetuned-ConvNext-Indian-food
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: indian_food_images
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9107332624867163
---
<!-- 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. -->
# finetuned-ConvNext-Indian-food
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the indian_food_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2977
- Accuracy: 0.9107
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3145 | 0.3 | 100 | 1.0460 | 0.8151 |
| 0.6694 | 0.6 | 200 | 0.5439 | 0.8757 |
| 0.5057 | 0.9 | 300 | 0.4398 | 0.8831 |
| 0.4381 | 1.2 | 400 | 0.4286 | 0.8820 |
| 0.4376 | 1.5 | 500 | 0.3400 | 0.9044 |
| 0.2499 | 1.8 | 600 | 0.3312 | 0.9065 |
| 0.2802 | 2.1 | 700 | 0.3338 | 0.9033 |
| 0.3014 | 2.4 | 800 | 0.3572 | 0.8948 |
| 0.2508 | 2.7 | 900 | 0.3432 | 0.9022 |
| 0.2012 | 3.0 | 1000 | 0.3060 | 0.9086 |
| 0.2634 | 3.3 | 1100 | 0.3451 | 0.9086 |
| 0.2483 | 3.6 | 1200 | 0.3550 | 0.9044 |
| 0.2273 | 3.9 | 1300 | 0.2977 | 0.9107 |
| 0.1214 | 4.2 | 1400 | 0.3265 | 0.9160 |
| 0.2048 | 4.5 | 1500 | 0.3126 | 0.9214 |
| 0.0997 | 4.8 | 1600 | 0.3164 | 0.9160 |
| 0.1145 | 5.11 | 1700 | 0.3055 | 0.9139 |
| 0.1578 | 5.41 | 1800 | 0.3195 | 0.9171 |
| 0.0615 | 5.71 | 1900 | 0.3401 | 0.9107 |
| 0.1537 | 6.01 | 2000 | 0.3428 | 0.9097 |
| 0.1278 | 6.31 | 2100 | 0.3058 | 0.9192 |
| 0.1274 | 6.61 | 2200 | 0.3189 | 0.9192 |
| 0.0877 | 6.91 | 2300 | 0.3370 | 0.9182 |
| 0.1058 | 7.21 | 2400 | 0.3225 | 0.9192 |
| 0.1742 | 7.51 | 2500 | 0.3341 | 0.9214 |
| 0.0949 | 7.81 | 2600 | 0.3126 | 0.9256 |
| 0.1732 | 8.11 | 2700 | 0.3078 | 0.9235 |
| 0.0894 | 8.41 | 2800 | 0.3098 | 0.9267 |
| 0.1257 | 8.71 | 2900 | 0.3030 | 0.9320 |
| 0.1747 | 9.01 | 3000 | 0.3106 | 0.9256 |
| 0.2119 | 9.31 | 3100 | 0.3037 | 0.9299 |
| 0.1074 | 9.61 | 3200 | 0.3049 | 0.9277 |
| 0.1275 | 9.91 | 3300 | 0.3046 | 0.9309 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
RamAnanth1/decision-transformers-walker2d-expert
|
RamAnanth1
| 2022-09-29T15:40:33Z | 64 | 0 |
transformers
|
[
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"deep-reinforcement-learning",
"reinforcement-learning",
"decision-transformer",
"gym-continous-control",
"dataset:decision_transformer_gym_replay",
"arxiv:2106.01345",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2022-09-29T15:35:33Z |
---
tags:
- generated_from_trainer
- deep-reinforcement-learning
- reinforcement-learning
- decision-transformer
- gym-continous-control
pipeline_tag: reinforcement-learning
datasets:
- decision_transformer_gym_replay
---
<!-- 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. -->
# Decision Transformer model trained on expert trajectories sampled from the Gym Walker2d environment
This is a trained [Decision Transformer](https://arxiv.org/abs/2106.01345) model trained from scratch on expert trajectories sampled from the Gym Walker2d environment based on the modified version of the example [training script](https://github.com/huggingface/blog/blob/main/notebooks/101_train-decision-transformers.ipynb) provided by HuggingFace
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- 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_ratio: 0.1
- num_epochs: 120
### Training results
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
model-attribution-challenge/roberta-base
|
model-attribution-challenge
| 2022-09-29T15:21:48Z | 170 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"roberta",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1907.11692",
"arxiv:1806.02847",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-11-09T20:15:07Z |
---
language: en
tags:
- exbert
license: mit
datasets:
- bookcorpus
- wikipedia
---
# RoBERTa base model
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it
makes a difference between english and English.
Disclaimer: The team releasing RoBERTa did not write a model card for this model so this model card has been written by
the Hugging Face team.
## Model description
RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means
it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model
randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict
the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one
after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to
learn a bidirectional representation of the sentence.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
See the [model hub](https://huggingface.co/models?filter=roberta) to look for fine-tuned versions on a task that
interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='roberta-base')
>>> unmasker("Hello I'm a <mask> model.")
[{'sequence': "<s>Hello I'm a male model.</s>",
'score': 0.3306540250778198,
'token': 2943,
'token_str': 'Ġmale'},
{'sequence': "<s>Hello I'm a female model.</s>",
'score': 0.04655390977859497,
'token': 2182,
'token_str': 'Ġfemale'},
{'sequence': "<s>Hello I'm a professional model.</s>",
'score': 0.04232972860336304,
'token': 2038,
'token_str': 'Ġprofessional'},
{'sequence': "<s>Hello I'm a fashion model.</s>",
'score': 0.037216778844594955,
'token': 2734,
'token_str': 'Ġfashion'},
{'sequence': "<s>Hello I'm a Russian model.</s>",
'score': 0.03253649175167084,
'token': 1083,
'token_str': 'ĠRussian'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaModel.from_pretrained('roberta-base')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = TFRobertaModel.from_pretrained('roberta-base')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from
neutral. Therefore, the model can have biased predictions:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='roberta-base')
>>> unmasker("The man worked as a <mask>.")
[{'sequence': '<s>The man worked as a mechanic.</s>',
'score': 0.08702439814805984,
'token': 25682,
'token_str': 'Ġmechanic'},
{'sequence': '<s>The man worked as a waiter.</s>',
'score': 0.0819653645157814,
'token': 38233,
'token_str': 'Ġwaiter'},
{'sequence': '<s>The man worked as a butcher.</s>',
'score': 0.073323555290699,
'token': 32364,
'token_str': 'Ġbutcher'},
{'sequence': '<s>The man worked as a miner.</s>',
'score': 0.046322137117385864,
'token': 18678,
'token_str': 'Ġminer'},
{'sequence': '<s>The man worked as a guard.</s>',
'score': 0.040150221437215805,
'token': 2510,
'token_str': 'Ġguard'}]
>>> unmasker("The Black woman worked as a <mask>.")
[{'sequence': '<s>The Black woman worked as a waitress.</s>',
'score': 0.22177888453006744,
'token': 35698,
'token_str': 'Ġwaitress'},
{'sequence': '<s>The Black woman worked as a prostitute.</s>',
'score': 0.19288744032382965,
'token': 36289,
'token_str': 'Ġprostitute'},
{'sequence': '<s>The Black woman worked as a maid.</s>',
'score': 0.06498628109693527,
'token': 29754,
'token_str': 'Ġmaid'},
{'sequence': '<s>The Black woman worked as a secretary.</s>',
'score': 0.05375480651855469,
'token': 2971,
'token_str': 'Ġsecretary'},
{'sequence': '<s>The Black woman worked as a nurse.</s>',
'score': 0.05245552211999893,
'token': 9008,
'token_str': 'Ġnurse'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The RoBERTa model was pretrained on the reunion of five datasets:
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books;
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers) ;
- [CC-News](https://commoncrawl.org/2016/10/news-dataset-available/), a dataset containing 63 millions English news
articles crawled between September 2016 and February 2019.
- [OpenWebText](https://github.com/jcpeterson/openwebtext), an opensource recreation of the WebText dataset used to
train GPT-2,
- [Stories](https://arxiv.org/abs/1806.02847) a dataset containing a subset of CommonCrawl data filtered to match the
story-like style of Winograd schemas.
Together theses datasets weight 160GB of text.
## Training procedure
### Preprocessing
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50,000. The inputs of
the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
with `<s>` and the end of one by `</s>`
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `<mask>`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
### Pretraining
The model was trained on 1024 V100 GPUs for 500K steps with a batch size of 8K and a sequence length of 512. The
optimizer used is Adam with a learning rate of 6e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and
\\(\epsilon = 1e-6\\), a weight decay of 0.01, learning rate warmup for 24,000 steps and linear decay of the learning
rate after.
## Evaluation results
When fine-tuned on downstream tasks, this model achieves the following results:
Glue test results:
| Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE |
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|
| | 87.6 | 91.9 | 92.8 | 94.8 | 63.6 | 91.2 | 90.2 | 78.7 |
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1907-11692,
author = {Yinhan Liu and
Myle Ott and
Naman Goyal and
Jingfei Du and
Mandar Joshi and
Danqi Chen and
Omer Levy and
Mike Lewis and
Luke Zettlemoyer and
Veselin Stoyanov},
title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
journal = {CoRR},
volume = {abs/1907.11692},
year = {2019},
url = {http://arxiv.org/abs/1907.11692},
archivePrefix = {arXiv},
eprint = {1907.11692},
timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<a href="https://huggingface.co/exbert/?model=roberta-base">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
JanRubenFischer/Klassifizierung-Gewerke
|
JanRubenFischer
| 2022-09-29T14:10:39Z | 100 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-28T14:24:38Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: Klassifizierung-Gewerke
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. -->
# Klassifizierung-Gewerke
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0964
- F1: 0.9822
## 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
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6216 | 1.0 | 91 | 0.1944 | 0.9415 |
| 0.1465 | 2.0 | 182 | 0.1180 | 0.9695 |
| 0.0651 | 3.0 | 273 | 0.0964 | 0.9822 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
philschmid/distilbert-base-uncased-emotion
|
philschmid
| 2022-09-29T14:01:38Z | 123 | 2 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"emotion",
"endpoints-template",
"en",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-18T05:56:21Z |
---
language:
- en
tags:
- text-classification
- emotion
- endpoints-template
license: apache-2.0
datasets:
- emotion
metrics:
- Accuracy, F1 Score
---
# Fork of [bhadresh-savani/distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion)
|
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