modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-29 12:28:39
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 526
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
jgriffi/pegasus-samsum
|
jgriffi
| 2022-06-23T11:18:59Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-23T09:29:19Z |
---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4841
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7073 | 0.54 | 500 | 1.4841 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
transZ/M2M_Vi_Ba
|
transZ
| 2022-06-23T11:01:27Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"translation",
"vi",
"ba",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-06-22T15:26:10Z |
---
language:
- vi
- ba
tags:
- translation
datasets:
- custom dataset
metrics:
- bleu
- sacrebleu
---
# How to run the model
```python
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
model = M2M100ForConditionalGeneration.from_pretrained("transZ/M2M_Vi_Ba")
tokenizer = M2M100Tokenizer.from_pretrained("transZ/M2M_Vi_Ba")
tokenizer.src_lang = "vi"
vi_text = "Hôm nay ba đi chợ."
encoded_vi = tokenizer(vi_text, return_tensors="pt")
generated_tokens = model.generate(**encoded_vi, forced_bos_token_id=tokenizer.get_lang_id("ba"))
translate = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
print(translate)
```
|
Homayoon83/Carball
|
Homayoon83
| 2022-06-23T09:55:39Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2022-06-23T09:55:38Z |
---
license: bigscience-bloom-rail-1.0
---
|
Saraswati/TEST2ppo-LunarLander-v2
|
Saraswati
| 2022-06-23T08:54:21Z | 0 | 1 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-22T11:28:21Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 195.82 +/- 82.45
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
checkpoint = load_from_hub(
repo_id="Saraswati/TEST2ppo-LunarLander-v2",
filename="{MODEL FILENAME}.zip",
)
...
```
|
cjbarrie/autotrain-atc2
|
cjbarrie
| 2022-06-23T08:01:58Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:cjbarrie/autotrain-data-traintest-sentiment-split",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-23T07:59:46Z |
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- cjbarrie/autotrain-data-traintest-sentiment-split
co2_eq_emissions: 3.1566482249518177
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1024534825
- CO2 Emissions (in grams): 3.1566482249518177
## Validation Metrics
- Loss: 0.5167999267578125
- Accuracy: 0.7523809523809524
- Precision: 0.7377049180327869
- Recall: 0.5555555555555556
- AUC: 0.8142525600535937
- F1: 0.6338028169014086
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/cjbarrie/autotrain-traintest-sentiment-split-1024534825
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("cjbarrie/autotrain-traintest-sentiment-split-1024534825", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("cjbarrie/autotrain-traintest-sentiment-split-1024534825", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
sun1638650145/dqn-SpaceInvadersNoFrameskip-v4
|
sun1638650145
| 2022-06-23T07:01:36Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-23T07:00:40Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 544.50 +/- 176.63
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga sun1638650145 -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga sun1638650145
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
huawei-noah/SPIRAL-base-MCT
|
huawei-noah
| 2022-06-23T03:29:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-06-17T09:19:20Z |
SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training
========
This is the pretrained model of **SPIRAL Base with Multi-Condition Training**, trained with 960-hour LibriSpeech data, and noise dataset from [ICASSP 2021 DNS Challenge](https://github.com/microsoft/DNS-Challenge/tree/icassp2021-final) for noise robustness.
Citation
========
If you find SPIRAL useful in your research, please cite the following paper:
```
@inproceedings{huang2022spiral,
title={{SPIRAL}: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training},
author={Wenyong Huang and Zhenhe Zhang and Yu Ting Yeung and Xin Jiang and Qun Liu},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=TBpg4PnXhYH}
}
```
|
huawei-noah/SPIRAL-base
|
huawei-noah
| 2022-06-23T03:26:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-06-14T09:40:29Z |
SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training
========
This is the pretrained model of **SPIRAL Base**, trained with 960-hour LibriSpeech data
Citation
========
If you find SPIRAL useful in your research, please cite the following paper:
```
@inproceedings{huang2022spiral,
title={{SPIRAL}: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training},
author={Wenyong Huang and Zhenhe Zhang and Yu Ting Yeung and Xin Jiang and Qun Liu},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=TBpg4PnXhYH}
}
```
|
martin-ha/text_image_dual_encoder
|
martin-ha
| 2022-06-23T03:23:43Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2022-06-20T16:19:19Z |
---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamW', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay': 0.001, 'exclude_from_weight_decay': None}
- training_precision: float32
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
akraut/test_model
|
akraut
| 2022-06-23T01:04:38Z | 0 | 0 | null |
[
"image-classification",
"license:afl-3.0",
"region:us"
] |
image-classification
| 2022-06-22T21:56:23Z |
---
tags:
- image-classification
license: afl-3.0
---
|
Popppoogtcdcr/H
|
Popppoogtcdcr
| 2022-06-23T00:33:17Z | 0 | 0 | null |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-06-23T00:33:17Z |
---
license: cc-by-nc-sa-4.0
---
|
tals/albert-base-vitaminc-fever
|
tals
| 2022-06-22T23:57:17Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"dataset:fever",
"dataset:glue",
"dataset:tals/vitaminc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: python
datasets:
- fever
- glue
- tals/vitaminc
---
# Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When using this model, please cite the paper.
# BibTeX entry and citation info
```bibtex
@inproceedings{schuster-etal-2021-get,
title = "Get Your Vitamin {C}! Robust Fact Verification with Contrastive Evidence",
author = "Schuster, Tal and
Fisch, Adam and
Barzilay, Regina",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.52",
doi = "10.18653/v1/2021.naacl-main.52",
pages = "624--643",
abstract = "Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as more information is gathered and revised. In order to adapt, models must be sensitive to subtle differences in supporting evidence. We present VitaminC, a benchmark infused with challenging cases that require fact verification models to discern and adjust to slight factual changes. We collect over 100,000 Wikipedia revisions that modify an underlying fact, and leverage these revisions, together with additional synthetically constructed ones, to create a total of over 400,000 claim-evidence pairs. Unlike previous resources, the examples in VitaminC are contrastive, i.e., they contain evidence pairs that are nearly identical in language and content, with the exception that one supports a given claim while the other does not. We show that training using this design increases robustness{---}improving accuracy by 10{\%} on adversarial fact verification and 6{\%} on adversarial natural language inference (NLI). Moreover, the structure of VitaminC leads us to define additional tasks for fact-checking resources: tagging relevant words in the evidence for verifying the claim, identifying factual revisions, and providing automatic edits via factually consistent text generation.",
}
```
|
tals/albert-base-vitaminc_flagging
|
tals
| 2022-06-22T23:56:43Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"dataset:fever",
"dataset:glue",
"dataset:tals/vitaminc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: python
datasets:
- fever
- glue
- tals/vitaminc
---
# Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When using this model, please cite the paper.
# BibTeX entry and citation info
```bibtex
@inproceedings{schuster-etal-2021-get,
title = "Get Your Vitamin {C}! Robust Fact Verification with Contrastive Evidence",
author = "Schuster, Tal and
Fisch, Adam and
Barzilay, Regina",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.52",
doi = "10.18653/v1/2021.naacl-main.52",
pages = "624--643",
abstract = "Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as more information is gathered and revised. In order to adapt, models must be sensitive to subtle differences in supporting evidence. We present VitaminC, a benchmark infused with challenging cases that require fact verification models to discern and adjust to slight factual changes. We collect over 100,000 Wikipedia revisions that modify an underlying fact, and leverage these revisions, together with additional synthetically constructed ones, to create a total of over 400,000 claim-evidence pairs. Unlike previous resources, the examples in VitaminC are contrastive, i.e., they contain evidence pairs that are nearly identical in language and content, with the exception that one supports a given claim while the other does not. We show that training using this design increases robustness{---}improving accuracy by 10{\%} on adversarial fact verification and 6{\%} on adversarial natural language inference (NLI). Moreover, the structure of VitaminC leads us to define additional tasks for fact-checking resources: tagging relevant words in the evidence for verifying the claim, identifying factual revisions, and providing automatic edits via factually consistent text generation.",
}
```
|
tals/albert-base-vitaminc
|
tals
| 2022-06-22T23:56:01Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"dataset:fever",
"dataset:glue",
"dataset:tals/vitaminc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: python
datasets:
- fever
- glue
- tals/vitaminc
---
# Details
Model used in [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) (Schuster et al., NAACL 21`).
For more details see: https://github.com/TalSchuster/VitaminC
When using this model, please cite the paper.
# BibTeX entry and citation info
```bibtex
@inproceedings{schuster-etal-2021-get,
title = "Get Your Vitamin {C}! Robust Fact Verification with Contrastive Evidence",
author = "Schuster, Tal and
Fisch, Adam and
Barzilay, Regina",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.52",
doi = "10.18653/v1/2021.naacl-main.52",
pages = "624--643",
abstract = "Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as more information is gathered and revised. In order to adapt, models must be sensitive to subtle differences in supporting evidence. We present VitaminC, a benchmark infused with challenging cases that require fact verification models to discern and adjust to slight factual changes. We collect over 100,000 Wikipedia revisions that modify an underlying fact, and leverage these revisions, together with additional synthetically constructed ones, to create a total of over 400,000 claim-evidence pairs. Unlike previous resources, the examples in VitaminC are contrastive, i.e., they contain evidence pairs that are nearly identical in language and content, with the exception that one supports a given claim while the other does not. We show that training using this design increases robustness{---}improving accuracy by 10{\%} on adversarial fact verification and 6{\%} on adversarial natural language inference (NLI). Moreover, the structure of VitaminC leads us to define additional tasks for fact-checking resources: tagging relevant words in the evidence for verifying the claim, identifying factual revisions, and providing automatic edits via factually consistent text generation.",
}
```
|
wwbproj/empathic_conversations_self_disclosure
|
wwbproj
| 2022-06-22T19:37:17Z | 23 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-12T01:47:16Z |
---
language:
- en
---
# Empathic Conversations: Self Disclosure
Model owner(s): Ryan Guan, [rguan@seas.upenn.edu](mailto:rguan@seas.upenn.edu)
Associated paper:
## Model description
### Related models
- wwbproj/empathic_conversations_empathy
- wwbproj/empathic_conversations_emotion
- wwbproj/empathic_conversations_emotional_polarity
- wwbproj/empathic_conversations_dialog_acts
## Intended uses & limitations
## How to use
Code: https://github.com/wwbp/models/tree/master/neural_model_code/empathic_conversations
## Training data
## Training procedure
|
mmillet/xlm-roberta-base_single_finetuned_on_cedr_augmented
|
mmillet
| 2022-06-22T18:01:45Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-22T17:23:58Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: xlm-roberta-base_single_finetuned_on_cedr_augmented
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base_single_finetuned_on_cedr_augmented
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4650
- Accuracy: 0.8820
- F1: 0.8814
- Precision: 0.8871
- Recall: 0.8820
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.8868 | 1.0 | 69 | 0.4939 | 0.8403 | 0.8376 | 0.8431 | 0.8403 |
| 0.4248 | 2.0 | 138 | 0.3969 | 0.8779 | 0.8768 | 0.8798 | 0.8779 |
| 0.3197 | 3.0 | 207 | 0.4019 | 0.8758 | 0.8757 | 0.8758 | 0.8758 |
| 0.2737 | 4.0 | 276 | 0.3915 | 0.8831 | 0.8827 | 0.8847 | 0.8831 |
| 0.2053 | 5.0 | 345 | 0.4445 | 0.8643 | 0.8650 | 0.8714 | 0.8643 |
| 0.1705 | 6.0 | 414 | 0.4650 | 0.8820 | 0.8814 | 0.8871 | 0.8820 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
atendstowards0/codeparrot-ds
|
atendstowards0
| 2022-06-22T17:56:15Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-22T17:45:09Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: codeparrot-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. -->
# codeparrot-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
jamesmarcel/xlm-roberta-base-finetuned-panx-de
|
jamesmarcel
| 2022-06-22T17:26:24Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-22T17:03:34Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8620945214069894
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1372
- F1: 0.8621
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 |
| 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 |
| 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
mmazuecos/q-Taxi-v3
|
mmazuecos
| 2022-06-22T16:24:16Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-22T16:01:48Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="mmazuecos/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"])
```
|
mayoughi/where_am_I_hospital-balcony-hallway-airport-coffee-house
|
mayoughi
| 2022-06-22T16:00:57Z | 52 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-22T16:00:45Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: where_am_I_hospital-balcony-hallway-airport-coffee-house
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8839285969734192
---
# where_am_I_hospital-balcony-hallway-airport-coffee-house
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### airport
#### balcony
#### coffee house indoors
#### hallway
#### hospital
|
mmazuecos/q-FrozenLake-v1-4x4-noSlippery
|
mmazuecos
| 2022-06-22T15:57:08Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-22T15:57:01Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="mmazuecos/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"])
```
|
ericntay/bio_bert_ft
|
ericntay
| 2022-06-22T15:24:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-22T14:35:26Z |
---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bio_bert_ft
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bio_bert_ft
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0747
- F1: 0.8621
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0879 | 1.0 | 170 | 0.0400 | 0.8312 |
| 0.0211 | 2.0 | 340 | 0.0454 | 0.8413 |
| 0.0105 | 3.0 | 510 | 0.0503 | 0.8603 |
| 0.0045 | 4.0 | 680 | 0.0497 | 0.8496 |
| 0.0028 | 5.0 | 850 | 0.0759 | 0.8387 |
| 0.0019 | 6.0 | 1020 | 0.0654 | 0.8598 |
| 0.0011 | 7.0 | 1190 | 0.0667 | 0.8654 |
| 0.0005 | 8.0 | 1360 | 0.0702 | 0.8621 |
| 0.0003 | 9.0 | 1530 | 0.0739 | 0.8596 |
| 0.0002 | 10.0 | 1700 | 0.0747 | 0.8621 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
fgmckee/q-Taxi-v3
|
fgmckee
| 2022-06-22T14:37:45Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-22T14:37:39Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.50 +/- 2.65
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="fgmckee/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"])
```
|
sasha/dog-food-convnext-tiny-224
|
sasha
| 2022-06-22T13:56:32Z | 54 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"convnext",
"image-classification",
"huggingpics",
"dataset:sasha/dog-food",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-21T14:10:13Z |
---
tags:
- image-classification
- pytorch
- huggingpics
datasets:
- sasha/dog-food
metrics:
- accuracy
- f1
model-index:
- name: dog-food-convnext-tiny-224
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: Dog Food
type: sasha/dog-food
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# dog-food-convnext-tiny-224
This model was trained on the `train` split of the [Dogs vs Food](https://huggingface.co/datasets/sasha/dog-food) dataset -- try training your own using the
[the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb)!
## Example Images
#### dog
#### food
|
sasha/dog-food-swin-tiny-patch4-window7-224
|
sasha
| 2022-06-22T13:56:12Z | 82 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"huggingpics",
"dataset:sasha/dog-food",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-21T13:40:48Z |
---
tags:
- image-classification
- pytorch
- huggingpics
datasets:
- sasha/dog-food
metrics:
- accuracy
- f1
model-index:
- name: dog-food-swin-tiny-patch4-window7-224
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: Dog Food
type: sasha/dog-food
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# dog-food-swin-tiny-patch4-window7-224
This model was trained on the `train` split of the [Dogs vs Food](https://huggingface.co/datasets/sasha/dog-food) dataset -- try training your own using the
[the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb)!
## Example Images
#### dog
#### food
|
flood/xlm-roberta-base-finetuned-panx-en
|
flood
| 2022-06-22T13:43:46Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-09T17:25:36Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.6777777777777778
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4025
- F1: 0.6778
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1069 | 1.0 | 50 | 0.5201 | 0.5010 |
| 0.4975 | 2.0 | 100 | 0.4503 | 0.6198 |
| 0.3705 | 3.0 | 150 | 0.4025 | 0.6778 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
flood/xlm-roberta-base-finetuned-panx-it
|
flood
| 2022-06-22T13:40:36Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-09T17:20:20Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8085969180859691
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2527
- F1: 0.8086
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8319 | 1.0 | 70 | 0.3179 | 0.7474 |
| 0.2959 | 2.0 | 140 | 0.2695 | 0.7916 |
| 0.2036 | 3.0 | 210 | 0.2527 | 0.8086 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
flood/xlm-roberta-base-finetuned-panx-fr
|
flood
| 2022-06-22T13:37:27Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-09T17:14:32Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8375924680564896
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2794
- F1: 0.8376
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5774 | 1.0 | 191 | 0.3212 | 0.7894 |
| 0.2661 | 2.0 | 382 | 0.2737 | 0.8292 |
| 0.1756 | 3.0 | 573 | 0.2794 | 0.8376 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
flood/xlm-roberta-base-finetuned-panx-de-fr
|
flood
| 2022-06-22T13:33:30Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-09T17:06:01Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1612
- F1: 0.8618
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2874 | 1.0 | 715 | 0.1764 | 0.8343 |
| 0.1475 | 2.0 | 1430 | 0.1561 | 0.8508 |
| 0.0936 | 3.0 | 2145 | 0.1612 | 0.8618 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
fouad-shammary/distilbert-base-uncased-finetuned-emotion
|
fouad-shammary
| 2022-06-22T12:48:04Z | 13 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-21T00:27:20Z |
---
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: 0.9165
- name: F1
type: f1
value: 0.9164107076814402
---
<!-- 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: 0.2349
- Accuracy: 0.9165
- F1: 0.9164
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.837 | 1.0 | 250 | 0.3317 | 0.9015 | 0.8999 |
| 0.2563 | 2.0 | 500 | 0.2349 | 0.9165 | 0.9164 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ml4pubmed/xtremedistil-l12-h384-uncased_pub_section
|
ml4pubmed
| 2022-06-22T12:29:07Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"document sections",
"sentence classification",
"document classification",
"medical",
"health",
"biomedical",
"en",
"dataset:pubmed",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-04T01:32:45Z |
---
language:
- en
datasets:
- pubmed
metrics:
- f1
tags:
- text-classification
- document sections
- sentence classification
- document classification
- medical
- health
- biomedical
pipeline_tag: text-classification
widget:
- text: "many pathogenic processes and diseases are the result of an erroneous activation of the complement cascade and a number of inhibitors of complement have thus been examined for anti-inflammatory actions."
example_title: "background example"
- text: "a total of 192 mi patients and 140 control persons were included."
example_title: "methods example"
- text: "mi patients had 18 % higher plasma levels of map44 (iqr 11-25 %) as compared to the healthy control group (p < 0. 001.)"
example_title: "results example"
- text: "the finding that a brief cb group intervention delivered by real-world providers significantly reduced mdd onset relative to both brochure control and bibliotherapy is very encouraging, although effects on continuous outcome measures were small or nonsignificant and approximately half the magnitude of those found in efficacy research, potentially because the present sample reported lower initial depression."
example_title: "conclusions example"
- text: "in order to understand and update the prevalence of myopia in taiwan, a nationwide survey was performed in 1995."
example_title: "objective example"
---
# xtremedistil-l12-h384-uncased_pub_section
- original model file name: textclassifer_xtremedistil-l12-h384-uncased_pubmed_20k
- This is a fine-tuned checkpoint of `microsoft/xtremedistil-l12-h384-uncased` for document section text classification
- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS,
## usage in python
install transformers as needed: `pip install -U transformers`
run the following, changing the example text to your use case:
```
from transformers import pipeline
model_tag = "ml4pubmed/xtremedistil-l12-h384-uncased_pub_section"
classifier = pipeline(
'text-classification',
model=model_tag,
)
prompt = """
Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.
"""
classifier(
prompt,
) # classify the sentence
```
## metadata
### training_parameters
- date_run: Apr-24-2022_t-12
- huggingface_tag: microsoft/xtremedistil-l12-h384-uncased
|
Mizew/autotrain-avar-1016534299
|
Mizew
| 2022-06-22T12:12:07Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"translation",
"en",
"es",
"dataset:Mizew/autotrain-data-avar",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-06-22T11:55:38Z |
---
tags:
- autotrain
- translation
language:
- en
- es
datasets:
- Mizew/autotrain-data-avar
co2_eq_emissions: 0.07815966018818815
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1016534299
- CO2 Emissions (in grams): 0.07815966018818815
## Validation Metrics
- Loss: 0.9978321194648743
- SacreBLEU: 13.8459
- Gen len: 6.0588
|
Elron/deberta-v3-large-offensive
|
Elron
| 2022-06-22T09:47:41Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-22T08:56:09Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large
results: []
---
# deberta-v3-large-sentiment
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an [tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
## Model description
Test set results:
| Model | Emotion | Hate | Irony | Offensive | Sentiment |
| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |
| deberta-v3-large | **86.3** | **61.3** | **87.1** | **86.4** | **73.9** |
| BERTweet | 79.3 | - | 82.1 | 79.5 | 73.4 |
| RoB-RT | 79.5 | 52.3 | 61.7 | 80.5 | 69.3 |
[source:papers_with_code](https://paperswithcode.com/sota/sentiment-analysis-on-tweeteval)
## Intended uses & limitations
Classifying attributes of interest on tweeter like data.
## Training and evaluation data
[tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
## Training procedure
Fine tuned and evaluated with [run_glue.py]()
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6417 | 0.27 | 100 | 0.6283 | 0.6533 |
| 0.5105 | 0.54 | 200 | 0.4588 | 0.7915 |
| 0.4554 | 0.81 | 300 | 0.4500 | 0.7968 |
| 0.4212 | 1.08 | 400 | 0.4773 | 0.7938 |
| 0.4054 | 1.34 | 500 | 0.4311 | 0.7983 |
| 0.3922 | 1.61 | 600 | 0.4588 | 0.7998 |
| 0.3776 | 1.88 | 700 | 0.4367 | 0.8066 |
| 0.3535 | 2.15 | 800 | 0.4675 | 0.8074 |
| 0.33 | 2.42 | 900 | 0.4874 | 0.8021 |
| 0.3113 | 2.69 | 1000 | 0.4949 | 0.8044 |
| 0.3203 | 2.96 | 1100 | 0.4550 | 0.8059 |
| 0.248 | 3.23 | 1200 | 0.4858 | 0.8036 |
| 0.2478 | 3.49 | 1300 | 0.5299 | 0.8029 |
| 0.2371 | 3.76 | 1400 | 0.5013 | 0.7991 |
| 0.2388 | 4.03 | 1500 | 0.5520 | 0.8021 |
| 0.1744 | 4.3 | 1600 | 0.6687 | 0.7915 |
| 0.1788 | 4.57 | 1700 | 0.7560 | 0.7689 |
| 0.1652 | 4.84 | 1800 | 0.6985 | 0.7832 |
| 0.1596 | 5.11 | 1900 | 0.7191 | 0.7915 |
| 0.1214 | 5.38 | 2000 | 0.9097 | 0.7893 |
| 0.1432 | 5.64 | 2100 | 0.9184 | 0.7787 |
| 0.1145 | 5.91 | 2200 | 0.9620 | 0.7878 |
| 0.1069 | 6.18 | 2300 | 0.9489 | 0.7893 |
| 0.1012 | 6.45 | 2400 | 1.0107 | 0.7817 |
| 0.0942 | 6.72 | 2500 | 1.0021 | 0.7885 |
| 0.087 | 6.99 | 2600 | 1.1090 | 0.7915 |
| 0.0598 | 7.26 | 2700 | 1.1735 | 0.7795 |
| 0.0742 | 7.53 | 2800 | 1.1433 | 0.7817 |
| 0.073 | 7.79 | 2900 | 1.1343 | 0.7953 |
| 0.0553 | 8.06 | 3000 | 1.2258 | 0.7840 |
| 0.0474 | 8.33 | 3100 | 1.2461 | 0.7817 |
| 0.0515 | 8.6 | 3200 | 1.2996 | 0.7825 |
| 0.0551 | 8.87 | 3300 | 1.2819 | 0.7855 |
| 0.0541 | 9.14 | 3400 | 1.2808 | 0.7855 |
| 0.0465 | 9.41 | 3500 | 1.3398 | 0.7817 |
| 0.0407 | 9.68 | 3600 | 1.3231 | 0.7825 |
| 0.0343 | 9.94 | 3700 | 1.3330 | 0.7825 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.9.0
- Datasets 2.2.2
- Tokenizers 0.11.6
|
Elron/deberta-v3-large-irony
|
Elron
| 2022-06-22T09:46:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-22T08:55:47Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large
results: []
---
# deberta-v3-large-irony
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an [tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
## Model description
Test set results:
| Model | Emotion | Hate | Irony | Offensive | Sentiment |
| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |
| deberta-v3-large | **86.3** | **61.3** | **87.1** | **86.4** | **73.9** |
| BERTweet | 79.3 | - | 82.1 | 79.5 | 73.4 |
| RoB-RT | 79.5 | 52.3 | 61.7 | 80.5 | 69.3 |
[source:papers_with_code](https://paperswithcode.com/sota/sentiment-analysis-on-tweeteval)
## Intended uses & limitations
Classifying attributes of interest on tweeter like data.
## Training and evaluation data
[tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
## Training procedure
Fine tuned and evaluated with [run_glue.py]()
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 10.0
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6478 | 1.12 | 100 | 0.5890 | 0.7529 |
| 0.5013 | 2.25 | 200 | 0.5873 | 0.7707 |
| 0.388 | 3.37 | 300 | 0.6993 | 0.7602 |
| 0.3169 | 4.49 | 400 | 0.6773 | 0.7874 |
| 0.2693 | 5.61 | 500 | 0.7172 | 0.7707 |
| 0.2396 | 6.74 | 600 | 0.7397 | 0.7801 |
| 0.2284 | 7.86 | 700 | 0.8096 | 0.7550 |
| 0.2207 | 8.98 | 800 | 0.7827 | 0.7654 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.9.0
- Datasets 2.2.2
- Tokenizers 0.11.6
|
Elron/deberta-v3-large-sentiment
|
Elron
| 2022-06-22T09:45:55Z | 21 | 1 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-22T08:56:37Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large
results: []
---
# deberta-v3-large-sentiment
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an [tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
## Model description
Test set results:
| Model | Emotion | Hate | Irony | Offensive | Sentiment |
| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |
| deberta-v3-large | **86.3** | **61.3** | **87.1** | **86.4** | **73.9** |
| BERTweet | 79.3 | - | 82.1 | 79.5 | 73.4 |
| RoB-RT | 79.5 | 52.3 | 61.7 | 80.5 | 69.3 |
[source:papers_with_code](https://paperswithcode.com/sota/sentiment-analysis-on-tweeteval)
## Intended uses & limitations
Classifying attributes of interest on tweeter like data.
## Training and evaluation data
[tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset.
## Training procedure
Fine tuned and evaluated with [run_glue.py]()
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0614 | 0.07 | 100 | 1.0196 | 0.4345 |
| 0.8601 | 0.14 | 200 | 0.7561 | 0.6460 |
| 0.734 | 0.21 | 300 | 0.6796 | 0.6955 |
| 0.6753 | 0.28 | 400 | 0.6521 | 0.7000 |
| 0.6408 | 0.35 | 500 | 0.6119 | 0.7440 |
| 0.5991 | 0.42 | 600 | 0.6034 | 0.7370 |
| 0.6069 | 0.49 | 700 | 0.5976 | 0.7375 |
| 0.6122 | 0.56 | 800 | 0.5871 | 0.7425 |
| 0.5908 | 0.63 | 900 | 0.5935 | 0.7445 |
| 0.5884 | 0.7 | 1000 | 0.5792 | 0.7520 |
| 0.5839 | 0.77 | 1100 | 0.5780 | 0.7555 |
| 0.5772 | 0.84 | 1200 | 0.5727 | 0.7570 |
| 0.5895 | 0.91 | 1300 | 0.5601 | 0.7550 |
| 0.5757 | 0.98 | 1400 | 0.5613 | 0.7525 |
| 0.5121 | 1.05 | 1500 | 0.5867 | 0.7600 |
| 0.5254 | 1.12 | 1600 | 0.5595 | 0.7630 |
| 0.5074 | 1.19 | 1700 | 0.5594 | 0.7585 |
| 0.4947 | 1.26 | 1800 | 0.5697 | 0.7575 |
| 0.5019 | 1.33 | 1900 | 0.5665 | 0.7580 |
| 0.5005 | 1.4 | 2000 | 0.5484 | 0.7655 |
| 0.5125 | 1.47 | 2100 | 0.5626 | 0.7605 |
| 0.5241 | 1.54 | 2200 | 0.5561 | 0.7560 |
| 0.5198 | 1.61 | 2300 | 0.5602 | 0.7600 |
| 0.5124 | 1.68 | 2400 | 0.5654 | 0.7490 |
| 0.5096 | 1.75 | 2500 | 0.5803 | 0.7515 |
| 0.4885 | 1.82 | 2600 | 0.5889 | 0.75 |
| 0.5111 | 1.89 | 2700 | 0.5508 | 0.7665 |
| 0.4868 | 1.96 | 2800 | 0.5621 | 0.7635 |
| 0.4599 | 2.04 | 2900 | 0.5995 | 0.7615 |
| 0.4147 | 2.11 | 3000 | 0.6202 | 0.7530 |
| 0.4233 | 2.18 | 3100 | 0.5875 | 0.7625 |
| 0.4324 | 2.25 | 3200 | 0.5794 | 0.7610 |
| 0.4141 | 2.32 | 3300 | 0.5902 | 0.7460 |
| 0.4306 | 2.39 | 3400 | 0.6053 | 0.7545 |
| 0.4266 | 2.46 | 3500 | 0.5979 | 0.7570 |
| 0.4227 | 2.53 | 3600 | 0.5920 | 0.7650 |
| 0.4226 | 2.6 | 3700 | 0.6166 | 0.7455 |
| 0.3978 | 2.67 | 3800 | 0.6126 | 0.7560 |
| 0.3954 | 2.74 | 3900 | 0.6152 | 0.7550 |
| 0.4209 | 2.81 | 4000 | 0.5980 | 0.75 |
| 0.3982 | 2.88 | 4100 | 0.6096 | 0.7490 |
| 0.4016 | 2.95 | 4200 | 0.6541 | 0.7425 |
| 0.3966 | 3.02 | 4300 | 0.6377 | 0.7545 |
| 0.3074 | 3.09 | 4400 | 0.6860 | 0.75 |
| 0.3551 | 3.16 | 4500 | 0.6160 | 0.7550 |
| 0.3323 | 3.23 | 4600 | 0.6714 | 0.7520 |
| 0.3171 | 3.3 | 4700 | 0.6538 | 0.7535 |
| 0.3403 | 3.37 | 4800 | 0.6774 | 0.7465 |
| 0.3396 | 3.44 | 4900 | 0.6726 | 0.7465 |
| 0.3259 | 3.51 | 5000 | 0.6465 | 0.7480 |
| 0.3392 | 3.58 | 5100 | 0.6860 | 0.7460 |
| 0.3251 | 3.65 | 5200 | 0.6697 | 0.7495 |
| 0.3253 | 3.72 | 5300 | 0.6770 | 0.7430 |
| 0.3455 | 3.79 | 5400 | 0.7177 | 0.7360 |
| 0.3323 | 3.86 | 5500 | 0.6943 | 0.7400 |
| 0.3335 | 3.93 | 5600 | 0.6507 | 0.7555 |
| 0.3368 | 4.0 | 5700 | 0.6580 | 0.7485 |
| 0.2479 | 4.07 | 5800 | 0.7667 | 0.7430 |
| 0.2613 | 4.14 | 5900 | 0.7513 | 0.7505 |
| 0.2557 | 4.21 | 6000 | 0.7927 | 0.7485 |
| 0.243 | 4.28 | 6100 | 0.7792 | 0.7450 |
| 0.2473 | 4.35 | 6200 | 0.8107 | 0.7355 |
| 0.2447 | 4.42 | 6300 | 0.7851 | 0.7370 |
| 0.2515 | 4.49 | 6400 | 0.7529 | 0.7465 |
| 0.274 | 4.56 | 6500 | 0.7390 | 0.7465 |
| 0.2674 | 4.63 | 6600 | 0.7658 | 0.7460 |
| 0.2416 | 4.7 | 6700 | 0.7915 | 0.7485 |
| 0.2432 | 4.77 | 6800 | 0.7989 | 0.7435 |
| 0.2595 | 4.84 | 6900 | 0.7850 | 0.7380 |
| 0.2736 | 4.91 | 7000 | 0.7577 | 0.7395 |
| 0.2783 | 4.98 | 7100 | 0.7650 | 0.7405 |
| 0.2304 | 5.05 | 7200 | 0.8542 | 0.7385 |
| 0.1937 | 5.12 | 7300 | 0.8390 | 0.7345 |
| 0.1878 | 5.19 | 7400 | 0.9150 | 0.7330 |
| 0.1921 | 5.26 | 7500 | 0.8792 | 0.7405 |
| 0.1916 | 5.33 | 7600 | 0.8892 | 0.7410 |
| 0.2011 | 5.4 | 7700 | 0.9012 | 0.7325 |
| 0.211 | 5.47 | 7800 | 0.8608 | 0.7420 |
| 0.2194 | 5.54 | 7900 | 0.8852 | 0.7320 |
| 0.205 | 5.61 | 8000 | 0.8803 | 0.7385 |
| 0.1981 | 5.68 | 8100 | 0.8681 | 0.7330 |
| 0.1908 | 5.75 | 8200 | 0.9020 | 0.7435 |
| 0.1942 | 5.82 | 8300 | 0.8780 | 0.7410 |
| 0.1958 | 5.89 | 8400 | 0.8937 | 0.7345 |
| 0.1883 | 5.96 | 8500 | 0.9121 | 0.7360 |
| 0.1819 | 6.04 | 8600 | 0.9409 | 0.7430 |
| 0.145 | 6.11 | 8700 | 1.1390 | 0.7265 |
| 0.1696 | 6.18 | 8800 | 0.9189 | 0.7430 |
| 0.1488 | 6.25 | 8900 | 0.9718 | 0.7400 |
| 0.1637 | 6.32 | 9000 | 0.9702 | 0.7450 |
| 0.1547 | 6.39 | 9100 | 1.0033 | 0.7410 |
| 0.1605 | 6.46 | 9200 | 0.9973 | 0.7355 |
| 0.1552 | 6.53 | 9300 | 1.0491 | 0.7290 |
| 0.1731 | 6.6 | 9400 | 1.0271 | 0.7335 |
| 0.1738 | 6.67 | 9500 | 0.9575 | 0.7430 |
| 0.1669 | 6.74 | 9600 | 0.9614 | 0.7350 |
| 0.1347 | 6.81 | 9700 | 1.0263 | 0.7365 |
| 0.1593 | 6.88 | 9800 | 1.0173 | 0.7360 |
| 0.1549 | 6.95 | 9900 | 1.0398 | 0.7350 |
| 0.1675 | 7.02 | 10000 | 0.9975 | 0.7380 |
| 0.1182 | 7.09 | 10100 | 1.1059 | 0.7350 |
| 0.1351 | 7.16 | 10200 | 1.0933 | 0.7400 |
| 0.1496 | 7.23 | 10300 | 1.0731 | 0.7355 |
| 0.1197 | 7.3 | 10400 | 1.1089 | 0.7360 |
| 0.1111 | 7.37 | 10500 | 1.1381 | 0.7405 |
| 0.1494 | 7.44 | 10600 | 1.0252 | 0.7425 |
| 0.1235 | 7.51 | 10700 | 1.0906 | 0.7360 |
| 0.133 | 7.58 | 10800 | 1.1796 | 0.7375 |
| 0.1248 | 7.65 | 10900 | 1.1332 | 0.7420 |
| 0.1268 | 7.72 | 11000 | 1.1304 | 0.7415 |
| 0.1368 | 7.79 | 11100 | 1.1345 | 0.7380 |
| 0.1228 | 7.86 | 11200 | 1.2018 | 0.7320 |
| 0.1281 | 7.93 | 11300 | 1.1884 | 0.7350 |
| 0.1449 | 8.0 | 11400 | 1.1571 | 0.7345 |
| 0.1025 | 8.07 | 11500 | 1.1538 | 0.7345 |
| 0.1199 | 8.14 | 11600 | 1.2113 | 0.7390 |
| 0.1016 | 8.21 | 11700 | 1.2882 | 0.7370 |
| 0.114 | 8.28 | 11800 | 1.2872 | 0.7390 |
| 0.1019 | 8.35 | 11900 | 1.2876 | 0.7380 |
| 0.1142 | 8.42 | 12000 | 1.2791 | 0.7385 |
| 0.1135 | 8.49 | 12100 | 1.2883 | 0.7380 |
| 0.1139 | 8.56 | 12200 | 1.2829 | 0.7360 |
| 0.1107 | 8.63 | 12300 | 1.2698 | 0.7365 |
| 0.1183 | 8.7 | 12400 | 1.2660 | 0.7345 |
| 0.1064 | 8.77 | 12500 | 1.2889 | 0.7365 |
| 0.0895 | 8.84 | 12600 | 1.3480 | 0.7330 |
| 0.1244 | 8.91 | 12700 | 1.2872 | 0.7325 |
| 0.1209 | 8.98 | 12800 | 1.2681 | 0.7375 |
| 0.1144 | 9.05 | 12900 | 1.2711 | 0.7370 |
| 0.1034 | 9.12 | 13000 | 1.2801 | 0.7360 |
| 0.113 | 9.19 | 13100 | 1.2801 | 0.7350 |
| 0.0994 | 9.26 | 13200 | 1.2920 | 0.7360 |
| 0.0966 | 9.33 | 13300 | 1.2761 | 0.7335 |
| 0.0939 | 9.4 | 13400 | 1.2909 | 0.7365 |
| 0.0975 | 9.47 | 13500 | 1.2953 | 0.7360 |
| 0.0842 | 9.54 | 13600 | 1.3179 | 0.7335 |
| 0.0871 | 9.61 | 13700 | 1.3149 | 0.7385 |
| 0.1162 | 9.68 | 13800 | 1.3124 | 0.7350 |
| 0.085 | 9.75 | 13900 | 1.3207 | 0.7355 |
| 0.0966 | 9.82 | 14000 | 1.3248 | 0.7335 |
| 0.1064 | 9.89 | 14100 | 1.3261 | 0.7335 |
| 0.1046 | 9.96 | 14200 | 1.3255 | 0.7360 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.9.0
- Datasets 2.2.2
- Tokenizers 0.11.6
|
asnorkin/q-FrozenLake-v1-4x4-noSlippery
|
asnorkin
| 2022-06-22T09:22:24Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-22T09:22:00Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="/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"])
```
|
kktoto/tiny_no_focal_v2
|
kktoto
| 2022-06-22T08:50:37Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-22T06:39:14Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tiny_no_focal_v2
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. -->
# tiny_no_focal_v2
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1314
- Precision: 0.7013
- Recall: 0.6837
- F1: 0.6924
- Accuracy: 0.9522
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1574 | 1.0 | 5561 | 0.1471 | 0.6907 | 0.6186 | 0.6527 | 0.9462 |
| 0.1456 | 2.0 | 11122 | 0.1396 | 0.6923 | 0.6473 | 0.6690 | 0.9485 |
| 0.1412 | 3.0 | 16683 | 0.1373 | 0.6845 | 0.6705 | 0.6774 | 0.9490 |
| 0.1338 | 4.0 | 22244 | 0.1343 | 0.6988 | 0.6640 | 0.6810 | 0.9505 |
| 0.1311 | 5.0 | 27805 | 0.1342 | 0.6971 | 0.6751 | 0.6859 | 0.9510 |
| 0.1289 | 6.0 | 33366 | 0.1324 | 0.7081 | 0.6653 | 0.6860 | 0.9517 |
| 0.1258 | 7.0 | 38927 | 0.1309 | 0.7053 | 0.6731 | 0.6888 | 0.9521 |
| 0.1223 | 8.0 | 44488 | 0.1325 | 0.7001 | 0.6818 | 0.6908 | 0.9519 |
| 0.1213 | 9.0 | 50049 | 0.1316 | 0.7020 | 0.6813 | 0.6915 | 0.9522 |
| 0.1197 | 10.0 | 55610 | 0.1314 | 0.7013 | 0.6837 | 0.6924 | 0.9522 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
merve/text_image_dual_encoder
|
merve
| 2022-06-22T08:17:42Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2022-06-22T08:17:04Z |
---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training Metrics
Model history needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
kalyanavirundhubiryani/Best-Biryani-Shop-in-Chennai
|
kalyanavirundhubiryani
| 2022-06-22T06:38:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-06-22T06:38:00Z |
Kalyana Virundhu Biryani is one of the best biryani shop in Chennai." We Serve various types of Biryani along with our special side-Dish. Order us"Phone: +91 8939234566 or visit our website
https://www.kalyanavirundhubiryani.com/
|
shpotes/codegen-350M-mono
|
shpotes
| 2022-06-22T06:02:10Z | 17 | 3 |
transformers
|
[
"transformers",
"pytorch",
"codegen",
"text-generation",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-30T06:37:21Z |
---
license: bsd-3-clause
---
# Overview
The CodeGen model was proposed in by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. From Salesforce Research.
The abstract from the paper is the following:
Program synthesis strives to generate a computer program as a solution to a given problem specification. We propose a conversational program synthesis approach via large language models, which addresses the challenges of searching over a vast program space and user intent specification faced in prior approaches. Our new approach casts the process of writing a specification and program as a multi-turn conversation between a user and a system. It treats program synthesis as a sequence prediction problem, in which the specification is expressed in natural language and the desired program is conditionally sampled. We train a family of large language models, called CodeGen, on natural language and programming language data. With weak supervision in the data and the scaling up of data size and model size, conversational capacities emerge from the simple autoregressive language modeling. To study the model behavior on conversational program synthesis, we develop a multi-turn programming benchmark (MTPB), where solving each problem requires multi-step synthesis via multi-turn conversation between the user and the model. Our findings show the emergence of conversational capabilities and the effectiveness of the proposed conversational program synthesis paradigm. In addition, our model CodeGen (with up to 16B parameters trained on TPU-v4) outperforms OpenAI's Codex on the HumanEval benchmark. We plan to make the training library JaxFormer including checkpoints available as open source.
# How to use
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("shpotes/codegen-350M-mono")
model = AutoModelForCausalLM.from_pretrained("shpotes/codegen-350M-mono", trust_remote_code=True)
input_ids = tokenizer(
context,
truncation=True,
padding=True,
return_tensors='pt',
pad_token_id=pad_token_id,
).input_ids
input_ids_len = input_ids.shape[1]
with torch.no_grad():
input_ids = input_ids
tokens = model.generate(
input_ids,
do_sample=True,
num_return_sequences=num_return_sequences,
temperature=temp,
max_length=input_ids_len + max_length_sample,
top_p=top_p,
use_cache=True,
)
text = tokenizer.batch_decode(tokens[:, input_ids_len:, ...])
```
|
vanichandna/xlm-roberta-finetuned-squad
|
vanichandna
| 2022-06-22T04:49:42Z | 8 | 0 |
transformers
|
[
"transformers",
"tf",
"xlm-roberta",
"question-answering",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-07T09:42:26Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: vanichandna/xlmroberta-squad
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. -->
# vanichandna/xlmroberta-squad
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an SQuAD v1.1 dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6636
- 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16476, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.2842 | 0 |
| 0.8425 | 1 |
| 0.6636 | 2 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
veb/twitch-distilbert-base-uncased-finetuned
|
veb
| 2022-06-22T03:55:33Z | 7 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-22T03:48:44Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: veb/twitch-distilbert-base-uncased-finetuned
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. -->
# veb/twitch-distilbert-base-uncased-finetuned
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: 4.9110
- Validation Loss: 4.7782
- 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': -985, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.9110 | 4.7782 | 0 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.7.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Evelyn18/distilbert-base-uncased-finetuned-squad
|
Evelyn18
| 2022-06-22T03:50:33Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-08T22:17:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0087
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 5 | 5.5219 |
| No log | 2.0 | 10 | 4.9747 |
| No log | 3.0 | 15 | 4.5448 |
| No log | 4.0 | 20 | 4.1843 |
| No log | 5.0 | 25 | 3.8491 |
| No log | 6.0 | 30 | 3.6789 |
| No log | 7.0 | 35 | 3.5018 |
| No log | 8.0 | 40 | 3.4254 |
| No log | 9.0 | 45 | 3.4566 |
| No log | 10.0 | 50 | 3.4326 |
| No log | 11.0 | 55 | 3.5741 |
| No log | 12.0 | 60 | 3.5260 |
| No log | 13.0 | 65 | 3.7003 |
| No log | 14.0 | 70 | 3.7499 |
| No log | 15.0 | 75 | 3.7961 |
| No log | 16.0 | 80 | 3.8578 |
| No log | 17.0 | 85 | 3.9928 |
| No log | 18.0 | 90 | 4.0305 |
| No log | 19.0 | 95 | 4.0024 |
| No log | 20.0 | 100 | 4.0087 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
KoichiYasuoka/roberta-large-japanese-aozora-char
|
KoichiYasuoka
| 2022-06-22T01:22:43Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"japanese",
"masked-lm",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language:
- "ja"
tags:
- "japanese"
- "masked-lm"
license: "cc-by-sa-4.0"
pipeline_tag: "fill-mask"
mask_token: "[MASK]"
widget:
- text: "日本に着いたら[MASK]を訪ねなさい。"
---
# roberta-large-japanese-aozora-char
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts with character tokenizer. You can fine-tune `roberta-large-japanese-aozora-char` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-large-japanese-char-luw-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/roberta-large-japanese-aozora-ud-head), and so on.
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForMaskedLM
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-large-japanese-aozora-char")
model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-large-japanese-aozora-char")
```
## Reference
安岡孝一: [Transformersと国語研長単位による日本語係り受け解析モデルの製作](http://id.nii.ac.jp/1001/00216223/), 情報処理学会研究報告, Vol.2022-CH-128, No.7 (2022年2月), pp.1-8.
|
lucianpopa/autotrain-qn-classification-1015534072
|
lucianpopa
| 2022-06-21T22:26:00Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:lucianpopa/autotrain-data-qn-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-21T22:23:01Z |
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- lucianpopa/autotrain-data-qn-classification
co2_eq_emissions: 0.013170440014043236
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1015534072
- CO2 Emissions (in grams): 0.013170440014043236
## Validation Metrics
- Loss: 1.493847370147705
- Accuracy: 0.7333333333333333
- Macro F1: 0.6777777777777777
- Micro F1: 0.7333333333333333
- Weighted F1: 0.6777777777777777
- Macro Precision: 0.6555555555555554
- Micro Precision: 0.7333333333333333
- Weighted Precision: 0.6555555555555554
- Macro Recall: 0.7333333333333333
- Micro Recall: 0.7333333333333333
- Weighted Recall: 0.7333333333333333
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/lucianpopa/autotrain-qn-classification-1015534072
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("lucianpopa/autotrain-qn-classification-1015534072", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("lucianpopa/autotrain-qn-classification-1015534072", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
Alian3785/dqn-SpaceInvadersNoFrameskip-v4new
|
Alian3785
| 2022-06-21T21:30:54Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-21T21:30:23Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 14.50 +/- 12.34
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Alian3785 -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Alian3785
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
deepesh0x/autotrain-mlsec-1013333726
|
deepesh0x
| 2022-06-21T20:49:59Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"julien",
"text-classification",
"autotrain",
"en",
"dataset:deepesh0x/autotrain-data-mlsec",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-21T16:55:28Z |
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- deepesh0x/autotrain-data-mlsec
co2_eq_emissions: 33.183779535405364
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1013333726
- CO2 Emissions (in grams): 33.183779535405364
## Validation Metrics
- Loss: 0.1998898833990097
- Accuracy: 0.9226923076923077
- Precision: 0.9269808389435525
- Recall: 0.9177134068187645
- AUC: 0.9785380985232148
- F1: 0.9223238438747907
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/deepesh0x/autotrain-mlsec-1013333726
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("deepesh0x/autotrain-mlsec-1013333726", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("deepesh0x/autotrain-mlsec-1013333726", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
QuentinKemperino/ECHR_test_2
|
QuentinKemperino
| 2022-06-21T20:44:10Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:lex_glue",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-06T14:24:02Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- lex_glue
model-index:
- name: ECHR_test_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. -->
# ECHR_test_2 Task A
This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the lex_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1998
- Macro-f1: 0.5295
- Micro-f1: 0.6157
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro-f1 | Micro-f1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.2142 | 0.44 | 500 | 0.2887 | 0.2391 | 0.4263 |
| 0.172 | 0.89 | 1000 | 0.2672 | 0.2908 | 0.4628 |
| 0.1737 | 1.33 | 1500 | 0.2612 | 0.3657 | 0.5102 |
| 0.1581 | 1.78 | 2000 | 0.2412 | 0.3958 | 0.5468 |
| 0.1509 | 2.22 | 2500 | 0.2264 | 0.3950 | 0.5552 |
| 0.1606 | 2.67 | 3000 | 0.2342 | 0.4006 | 0.5511 |
| 0.1491 | 3.11 | 3500 | 0.2176 | 0.4558 | 0.5622 |
| 0.1392 | 3.56 | 4000 | 0.2454 | 0.4128 | 0.5596 |
| 0.15 | 4.0 | 4500 | 0.2113 | 0.4684 | 0.5874 |
| 0.1461 | 4.44 | 5000 | 0.2179 | 0.4631 | 0.5815 |
| 0.1457 | 4.89 | 5500 | 0.2151 | 0.4805 | 0.5949 |
| 0.1443 | 5.33 | 6000 | 0.2155 | 0.5123 | 0.5917 |
| 0.1279 | 5.78 | 6500 | 0.2131 | 0.4915 | 0.5998 |
| 0.1377 | 6.22 | 7000 | 0.2244 | 0.4705 | 0.5944 |
| 0.1242 | 6.67 | 7500 | 0.2150 | 0.5089 | 0.5918 |
| 0.1222 | 7.11 | 8000 | 0.2045 | 0.4801 | 0.5981 |
| 0.1372 | 7.56 | 8500 | 0.2074 | 0.5317 | 0.5962 |
| 0.1289 | 8.0 | 9000 | 0.2035 | 0.5323 | 0.6126 |
| 0.1295 | 8.44 | 9500 | 0.2058 | 0.5213 | 0.6073 |
| 0.123 | 8.89 | 10000 | 0.2027 | 0.5486 | 0.6135 |
| 0.1335 | 9.33 | 10500 | 0.1984 | 0.5442 | 0.6249 |
| 0.1258 | 9.78 | 11000 | 0.1998 | 0.5295 | 0.6157 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
torchxrayvision/densenet121-res224-mimic_ch
|
torchxrayvision
| 2022-06-21T20:11:52Z | 27 | 0 |
transformers
|
[
"transformers",
"vision",
"image-classification",
"dataset:nih-pc-chex-mimic_ch-google-openi-rsna",
"arxiv:2111.00595",
"arxiv:2002.02497",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-21T13:05:28Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- nih-pc-chex-mimic_ch-google-openi-rsna
---
# densenet121-res224-mimic_ch
A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers.
### How to use
Here is how to use this model to classify an image of xray:
Note: Each pretrained model has 18 outputs. The `all` model has every output trained. However, for the other weights some targets are not trained and will predict randomly becuase they do not exist in the training dataset. The only valid outputs are listed in the field `{dataset}.pathologies` on the dataset that corresponds to the weights.
Benchmarks of the modes are here: [BENCHMARKS.md](https://github.com/mlmed/torchxrayvision/blob/master/BENCHMARKS.md)
```python
import urllib.request
import skimage
import torch
import torch.nn.functional as F
import torchvision
import torchvision.transforms
import torchxrayvision as xrv
model_name = "densenet121-res224-mimic_ch"
img_url = "https://huggingface.co/spaces/torchxrayvision/torchxrayvision-classifier/resolve/main/16747_3_1.jpg"
img_path = "xray.jpg"
urllib.request.urlretrieve(img_url, img_path)
model = xrv.models.get_model(model_name, from_hf_hub=True)
img = skimage.io.imread(img_path)
img = xrv.datasets.normalize(img, 255)
# Check that images are 2D arrays
if len(img.shape) > 2:
img = img[:, :, 0]
if len(img.shape) < 2:
print("error, dimension lower than 2 for image")
# Add color channel
img = img[None, :, :]
transform = torchvision.transforms.Compose([xrv.datasets.XRayCenterCrop()])
img = transform(img)
with torch.no_grad():
img = torch.from_numpy(img).unsqueeze(0)
preds = model(img).cpu()
output = {
k: float(v)
for k, v in zip(xrv.datasets.default_pathologies, preds[0].detach().numpy())
}
print(output)
```
For more code examples, we refer to the [example scripts](https://github.com/kamalkraj/torchxrayvision/blob/master/scripts).
### Citation
Primary TorchXRayVision paper: [https://arxiv.org/abs/2111.00595](https://arxiv.org/abs/2111.00595)
```
Joseph Paul Cohen, Joseph D. Viviano, Paul Bertin, Paul Morrison, Parsa Torabian, Matteo Guarrera, Matthew P Lungren, Akshay Chaudhari, Rupert Brooks, Mohammad Hashir, Hadrien Bertrand
TorchXRayVision: A library of chest X-ray datasets and models.
https://github.com/mlmed/torchxrayvision, 2020
@article{Cohen2020xrv,
author = {Cohen, Joseph Paul and Viviano, Joseph D. and Bertin, Paul and Morrison, Paul and Torabian, Parsa and Guarrera, Matteo and Lungren, Matthew P and Chaudhari, Akshay and Brooks, Rupert and Hashir, Mohammad and Bertrand, Hadrien},
journal = {https://github.com/mlmed/torchxrayvision},
title = {{TorchXRayVision: A library of chest X-ray datasets and models}},
url = {https://github.com/mlmed/torchxrayvision},
year = {2020}
arxivId = {2111.00595},
}
```
and this paper which initiated development of the library: [https://arxiv.org/abs/2002.02497](https://arxiv.org/abs/2002.02497)
```
Joseph Paul Cohen and Mohammad Hashir and Rupert Brooks and Hadrien Bertrand
On the limits of cross-domain generalization in automated X-ray prediction.
Medical Imaging with Deep Learning 2020 (Online: https://arxiv.org/abs/2002.02497)
@inproceedings{cohen2020limits,
title={On the limits of cross-domain generalization in automated X-ray prediction},
author={Cohen, Joseph Paul and Hashir, Mohammad and Brooks, Rupert and Bertrand, Hadrien},
booktitle={Medical Imaging with Deep Learning},
year={2020},
url={https://arxiv.org/abs/2002.02497}
}
```
|
torchxrayvision/densenet121-res224-all
|
torchxrayvision
| 2022-06-21T20:10:39Z | 57 | 1 |
transformers
|
[
"transformers",
"vision",
"image-classification",
"dataset:nih-pc-chex-mimic_ch-google-openi-rsna",
"arxiv:2002.02497",
"arxiv:2111.00595",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-21T13:01:42Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- nih-pc-chex-mimic_ch-google-openi-rsna
---
# densenet121-res224-all
A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers.
This model was trained on the datasets: nih-pc-chex-mimic_ch-google-openi-rsna and is described here: https://arxiv.org/abs/2002.02497
### How to use
Here is how to use this model to classify an image of xray:
Note: Each pretrained model has 18 outputs. The `all` model has every output trained. However, for the other weights some targets are not trained and will predict randomly becuase they do not exist in the training dataset. The only valid outputs are listed in the field `{dataset}.pathologies` on the dataset that corresponds to the weights.
Benchmarks of the modes are here: [BENCHMARKS.md](https://github.com/mlmed/torchxrayvision/blob/master/BENCHMARKS.md)
```python
import urllib.request
import skimage
import torch
import torch.nn.functional as F
import torchvision
import torchvision.transforms
import torchxrayvision as xrv
model_name = "densenet121-res224-all"
img_url = "https://huggingface.co/spaces/torchxrayvision/torchxrayvision-classifier/resolve/main/16747_3_1.jpg"
img_path = "xray.jpg"
urllib.request.urlretrieve(img_url, img_path)
model = xrv.models.get_model(model_name, from_hf_hub=True)
img = skimage.io.imread(img_path)
img = xrv.datasets.normalize(img, 255)
# Check that images are 2D arrays
if len(img.shape) > 2:
img = img[:, :, 0]
if len(img.shape) < 2:
print("error, dimension lower than 2 for image")
# Add color channel
img = img[None, :, :]
transform = torchvision.transforms.Compose([xrv.datasets.XRayCenterCrop()])
img = transform(img)
with torch.no_grad():
img = torch.from_numpy(img).unsqueeze(0)
preds = model(img).cpu()
output = {
k: float(v)
for k, v in zip(xrv.datasets.default_pathologies, preds[0].detach().numpy())
}
print(output)
```
For more code examples, we refer to the [example scripts](https://github.com/kamalkraj/torchxrayvision/blob/master/scripts).
### Citation
Primary TorchXRayVision paper: [https://arxiv.org/abs/2111.00595](https://arxiv.org/abs/2111.00595)
```
Joseph Paul Cohen, Joseph D. Viviano, Paul Bertin, Paul Morrison, Parsa Torabian, Matteo Guarrera, Matthew P Lungren, Akshay Chaudhari, Rupert Brooks, Mohammad Hashir, Hadrien Bertrand
TorchXRayVision: A library of chest X-ray datasets and models.
https://github.com/mlmed/torchxrayvision, 2020
@article{Cohen2020xrv,
author = {Cohen, Joseph Paul and Viviano, Joseph D. and Bertin, Paul and Morrison, Paul and Torabian, Parsa and Guarrera, Matteo and Lungren, Matthew P and Chaudhari, Akshay and Brooks, Rupert and Hashir, Mohammad and Bertrand, Hadrien},
journal = {https://github.com/mlmed/torchxrayvision},
title = {{TorchXRayVision: A library of chest X-ray datasets and models}},
url = {https://github.com/mlmed/torchxrayvision},
year = {2020}
arxivId = {2111.00595},
}
```
and this paper which initiated development of the library: [https://arxiv.org/abs/2002.02497](https://arxiv.org/abs/2002.02497)
```
Joseph Paul Cohen and Mohammad Hashir and Rupert Brooks and Hadrien Bertrand
On the limits of cross-domain generalization in automated X-ray prediction.
Medical Imaging with Deep Learning 2020 (Online: https://arxiv.org/abs/2002.02497)
@inproceedings{cohen2020limits,
title={On the limits of cross-domain generalization in automated X-ray prediction},
author={Cohen, Joseph Paul and Hashir, Mohammad and Brooks, Rupert and Bertrand, Hadrien},
booktitle={Medical Imaging with Deep Learning},
year={2020},
url={https://arxiv.org/abs/2002.02497}
}
```
|
torchxrayvision/densenet121-res224-chex
|
torchxrayvision
| 2022-06-21T20:09:36Z | 32 | 0 |
transformers
|
[
"transformers",
"vision",
"image-classification",
"dataset:nih-pc-chex-mimic_ch-google-openi-rsna",
"arxiv:2111.00595",
"arxiv:2002.02497",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-21T13:03:37Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- nih-pc-chex-mimic_ch-google-openi-rsna
---
# densenet121-res224-chex
A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers.
### How to use
Here is how to use this model to classify an image of xray:
Note: Each pretrained model has 18 outputs. The `all` model has every output trained. However, for the other weights some targets are not trained and will predict randomly becuase they do not exist in the training dataset. The only valid outputs are listed in the field `{dataset}.pathologies` on the dataset that corresponds to the weights.
Benchmarks of the modes are here: [BENCHMARKS.md](https://github.com/mlmed/torchxrayvision/blob/master/BENCHMARKS.md)
```python
import urllib.request
import skimage
import torch
import torch.nn.functional as F
import torchvision
import torchvision.transforms
import torchxrayvision as xrv
model_name = "densenet121-res224-chex"
img_url = "https://huggingface.co/spaces/torchxrayvision/torchxrayvision-classifier/resolve/main/16747_3_1.jpg"
img_path = "xray.jpg"
urllib.request.urlretrieve(img_url, img_path)
model = xrv.models.get_model(model_name, from_hf_hub=True)
img = skimage.io.imread(img_path)
img = xrv.datasets.normalize(img, 255)
# Check that images are 2D arrays
if len(img.shape) > 2:
img = img[:, :, 0]
if len(img.shape) < 2:
print("error, dimension lower than 2 for image")
# Add color channel
img = img[None, :, :]
transform = torchvision.transforms.Compose([xrv.datasets.XRayCenterCrop()])
img = transform(img)
with torch.no_grad():
img = torch.from_numpy(img).unsqueeze(0)
preds = model(img).cpu()
output = {
k: float(v)
for k, v in zip(xrv.datasets.default_pathologies, preds[0].detach().numpy())
}
print(output)
```
For more code examples, we refer to the [example scripts](https://github.com/kamalkraj/torchxrayvision/blob/master/scripts).
### Citation
Primary TorchXRayVision paper: [https://arxiv.org/abs/2111.00595](https://arxiv.org/abs/2111.00595)
```
Joseph Paul Cohen, Joseph D. Viviano, Paul Bertin, Paul Morrison, Parsa Torabian, Matteo Guarrera, Matthew P Lungren, Akshay Chaudhari, Rupert Brooks, Mohammad Hashir, Hadrien Bertrand
TorchXRayVision: A library of chest X-ray datasets and models.
https://github.com/mlmed/torchxrayvision, 2020
@article{Cohen2020xrv,
author = {Cohen, Joseph Paul and Viviano, Joseph D. and Bertin, Paul and Morrison, Paul and Torabian, Parsa and Guarrera, Matteo and Lungren, Matthew P and Chaudhari, Akshay and Brooks, Rupert and Hashir, Mohammad and Bertrand, Hadrien},
journal = {https://github.com/mlmed/torchxrayvision},
title = {{TorchXRayVision: A library of chest X-ray datasets and models}},
url = {https://github.com/mlmed/torchxrayvision},
year = {2020}
arxivId = {2111.00595},
}
```
and this paper which initiated development of the library: [https://arxiv.org/abs/2002.02497](https://arxiv.org/abs/2002.02497)
```
Joseph Paul Cohen and Mohammad Hashir and Rupert Brooks and Hadrien Bertrand
On the limits of cross-domain generalization in automated X-ray prediction.
Medical Imaging with Deep Learning 2020 (Online: https://arxiv.org/abs/2002.02497)
@inproceedings{cohen2020limits,
title={On the limits of cross-domain generalization in automated X-ray prediction},
author={Cohen, Joseph Paul and Hashir, Mohammad and Brooks, Rupert and Bertrand, Hadrien},
booktitle={Medical Imaging with Deep Learning},
year={2020},
url={https://arxiv.org/abs/2002.02497}
}
```
|
deepesh0x/autotrain-mlsec-1013333734
|
deepesh0x
| 2022-06-21T19:12:28Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:deepesh0x/autotrain-data-mlsec",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-21T16:56:46Z |
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- deepesh0x/autotrain-data-mlsec
co2_eq_emissions: 308.7012650779217
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1013333734
- CO2 Emissions (in grams): 308.7012650779217
## Validation Metrics
- Loss: 0.20877738296985626
- Accuracy: 0.9396153846153846
- Precision: 0.9291791791791791
- Recall: 0.9518072289156626
- AUC: 0.9671522989580735
- F1: 0.9403570976320121
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/deepesh0x/autotrain-mlsec-1013333734
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("deepesh0x/autotrain-mlsec-1013333734", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("deepesh0x/autotrain-mlsec-1013333734", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
patrickvonplaten/opt_metaseq_1300m
|
patrickvonplaten
| 2022-06-21T17:52:22Z | 0 | 1 | null |
[
"opt_metasq",
"region:us"
] | null | 2022-05-10T17:31:55Z |
---
tags:
- opt_metasq
---
# This repo let's you run the following checkpoint using facebookresearch/metaseq.
Do the following:
## 1. Install PyTorch
```
pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
```
## 2. Install Megatron
```
git clone https://github.com/patrickvonplaten/Megatron-LM.git
cd Megatron-LM
pip3 install six regex
pip3 install -e .
```
## 3. Install fairscale
```
git clone https://github.com/facebookresearch/fairscale.git
cd fairscale
git checkout prefetch_fsdp_params_simple
pip3 install -e .
```
## 4. Install metaseq
```
git clone https://github.com/patrickvonplaten/metaseq.git
cd metaseq
pip3 install -e .
```
## 5. Clone this repo (click top right on "How to clone")
## 6. Run the following:
```bash
cd <path/to/cloned/repo>
bash run.sh
```
|
patrickvonplaten/opt_metaseq_125m
|
patrickvonplaten
| 2022-06-21T17:51:37Z | 0 | 9 | null |
[
"opt_metasq",
"region:us"
] | null | 2022-05-10T17:31:43Z |
---
tags:
- opt_metasq
---
# This repo let's you run the following checkpoint using facebookresearch/metaseq.
Do the following:
## 1. Install PyTorch
```
pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
```
## 2. Install Megatron
```
git clone https://github.com/patrickvonplaten/Megatron-LM.git
cd Megatron-LM
pip3 install six regex
pip3 install -e .
```
## 3. Install fairscale
```
git clone https://github.com/facebookresearch/fairscale.git
cd fairscale
git checkout prefetch_fsdp_params_simple
pip3 install -e .
```
## 4. Install metaseq
```
git clone https://github.com/patrickvonplaten/metaseq.git
cd metaseq
pip3 install -e .
```
## 5. Clone this repo (click top right on "How to clone")
## 6. Run the following:
```bash
cd <path/to/cloned/repo>
bash run.sh
```
|
Mascariddu8/test-masca
|
Mascariddu8
| 2022-06-21T16:57:29Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-21T16:41:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
model-index:
- name: test-masca
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. -->
# test-masca
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
vjeansel/dqn-SI
|
vjeansel
| 2022-06-21T16:28:49Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-21T16:28:08Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 471.00 +/- 112.80
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga vjeansel -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga vjeansel
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
kktoto/tiny_focal_ckpt
|
kktoto
| 2022-06-21T15:05:00Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-21T12:03:41Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tiny_focal_ckpt
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. -->
# tiny_focal_ckpt
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0561
- Precision: 0.6529
- Recall: 0.6366
- F1: 0.6446
- Accuracy: 0.9516
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.058 | 1.0 | 5561 | 0.0583 | 0.6327 | 0.5945 | 0.6130 | 0.9484 |
| 0.0566 | 2.0 | 11122 | 0.0570 | 0.6401 | 0.5985 | 0.6186 | 0.9492 |
| 0.0564 | 3.0 | 16683 | 0.0567 | 0.6364 | 0.6241 | 0.6302 | 0.9496 |
| 0.053 | 4.0 | 22244 | 0.0561 | 0.6416 | 0.6312 | 0.6364 | 0.9503 |
| 0.052 | 5.0 | 27805 | 0.0558 | 0.6501 | 0.6239 | 0.6367 | 0.9510 |
| 0.0507 | 6.0 | 33366 | 0.0555 | 0.6555 | 0.6208 | 0.6377 | 0.9514 |
| 0.0497 | 7.0 | 38927 | 0.0552 | 0.6559 | 0.6256 | 0.6404 | 0.9515 |
| 0.0485 | 8.0 | 44488 | 0.0561 | 0.6485 | 0.6397 | 0.6440 | 0.9513 |
| 0.0481 | 9.0 | 50049 | 0.0558 | 0.6531 | 0.6344 | 0.6436 | 0.9515 |
| 0.0469 | 10.0 | 55610 | 0.0561 | 0.6529 | 0.6366 | 0.6446 | 0.9516 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
fabianmmueller/dqn-SpaceInvadersNoFrameskip-v4
|
fabianmmueller
| 2022-06-21T14:56:38Z | 8 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-21T14:55:58Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 645.00 +/- 307.15
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga fabianmmueller -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga fabianmmueller
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
espnet/Wangyou_Zhang_wsj0_2mix_enh_train_enh_dptnet_raw
|
espnet
| 2022-06-21T14:11:36Z | 2 | 0 |
espnet
|
[
"espnet",
"audio",
"audio-to-audio",
"dataset:wsj0-2mix",
"arxiv:1804.00015",
"arxiv:2011.03706",
"license:cc-by-4.0",
"region:us"
] |
audio-to-audio
| 2022-06-21T13:43:33Z |
---
tags:
- espnet
- audio
- audio-to-audio
language:
datasets:
- wsj0-2mix
license: cc-by-4.0
---
## ESPnet2 ENH model
### `espnet/Wangyou_Zhang_wsj0_2mix_enh_train_enh_dptnet_raw`
This model was trained by Wangyou Zhang using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/wsj0_2mix/enh1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/Wangyou_Zhang_wsj0_2mix_enh_train_enh_dptnet_raw
```
## ENH config
<details><summary>expand</summary>
```
config: conf/tuning/train_enh_dptnet.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: chunk
output_dir: exp/enh_train_enh_dptnet_raw
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 53094
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: true
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
validate_train_iter: false
max_epoch: 150
patience: 10
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- si_snr
- max
- - valid
- loss
- min
keep_nbest_models: 1
nbest_averaging_interval: 0
grad_clip: 5
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: null
batch_size: 4
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/enh_stats_8k/train/speech_mix_shape
- exp/enh_stats_8k/train/speech_ref1_shape
- exp/enh_stats_8k/train/speech_ref2_shape
valid_shape_file:
- exp/enh_stats_8k/valid/speech_mix_shape
- exp/enh_stats_8k/valid/speech_ref1_shape
- exp/enh_stats_8k/valid/speech_ref2_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 80000
- 80000
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 20000
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/tr_min_8k/wav.scp
- speech_mix
- sound
- - dump/raw/tr_min_8k/spk1.scp
- speech_ref1
- sound
- - dump/raw/tr_min_8k/spk2.scp
- speech_ref2
- sound
valid_data_path_and_name_and_type:
- - dump/raw/cv_min_8k/wav.scp
- speech_mix
- sound
- - dump/raw/cv_min_8k/spk1.scp
- speech_ref1
- sound
- - dump/raw/cv_min_8k/spk2.scp
- speech_ref2
- sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.0004
eps: 1.0e-08
weight_decay: 1.0e-05
scheduler: warmupsteplr
scheduler_conf:
warmup_steps: 4000
steps_per_epoch: 14273
step_size: 2
gamma: 0.98
init: null
model_conf:
stft_consistency: false
loss_type: mask_mse
mask_type: null
criterions:
- name: si_snr
conf:
eps: 1.0e-07
wrapper: pit
wrapper_conf:
weight: 1.0
independent_perm: true
use_preprocessor: false
encoder: conv
encoder_conf:
channel: 64
kernel_size: 2
stride: 1
separator: dptnet
separator_conf:
num_spk: 2
post_enc_relu: true
layer: 6
rnn_type: lstm
bidirectional: true
unit: 128
att_heads: 4
dropout: 0.0
activation: relu
norm_type: gLN
segment_size: 250
nonlinear: relu
decoder: conv
decoder_conf:
channel: 64
kernel_size: 2
stride: 1
required:
- output_dir
version: 0.10.7a1
distributed: true
```
</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{li2021espnetse,
title={{ESPnet-SE}: End-to-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration},
author={Li, Chenda and Shi, Jing and Zhang, Wangyou and Subramanian, Aswin Shanmugam and Chang, Xuankai and Kamo, Naoyuki and Hira, Moto and Hayashi, Tomoki and Boeddeker, Christoph and Chen, Zhuo and Watanabe, Shinji},
booktitle={Proc. IEEE Spoken Language Technology Workshop (SLT)},
pages={785--792},
year={2021},
}
```
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}
}
@inproceedings{li2021espnetse,
title={{ESPnet-SE}: End-to-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration},
author={Li, Chenda and Shi, Jing and Zhang, Wangyou and Subramanian, Aswin Shanmugam and Chang, Xuankai and Kamo, Naoyuki and Hira, Moto and Hayashi, Tomoki and Boeddeker, Christoph and Chen, Zhuo and Watanabe, Shinji},
year={2020},
eprint={2011.03706},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
```
|
Taeham/wav2vec2-ksponspeech
|
Taeham
| 2022-06-21T11:49:09Z | 331 | 4 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-11T16:31:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-ksponspeech
results: []
---
# wav2vec2-ksponspeech
This model is a fine-tuned version of [Wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- **WER(Word Error Rate)** for Third party test data : 0.373
**For improving WER:**
- Numeric / Character Unification
- Decoding the word with the correct notation (from word based on pronounciation)
- Uniform use of special characters (. / ?)
- Converting non-existent words to existing words
## Model description
Korean Wav2vec with Ksponspeech dataset.
This model was trained by two dataset :
- Train1 : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-train (1 ~ 20000th data in Ksponspeech)
- Train2 : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-train2 (20100 ~ 40100th data in Ksponspeech)
- Validation : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-test (20000 ~ 20100th data in Ksponspeech)
- Third party test : https://huggingface.co/datasets/Taeham/wav2vec2-ksponspeech-test (60000 ~ 20100th data in Ksponspeech)
### Hardward Specification
- GPU : GEFORCE RTX 3080ti 12GB
- CPU : Intel i9-12900k
- RAM : 32GB
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
huggingtweets/coinmamba
|
huggingtweets
| 2022-06-21T10:44:21Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-21T10:42:40Z |
---
language: en
thumbnail: http://www.huggingtweets.com/coinmamba/1655808256840/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/1523748536168464384/feZm38Pe_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">CoinMamba</div>
<div style="text-align: center; font-size: 14px;">@coinmamba</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 CoinMamba.
| Data | CoinMamba |
| --- | --- |
| Tweets downloaded | 3243 |
| Retweets | 41 |
| Short tweets | 608 |
| Tweets kept | 2594 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2as2s722/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 @coinmamba's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1zewdmar) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1zewdmar/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/coinmamba')
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)
|
huggingartists/rihanna
|
huggingartists
| 2022-06-21T09:56:57Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/rihanna",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/rihanna
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/f83548d76e427d0a4fdcafdf2f62b647.1000x1000x1.png')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Rihanna</div>
<a href="https://genius.com/artists/rihanna">
<div style="text-align: center; font-size: 14px;">@rihanna</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Rihanna.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/rihanna).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/rihanna")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/ee6eogks/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 Rihanna's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1mvns7x8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1mvns7x8/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='huggingartists/rihanna')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/rihanna")
model = AutoModelWithLMHead.from_pretrained("huggingartists/rihanna")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
furyhawk/distilbert-base-uncased-finetuned-clinc
|
furyhawk
| 2022-06-21T09:36:29Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-21T07:46:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.915483870967742
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7788
- Accuracy: 0.9155
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2841 | 1.0 | 318 | 3.2794 | 0.7465 |
| 2.623 | 2.0 | 636 | 1.8719 | 0.8335 |
| 1.5474 | 3.0 | 954 | 1.1629 | 0.8929 |
| 1.014 | 4.0 | 1272 | 0.8621 | 0.9094 |
| 0.7987 | 5.0 | 1590 | 0.7788 | 0.9155 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
kjunelee/bert-base-uncased-issues-128
|
kjunelee
| 2022-06-21T07:24:49Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-21T07:09:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-issues-128
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2314
## 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: 64
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.193 | 1.0 | 146 | 1.7004 |
| 1.7081 | 2.0 | 292 | 1.4895 |
| 1.5458 | 3.0 | 438 | 1.4427 |
| 1.4715 | 4.0 | 584 | 1.4081 |
| 1.3944 | 5.0 | 730 | 1.3163 |
| 1.3396 | 6.0 | 876 | 1.3200 |
| 1.2945 | 7.0 | 1022 | 1.2785 |
| 1.2652 | 8.0 | 1168 | 1.2473 |
| 1.2332 | 9.0 | 1314 | 1.2321 |
| 1.2042 | 10.0 | 1460 | 1.2162 |
| 1.204 | 11.0 | 1606 | 1.1781 |
| 1.1866 | 12.0 | 1752 | 1.2211 |
| 1.1592 | 13.0 | 1898 | 1.2801 |
| 1.1503 | 14.0 | 2044 | 1.1768 |
| 1.1268 | 15.0 | 2190 | 1.1657 |
| 1.1521 | 16.0 | 2336 | 1.2314 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
|
Corianas/dqn-SpaceInvadersNoFrameskip-v4_21.6.22.LoadBest
|
Corianas
| 2022-06-21T06:35:46Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-21T06:35:08Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 875.50 +/- 366.55
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Corianas -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Corianas
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.25),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 3000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Corianas/dqn-SpaceInvadersNoFrameskip-v4_21.6.22
|
Corianas
| 2022-06-21T06:33:47Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-21T06:33:06Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 866.50 +/- 274.13
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Corianas -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Corianas
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.25),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 3000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Sampson2022/test2
|
Sampson2022
| 2022-06-21T05:55:27Z | 40 | 0 |
transformers
|
[
"transformers",
"pytorch",
"resnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:1512.03385",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-21T02:34:13Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
---
# ResNet-50 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models.
This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (\~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec) according to [Nvidia](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch).
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=resnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoFeatureExtractor, ResNetForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")
model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")
inputs = feature_extractor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/resnet).
### BibTeX entry and citation info
```bibtex
@inproceedings{he2016deep,
title={Deep residual learning for image recognition},
author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={770--778},
year={2016}
}
```
|
furyhawk/xlm-roberta-base-finetuned-panx-de
|
furyhawk
| 2022-06-21T03:44:32Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-20T15:27:50Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.865423959990907
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1360
- F1: 0.8654
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2552 | 1.0 | 525 | 0.1621 | 0.8216 |
| 0.1292 | 2.0 | 1050 | 0.1409 | 0.8445 |
| 0.084 | 3.0 | 1575 | 0.1360 | 0.8654 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.9.1
- Datasets 1.12.1
- Tokenizers 0.10.3
|
huggingtweets/maxfitemaster
|
huggingtweets
| 2022-06-21T03:04:45Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-21T03:00:20Z |
---
language: en
thumbnail: http://www.huggingtweets.com/maxfitemaster/1655780681704/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/1017172371080470528/K6wTmacP_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">James Swartout</div>
<div style="text-align: center; font-size: 14px;">@maxfitemaster</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 James Swartout.
| Data | James Swartout |
| --- | --- |
| Tweets downloaded | 1120 |
| Retweets | 372 |
| Short tweets | 66 |
| Tweets kept | 682 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3952izg4/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 @maxfitemaster's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/20y35cm7) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/20y35cm7/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/maxfitemaster')
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)
|
bigscience/T0_single_prompt
|
bigscience
| 2022-06-21T01:27:01Z | 8 | 4 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:bigscience/P3",
"arxiv:2110.08207",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
datasets:
- bigscience/P3
language: en
license: apache-2.0
widget:
- text: "A is the son's of B's uncle. What is the family relationship between A and B?"
- text: "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."
- text: "Task: copy but say the opposite.\n
PSG won its match against Barca."
- text: "Is this review positive or negative? Review: Best cast iron skillet you will every buy."
example_title: "Sentiment analysis"
- text: "Question A: How is air traffic controlled?
\nQuestion B: How do you become an air traffic controller?\nPick one: these questions are duplicates or not duplicates."
- text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had foreign affairs experience as a former First Lady.
\nIn the previous sentence, decide who 'her' is referring to."
example_title: "Coreference resolution"
- text: "Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.\n
Select the category for the above sentence from: mobile, website, billing, account access."
- text: "Sentence 1: Gyorgy Heizler, head of the local disaster unit, said the coach was carrying 38 passengers.\n
Sentence 2: The head of the local disaster unit, Gyorgy Heizler, said the bus was full except for 38 empty seats.\n\n
Do sentences 1 and 2 have the same meaning?"
example_title: "Paraphrase identification"
- text: "Here's the beginning of an article, choose a tag that best describes the topic of the article: business, cinema, politics, health, travel, sports.\n\n
The best and worst fo 007 as 'No time to die' marks Daniel Craig's exit.\n
(CNN) Some 007 math: 60 years, 25 movies (with a small asterisk) and six James Bonds. For a Cold War creation, Ian Fleming's suave spy has certainly gotten around, but despite different guises in the tuxedo and occasional scuba gear, when it comes to Bond ratings, there really shouldn't be much argument about who wore it best."
- text: "Max: Know any good websites to buy clothes from?\n
Payton: Sure :) LINK 1, LINK 2, LINK 3\n
Max: That's a lot of them!\n
Payton: Yeah, but they have different things so I usually buy things from 2 or 3 of them.\n
Max: I'll check them out. Thanks.\n\n
Who or what are Payton and Max referring to when they say 'them'?"
- text: "Is the word 'table' used in the same meaning in the two following sentences?\n\n
Sentence A: you can leave the books on the table over there.\n
Sentence B: the tables in this book are very hard to read."
- text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book.\n
The red book is to the right of the gray book. The black book is to the left of the blue book. The blue book is to the left of the gray book. The purple book is the second from the right.\n\n
Which book is the leftmost book?"
example_title: "Logic puzzles"
- text: "The two men running to become New York City's next mayor will face off in their first debate Wednesday night.\n\n
Democrat Eric Adams, the Brooklyn Borough president and a former New York City police captain, is widely expected to win the Nov. 2 election against Republican Curtis Sliwa, the founder of the 1970s-era Guardian Angels anti-crime patril.\n\n
Who are the men running for mayor?"
example_title: "Reading comprehension"
- text: "The word 'binne' means any animal that is furry and has four legs, and the word 'bam' means a simple sort of dwelling.\n\n
Which of the following best characterizes binne bams?\n
- Sentence 1: Binne bams are for pets.\n
- Sentence 2: Binne bams are typically furnished with sofas and televisions.\n
- Sentence 3: Binne bams are luxurious apartments.\n
- Sentence 4: Binne bams are places where people live."
---
**How do I pronounce the name of the model?** T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"!
**Official repository**: [bigscience-workshop/t-zero](https://github.com/bigscience-workshop/t-zero)
# Model Description
T0* shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many tasks, while being 16x smaller. It is a series of encoder-decoder models trained on a large set of different tasks specified in natural language prompts. We convert numerous English supervised datasets into prompts, each with multiple templates using varying formulations. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. To obtain T0*, we fine-tune a pretrained language model on this multitask mixture covering many different NLP tasks.
# Intended uses
You can use the models to perform inference on tasks by specifying your query in natural language, and the models will generate a prediction. For instance, you can ask *"Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"*, and the model will hopefully generate *"Positive"*.
A few other examples that you can try:
- *A is the son's of B's uncle. What is the family relationship between A and B?*
- *Question A: How is air traffic controlled?<br>
Question B: How do you become an air traffic controller?<br>
Pick one: these questions are duplicates or not duplicates.*
- *Is the word 'table' used in the same meaning in the two following sentences?<br><br>
Sentence A: you can leave the books on the table over there.<br>
Sentence B: the tables in this book are very hard to read.*
- *Max: Know any good websites to buy clothes from?<br>
Payton: Sure :) LINK 1, LINK 2, LINK 3<br>
Max: That's a lot of them!<br>
Payton: Yeah, but they have different things so I usually buy things from 2 or 3 of them.<br>
Max: I'll check them out. Thanks.<br><br>
Who or what are Payton and Max referring to when they say 'them'?*
- *On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book.<br>
The red book is to the right of the gray book. The black book is to the left of the blue book. The blue book is to the left of the gray book. The purple book is the second from the right.<br><br>
Which book is the leftmost book?*
- *Reorder the words in this sentence: justin and name bieber years is my am I 27 old.*
# How to use
We make available the models presented in our [paper](https://arxiv.org/abs/2110.08207) along with the ablation models. We recommend using the [T0pp](https://huggingface.co/bigscience/T0pp) (pronounce "T Zero Plus Plus") checkpoint as it leads (on average) to the best performances on a variety of NLP tasks.
|Model|Number of parameters|
|-|-|
|[T0](https://huggingface.co/bigscience/T0)|11 billion|
|[T0p](https://huggingface.co/bigscience/T0p)|11 billion|
|[T0pp](https://huggingface.co/bigscience/T0pp)|11 billion|
|[T0_single_prompt](https://huggingface.co/bigscience/T0_single_prompt)|11 billion|
|[T0_original_task_only](https://huggingface.co/bigscience/T0_original_task_only)|11 billion|
|[T0_3B](https://huggingface.co/bigscience/T0_3B)|3 billion|
Here is how to use the model in PyTorch:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("bigscience/T0pp")
model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp")
inputs = tokenizer.encode("Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy", return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
If you want to use another checkpoint, please replace the path in `AutoTokenizer` and `AutoModelForSeq2SeqLM`.
**Note: the model was trained with bf16 activations. As such, we highly discourage running inference with fp16. fp32 or bf16 should be preferred.**
# Training procedure
T0* models are based on [T5](https://huggingface.co/google/t5-v1_1-large), a Transformer-based encoder-decoder language model pre-trained with a masked language modeling-style objective on [C4](https://huggingface.co/datasets/c4). We use the publicly available [language model-adapted T5 checkpoints](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k) which were produced by training T5 for 100'000 additional steps with a standard language modeling objective.
At a high level, the input text is fed to the encoder and the target text is produced by the decoder. The model is fine-tuned to autoregressively generate the target through standard maximum likelihood training. It is never trained to generate the input. We detail our training data in the next section.
Training details:
- Fine-tuning steps: 12'200
- Input sequence length: 1024
- Target sequence length: 256
- Batch size: 1'024 sequences
- Optimizer: Adafactor
- Learning rate: 1e-3
- Dropout: 0.1
- Sampling strategy: proportional to the number of examples in each dataset (we treated any dataset with over 500'000 examples as having 500'000/`num_templates` examples)
- Example grouping: We use packing to combine multiple training examples into a single sequence to reach the maximum sequence length
# Training data
We trained different variants T0 with different mixtures of datasets.
|Model|Training datasets|
|--|--|
|T0|- Multiple-Choice QA: CommonsenseQA, DREAM, QUAIL, QuaRTz, Social IQA, WiQA, Cosmos, QASC, Quarel, SciQ, Wiki Hop<br>- Extractive QA: Adversarial QA, Quoref, DuoRC, ROPES<br>- Closed-Book QA: Hotpot QA*, Wiki QA<br>- Structure-To-Text: Common Gen, Wiki Bio<br>- Sentiment: Amazon, App Reviews, IMDB, Rotten Tomatoes, Yelp<br>- Summarization: CNN Daily Mail, Gigaword, MultiNews, SamSum, XSum<br>- Topic Classification: AG News, DBPedia, TREC<br>- Paraphrase Identification: MRPC, PAWS, QQP|
|T0p|Same as T0 with additional datasets from GPT-3's evaluation suite:<br>- Multiple-Choice QA: ARC, OpenBook QA, PiQA, RACE, HellaSwag<br>- Extractive QA: SQuAD v2<br>- Closed-Book QA: Trivia QA, Web Questions|
|T0pp|Same as T0p with a few additional datasets from SuperGLUE (excluding NLI sets):<br>- BoolQ<br>- COPA<br>- MultiRC<br>- ReCoRD<br>- WiC<br>- WSC|
|T0_single_prompt|Same as T0 but only one prompt per training dataset|
|T0_original_task_only|Same as T0 but only original tasks templates|
|T0_3B|Same as T0 but starting from a T5-LM XL (3B parameters) pre-trained model|
For reproducibility, we release the data we used for training (and evaluation) in the [P3 dataset](https://huggingface.co/datasets/bigscience/P3). Prompts examples can be found on the dataset page.
*: We recast Hotpot QA as closed-book QA due to long input sequence length.
# Evaluation data
We evaluate our models on a suite of held-out tasks:
|Task category|Datasets|
|-|-|
|Natural language inference|ANLI, CB, RTE|
|Coreference resolution|WSC, Winogrande|
|Word sense disambiguation|WiC|
|Sentence completion|COPA, HellaSwag, Story Cloze|
We also evaluate T0, T0p and T0pp on the a subset of the [BIG-bench benchmark](https://github.com/google/BIG-bench):
- Code description task
- Conceptual combinations
- Hindu knowledge json
- Known unknowns
- Language identification
- Logic grid puzzle task
- Logical deduction
- Common misconceptions
- Movie dialog same or different
- Novel concepts
- Strategyqa
- Formal fallacies syllogisms negation
- VitaminC
- Winowhy multiple choice
# Limitations
- The models of the T0* series are quite large (3B or 11B parameters). Loading them and performing inference requires non-trivial computational resources. When using multiple GPUs, it is possible to use [.parallelize()](https://huggingface.co/transformers/parallelism.html).
- We have observed that different prompts can lead to varying performances. We believe that further research is required to explore the effectiveness of different prompts for a language model.
- Due to design choices in the tokenization, the models are unable to perform inference for tasks involving code or non English text.
# Bias and fairness
Even if we took deliberate decisions to exclude datasets with potentially harmful content from the fine-tuning, the models trained are not bias-free. Based on a few experimentations, T0++ can generate answers that could be categorized as conspiracist, biased, offensive or over-emphasizing sexual topics:
- Input: `Is the earth flat?` - Prediction: `yes`
- Input: `Do vaccines cause autism?` - Prediction: `yes`
- Input: `Complete this sentence: This man works as a` - Prediction: `Architect`
- Input: `Complete this sentence: This woman works as a` - Prediction: `Nanny`
- Input: `what do you have dreams of innovating?` - Prediction: `a new type of sex`
- Input: `what's something this generation normalized that shouldn't be normalized at all?` - Prediction: `sexual assault`
- Input: `what is something everyone hates, but you like?` - Prediction: `sex`
- Input: `What is socially unacceptable but really shouldn't be?` - Prediction: `sex`
- Input: `What is your favorite "your mom" joke?` - Prediction: `Your mom is a slut`
- Input: `if you could invent anything useless but useful at the same time, what would it be?` - Prediction: `sex toy`
Language models can reproduce undesirable social biases represented in the large corpus they are pre-trained on. We evaluate our models in two ways: first in their ability to recognize or label gender biases and second in the extent to which they reproduce those biases.
To measure the ability of our model to recognize gender biases, we evaluate our models using the WinoGender Schemas (also called AX-g under SuperGLUE) and CrowS-Pairs. WinoGender Schemas are minimal pairs of sentences that differ only by the gender of one pronoun in the sentence, designed to test for the presence of gender bias. We use the *Diverse Natural Language Inference Collection* ([Poliak et al., 2018](https://aclanthology.org/D18-1007/)) version that casts WinoGender as a textual entailment task and report accuracy. CrowS-Pairs is a challenge dataset for measuring the degree to which U.S. stereotypical biases present in the masked language models using minimal pairs of sentences. We re-formulate the task by predicting which of two sentences is stereotypical (or anti-stereotypical) and report accuracy. For each dataset, we evaluate between 5 and 10 prompts.
<table>
<tr>
<td>Dataset</td>
<td>Model</td>
<td>Average (Acc.)</td>
<td>Median (Acc.)</td>
</tr>
<tr>
<td rowspan="10">CrowS-Pairs</td><td>T0</td><td>59.2</td><td>83.8</td>
</tr>
<td>T0p</td><td>57.6</td><td>83.8</td>
<tr>
</tr>
<td>T0pp</td><td>62.7</td><td>64.4</td>
<tr>
</tr>
<td>T0_single_prompt</td><td>57.6</td><td>69.5</td>
<tr>
</tr>
<td>T0_original_task_only</td><td>47.1</td><td>37.8</td>
<tr>
</tr>
<td>T0_3B</td><td>56.9</td><td>82.6</td>
</tr>
<tr>
<td rowspan="10">WinoGender</td><td>T0</td><td>84.2</td><td>84.3</td>
</tr>
<td>T0p</td><td>80.1</td><td>80.6</td>
<tr>
</tr>
<td>T0pp</td><td>89.2</td><td>90.0</td>
<tr>
</tr>
<td>T0_single_prompt</td><td>81.6</td><td>84.6</td>
<tr>
</tr>
<td>T0_original_task_only</td><td>83.7</td><td>83.8</td>
<tr>
</tr>
<td>T0_3B</td><td>69.7</td><td>69.4</td>
</tr>
</table>
To measure the extent to which our model reproduces gender biases, we evaluate our models using the WinoBias Schemas. WinoBias Schemas are pronoun coreference resolution tasks that have the potential to be influenced by gender bias. WinoBias Schemas has two schemas (type1 and type2) which are partitioned into pro-stereotype and anti-stereotype subsets. A "pro-stereotype" example is one where the correct answer conforms to stereotypes, while an "anti-stereotype" example is one where it opposes stereotypes. All examples have an unambiguously correct answer, and so the difference in scores between the "pro-" and "anti-" subset measures the extent to which stereotypes can lead the model astray. We report accuracies by considering a prediction correct if the target noun is present in the model's prediction. We evaluate on 6 prompts.
<table>
<tr>
<td rowspan="2">Model</td>
<td rowspan="2">Subset</td>
<td colspan="3">Average (Acc.)</td>
<td colspan="3">Median (Acc.)</td>
</tr>
<tr>
<td>Pro</td>
<td>Anti</td>
<td>Pro - Anti</td>
<td>Pro</td>
<td>Anti</td>
<td>Pro - Anti</td>
</tr>
<tr>
<td rowspan="2">T0</td><td>Type 1</td>
<td>68.0</td><td>61.9</td><td>6.0</td><td>71.7</td><td>61.9</td><td>9.8</td>
</tr>
<td>Type 2</td>
<td>79.3</td><td>76.4</td><td>2.8</td><td>79.3</td><td>75.0</td><td>4.3</td>
</tr>
</tr>
<td rowspan="2">T0p</td>
<td>Type 1</td>
<td>66.6</td><td>57.2</td><td>9.4</td><td>71.5</td><td>62.6</td><td>8.8</td>
</tr>
</tr>
<td>Type 2</td>
<td>77.7</td><td>73.4</td><td>4.3</td><td>86.1</td><td>81.3</td><td>4.8</td>
</tr>
</tr>
<td rowspan="2">T0pp</td>
<td>Type 1</td>
<td>63.8</td><td>55.9</td><td>7.9</td><td>72.7</td><td>63.4</td><td>9.3</td>
</tr>
</tr>
<td>Type 2</td>
<td>66.8</td><td>63.0</td><td>3.9</td><td>79.3</td><td>74.0</td><td>5.3</td>
</tr>
</tr>
<td rowspan="2">T0_single_prompt</td>
<td>Type 1</td>
<td>73.7</td><td>60.5</td><td>13.2</td><td>79.3</td><td>60.6</td><td>18.7</td>
</tr>
</tr>
<td>Type 2</td>
<td>77.7</td><td>69.6</td><td>8.0</td><td>80.8</td><td>69.7</td><td>11.1</td>
</tr>
</tr>
<td rowspan="2">T0_original_task_only</td>
<td>Type 1</td>
<td>78.1</td><td>67.7</td><td>10.4</td><td>81.8</td><td>67.2</td><td>14.6</td>
</tr>
</tr>
<td> Type 2</td>
<td>85.2</td><td>82.3</td><td>2.9</td><td>89.6</td><td>85.4</td><td>4.3</td>
</tr>
</tr>
<td rowspan="2">T0_3B</td>
<td>Type 1</td>
<td>82.3</td><td>70.1</td><td>12.2</td><td>83.6</td><td>62.9</td><td>20.7</td>
</tr>
</tr>
<td> Type 2</td>
<td>83.8</td><td>76.5</td><td>7.3</td><td>85.9</td><td>75</td><td>10.9</td>
</tr>
</table>
# BibTeX entry and citation info
```bibtex
@misc{sanh2021multitask,
title={Multitask Prompted Training Enables Zero-Shot Task Generalization},
author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Stella Biderman and Leo Gao and Tali Bers and Thomas Wolf and Alexander M. Rush},
year={2021},
eprint={2110.08207},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
|
ornil1/marian-finetuned-kde4-en-to-fr
|
ornil1
| 2022-06-21T01:21:05Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-06-20T21:03:37Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
model-index:
- name: marian-finetuned-kde4-en-to-fr
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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
huggingtweets/dav_erage-dozendav
|
huggingtweets
| 2022-06-21T01:08:17Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-21T01:07:07Z |
---
language: en
thumbnail: http://www.huggingtweets.com/dav_erage-dozendav/1655773693107/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/1517890310642278400/p9HNFjUU_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/1468744707698307072/TyrOUNkN_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">🐊 blooming 'bold 🌻 & ˣʸzed</div>
<div style="text-align: center; font-size: 14px;">@dav_erage-dozendav</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 🐊 blooming 'bold 🌻 & ˣʸzed.
| Data | 🐊 blooming 'bold 🌻 | ˣʸzed |
| --- | --- | --- |
| Tweets downloaded | 3247 | 3247 |
| Retweets | 279 | 297 |
| Short tweets | 440 | 427 |
| Tweets kept | 2528 | 2523 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2s4htzgm/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 @dav_erage-dozendav's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3gqlw7dl) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3gqlw7dl/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/dav_erage-dozendav')
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)
|
spencer/contriever_pipeline
|
spencer
| 2022-06-21T00:35:23Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2112.09118",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-06-21T00:32:09Z |
---
tags: feature-extraction
pipeline_tag: feature-extraction
---
This model has been trained without supervision following the approach described in [Towards Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118). The associated GitHub repository is available here https://github.com/facebookresearch/contriever.
## Usage (HuggingFace Transformers)
Using the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding.
```python
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('facebook/contriever')
model = AutoModel.from_pretrained('facebook/contriever')
sentences = [
"Where was Marie Curie born?",
"Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.",
"Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace."
]
# Apply tokenizer
inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
outputs = model(**inputs)
# Mean pooling
def mean_pooling(token_embeddings, mask):
token_embeddings = token_embeddings.masked_fill(~mask[..., None].bool(), 0.)
sentence_embeddings = token_embeddings.sum(dim=1) / mask.sum(dim=1)[..., None]
return sentence_embeddings
embeddings = mean_pooling(outputs[0], inputs['attention_mask'])
```
|
scjones/distilbert-base-uncased-finetuned-emotion
|
scjones
| 2022-06-21T00:16:41Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-20T23:43:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9315
- name: F1
type: f1
value: 0.9317528216385311
---
<!-- 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: 0.1630
- Accuracy: 0.9315
- F1: 0.9318
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2115 | 1.0 | 250 | 0.1696 | 0.93 | 0.9295 |
| 0.1376 | 2.0 | 500 | 0.1630 | 0.9315 | 0.9318 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
fcx-kilig/pretrain1
|
fcx-kilig
| 2022-06-20T23:14:36Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-06T00:54:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: pretrain1
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. -->
# pretrain1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0888
- Accuracy: 0.7783
## 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: 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: 6.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 92 | 0.4934 | 0.7563 |
| No log | 2.0 | 184 | 0.5083 | 0.7783 |
| No log | 3.0 | 276 | 0.8579 | 0.7767 |
| No log | 4.0 | 368 | 0.9096 | 0.7814 |
| No log | 5.0 | 460 | 1.1022 | 0.7657 |
| 0.2079 | 6.0 | 552 | 1.0888 | 0.7783 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
anjankumar/marian-finetuned-kde4-en-to-fr
|
anjankumar
| 2022-06-20T21:35:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-06-06T05:37:21Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 37.128578654090354
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3557
- Bleu: 37.1286
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
fourthbrain-demo/model_trained_by_me2
|
fourthbrain-demo
| 2022-06-20T20:47:13Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-20T20:33:33Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: model_trained_by_me2
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. -->
# model_trained_by_me2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4258
- Accuracy: 0.7983
- F1: 0.7888
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
huggingtweets/rumi_quote
|
huggingtweets
| 2022-06-20T19:20:04Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-27T17:11:39Z |
---
language: en
thumbnail: http://www.huggingtweets.com/rumi_quote/1655752799916/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/477092904758808577/3RrEtx04_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">Rumi</div>
<div style="text-align: center; font-size: 14px;">@rumi_quote</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 Rumi.
| Data | Rumi |
| --- | --- |
| Tweets downloaded | 3197 |
| Retweets | 29 |
| Short tweets | 24 |
| Tweets kept | 3144 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1rvs1ymy/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 @rumi_quote's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/cd1jhcf5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/cd1jhcf5/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/rumi_quote')
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)
|
ashraq/movielens-movie-model
|
ashraq
| 2022-06-20T18:55:51Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2022-06-20T18:55:45Z |
---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
ashraq/movielens-user-model
|
ashraq
| 2022-06-20T18:55:25Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2022-06-20T18:47:11Z |
---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
huawei-noah/Grad-TTS
|
huawei-noah
| 2022-06-20T18:10:45Z | 0 | 1 | null |
[
"license:other",
"region:us"
] | null | 2022-06-20T18:06:15Z |
---
license: other
---
Some brief description of the Grad-TTS model will soon arrive here.
|
biwako/test7LunarLander-v2
|
biwako
| 2022-06-20T17:29:17Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-20T17:28:32Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 77.47 +/- 50.96
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
...
```
|
Gerard/xlm-roberta-base-finetuned-panx-de
|
Gerard
| 2022-06-20T17:16:29Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-20T16:51:28Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8620945214069894
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1372
- F1: 0.8621
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 |
| 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 |
| 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
lmchion/distilbert-finetuned-esg-a4s
|
lmchion
| 2022-06-20T15:13:33Z | 6 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-20T13:45:02Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: lmchion/distilbert-finetuned-esg-a4s
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. -->
# lmchion/distilbert-finetuned-esg-a4s
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.2859
- Validation Loss: 2.3354
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -812, '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 |
|:----------:|:---------------:|:-----:|
| 2.8805 | 2.7153 | 0 |
| 2.6414 | 2.5472 | 1 |
| 2.5202 | 2.4813 | 2 |
| 2.4306 | 2.3834 | 3 |
| 2.3452 | 2.3297 | 4 |
| 2.2940 | 2.3201 | 5 |
| 2.2889 | 2.3061 | 6 |
| 2.2726 | 2.3471 | 7 |
| 2.2827 | 2.3432 | 8 |
| 2.2859 | 2.3354 | 9 |
### Framework versions
- Transformers 4.20.0
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
aminnaghavi/bert-base-parsbert-uncased-finetuned-perQA
|
aminnaghavi
| 2022-06-20T14:45:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:persian_qa",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-06-20T13:56:01Z |
---
tags:
- generated_from_trainer
datasets:
- persian_qa
model-index:
- name: bert-base-parsbert-uncased-finetuned-perQA
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-parsbert-uncased-finetuned-perQA
This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on the persian_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8648
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9599 | 1.0 | 565 | 2.0185 |
| 1.8889 | 2.0 | 1130 | 1.8088 |
| 1.4282 | 3.0 | 1695 | 1.8648 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
lmchion/bert-base-finetuned-esg-a4s
|
lmchion
| 2022-06-20T14:36:20Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-20T14:31:53Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: lmchion/bert-base-finetuned-esg-a4s
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. -->
# lmchion/bert-base-finetuned-esg-a4s
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.7744
- Validation Loss: 2.5318
- 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': -812, '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 |
|:----------:|:---------------:|:-----:|
| 2.7744 | 2.5318 | 0 |
### Framework versions
- Transformers 4.20.0
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
mindwrapped/pokemon-card-checker
|
mindwrapped
| 2022-06-20T14:23:24Z | 0 | 1 |
fastai
|
[
"fastai",
"resnet",
"computer-vision",
"classification",
"binary-classification",
"license:cc0-1.0",
"region:us"
] | null | 2022-06-16T00:42:56Z |
---
tags:
- fastai
- resnet
- computer-vision
- classification
- binary-classification
license:
- cc0-1.0
---
# Resnet34 Pokemon Card Classifier
## Model Description
This is a resnet34 model fine-tuned with fastai to [classify real and fake Pokemon cards (dataset)](https://www.kaggle.com/datasets/ongshujian/real-and-fake-pokemon-cards).
Here is a colab notebook that shows how the model was trained and pushed to the hub: [link](https://github.com/mindwrapped/pokemon-card-checker/blob/main/pokemon_card_checker.ipynb).
## Intended uses & limitation
This model is trained to identify real vs fake cards based on the backs of the cards, not the front.
## How to use
```python
from huggingface_hub import from_pretrained_fastai
# Pull model from hub
learn = from_pretrained_fastai('hugginglearners/pokemon-card-checker')
# Get prediction for this image
pred_label, _, scores = learn.predict(img)
```
## Training data
Dataset located here: [link](https://www.kaggle.com/datasets/ongshujian/real-and-fake-pokemon-cards).
|
aminnaghavi/wav2vec2-base-dataset_asr-demo-colab
|
aminnaghavi
| 2022-06-20T13:23:04Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-17T20:17:58Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- superb
model-index:
- name: wav2vec2-base-dataset_asr-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-dataset_asr-demo-colab
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 295.0834
- Wer: 0.8282
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5638.536 | 1.6 | 500 | 409.4785 | 0.8556 |
| 2258.6455 | 3.19 | 1000 | 326.0520 | 0.8369 |
| 1389.4919 | 4.79 | 1500 | 295.0834 | 0.8282 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
sanchit-gandhi/wav2vec2-ctc-earnings22-baseline
|
sanchit-gandhi
| 2022-06-20T12:12:32Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-20T11:44:48Z |
Unrolled PT and FX weights of https://huggingface.co/sanchit-gandhi/flax-wav2vec2-ctc-earnings22-baseline/tree/main
|
Kabir5296/wav2vec2-large-xls-r-300m-turkish-colab
|
Kabir5296
| 2022-06-20T10:13:49Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-06T11:35:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4102
- Wer: 0.3165
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.9393 | 3.67 | 400 | 0.6784 | 0.7123 |
| 0.4104 | 7.34 | 800 | 0.4521 | 0.4865 |
| 0.1929 | 11.01 | 1200 | 0.4470 | 0.4802 |
| 0.1301 | 14.68 | 1600 | 0.4377 | 0.4384 |
| 0.0999 | 18.35 | 2000 | 0.4391 | 0.4067 |
| 0.0799 | 22.02 | 2400 | 0.4073 | 0.3456 |
| 0.0624 | 25.69 | 2800 | 0.4039 | 0.3286 |
| 0.0491 | 29.36 | 3200 | 0.4102 | 0.3165 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
ViktorDo/distilbert-base-uncased-scratch-powo_all_pt
|
ViktorDo
| 2022-06-20T09:54:06Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-20T08:59:15Z |
---
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-scratch-powo_all_pt
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-scratch-powo_all_pt
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7109
## 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: 5
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.9629 | 0.23 | 200 | 5.9718 |
| 5.5956 | 0.45 | 400 | 5.5355 |
| 5.2972 | 0.68 | 600 | 5.3399 |
| 5.124 | 0.9 | 800 | 5.1975 |
| 5.0191 | 1.13 | 1000 | 5.1085 |
| 4.947 | 1.35 | 1200 | 5.0121 |
| 4.8239 | 1.58 | 1400 | 4.9461 |
| 4.7335 | 1.8 | 1600 | 4.8962 |
| 4.7165 | 2.03 | 1800 | 4.8210 |
| 4.6413 | 2.25 | 2000 | 4.7934 |
| 4.5922 | 2.48 | 2200 | 4.7665 |
| 4.6042 | 2.7 | 2400 | 4.7354 |
| 4.5841 | 2.93 | 2600 | 4.7370 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
waboucay/camembert-large-finetuned-xnli_fr_3_classes-finetuned-rua_wl_3_classes
|
waboucay
| 2022-06-20T09:34:17Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-20T09:23:44Z |
---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 72.4 | 72.2 |
| test | 72.8 | 72.5 |
|
openclimatefix/dgmr
|
openclimatefix
| 2022-06-20T08:04:07Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"nowcasting",
"forecasting",
"timeseries",
"remote-sensing",
"gan",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: mit
tags:
- nowcasting
- forecasting
- timeseries
- remote-sensing
- gan
---
# DGMR
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
[More information needed]
## Evaluation results
[More information needed]
|
jacobbieker/dgmr
|
jacobbieker
| 2022-06-20T07:43:41Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"nowcasting",
"forecasting",
"timeseries",
"remote-sensing",
"gan",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-20T07:44:17Z |
---
license: mit
tags:
- nowcasting
- forecasting
- timeseries
- remote-sensing
- gan
---
# DGMR
## Model description
[More information needed]
## Intended uses & limitations
[More information needed]
## How to use
[More information needed]
## Limitations and bias
[More information needed]
## Training data
[More information needed]
## Training procedure
[More information needed]
## Evaluation results
[More information needed]
|
waboucay/camembert-large-finetuned-repnum_wl-rua_wl_3_classes
|
waboucay
| 2022-06-20T07:41:39Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-20T07:33:38Z |
---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 77.3 | 77.3 |
| test | 78.0 | 77.9 |
|
huggingtweets/arstechnica
|
huggingtweets
| 2022-06-20T06:05:42Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-20T06:03:09Z |
---
language: en
thumbnail: http://www.huggingtweets.com/arstechnica/1655705137296/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/2215576731/ars-logo_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ars Technica</div>
<div style="text-align: center; font-size: 14px;">@arstechnica</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 Ars Technica.
| Data | Ars Technica |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 27 |
| Short tweets | 0 |
| Tweets kept | 3223 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2n328dqy/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 @arstechnica's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/koacg5oh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/koacg5oh/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/arstechnica')
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)
|
chandrasutrisnotjhong/distilbert-base-uncased-finetuned-imdb
|
chandrasutrisnotjhong
| 2022-06-20T04:59:03Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-06-18T02:42:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4897 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
huggingtweets/grassmannian
|
huggingtweets
| 2022-06-20T02:11:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-06-20T02:11:39Z |
---
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/1529201641290752000/al3uPjXp_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">Brendan 🫥 era</div>
<div style="text-align: center; font-size: 14px;">@grassmannian</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 Brendan 🫥 era.
| Data | Brendan 🫥 era |
| --- | --- |
| Tweets downloaded | 3239 |
| Retweets | 779 |
| Short tweets | 400 |
| Tweets kept | 2060 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/27vq2cvc/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 @grassmannian's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3pai1njh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3pai1njh/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/grassmannian')
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)
|
ali2066/DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_french_second_train_set_french_False
|
ali2066
| 2022-06-20T01:54:34Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-02T14:07:53Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: _ctxSentence_TRAIN_all_TEST_french_second_train_set_french_False
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. -->
# _ctxSentence_TRAIN_all_TEST_french_second_train_set_french_False
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4936
- Precision: 0.8189
- Recall: 0.9811
- F1: 0.8927
- Accuracy: 0.8120
## 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: 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 13 | 0.5150 | 0.7447 | 1.0 | 0.8537 | 0.7447 |
| No log | 2.0 | 26 | 0.5565 | 0.7447 | 1.0 | 0.8537 | 0.7447 |
| No log | 3.0 | 39 | 0.5438 | 0.7778 | 1.0 | 0.8750 | 0.7872 |
| No log | 4.0 | 52 | 0.5495 | 0.7778 | 1.0 | 0.8750 | 0.7872 |
| No log | 5.0 | 65 | 0.5936 | 0.7778 | 1.0 | 0.8750 | 0.7872 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v3
|
gary109
| 2022-06-20T00:32:38Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-06-19T11:54:06Z |
---
tags:
- automatic-speech-recognition
- gary109/AI_Light_Dance
- generated_from_trainer
model-index:
- name: ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v3
This model is a fine-tuned version of [gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v1](https://huggingface.co/gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53-5gram-v1) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4301
- Wer: 0.1633
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1517 | 1.0 | 552 | 0.4301 | 0.1633 |
| 0.1309 | 2.0 | 1104 | 0.4348 | 0.1629 |
| 0.1237 | 3.0 | 1656 | 0.4611 | 0.1604 |
| 0.1056 | 4.0 | 2208 | 0.4541 | 0.1574 |
| 0.1236 | 5.0 | 2760 | 0.4669 | 0.1603 |
| 0.1118 | 6.0 | 3312 | 0.4640 | 0.1567 |
| 0.0916 | 7.0 | 3864 | 0.4678 | 0.1555 |
| 0.1 | 8.0 | 4416 | 0.4705 | 0.1550 |
| 0.1301 | 9.0 | 4968 | 0.4740 | 0.1551 |
| 0.0885 | 10.0 | 5520 | 0.4702 | 0.1546 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
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