modelId
stringlengths 4
81
| tags
sequence | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
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AlexDemon/Alex | [] | null | {
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} | 0 | null | ---
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)
``` |
AlexMaclean/sentence-compression-roberta | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
] | token-classification | {
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} | 13 | null | ---
library_name: stable-baselines3
tags:
- BeamRiderNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: QRDQN
results:
- metrics:
- type: mean_reward
value: 13335.00 +/- 5701.88
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BeamRiderNoFrameskip-v4
type: BeamRiderNoFrameskip-v4
---
# **QRDQN** Agent playing **BeamRiderNoFrameskip-v4**
This is a trained model of a **QRDQN** agent playing **BeamRiderNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo qrdqn --env BeamRiderNoFrameskip-v4 -orga Corianas -f logs/
python enjoy.py --algo qrdqn --env BeamRiderNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo qrdqn --env BeamRiderNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo qrdqn --env BeamRiderNoFrameskip-v4 -f logs/ -orga Corianas
```
## Hyperparameters
```python
OrderedDict([('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_fraction', 0.025),
('frame_stack', 3),
('n_timesteps', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('normalize', False)])
```
|
AlexN/xls-r-300m-fr-0 | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
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}
} | 4 | null | ---
language:
- en
- ru
license: apache-2.0
tags:
- gpt
- NLG
---
# YaLM 100B
https://github.com/yandex/YaLM-100B
**YaLM 100B** is a GPT-like neural network for generating and processing text. It can be used freely by developers and researchers from all over the world.
The model leverages 100 billion parameters. It took 65 days to train the model on a cluster of 800 A100 graphics cards and 1.7 TB of online texts, books, and countless other sources in both English and Russian.
Training details and best practices on acceleration and stabilizations can be found on **[Medium](https://medium.com/p/d1df53d0e9a6)** (English) and **[Habr](https://habr.com/ru/company/yandex/blog/672396/)** (Russian) articles.
|
Alexander-Learn/bert-finetuned-ner-accelerate | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
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"BertForTokenClassification"
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}
} | 4 | null | ### 作文模型
使用方法，请参考[Python 自动写作文库](https://github.com/WindowsRegedit/zuowen)
|
AliReza/distilbert-emotion | [] | null | {
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} | 0 | 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
|
Alicanke/Wyau | [] | null | {
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} | 0 | 2022-06-23T09:32:13Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 274.50 +/- 31.50
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 dfomin -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 dfomin
```
## 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', 100000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Alireza1044/albert-base-v2-stsb | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
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"AlbertForSequenceClassification"
],
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} | 37 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6895
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7852 | 1.0 | 2334 | 3.6895 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Alireza1044/dwight_bert_lm | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
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} | 14 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-erichmariaremarque
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-erichmariaremarque
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Alireza1044/michael_bert_lm | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 10 | null | ---
language:
- en
tags:
- pytorch
- text-generation
- causal-lm
- rwkv
license: apache-2.0
datasets:
- The Pile
---
# RWKV-3 1.5B
## Model Description
RWKV-3 1.5B is a L24-D2048 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details.
RWKV-4 1.5B is out: https://huggingface.co/BlinkDL/rwkv-4-pile-1b5
At this moment you have to use my Github code (https://github.com/BlinkDL/RWKV-v2-RNN-Pile) to run it.
ctx_len = 896
n_layer = 24
n_embd = 2048
Preview checkpoint: RWKV-3-Pile-20220723-3542.pth : Trained on the Pile for 127B tokens.
* Pile loss 2.102
* LAMBADA ppl 7.52, acc 54.71%
* PIQA acc 71.11%
* SC2016 acc 67.24%
* Hellaswag acc_norm 50.45%
Preview checkpoint: 20220708-1905.pth : Trained on the Pile for 68B tokens.
* Pile loss 2.148
* LAMBADA ppl 8.41, acc 53.17%
* PIQA acc 69.64%
* SC2016 acc 67.08%
* Hellaswag acc_norm 48.20%
(I am still training it) |
Amba/wav2vec2-large-xls-r-300m-turkish-colab | [] | null | {
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} | 0 | null | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- hellennamulinda/autotrain-data-agric-eng-lug
co2_eq_emissions: 0.04087910671538076
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1026034854
- CO2 Emissions (in grams): 0.04087910671538076
## Validation Metrics
- Loss: 1.0871405601501465
- Rouge1: 55.8225
- Rouge2: 34.1547
- RougeL: 54.4274
- RougeLsum: 54.408
- Gen Len: 23.178
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/hellennamulinda/autotrain-agric-eng-lug-1026034854
``` |
AmirHussein/test | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
- imagenet-21k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# ONNX convert of ViT (base-sized model)
Conversion of [ViT-base](https://huggingface.co/google/vit-base-patch16-224), which has a classification head to perform **image classification**.
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) 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
from optimum.onnxruntime import ORTModelForImageClassification
from optimum.pipelines import pipeline
feature_extractor = AutoFeatureExtractor.from_pretrained("optimum/vit-base-patch16-224")
# Loading already converted and optimized ORT checkpoint for inference
model = ORTModelForImageClassification.from_pretrained("optimum/vit-base-patch16-224")
onnx_img_classif = pipeline(
"image-classification", model=model, feature_extractor=feature_extractor
)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
pred = onnx_img_classif(url)
print("Top-5 predicted classes:", pred)
```
## Training data
The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
``` |
Andrija/SRoBERTa-XL-NER | [
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: prahlad/rotten_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# prahlad/rotten_model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on rotten_tomatoes movie review dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4876
- Train Accuracy: 0.7620
- Validation Loss: 0.5001
- Validation Accuracy: 0.7842
- 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': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 12795, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4876 | 0.7620 | 0.5001 | 0.7842 | 0 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AnonymousSub/AR_rule_based_roberta_bert_triplet_epochs_1_shard_1 | [
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} | 4 | null | ---
license: apache-2.0
tags:
- automatic-speech-recognition
- gary109/AI_Light_Dance
- generated_from_trainer
model-index:
- name: ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-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. -->
# ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v2
This model is a fine-tuned version of [gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v1](https://huggingface.co/gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v1) on the GARY109/AI_LIGHT_DANCE - ONSET-STEPMANIA2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0753
- Wer: 0.7017
## 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: 10
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 160
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.945 | 1.0 | 150 | 1.0767 | 0.7282 |
| 0.9445 | 2.0 | 300 | 1.0773 | 0.7165 |
| 0.9392 | 3.0 | 450 | 1.0813 | 0.7141 |
| 0.933 | 4.0 | 600 | 1.0858 | 0.7032 |
| 0.921 | 5.0 | 750 | 1.0753 | 0.7017 |
| 0.9241 | 6.0 | 900 | 1.0787 | 0.6976 |
| 0.9282 | 7.0 | 1050 | 1.0825 | 0.6959 |
| 0.9184 | 8.0 | 1200 | 1.0760 | 0.6930 |
| 0.915 | 9.0 | 1350 | 1.0773 | 0.6906 |
| 0.9094 | 10.0 | 1500 | 1.0786 | 0.6900 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
AnonymousSub/AR_rule_based_roberta_bert_triplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 10 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: jwang/tuned-t5
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. -->
# jwang/tuned-t5
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.6386
- Validation Loss: 3.3773
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.7547 | 3.4438 | 0 |
| 4.6135 | 3.4096 | 1 |
| 4.6386 | 3.3773 | 2 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AnonymousSub/AR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 6 | null | ---
license: bsl-1.0
---
https://www.humhealth.com/remote-patient-monitoring/
https://www.humhealth.com/chronic-care-management/
|
AnonymousSub/SR_rule_based_roberta_twostagetriplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"RobertaModel"
],
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} | 8 | null | Access to model MahmoudAbdullah99/wav2vec-speech-model is restricted and you are not in the authorized list. Visit https://huggingface.co/MahmoudAbdullah99/wav2vec-speech-model to ask for access. |
AnonymousSub/T5_pubmedqa_question_generation | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"T5ForConditionalGeneration"
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},
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"prefix": "translate English to French: "
},
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"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 6 | null | ---
license: apache-2.0
language: en
tags:
- generated_from_trainer
datasets:
- speech_commands
metrics:
- accuracy
model-index:
- name: wav2vec2-conformer-rel-pos-large-finetuned-speech-commands
results:
- task:
type: audio-classification
name: audio classification
dataset:
type: speech_commands
name: speech_commands
split: v0.02
metrics:
- type: accuracy
value: 0.9724
name: accuracy
---
# wav2vec2-conformer-rel-pos-large-finetuned-speech-commands
### Model description
This model is a fine-tuned version of [facebook/wav2vec2-conformer-rel-pos-large](https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large) on the [speech_commands](https://huggingface.co/datasets/speech_commands) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5245
- Accuracy: 0.9724
#### Intended uses & limitations
The model can spot one of the following keywords: "Yes", "No", "Up", "Down", "Left", "Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine", "Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila", "Tree", "Wow", "Backward", "Forward", "Follow", "Learn", "Visual".
The repository includes sample files that I recorded (WAV, 16Khz sampling rate, mono). The simplest way to use the model is with the ```pipeline``` API:
```
>>> from transformers import pipeline
>>> p = pipeline("audio-classification", model="juliensimon/wav2vec2-conformer-rel-pos-large-finetuned-speech-commands")
>>> p("up16k.wav")
[{'score': 0.7008192539215088, 'label': 'up'}, {'score': 0.04346614331007004, 'label': 'off'}, {'score': 0.029526518657803535, 'label': 'left'}, {'score': 0.02905120886862278, 'label': 'stop'}, {'score': 0.027142534032464027, 'label': 'on'}]
>>> p("stop16k.wav")
[{'score': 0.6969656944274902, 'label': 'stop'}, {'score': 0.03391443192958832, 'label': 'up'}, {'score': 0.027382319793105125, 'label': 'seven'}, {'score': 0.020835857838392258, 'label': 'five'}, {'score': 0.018051736056804657, 'label': 'down'}]
>>> p("marvin16k.wav")
[{'score': 0.5276530981063843, 'label': 'marvin'}, {'score': 0.04645705968141556, 'label': 'down'}, {'score': 0.038583893328905106, 'label': 'backward'}, {'score': 0.03578080236911774, 'label': 'wow'}, {'score': 0.03178196772933006, 'label': 'bird'}]
```
You can also use them with the ```Auto```API:
```
>>> import torch, librosa
>>> from transformers import AutoModelForAudioClassification, Wav2Vec2FeatureExtractor
>>> feature_extractor = Wav2Vec2FeatureExtractor()
>>> model = AutoModelForAudioClassification.from_pretrained("juliensimon/wav2vec2-conformer-rel-pos-large-finetuned-speech-commands")
>>> audio, rate = librosa.load("up16k.wav", sr = 16000)
>>> inputs = feature_extractor(audio, sampling_rate=16000, return_tensors = "pt")
>>> logits = model(inputs['input_values'])
>>> logits
SequenceClassifierOutput(loss=None, logits=tensor([[-0.4635, -1.0112, 4.7935, 0.8528, 1.6265, 0.6456, 1.5423, 2.0132,
1.6103, 0.5847, -2.2526, 0.8839, 0.8163, -1.5655, -1.4160, -0.4196,
-0.1097, -1.8827, 0.6609, -0.2022, 0.0971, -0.6205, 0.4492, 0.0926,
-2.4848, 0.2630, -0.4584, -2.4327, -1.1654, 0.3897, -0.3374, -1.2418,
-0.1045, 0.2827, -1.5667, -0.0963]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)
>>> classes = torch.softmax(logits.logits, dim=1)
>>> torch.set_printoptions(precision=3, sci_mode=False)
>>> classes
tensor([[ 0.004, 0.002, 0.701, 0.014, 0.030, 0.011,
0.027, 0.043, 0.029, 0.010, 0.001, 0.014,
0.013, 0.001, 0.001, 0.004, 0.005, 0.001,
0.011, 0.005, 0.006, 0.003, 0.009, 0.006,
0.000, 0.008, 0.004, 0.001, 0.002, 0.009,
0.004, 0.002, 0.005, 0.008, 0.001, 0.005]],
grad_fn=<SoftmaxBackward0>)
>>> top_class = torch.argmax(logits.logits, dim=1)
>>> top_class
tensor([2])
>>> model.config.id2label[top_class.numpy()[0]]
'up'
```
### Training and evaluation data
- subset: v0.02
- full training set
- full validation set
### Training procedure
The model was fine-tuned on [Amazon SageMaker](https://aws.amazon.com/sagemaker), using an [ml.p3dn.24xlarge](https://aws.amazon.com/fr/ec2/instance-types/p3/) instance (8 NVIDIA V100 GPUs). Total training time for 10 epochs was 4.5 hours.
#### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
#### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2901 | 1.0 | 83 | 2.0542 | 0.8875 |
| 1.8375 | 2.0 | 166 | 1.5610 | 0.9316 |
| 1.4957 | 3.0 | 249 | 1.1850 | 0.9558 |
| 1.1917 | 4.0 | 332 | 0.9159 | 0.9695 |
| 1.0449 | 5.0 | 415 | 0.7624 | 0.9687 |
| 0.9319 | 6.0 | 498 | 0.6444 | 0.9715 |
| 0.8559 | 7.0 | 581 | 0.5806 | 0.9711 |
| 0.8199 | 8.0 | 664 | 0.5394 | 0.9721 |
| 0.7949 | 9.0 | 747 | 0.5245 | 0.9724 |
| 0.7975 | 10.0 | 830 | 0.5256 | 0.9721 |
#### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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} | 4 | 2022-06-24T13:25:01Z | ---
tags:
- text-classification
- generated_from_trainer
model-index:
- name: BioM-ALBERT-xxlarge-finetuned-DAGPap22
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. -->
# BioM-ALBERT-xxlarge-finetuned-DAGPap22
This model is a fine-tuned version of [sultan/BioM-ALBERT-xxlarge](https://huggingface.co/sultan/BioM-ALBERT-xxlarge) 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: 2e-05
- 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
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
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} | 4 | null | ---
tags:
- fastai
title: Blurr Sentiment Classification
emoji: 🐠
colorFrom: green
colorTo: indigo
sdk: gradio
sdk_version: 2.9.4
app_file: app.py
pinned: false
license: apache-2.0
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
AnonymousSub/declutr-model | [
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} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0709
- Precision: 0.8442
- Recall: 0.8364
- F1: 0.8403
- Accuracy: 0.9794
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0442 | 1.0 | 1875 | 0.0772 | 0.7945 | 0.7627 | 0.7783 | 0.9739 |
| 0.0272 | 2.0 | 3750 | 0.0679 | 0.8465 | 0.8551 | 0.8507 | 0.9791 |
| 0.0175 | 3.0 | 5625 | 0.0709 | 0.8442 | 0.8364 | 0.8403 | 0.9794 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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} | 2 | 2022-06-24T17:27:34Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec_cv
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. -->
# wav2vec_cv
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1760
- Wer: 1.0
## 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:
- learning_rate: 0.003
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 7.1467 | 4.29 | 30 | 4.2173 | 1.0 |
| 6.8918 | 8.57 | 60 | 4.2004 | 1.0 |
| 5.4913 | 12.86 | 90 | 4.2007 | 1.0 |
| 5.3906 | 17.14 | 120 | 4.1765 | 1.0 |
| 4.9212 | 21.43 | 150 | 4.1714 | 1.0 |
| 4.3916 | 25.71 | 180 | 4.1811 | 1.0 |
| 5.2255 | 30.0 | 210 | 4.1633 | 1.0 |
| 4.501 | 34.29 | 240 | 4.2050 | 1.0 |
| 4.4328 | 38.57 | 270 | 4.1572 | 1.0 |
| 4.2136 | 42.86 | 300 | 4.1698 | 1.0 |
| 4.3353 | 47.14 | 330 | 4.1721 | 1.0 |
| 4.1805 | 51.43 | 360 | 4.1804 | 1.0 |
| 4.1695 | 55.71 | 390 | 4.1801 | 1.0 |
| 4.2978 | 60.0 | 420 | 4.1760 | 1.0 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
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} | 4 | null | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- deepesh0x/autotrain-data-finetunedmodelbert
co2_eq_emissions: 7.1805069109958835
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1034335535
- CO2 Emissions (in grams): 7.1805069109958835
## Validation Metrics
- Loss: 0.05866553634405136
- Accuracy: 0.9793615441722346
- Precision: 0.9811170212765957
- Recall: 0.9819004524886877
- AUC: 0.9976735725727466
- F1: 0.9815085805507516
## 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-finetunedmodelbert-1034335535
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("deepesh0x/autotrain-finetunedmodelbert-1034335535", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("deepesh0x/autotrain-finetunedmodelbert-1034335535", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
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} | 39 | null | ---
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> |
AnonymousSub/rule_based_bert_hier_diff_equal_wts_epochs_1_shard_10 | [
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} | 6 | null | ---
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> |
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: hsohn3/ehr-bert-base-uncased-cchs-wordlevel
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. -->
# hsohn3/ehr-bert-base-uncased-cchs-wordlevel
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: 3.7374
- Epoch: 9
## Model description
- model: bert-base-uncased (train from scratch)
- tokenizer: BertTokenizer + WordLevel splitter
## Intended uses & limitations
More information needed
## Training and evaluation data
- data_source: cchs (10,000 visits)
- data_format: visit-level texts concatenated by `[SEP]` token
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
- block_size: 512
- batch_size: 4
- num_epochs: 10
- mlm_probability: 0.15
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 3.8857 | 0 |
| 3.7525 | 1 |
| 3.7505 | 2 |
| 3.7493 | 3 |
| 3.7412 | 4 |
| 3.7432 | 5 |
| 3.7428 | 6 |
| 3.7409 | 7 |
| 3.7394 | 8 |
| 3.7374 | 9 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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} | 3 | null | | Feature | Description |
| --- | --- |
| **Name** | `en_ethicalads_topics` |
| **Version** | `20221006_18_20_26` |
| **spaCy** | `>=3.4.1,<3.5.0` |
| **Default Pipeline** | `transformer`, `textcat_multilabel` |
| **Components** | `transformer`, `textcat_multilabel` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (6 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`textcat_multilabel`** | `datascience`, `backend`, `frontend`, `security`, `devops`, `blockchain` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `CATS_SCORE` | 94.59 |
| `CATS_MICRO_P` | 88.33 |
| `CATS_MICRO_R` | 60.23 |
| `CATS_MICRO_F` | 71.62 |
| `CATS_MACRO_P` | 89.62 |
| `CATS_MACRO_R` | 62.99 |
| `CATS_MACRO_F` | 72.15 |
| `CATS_MACRO_AUC` | 94.59 |
| `CATS_MACRO_AUC_PER_TYPE` | 0.00 |
| `TRANSFORMER_LOSS` | 6.77 |
| `TEXTCAT_MULTILABEL_LOSS` | 630.05 | |
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1_wikiqa | [
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} | 31 | 2022-06-24T20:59:45Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-wikitext2
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. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.1099
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.5562 | 1.0 | 2249 | 6.4689 |
| 6.1912 | 2.0 | 4498 | 6.2003 |
| 6.0155 | 3.0 | 6747 | 6.1099 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1_wikiqa_copy | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
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} | 1 | null | ---
library_name: generic
tags:
- text-classification
--- |
AnonymousSub/rule_based_hier_quadruplet_0.1_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
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} | 4 | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: nlp-esg-scoring/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. -->
# nlp-esg-scoring/bert-base-finetuned-esg-a4s
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.9437
- Validation Loss: 1.9842
- 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 |
|:----------:|:---------------:|:-----:|
| 1.9200 | 2.0096 | 0 |
| 1.9249 | 1.9926 | 1 |
| 1.9366 | 2.0100 | 2 |
| 1.9327 | 1.9814 | 3 |
| 1.9266 | 2.0152 | 4 |
| 1.9332 | 2.0519 | 5 |
| 1.9203 | 2.0437 | 6 |
| 1.9238 | 2.0118 | 7 |
| 1.9290 | 2.0019 | 8 |
| 1.9437 | 1.9842 | 9 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_10 | [
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"bert",
"feature-extraction",
"transformers"
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} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-wikitext2
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-cased-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.8574
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.0916 | 1.0 | 2346 | 7.0492 |
| 6.9039 | 2.0 | 4692 | 6.8751 |
| 6.8845 | 3.0 | 7038 | 6.8929 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
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} | 30 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_hier_triplet_0.1_epochs_1_shard_1_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
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} | 2 | 2022-06-24T23:01:01Z | ---
license: mit
tags:
- text-classification
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: deberta-v3-xsmall-finetuned-DAGPap22
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. -->
# deberta-v3-xsmall-finetuned-DAGPap22
This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0798
- Accuracy: 0.9907
- F1: 0.9934
## 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: 4.5e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 402 | 0.1626 | 0.9477 | 0.9616 |
| 0.4003 | 2.0 | 804 | 0.0586 | 0.9794 | 0.9853 |
| 0.1075 | 3.0 | 1206 | 0.0342 | 0.9907 | 0.9933 |
| 0.0581 | 4.0 | 1608 | 0.1140 | 0.9776 | 0.9838 |
| 0.0245 | 5.0 | 2010 | 0.1409 | 0.9776 | 0.9842 |
| 0.0245 | 6.0 | 2412 | 0.0732 | 0.9832 | 0.9881 |
| 0.0167 | 7.0 | 2814 | 0.1996 | 0.9682 | 0.9778 |
| 0.0139 | 8.0 | 3216 | 0.1219 | 0.9850 | 0.9894 |
| 0.006 | 9.0 | 3618 | 0.0670 | 0.9907 | 0.9934 |
| 0.0067 | 10.0 | 4020 | 0.1036 | 0.9869 | 0.9907 |
| 0.0067 | 11.0 | 4422 | 0.1220 | 0.9776 | 0.9838 |
| 0.0041 | 12.0 | 4824 | 0.1768 | 0.9776 | 0.9839 |
| 0.0007 | 13.0 | 5226 | 0.0943 | 0.9888 | 0.9920 |
| 0.0 | 14.0 | 5628 | 0.0959 | 0.9907 | 0.9934 |
| 0.0054 | 15.0 | 6030 | 0.0915 | 0.9888 | 0.9921 |
| 0.0054 | 16.0 | 6432 | 0.1618 | 0.9794 | 0.9855 |
| 0.0019 | 17.0 | 6834 | 0.0794 | 0.9907 | 0.9934 |
| 0.0 | 18.0 | 7236 | 0.0799 | 0.9907 | 0.9934 |
| 0.0 | 19.0 | 7638 | 0.0797 | 0.9907 | 0.9934 |
| 0.0 | 20.0 | 8040 | 0.0798 | 0.9907 | 0.9934 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
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} | 6 | null | ---
tags: autotrain
language: zh
widget:
- text: "I love AutoTrain 🤗"
datasets:
- AI-Prize-Challenges/autotrain-data-finetuned1
co2_eq_emissions: 0.03608660562919794
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1035435583
- CO2 Emissions (in grams): 0.03608660562919794
## Validation Metrics
- Loss: 0.31551286578178406
- Accuracy: 0.8816629547141797
- Precision: 0.8965702036441586
- Recall: 0.8906042054830983
- AUC: 0.9449180200540812
- F1: 0.8935772466283884
## 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/AI-Prize-Challenges/autotrain-finetuned1-1035435583
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("AI-Prize-Challenges/autotrain-finetuned1-1035435583", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("AI-Prize-Challenges/autotrain-finetuned1-1035435583", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
AnonymousSub/rule_based_only_classfn_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
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} | 7 | null | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/TextbookInformalFormalEnglish")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/TextbookInformalFormalEnglish")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
``` |
AnonymousSub/rule_based_only_classfn_epochs_1_shard_1_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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"BertForQuestionAnswering"
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} | 2 | null | ---
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: 2.3811
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7046 | 1.0 | 157 | 2.4782 |
| 2.5679 | 2.0 | 314 | 2.4108 |
| 2.5028 | 3.0 | 471 | 2.4121 |
| 2.4825 | 4.0 | 628 | 2.3589 |
| 2.4593 | 5.0 | 785 | 2.4074 |
| 2.4294 | 6.0 | 942 | 2.3742 |
| 2.4258 | 7.0 | 1099 | 2.3706 |
| 2.4152 | 8.0 | 1256 | 2.3315 |
| 2.409 | 9.0 | 1413 | 2.3809 |
| 2.3908 | 10.0 | 1570 | 2.3394 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 2 | null | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tiny_focal_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. -->
# tiny_focal_v3
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0023
- Precision: 0.6975
- Recall: 0.6822
- F1: 0.6898
- Accuracy: 0.9515
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.004 | 1.0 | 5561 | 0.0032 | 0.6900 | 0.6102 | 0.6477 | 0.9454 |
| 0.0032 | 2.0 | 11122 | 0.0028 | 0.6901 | 0.6406 | 0.6644 | 0.9477 |
| 0.0029 | 3.0 | 16683 | 0.0026 | 0.6956 | 0.6509 | 0.6725 | 0.9490 |
| 0.0025 | 4.0 | 22244 | 0.0025 | 0.6838 | 0.6764 | 0.6801 | 0.9493 |
| 0.0024 | 5.0 | 27805 | 0.0024 | 0.6954 | 0.6715 | 0.6832 | 0.9504 |
| 0.0023 | 6.0 | 33366 | 0.0024 | 0.7125 | 0.6524 | 0.6811 | 0.9512 |
| 0.0021 | 7.0 | 38927 | 0.0023 | 0.6999 | 0.6748 | 0.6872 | 0.9514 |
| 0.0019 | 8.0 | 44488 | 0.0024 | 0.6962 | 0.6820 | 0.6890 | 0.9513 |
| 0.0019 | 9.0 | 50049 | 0.0023 | 0.7005 | 0.6775 | 0.6888 | 0.9516 |
| 0.0018 | 10.0 | 55610 | 0.0023 | 0.6975 | 0.6822 | 0.6898 | 0.9515 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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"RobertaForQuestionAnswering"
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} | 3 | null | ---
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 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
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}
} | 28 | null | ---
language:
- en
tag: fill-mask
widget:
- text: "Paris is the <mask> of France."
example_title: "Capital"
---
|
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1_wikiqa_copy | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"RobertaModel"
],
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}
}
} | 2 | null | ---
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: 0.1631
- F1: 0.8579
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2878 | 1.0 | 715 | 0.1840 | 0.8247 |
| 0.1456 | 2.0 | 1430 | 0.1596 | 0.8473 |
| 0.0925 | 3.0 | 2145 | 0.1631 | 0.8579 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 1 | null | ---
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: 0.9241871401929781
---
<!-- 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: 0.1013
- F1: 0.9242
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5667 | 1.0 | 191 | 0.2318 | 0.8415 |
| 0.2539 | 2.0 | 382 | 0.1428 | 0.8988 |
| 0.1739 | 3.0 | 573 | 0.1013 | 0.9242 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
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} | 2 | null | ---
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: 0.8223225276979894
---
<!-- 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: 0.2562
- F1: 0.8223
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8175 | 1.0 | 70 | 0.3331 | 0.7147 |
| 0.2807 | 2.0 | 140 | 0.2745 | 0.8045 |
| 0.1836 | 3.0 | 210 | 0.2562 | 0.8223 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
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} | 4 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 598.00 +/- 147.67
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 NikitaErmolaev -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 NikitaErmolaev
```
## 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)])
```
|
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
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} | 24 | null | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bert2gpt2_med_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. -->
<img src="https://huggingface.co/Chemsseddine/bert2gpt2_med_ml_orange_summ-finetuned_med_sum_new-finetuned_med_sum_new/resolve/main/logobert2gpt2.png" alt="Map of positive probabilities per country." width="200"/>
# bert2gpt2_med_v2
This model is a fine-tuned version of [Chemsseddine/bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum](https://huggingface.co/Chemsseddine/bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0684
- Rouge1: 34.1248
- Rouge2: 17.7006
- Rougel: 33.4661
- Rougelsum: 33.4419
- Gen Len: 22.6429
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.9107 | 1.0 | 1000 | 2.0877 | 30.4547 | 14.4024 | 30.3642 | 30.3788 | 21.9714 |
| 1.8782 | 2.0 | 2000 | 1.8151 | 32.6607 | 16.8089 | 32.3844 | 32.4762 | 21.7714 |
| 1.291 | 3.0 | 3000 | 1.7523 | 33.6391 | 16.7866 | 32.4256 | 32.3306 | 22.7429 |
| 0.819 | 4.0 | 4000 | 1.7650 | 35.0633 | 19.1222 | 34.4902 | 34.6796 | 22.4714 |
| 0.4857 | 5.0 | 5000 | 1.8129 | 33.8763 | 16.9303 | 32.8845 | 32.9225 | 22.3857 |
| 0.3232 | 6.0 | 6000 | 1.9339 | 33.9272 | 17.1784 | 32.9301 | 33.0253 | 22.4286 |
| 0.2022 | 7.0 | 7000 | 1.9634 | 33.9869 | 16.4238 | 33.7336 | 33.65 | 22.6429 |
| 0.1452 | 8.0 | 8000 | 2.0090 | 33.8892 | 18.2723 | 33.7514 | 33.6531 | 22.5714 |
| 0.0845 | 9.0 | 9000 | 2.0337 | 33.9649 | 17.1339 | 33.5061 | 33.4157 | 22.7857 |
| 0.0531 | 10.0 | 10000 | 2.0684 | 34.1248 | 17.7006 | 33.4661 | 33.4419 | 22.6429 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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} | 5 | null | ---
language:
- "ja"
tags:
- "japanese"
- "wikipedia"
- "question-answering"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "cc-by-sa-4.0"
pipeline_tag: "question-answering"
inference:
parameters:
align_to_words: false
widget:
- text: "国語"
context: "全学年にわたって小学校の国語の教科書に挿し絵が用いられている"
- text: "教科書"
context: "全学年にわたって小学校の国語の教科書に挿し絵が用いられている"
- text: "の"
context: "全学年にわたって小学校の国語[MASK]教科書に挿し絵が用いられている"
---
# deberta-base-japanese-wikipedia-ud-head
## Model Description
This is a DeBERTa(V2) model pretrained on Japanese Wikipedia and 青空文庫 texts for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [deberta-base-japanese-wikipedia](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-wikipedia) and [UD_Japanese-GSDLUW](https://github.com/UniversalDependencies/UD_Japanese-GSDLUW). Use [MASK] inside `context` to avoid ambiguity when specifying a multiple-used word as `question`.
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForQuestionAnswering,QuestionAnsweringPipeline
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia-ud-head")
model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia-ud-head")
qap=QuestionAnsweringPipeline(tokenizer=tokenizer,model=model,align_to_words=False)
print(qap(question="国語",context="全学年にわたって小学校の国語の教科書に挿し絵が用いられている"))
```
or (with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/))
```py
class TransformersUD(object):
def __init__(self,bert):
import os
from transformers import (AutoTokenizer,AutoModelForQuestionAnswering,
AutoModelForTokenClassification,AutoConfig,TokenClassificationPipeline)
self.tokenizer=AutoTokenizer.from_pretrained(bert)
self.model=AutoModelForQuestionAnswering.from_pretrained(bert)
x=AutoModelForTokenClassification.from_pretrained
if os.path.isdir(bert):
d,t=x(os.path.join(bert,"deprel")),x(os.path.join(bert,"tagger"))
else:
from transformers.utils import cached_file
c=AutoConfig.from_pretrained(cached_file(bert,"deprel/config.json"))
d=x(cached_file(bert,"deprel/pytorch_model.bin"),config=c)
s=AutoConfig.from_pretrained(cached_file(bert,"tagger/config.json"))
t=x(cached_file(bert,"tagger/pytorch_model.bin"),config=s)
self.deprel=TokenClassificationPipeline(model=d,tokenizer=self.tokenizer,
aggregation_strategy="simple")
self.tagger=TokenClassificationPipeline(model=t,tokenizer=self.tokenizer)
def __call__(self,text):
import numpy,torch,ufal.chu_liu_edmonds
w=[(t["start"],t["end"],t["entity_group"]) for t in self.deprel(text)]
z,n={t["start"]:t["entity"].split("|") for t in self.tagger(text)},len(w)
r,m=[text[s:e] for s,e,p in w],numpy.full((n+1,n+1),numpy.nan)
v,c=self.tokenizer(r,add_special_tokens=False)["input_ids"],[]
for i,t in enumerate(v):
q=[self.tokenizer.cls_token_id]+t+[self.tokenizer.sep_token_id]
c.append([q]+v[0:i]+[[self.tokenizer.mask_token_id]]+v[i+1:]+[[q[-1]]])
b=[[len(sum(x[0:j+1],[])) for j in range(len(x))] for x in c]
with torch.no_grad():
d=self.model(input_ids=torch.tensor([sum(x,[]) for x in c]),
token_type_ids=torch.tensor([[0]*x[0]+[1]*(x[-1]-x[0]) for x in b]))
s,e=d.start_logits.tolist(),d.end_logits.tolist()
for i in range(n):
for j in range(n):
m[i+1,0 if i==j else j+1]=s[i][b[i][j]]+e[i][b[i][j+1]-1]
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
if [0 for i in h if i==0]!=[0]:
i=([p for s,e,p in w]+["root"]).index("root")
j=i+1 if i<n else numpy.nanargmax(m[:,0])
m[0:j,0]=m[j+1:,0]=numpy.nan
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
u="# text = "+text.replace("\n"," ")+"\n"
for i,(s,e,p) in enumerate(w,1):
p="root" if h[i]==0 else "dep" if p=="root" else p
u+="\t".join([str(i),r[i-1],"_",z[s][0][2:],"_","|".join(z[s][1:]),
str(h[i]),p,"_","_" if i<n and e<w[i][0] else "SpaceAfter=No"])+"\n"
return u+"\n"
nlp=TransformersUD("KoichiYasuoka/deberta-base-japanese-wikipedia-ud-head")
print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている"))
```
## Reference
安岡孝一: [青空文庫DeBERTaモデルによる国語研長単位係り受け解析](http://hdl.handle.net/2433/275409), 東洋学へのコンピュータ利用, 第35回研究セミナー (2022年7月), pp.29-43.
|
AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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} | 4 | null | ---
language: fa
datasets:
- common_voice_6_1
tags:
- audio
- automatic-speech-recognition
license: mit
widget:
- example_title: Common Voice Sample 1
src: https://datasets-server.huggingface.co/assets/common_voice/--/fa/train/0/audio/audio.mp3
- example_title: Common Voice Sample 2
src: https://datasets-server.huggingface.co/assets/common_voice/--/fa/train/1/audio/audio.mp3
model-index:
- name: Sharif-wav2vec2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice Corpus 6.1 (clean)
type: common_voice_6_1
config: clean
split: test
args:
language: fa
metrics:
- name: Test WER
type: wer
value: 6.0
---
# Sharif-wav2vec2
This is a fine-tuned version of Sharif Wav2vec2 for Farsi. The base model went through a fine-tuning process in which 108 hours of Commonvoice's Farsi samples with a sampling rate equal to 16kHz. Afterward, we trained a 5gram using [kenlm](https://github.com/kpu/kenlm) toolkit and used it in the processor which increased our accuracy on online ASR.
## Usage
When using the model, ensure that your speech input is sampled at 16Khz. Prior to the usage, you may need to install the below dependencies:
```shell
pip install pyctcdecode
pip install pypi-kenlm
```
For testing, you can use the hosted inference API at the hugging face (There are provided examples from common-voice). It may take a while to transcribe the given voice; Or you can use the bellow code for a local run:
```python
import tensorflow
import torchaudio
import torch
import numpy as np
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("SLPL/Sharif-wav2vec2")
model = AutoModelForCTC.from_pretrained("SLPL/Sharif-wav2vec2")
speech_array, sampling_rate = torchaudio.load("path/to/your.wav")
speech_array = speech_array.squeeze().numpy()
features = processor(
speech_array,
sampling_rate=processor.feature_extractor.sampling_rate,
return_tensors="pt",
padding=True)
with torch.no_grad():
logits = model(
features.input_values,
attention_mask=features.attention_mask).logits
prediction = processor.batch_decode(logits.numpy()).text
print(prediction[0])
# تست
```
## Evaluation
For the evaluation, you can use the code below. Ensure your dataset to be in following form in order to avoid any further conflict:
| path | reference|
|:----:|:--------:|
| path/to/audio_file.wav | "TRANSCRIPTION" |
also, make sure you have installed `pip install jiwer` prior to running.
```python
import tensorflow
import torchaudio
import torch
import librosa
from datasets import load_dataset,load_metric
import numpy as np
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from transformers import Wav2Vec2ProcessorWithLM
model = Wav2Vec2ForCTC.from_pretrained("SLPL/Sharif-wav2vec2")
processor = Wav2Vec2ProcessorWithLM.from_pretrained("SLPL/Sharif-wav2vec2")
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
speech_array = speech_array.squeeze().numpy()
speech_array = librosa.resample(
np.asarray(speech_array),
sampling_rate,
processor.feature_extractor.sampling_rate)
batch["speech"] = speech_array
return batch
def predict(batch):
features = processor(
batch["speech"],
sampling_rate=processor.feature_extractor.sampling_rate,
return_tensors="pt",
padding=True
)
with torch.no_grad():
logits = model(
features.input_values,
attention_mask=features.attention_mask).logits
batch["prediction"] = processor.batch_decode(logits.numpy()).text
return batch
dataset = load_dataset(
"csv",
data_files={"test":"dataset.eval.csv"},
delimiter=",")["test"]
dataset = dataset.map(speech_file_to_array_fn)
result = dataset.map(predict, batched=True, batch_size=4)
wer = load_metric("wer")
print("WER: {:.2f}".format(wer.compute(
predictions=result["prediction"],
references=result["reference"])))
```
*Result (WER) on common-voice 6.1*:
| cleaned | other |
|:---:|:---:|
| 0.06 | 0.16 |
## Citation
If you want to cite this model you can use this:
```bibtex
?
```
### Contributions
Thanks to [@sarasadeghii](https://github.com/Sarasadeghii) and [@sadrasabouri](https://github.com/sadrasabouri) for adding this dataset.
|
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} | 0 | 2022-06-25T20:07:43Z | ---
language: es
license: cc-by-sa-4.0
datasets:
- wikipedia
- cc100
widget:
- text: "Yo vivo en <mask>."
- text: "Quiero <mask> contigo ?"
- text: "Es clima es <mask>."
- text: "Me llamo <mask>."
- text: "Las negociaciones están <mask>."
---
## RoBERTa Spanish base model (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses RoBERTa base setttings except vocabulary size.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* [wiki40b/es](https://www.tensorflow.org/datasets/catalog/wiki40b#wiki40bes) (Spanish Wikipedia)
* Subset of [CC-100/es](https://data.statmt.org/cc-100/) : Monolingual Datasets from Web Crawl Data
### Usage
```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='ClassCat/roberta-base-spanish')
unmasker("Yo soy <mask>.")
``` |
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9292895994725564
- name: Recall
type: recall
value: 0.9488387748232918
- name: F1
type: f1
value: 0.9389624448330418
- name: Accuracy
type: accuracy
value: 0.9863572143403779
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0602
- Precision: 0.9293
- Recall: 0.9488
- F1: 0.9390
- Accuracy: 0.9864
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0827 | 1.0 | 1756 | 0.0639 | 0.9167 | 0.9359 | 0.9262 | 0.9828 |
| 0.0413 | 2.0 | 3512 | 0.0565 | 0.9262 | 0.9465 | 0.9362 | 0.9859 |
| 0.0188 | 3.0 | 5268 | 0.0602 | 0.9293 | 0.9488 | 0.9390 | 0.9864 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-large-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9476811355009077
- name: Recall
type: recall
value: 0.9663412992258499
- name: F1
type: f1
value: 0.9569202566452795
- name: Accuracy
type: accuracy
value: 0.990656929827253
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-finetuned-ner
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0495
- Precision: 0.9477
- Recall: 0.9663
- F1: 0.9569
- Accuracy: 0.9907
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.078 | 1.0 | 1756 | 0.0577 | 0.9246 | 0.9536 | 0.9389 | 0.9865 |
| 0.0382 | 2.0 | 3512 | 0.0528 | 0.9414 | 0.9620 | 0.9516 | 0.9890 |
| 0.021 | 3.0 | 5268 | 0.0495 | 0.9477 | 0.9663 | 0.9569 | 0.9907 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Aran/DialoGPT-medium-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5443893754588841
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7548
- Matthews Correlation: 0.5444
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5303 | 1.0 | 535 | 0.5510 | 0.3636 |
| 0.3527 | 2.0 | 1070 | 0.5543 | 0.4886 |
| 0.2366 | 3.0 | 1605 | 0.5738 | 0.5311 |
| 0.1761 | 4.0 | 2140 | 0.7548 | 0.5444 |
| 0.128 | 5.0 | 2675 | 0.8436 | 0.5380 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Aran/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
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}
} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-common-voice-40p-persian-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-common-voice-40p-persian-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1805
- Wer: 0.6024
## 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: 0.00018
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.9643 | 1.05 | 200 | 3.0107 | 1.0 |
| 2.7552 | 2.11 | 400 | 2.7370 | 0.9997 |
| 1.9144 | 3.16 | 600 | 1.8266 | 0.9703 |
| 1.502 | 4.21 | 800 | 1.3981 | 0.8996 |
| 1.3155 | 5.26 | 1000 | 1.2148 | 0.8507 |
| 0.9471 | 6.32 | 1200 | 1.1698 | 0.7860 |
| 0.8391 | 7.37 | 1400 | 1.1106 | 0.7857 |
| 0.7986 | 8.42 | 1600 | 1.1858 | 0.7769 |
| 0.7692 | 9.47 | 1800 | 1.1227 | 0.7603 |
| 0.7871 | 10.53 | 2000 | 1.0626 | 0.7612 |
| 0.6795 | 11.58 | 2200 | 1.1249 | 0.7209 |
| 0.4842 | 12.63 | 2400 | 1.1626 | 0.7336 |
| 0.492 | 13.68 | 2600 | 1.0995 | 0.7212 |
| 0.5117 | 14.74 | 2800 | 1.1406 | 0.7105 |
| 0.5649 | 15.79 | 3000 | 1.0603 | 0.6819 |
| 0.3232 | 16.84 | 3200 | 1.1781 | 0.7070 |
| 0.4098 | 17.89 | 3400 | 1.1182 | 0.6764 |
| 0.3917 | 18.95 | 3600 | 1.1320 | 0.6750 |
| 0.3712 | 20.0 | 3800 | 1.1920 | 0.6724 |
| 0.3157 | 21.05 | 4000 | 1.1102 | 0.6786 |
| 0.2397 | 22.11 | 4200 | 1.1924 | 0.6519 |
| 0.2751 | 23.16 | 4400 | 1.1497 | 0.6468 |
| 0.2279 | 24.21 | 4600 | 1.2274 | 0.6400 |
| 0.393 | 25.26 | 4800 | 1.1741 | 0.6436 |
| 0.1748 | 26.32 | 5000 | 1.2038 | 0.6327 |
| 0.1727 | 27.37 | 5200 | 1.1639 | 0.6347 |
| 0.255 | 28.42 | 5400 | 1.1948 | 0.6367 |
| 0.2261 | 29.47 | 5600 | 1.1560 | 0.6362 |
| 0.2359 | 30.53 | 5800 | 1.1227 | 0.6269 |
| 0.1668 | 31.58 | 6000 | 1.1861 | 0.6295 |
| 0.1699 | 32.63 | 6200 | 1.2442 | 0.6314 |
| 0.14 | 33.68 | 6400 | 1.1340 | 0.6277 |
| 0.1919 | 34.74 | 6600 | 1.1691 | 0.6139 |
| 0.2527 | 35.79 | 6800 | 1.1511 | 0.6110 |
| 0.1219 | 36.84 | 7000 | 1.2062 | 0.6139 |
| 0.1389 | 37.89 | 7200 | 1.2142 | 0.6072 |
| 0.135 | 38.95 | 7400 | 1.1967 | 0.6040 |
| 0.1563 | 40.0 | 7600 | 1.1805 | 0.6024 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
ArashEsk95/bert-base-uncased-finetuned-cola | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 650.00 +/- 154.00
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 vebie91 -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 vebie91
```
## 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', 2000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
ArcQ/gpt-experiments | [] | null | {
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} | 0 | null | ---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="kavi12/q-FrozenLake-v1-8x8-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
ArthurBaia/bert-base-portuguese-cased-finetuned-squad | [] | null | {
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}
} | 0 | null | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
ArthurcJP/DialoGPT-small-YODA | [] | null | {
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}
} | 0 | 2022-06-26T15:00:58Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 287.72 +/- 15.68
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
...
```
|
AryanLala/autonlp-Scientific_Title_Generator-34558227 | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"dataset:AryanLala/autonlp-data-Scientific_Title_Generator",
"transformers",
"autonlp",
"co2_eq_emissions",
"autotrain_compatible",
"has_space"
] | text2text-generation | {
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"PegasusForConditionalGeneration"
],
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}
}
} | 103 | null | ---
tags: autotrain
language: zh
widget:
- text: "I love AutoTrain 🤗"
datasets:
- p123/autotrain-data-my-sum
co2_eq_emissions: 326.52733725745725
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1040935781
- CO2 Emissions (in grams): 326.52733725745725
## Validation Metrics
- Loss: 1.9157543182373047
- Rouge1: 0.4843
- Rouge2: 0.0
- RougeL: 0.4843
- RougeLsum: 0.4843
- Gen Len: 10.9718
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/p123/autotrain-my-sum-1040935781
``` |
AshLukass/AshLukass | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper_batch_8_e8
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. -->
# exper_batch_8_e8
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4608
- Accuracy: 0.9052
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 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: 8
- mixed_precision_training: Apex, opt level O1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 4.2202 | 0.08 | 100 | 4.1245 | 0.1237 |
| 3.467 | 0.16 | 200 | 3.5622 | 0.2143 |
| 3.3469 | 0.23 | 300 | 3.1688 | 0.2675 |
| 2.8086 | 0.31 | 400 | 2.8965 | 0.3034 |
| 2.6291 | 0.39 | 500 | 2.5858 | 0.4025 |
| 2.2382 | 0.47 | 600 | 2.2908 | 0.4133 |
| 1.9259 | 0.55 | 700 | 2.2007 | 0.4676 |
| 1.8088 | 0.63 | 800 | 2.0419 | 0.4742 |
| 1.9462 | 0.7 | 900 | 1.6793 | 0.5578 |
| 1.5392 | 0.78 | 1000 | 1.5460 | 0.6079 |
| 1.561 | 0.86 | 1100 | 1.5793 | 0.5690 |
| 1.2135 | 0.94 | 1200 | 1.4663 | 0.5929 |
| 1.0725 | 1.02 | 1300 | 1.2974 | 0.6534 |
| 0.8696 | 1.1 | 1400 | 1.2406 | 0.6569 |
| 0.8758 | 1.17 | 1500 | 1.2127 | 0.6623 |
| 1.1737 | 1.25 | 1600 | 1.2243 | 0.6550 |
| 0.8242 | 1.33 | 1700 | 1.1371 | 0.6735 |
| 1.0141 | 1.41 | 1800 | 1.0536 | 0.7024 |
| 0.9855 | 1.49 | 1900 | 0.9885 | 0.7205 |
| 0.805 | 1.57 | 2000 | 0.9048 | 0.7479 |
| 0.7207 | 1.64 | 2100 | 0.8842 | 0.7490 |
| 0.7101 | 1.72 | 2200 | 0.8954 | 0.7436 |
| 0.5946 | 1.8 | 2300 | 0.9174 | 0.7386 |
| 0.6937 | 1.88 | 2400 | 0.7818 | 0.7760 |
| 0.5593 | 1.96 | 2500 | 0.7449 | 0.7934 |
| 0.4139 | 2.04 | 2600 | 0.7787 | 0.7830 |
| 0.2929 | 2.11 | 2700 | 0.7122 | 0.7945 |
| 0.4159 | 2.19 | 2800 | 0.7446 | 0.7907 |
| 0.4079 | 2.27 | 2900 | 0.7354 | 0.7938 |
| 0.516 | 2.35 | 3000 | 0.7499 | 0.8007 |
| 0.2728 | 2.43 | 3100 | 0.6851 | 0.8061 |
| 0.4159 | 2.51 | 3200 | 0.7258 | 0.7999 |
| 0.3396 | 2.58 | 3300 | 0.7455 | 0.7972 |
| 0.1918 | 2.66 | 3400 | 0.6793 | 0.8119 |
| 0.1228 | 2.74 | 3500 | 0.6696 | 0.8134 |
| 0.2671 | 2.82 | 3600 | 0.6306 | 0.8285 |
| 0.4986 | 2.9 | 3700 | 0.6111 | 0.8296 |
| 0.3699 | 2.98 | 3800 | 0.5600 | 0.8508 |
| 0.0444 | 3.05 | 3900 | 0.6021 | 0.8331 |
| 0.1489 | 3.13 | 4000 | 0.5599 | 0.8516 |
| 0.15 | 3.21 | 4100 | 0.6377 | 0.8365 |
| 0.2535 | 3.29 | 4200 | 0.5752 | 0.8543 |
| 0.2679 | 3.37 | 4300 | 0.5677 | 0.8608 |
| 0.0989 | 3.45 | 4400 | 0.6325 | 0.8396 |
| 0.0825 | 3.52 | 4500 | 0.5979 | 0.8524 |
| 0.0427 | 3.6 | 4600 | 0.5903 | 0.8516 |
| 0.1806 | 3.68 | 4700 | 0.5323 | 0.8628 |
| 0.2672 | 3.76 | 4800 | 0.5688 | 0.8604 |
| 0.2674 | 3.84 | 4900 | 0.5369 | 0.8635 |
| 0.2185 | 3.92 | 5000 | 0.4743 | 0.8820 |
| 0.2195 | 3.99 | 5100 | 0.5340 | 0.8709 |
| 0.0049 | 4.07 | 5200 | 0.5883 | 0.8608 |
| 0.0204 | 4.15 | 5300 | 0.6102 | 0.8539 |
| 0.0652 | 4.23 | 5400 | 0.5659 | 0.8670 |
| 0.028 | 4.31 | 5500 | 0.4916 | 0.8840 |
| 0.0423 | 4.39 | 5600 | 0.5706 | 0.8736 |
| 0.0087 | 4.46 | 5700 | 0.5653 | 0.8697 |
| 0.0964 | 4.54 | 5800 | 0.5423 | 0.8755 |
| 0.0841 | 4.62 | 5900 | 0.5160 | 0.8743 |
| 0.0945 | 4.7 | 6000 | 0.5532 | 0.8697 |
| 0.0311 | 4.78 | 6100 | 0.4947 | 0.8867 |
| 0.0423 | 4.86 | 6200 | 0.5063 | 0.8843 |
| 0.1348 | 4.93 | 6300 | 0.5619 | 0.8743 |
| 0.049 | 5.01 | 6400 | 0.5800 | 0.8732 |
| 0.0053 | 5.09 | 6500 | 0.5499 | 0.8770 |
| 0.0234 | 5.17 | 6600 | 0.5102 | 0.8874 |
| 0.0192 | 5.25 | 6700 | 0.5447 | 0.8836 |
| 0.0029 | 5.32 | 6800 | 0.4787 | 0.8936 |
| 0.0249 | 5.4 | 6900 | 0.5232 | 0.8870 |
| 0.0671 | 5.48 | 7000 | 0.4766 | 0.8975 |
| 0.0056 | 5.56 | 7100 | 0.5136 | 0.8894 |
| 0.003 | 5.64 | 7200 | 0.5085 | 0.8882 |
| 0.0015 | 5.72 | 7300 | 0.4832 | 0.8971 |
| 0.0014 | 5.79 | 7400 | 0.4648 | 0.8998 |
| 0.0065 | 5.87 | 7500 | 0.4739 | 0.8978 |
| 0.0011 | 5.95 | 7600 | 0.5349 | 0.8867 |
| 0.0021 | 6.03 | 7700 | 0.5460 | 0.8847 |
| 0.0012 | 6.11 | 7800 | 0.5309 | 0.8890 |
| 0.0011 | 6.19 | 7900 | 0.4852 | 0.8998 |
| 0.0093 | 6.26 | 8000 | 0.4751 | 0.8998 |
| 0.003 | 6.34 | 8100 | 0.4934 | 0.8963 |
| 0.0027 | 6.42 | 8200 | 0.4882 | 0.9029 |
| 0.0009 | 6.5 | 8300 | 0.4806 | 0.9021 |
| 0.0009 | 6.58 | 8400 | 0.4974 | 0.9029 |
| 0.0009 | 6.66 | 8500 | 0.4748 | 0.9075 |
| 0.0008 | 6.73 | 8600 | 0.4723 | 0.9094 |
| 0.001 | 6.81 | 8700 | 0.4692 | 0.9098 |
| 0.0007 | 6.89 | 8800 | 0.4726 | 0.9075 |
| 0.0011 | 6.97 | 8900 | 0.4686 | 0.9067 |
| 0.0006 | 7.05 | 9000 | 0.4653 | 0.9056 |
| 0.0006 | 7.13 | 9100 | 0.4755 | 0.9029 |
| 0.0007 | 7.2 | 9200 | 0.4633 | 0.9036 |
| 0.0067 | 7.28 | 9300 | 0.4611 | 0.9036 |
| 0.0007 | 7.36 | 9400 | 0.4608 | 0.9052 |
| 0.0007 | 7.44 | 9500 | 0.4623 | 0.9044 |
| 0.0005 | 7.52 | 9600 | 0.4621 | 0.9056 |
| 0.0005 | 7.6 | 9700 | 0.4615 | 0.9056 |
| 0.0005 | 7.67 | 9800 | 0.4612 | 0.9059 |
| 0.0005 | 7.75 | 9900 | 0.4626 | 0.9075 |
| 0.0004 | 7.83 | 10000 | 0.4626 | 0.9075 |
| 0.0005 | 7.91 | 10100 | 0.4626 | 0.9075 |
| 0.0006 | 7.99 | 10200 | 0.4626 | 0.9079 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.5.1
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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} | 0 | null | ---
tags:
- conversational
---
# Rick and Morty DialoGPT Model |
Augustvember/WokkaBot3 | [
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} | 0 | null | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper_batch_16_e8
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. -->
# exper_batch_16_e8
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3951
- Accuracy: 0.9129
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Apex, opt level O1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.8115 | 0.16 | 100 | 3.7948 | 0.1862 |
| 3.1194 | 0.31 | 200 | 3.0120 | 0.3281 |
| 2.3703 | 0.47 | 300 | 2.4791 | 0.4426 |
| 2.07 | 0.63 | 400 | 2.1720 | 0.5 |
| 1.6847 | 0.78 | 500 | 1.7291 | 0.5956 |
| 1.3821 | 0.94 | 600 | 1.4777 | 0.6299 |
| 0.9498 | 1.1 | 700 | 1.2935 | 0.6681 |
| 0.8741 | 1.25 | 800 | 1.1353 | 0.7051 |
| 0.8875 | 1.41 | 900 | 0.9951 | 0.7448 |
| 0.7233 | 1.56 | 1000 | 0.9265 | 0.7487 |
| 0.6696 | 1.72 | 1100 | 0.8660 | 0.7625 |
| 0.7364 | 1.88 | 1200 | 0.8710 | 0.7579 |
| 0.3933 | 2.03 | 1300 | 0.7162 | 0.8038 |
| 0.3443 | 2.19 | 1400 | 0.6305 | 0.8300 |
| 0.3376 | 2.35 | 1500 | 0.6273 | 0.8315 |
| 0.3071 | 2.5 | 1600 | 0.5988 | 0.8319 |
| 0.2863 | 2.66 | 1700 | 0.6731 | 0.8153 |
| 0.3017 | 2.82 | 1800 | 0.6042 | 0.8315 |
| 0.2382 | 2.97 | 1900 | 0.5118 | 0.8712 |
| 0.1578 | 3.13 | 2000 | 0.4917 | 0.8736 |
| 0.1794 | 3.29 | 2100 | 0.5302 | 0.8631 |
| 0.1093 | 3.44 | 2200 | 0.5035 | 0.8635 |
| 0.1076 | 3.6 | 2300 | 0.5186 | 0.8674 |
| 0.1219 | 3.76 | 2400 | 0.4723 | 0.8801 |
| 0.1017 | 3.91 | 2500 | 0.5132 | 0.8712 |
| 0.0351 | 4.07 | 2600 | 0.4709 | 0.8728 |
| 0.0295 | 4.23 | 2700 | 0.4674 | 0.8824 |
| 0.0416 | 4.38 | 2800 | 0.4836 | 0.8805 |
| 0.0386 | 4.54 | 2900 | 0.4663 | 0.8828 |
| 0.0392 | 4.69 | 3000 | 0.4003 | 0.8990 |
| 0.0383 | 4.85 | 3100 | 0.4187 | 0.8948 |
| 0.0624 | 5.01 | 3200 | 0.4460 | 0.8874 |
| 0.0188 | 5.16 | 3300 | 0.4169 | 0.9029 |
| 0.0174 | 5.32 | 3400 | 0.4098 | 0.8951 |
| 0.0257 | 5.48 | 3500 | 0.4289 | 0.8951 |
| 0.0123 | 5.63 | 3600 | 0.4295 | 0.9029 |
| 0.0052 | 5.79 | 3700 | 0.4395 | 0.8994 |
| 0.0081 | 5.95 | 3800 | 0.4217 | 0.9082 |
| 0.0032 | 6.1 | 3900 | 0.4216 | 0.9056 |
| 0.0033 | 6.26 | 4000 | 0.4113 | 0.9082 |
| 0.0024 | 6.42 | 4100 | 0.4060 | 0.9102 |
| 0.0022 | 6.57 | 4200 | 0.4067 | 0.9090 |
| 0.0031 | 6.73 | 4300 | 0.4005 | 0.9113 |
| 0.0021 | 6.89 | 4400 | 0.4008 | 0.9129 |
| 0.0021 | 7.04 | 4500 | 0.3967 | 0.9113 |
| 0.0043 | 7.2 | 4600 | 0.3960 | 0.9121 |
| 0.0022 | 7.36 | 4700 | 0.3962 | 0.9125 |
| 0.0021 | 7.51 | 4800 | 0.3992 | 0.9121 |
| 0.002 | 7.67 | 4900 | 0.3951 | 0.9129 |
| 0.0023 | 7.82 | 5000 | 0.3952 | 0.9125 |
| 0.0021 | 7.98 | 5100 | 0.3952 | 0.9129 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.5.1
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.922
- name: F1
type: f1
value: 0.9219009840141562
---
<!-- 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: 0.2226
- Accuracy: 0.922
- F1: 0.9219
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8435 | 1.0 | 250 | 0.3324 | 0.897 | 0.8930 |
| 0.2578 | 2.0 | 500 | 0.2226 | 0.922 | 0.9219 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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} | 0 | 2022-06-26T21:24:54Z | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bert2gpt2_med_v4
results: []
---
<img src="https://huggingface.co/Chemsseddine/bert2gpt2_med_ml_orange_summ-finetuned_med_sum_new-finetuned_med_sum_new/resolve/main/logobert2gpt2.png" alt="Map of positive probabilities per country." width="200"/>
# bert2gpt2_med_v4
This model is a fine-tuned version of [Chemsseddine/bert2gpt2_med_v3](https://huggingface.co/Chemsseddine/bert2gpt2_med_v3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4780
- Rouge1: 36.7502
- Rouge2: 18.5992
- Rougel: 36.2566
- Rougelsum: 36.161
- Gen Len: 22.96
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 169 | 1.4796 | 33.9893 | 16.2462 | 33.5685 | 33.4738 | 22.42 |
| No log | 2.0 | 338 | 1.4404 | 34.0811 | 16.219 | 34.0206 | 33.9139 | 22.76 |
| 1.0815 | 3.0 | 507 | 1.4078 | 35.2755 | 18.2266 | 34.9186 | 34.9052 | 22.63 |
| 1.0815 | 4.0 | 676 | 1.4207 | 34.0146 | 17.4167 | 33.9904 | 33.9735 | 22.92 |
| 1.0815 | 5.0 | 845 | 1.4285 | 35.2093 | 17.3269 | 35.1023 | 35.222 | 22.75 |
| 0.4699 | 6.0 | 1014 | 1.4607 | 34.5503 | 16.9067 | 34.6404 | 34.5957 | 22.8 |
| 0.4699 | 7.0 | 1183 | 1.4469 | 35.0539 | 17.0677 | 34.7607 | 34.8734 | 22.73 |
| 0.4699 | 8.0 | 1352 | 1.4632 | 35.2308 | 17.9663 | 35.1657 | 35.1012 | 22.9 |
| 0.2522 | 9.0 | 1521 | 1.4734 | 35.5699 | 18.53 | 35.4927 | 35.3747 | 22.84 |
| 0.2522 | 10.0 | 1690 | 1.4780 | 36.7502 | 18.5992 | 36.2566 | 36.161 | 22.96 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.87
- name: F1
type: f1
value: 0.8695652173913044
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3120
- Accuracy: 0.87
- F1: 0.8696
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Augustvember/test | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 12 | 2022-06-26T22:20:11Z | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper_batch_32_e4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# exper_batch_32_e4
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3909
- Accuracy: 0.9067
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Apex, opt level O1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.4295 | 0.31 | 100 | 3.4027 | 0.2837 |
| 2.5035 | 0.62 | 200 | 2.4339 | 0.5247 |
| 1.6542 | 0.94 | 300 | 1.7690 | 0.6388 |
| 1.1589 | 1.25 | 400 | 1.3106 | 0.7460 |
| 0.9363 | 1.56 | 500 | 0.9977 | 0.7803 |
| 0.6946 | 1.88 | 600 | 0.8138 | 0.8207 |
| 0.3488 | 2.19 | 700 | 0.6593 | 0.8489 |
| 0.2935 | 2.5 | 800 | 0.5725 | 0.8662 |
| 0.2557 | 2.81 | 900 | 0.5088 | 0.8855 |
| 0.1509 | 3.12 | 1000 | 0.4572 | 0.8971 |
| 0.1367 | 3.44 | 1100 | 0.4129 | 0.9090 |
| 0.1078 | 3.75 | 1200 | 0.3909 | 0.9067 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.5.1
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Augustvember/wokka | [
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 4 | null | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: exper_batch_32_e8
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. -->
# exper_batch_32_e8
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3520
- Accuracy: 0.9113
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Apex, opt level O1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.3787 | 0.31 | 100 | 3.3100 | 0.3566 |
| 2.3975 | 0.62 | 200 | 2.3196 | 0.5717 |
| 1.5578 | 0.94 | 300 | 1.6764 | 0.6461 |
| 1.0291 | 1.25 | 400 | 1.1713 | 0.7463 |
| 0.8185 | 1.56 | 500 | 0.9292 | 0.7953 |
| 0.6181 | 1.88 | 600 | 0.7732 | 0.8169 |
| 0.3873 | 2.19 | 700 | 0.6877 | 0.8277 |
| 0.2979 | 2.5 | 800 | 0.6250 | 0.8404 |
| 0.2967 | 2.81 | 900 | 0.6151 | 0.8365 |
| 0.1874 | 3.12 | 1000 | 0.5401 | 0.8608 |
| 0.2232 | 3.44 | 1100 | 0.5032 | 0.8712 |
| 0.1109 | 3.75 | 1200 | 0.4635 | 0.8774 |
| 0.0539 | 4.06 | 1300 | 0.4495 | 0.8843 |
| 0.0668 | 4.38 | 1400 | 0.4273 | 0.8951 |
| 0.0567 | 4.69 | 1500 | 0.4427 | 0.8867 |
| 0.0285 | 5.0 | 1600 | 0.4092 | 0.8955 |
| 0.0473 | 5.31 | 1700 | 0.3720 | 0.9071 |
| 0.0225 | 5.62 | 1800 | 0.3691 | 0.9063 |
| 0.0196 | 5.94 | 1900 | 0.3775 | 0.9048 |
| 0.0173 | 6.25 | 2000 | 0.3641 | 0.9040 |
| 0.0092 | 6.56 | 2100 | 0.3551 | 0.9090 |
| 0.008 | 6.88 | 2200 | 0.3591 | 0.9125 |
| 0.0072 | 7.19 | 2300 | 0.3542 | 0.9121 |
| 0.007 | 7.5 | 2400 | 0.3532 | 0.9106 |
| 0.007 | 7.81 | 2500 | 0.3520 | 0.9113 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.5.1
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Augustvember/wokka2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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} | 12 | null | ---
license: mit
widget:
- text: "Jens Peter Hansen kommer fra Danmark"
---
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-google-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-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5206
- Wer: 0.3388
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5597 | 1.0 | 500 | 2.3415 | 0.9991 |
| 0.9759 | 2.01 | 1000 | 0.5556 | 0.5382 |
| 0.4587 | 3.01 | 1500 | 0.7690 | 0.4781 |
| 0.3156 | 4.02 | 2000 | 0.7994 | 0.4412 |
| 0.2272 | 5.02 | 2500 | 0.8948 | 0.4120 |
| 0.1921 | 6.02 | 3000 | 0.7065 | 0.3940 |
| 0.1618 | 7.03 | 3500 | 0.4333 | 0.3855 |
| 0.1483 | 8.03 | 4000 | 0.4232 | 0.3872 |
| 0.156 | 9.04 | 4500 | 0.4172 | 0.3749 |
| 0.1138 | 10.04 | 5000 | 0.4084 | 0.3758 |
| 0.1045 | 11.04 | 5500 | 0.4665 | 0.3623 |
| 0.0908 | 12.05 | 6000 | 0.4416 | 0.3684 |
| 0.0788 | 13.05 | 6500 | 0.4801 | 0.3659 |
| 0.0773 | 14.06 | 7000 | 0.4560 | 0.3583 |
| 0.0684 | 15.06 | 7500 | 0.4878 | 0.3610 |
| 0.0645 | 16.06 | 8000 | 0.4635 | 0.3567 |
| 0.0577 | 17.07 | 8500 | 0.5245 | 0.3548 |
| 0.0547 | 18.07 | 9000 | 0.5265 | 0.3639 |
| 0.0466 | 19.08 | 9500 | 0.5161 | 0.3546 |
| 0.0432 | 20.08 | 10000 | 0.5263 | 0.3558 |
| 0.0414 | 21.08 | 10500 | 0.4874 | 0.3500 |
| 0.0365 | 22.09 | 11000 | 0.5266 | 0.3472 |
| 0.0321 | 23.09 | 11500 | 0.5422 | 0.3458 |
| 0.0325 | 24.1 | 12000 | 0.5201 | 0.3428 |
| 0.0262 | 25.1 | 12500 | 0.5208 | 0.3398 |
| 0.0249 | 26.1 | 13000 | 0.5034 | 0.3429 |
| 0.0262 | 27.11 | 13500 | 0.5055 | 0.3396 |
| 0.0248 | 28.11 | 14000 | 0.5164 | 0.3404 |
| 0.0222 | 29.12 | 14500 | 0.5206 | 0.3388 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
Aurora/asdawd | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0337
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 46 | 0.4284 |
| No log | 2.0 | 92 | 0.0573 |
| No log | 3.0 | 138 | 0.0337 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Aurora/community.afpglobal | [] | null | {
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} | 0 | null | ---
license: apache-2.0
---
There are two folders now:
- conformer: Conformer A3T trained with all VCTK training data.
- unseen_conformer: Conformer A3T trained by excluding some speakers during the training.
|
Aviora/news2vec | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- QbertNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 19340.00 +/- 862.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: QbertNoFrameskip-v4
type: QbertNoFrameskip-v4
---
# **PPO** Agent playing **QbertNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **QbertNoFrameskip-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 ppo --env QbertNoFrameskip-v4 -orga Corianas -f logs/
python enjoy.py --algo ppo --env QbertNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo ppo --env QbertNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo ppo --env QbertNoFrameskip-v4 -f logs/ -orga Corianas
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('clip_range', 'lin_0.1'),
('ent_coef', 0.01),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('frame_stack', 4),
('learning_rate', 'lin_2.5e-4'),
('n_envs', 8),
('n_epochs', 4),
('n_steps', 128),
('n_timesteps', 10000000.0),
('policy', 'CnnPolicy'),
('vf_coef', 0.5),
('normalize', False)])
```
|
Awsaf/DialoGPT-medium-eren | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 12 | 2022-06-27T01:25:28Z | ---
tags: autotrain
language: zh
widget:
- text: "I love AutoTrain 🤗"
datasets:
- zyxzyx/autotrain-data-sum
co2_eq_emissions: 426.15271368095927
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1042335811
- CO2 Emissions (in grams): 426.15271368095927
## Validation Metrics
- Loss: 1.7748287916183472
- Rouge1: 0.536
- Rouge2: 0.0
- RougeL: 0.536
- RougeLsum: 0.536
- Gen Len: 10.9089
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/zyxzyx/autotrain-sum-1042335811
``` |
Awsaf/large-eren | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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} | 10 | 2022-06-27T11:44:44Z | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tiny_focal_alpah
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_alpah
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0492
- Precision: 0.6951
- Recall: 0.6796
- F1: 0.6873
- Accuracy: 0.9512
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0588 | 1.0 | 5561 | 0.0548 | 0.6801 | 0.6235 | 0.6506 | 0.9453 |
| 0.054 | 2.0 | 11122 | 0.0521 | 0.6850 | 0.6478 | 0.6659 | 0.9476 |
| 0.0525 | 3.0 | 16683 | 0.0509 | 0.6834 | 0.6676 | 0.6754 | 0.9486 |
| 0.0492 | 4.0 | 22244 | 0.0503 | 0.6829 | 0.6754 | 0.6791 | 0.9491 |
| 0.0482 | 5.0 | 27805 | 0.0500 | 0.6917 | 0.6727 | 0.6820 | 0.9501 |
| 0.0471 | 6.0 | 33366 | 0.0491 | 0.7085 | 0.6546 | 0.6805 | 0.9510 |
| 0.0459 | 7.0 | 38927 | 0.0486 | 0.6964 | 0.6746 | 0.6853 | 0.9510 |
| 0.0448 | 8.0 | 44488 | 0.0495 | 0.6922 | 0.6813 | 0.6867 | 0.9509 |
| 0.044 | 9.0 | 50049 | 0.0491 | 0.6961 | 0.6755 | 0.6857 | 0.9511 |
| 0.0433 | 10.0 | 55610 | 0.0492 | 0.6951 | 0.6796 | 0.6873 | 0.9512 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Axcel/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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} | 14 | 2022-06-27T01:39:58Z | ---
language: multilingual
thumbnail:
tags:
- audio-classification
license: "apache-2.0"
datasets:
- AudioSet
---
copy of https://tfhub.dev/google/yamnet/1, https://tfhub.dev/google/coral-model/yamnet/classification/coral/1 |
Axon/resnet18-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
] | null | {
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} | 0 | 2022-06-27T01:44:58Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 565.50 +/- 141.39
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 tjscollins -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 tjscollins
```
## 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)])
```
|
Ayah/GPT2-DBpedia | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 6 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: ra-XOr/Unity-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Aybars/ModelOnTquad | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
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} | 8 | null | Toxicity LD50 prediction (regression model) based on <a href = "https://tdcommons.ai/single_pred_tasks/tox/"> Acute Toxicity LD50 </a> dataset.
For now, for the purpose of prediction, download the model. In the future, an easy colab notebook will be available. |
Ayham/albert_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
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}
} | 12 | null | ---
license: mit
---
Base model: [gpt2-large](https://huggingface.co/gpt2-large)
Fine-tuned to generate responses on a dataset of [Vaccine public health tweets](https://github.com/TheRensselaerIDEA/generative-response-modeling). For more information about the dataset, task and training, see [our paper](https://arxiv.org/abs/2204.04353). This checkpoint corresponds to the lowest validation perplexity (2.82 at 2 epochs) seen during training. See Training metrics for Tensorboard logs.
For input format and usage examples, see our [COVID-19 public health tweet response model](https://huggingface.co/TheRensselaerIDEA/gpt2-large-covid-tweet-response).
|
Ayham/albert_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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}
} | 6 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.46 +/- 2.70
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="jcmc/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"])
```
|
Ayham/bert_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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"EncoderDecoderModel"
],
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} | 6 | null | ---
tags:
- generated_from_trainer
datasets:
- uob_singlish
model-index:
- name: Malaya-speech_fine-tune_realcase_27_Jun
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. -->
# Malaya-speech_fine-tune_realcase_27_Jun
This model is a fine-tuned version of [malay-huggingface/wav2vec2-xls-r-300m-mixed](https://huggingface.co/malay-huggingface/wav2vec2-xls-r-300m-mixed) on the uob_singlish dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9159
- Wer: 0.3819
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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 | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.3176 | 1.82 | 20 | 0.8928 | 0.3542 |
| 0.6716 | 3.64 | 40 | 0.9123 | 0.3681 |
| 0.3484 | 5.45 | 60 | 0.9509 | 0.3681 |
| 0.3064 | 7.27 | 80 | 0.9227 | 0.3958 |
| 0.3017 | 9.09 | 100 | 0.9159 | 0.3819 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Ayham/bert_gpt2_summarization_cnndm_new | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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],
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}
} | 8 | 2022-06-27T05:22:57Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-google-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-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5501
- Wer: 0.3424
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5448 | 1.0 | 500 | 2.5044 | 1.0 |
| 1.0167 | 2.01 | 1000 | 0.5435 | 0.5278 |
| 0.4453 | 3.01 | 1500 | 0.4450 | 0.4534 |
| 0.3 | 4.02 | 2000 | 0.4401 | 0.4245 |
| 0.2304 | 5.02 | 2500 | 0.4146 | 0.4022 |
| 0.1889 | 6.02 | 3000 | 0.4241 | 0.3927 |
| 0.1573 | 7.03 | 3500 | 0.4545 | 0.3878 |
| 0.1363 | 8.03 | 4000 | 0.4936 | 0.3940 |
| 0.1213 | 9.04 | 4500 | 0.4964 | 0.3806 |
| 0.108 | 10.04 | 5000 | 0.4931 | 0.3826 |
| 0.0982 | 11.04 | 5500 | 0.5373 | 0.3778 |
| 0.0883 | 12.05 | 6000 | 0.4978 | 0.3733 |
| 0.0835 | 13.05 | 6500 | 0.5189 | 0.3728 |
| 0.0748 | 14.06 | 7000 | 0.4608 | 0.3692 |
| 0.068 | 15.06 | 7500 | 0.4827 | 0.3608 |
| 0.0596 | 16.06 | 8000 | 0.5022 | 0.3661 |
| 0.056 | 17.07 | 8500 | 0.5482 | 0.3646 |
| 0.0565 | 18.07 | 9000 | 0.5158 | 0.3573 |
| 0.0487 | 19.08 | 9500 | 0.4910 | 0.3513 |
| 0.0444 | 20.08 | 10000 | 0.5771 | 0.3580 |
| 0.045 | 21.08 | 10500 | 0.5160 | 0.3539 |
| 0.0363 | 22.09 | 11000 | 0.5367 | 0.3503 |
| 0.0313 | 23.09 | 11500 | 0.5773 | 0.3500 |
| 0.0329 | 24.1 | 12000 | 0.5683 | 0.3508 |
| 0.0297 | 25.1 | 12500 | 0.5355 | 0.3464 |
| 0.0272 | 26.1 | 13000 | 0.5317 | 0.3450 |
| 0.0256 | 27.11 | 13500 | 0.5602 | 0.3443 |
| 0.0242 | 28.11 | 14000 | 0.5586 | 0.3419 |
| 0.0239 | 29.12 | 14500 | 0.5501 | 0.3424 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
Ayham/distilbert_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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"EncoderDecoderModel"
],
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}
} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: gopalkalpande/t5-small-finetuned-xsum
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. -->
# gopalkalpande/t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.0422
- Validation Loss: 0.4407
- Train Rouge1: 19.5311
- Train Rouge2: 14.2402
- Train Rougel: 17.9781
- Train Rougelsum: 18.1546
- Train Gen Len: 19.0
- 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 1.0422 | 0.4407 | 19.5311 | 14.2402 | 17.9781 | 18.1546 | 19.0 | 0 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Ayham/distilbert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
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} | 8 | null | ---
license: apache-2.0
---
See https://github.com/k2-fsa/icefall/pull/380 |
Ayham/distilbert_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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} | 14 | 2022-06-27T06:33:04Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: token_final_tunned
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. -->
# token_final_tunned
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4670
- Precision: 0.8269
- Recall: 0.8442
- F1: 0.8355
- Accuracy: 0.8516
## 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 108 | 0.7286 | 0.6581 | 0.7117 | 0.6838 | 0.7272 |
| No log | 2.0 | 216 | 0.5497 | 0.7529 | 0.7823 | 0.7673 | 0.8053 |
| No log | 3.0 | 324 | 0.4884 | 0.7911 | 0.8145 | 0.8026 | 0.8277 |
| No log | 4.0 | 432 | 0.4723 | 0.8144 | 0.8278 | 0.8210 | 0.8408 |
| 0.6038 | 5.0 | 540 | 0.4597 | 0.8032 | 0.8315 | 0.8171 | 0.8428 |
| 0.6038 | 6.0 | 648 | 0.4583 | 0.8208 | 0.8322 | 0.8264 | 0.8480 |
| 0.6038 | 7.0 | 756 | 0.4641 | 0.8290 | 0.8442 | 0.8365 | 0.8520 |
| 0.6038 | 8.0 | 864 | 0.4670 | 0.8269 | 0.8442 | 0.8355 | 0.8516 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Ayham/roberta_distilgpt2_summarization_cnn_dailymail | [
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"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
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} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- big_patent
model-index:
- name: bigbird-base-finetuned-big_patent
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. -->
# bigbird-base-finetuned-big_patent
This model is a fine-tuned version of [robingeibel/bigbird-base-finetuned-big_patent](https://huggingface.co/robingeibel/bigbird-base-finetuned-big_patent) on the big_patent dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0686
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 1.1432 | 1.0 | 154482 | 1.0686 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Ayham/roberta_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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} | 6 | 2022-06-27T07:06:21Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- elsevier-oa-cc-by
model-index:
- name: roberta-base-finetuned-academic
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-academic
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the elsevier-oa-cc-by dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1158
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.1903 | 0.25 | 1025 | 2.0998 |
| 2.1752 | 0.5 | 2050 | 2.1186 |
| 2.1864 | 0.75 | 3075 | 2.1073 |
| 2.1874 | 1.0 | 4100 | 2.1177 |
| 2.1669 | 1.25 | 5125 | 2.1091 |
| 2.1859 | 1.5 | 6150 | 2.1212 |
| 2.1783 | 1.75 | 7175 | 2.1096 |
| 2.1734 | 2.0 | 8200 | 2.0998 |
| 2.1712 | 2.25 | 9225 | 2.0972 |
| 2.1812 | 2.5 | 10250 | 2.1051 |
| 2.1811 | 2.75 | 11275 | 2.1150 |
| 2.1826 | 3.0 | 12300 | 2.1097 |
| 2.172 | 3.25 | 13325 | 2.1115 |
| 2.1745 | 3.5 | 14350 | 2.1098 |
| 2.1758 | 3.75 | 15375 | 2.1101 |
| 2.1834 | 4.0 | 16400 | 2.1232 |
| 2.1836 | 4.25 | 17425 | 2.1052 |
| 2.1791 | 4.5 | 18450 | 2.1186 |
| 2.172 | 4.75 | 19475 | 2.1039 |
| 2.1797 | 5.0 | 20500 | 2.1015 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Ayham/xlnet_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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}
} | 13 | null | ---
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-nsc-final_1-google-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-nsc-final_1-google-colab
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- 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: 1000
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.10.3
|
Ayham/xlnet_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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} | 13 | null | ---
language:
- en
license: mit
tags:
- generated_from_trainer
model-index:
- name: reproduce-unsup-roberta-base-avg
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. -->
# reproduce-unsup-roberta-base-avg
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 512
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Ayran/DialoGPT-small-harry-potter-1-through-3 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9329136988570482
- name: Recall
type: recall
value: 0.9478290138000673
- name: F1
type: f1
value: 0.9403122130394858
- name: Accuracy
type: accuracy
value: 0.9855477718255137
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0663
- Precision: 0.9329
- Recall: 0.9478
- F1: 0.9403
- Accuracy: 0.9855
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0837 | 1.0 | 1756 | 0.0656 | 0.9151 | 0.9392 | 0.9270 | 0.9834 |
| 0.0388 | 2.0 | 3512 | 0.0619 | 0.9249 | 0.9475 | 0.9361 | 0.9855 |
| 0.0198 | 3.0 | 5268 | 0.0663 | 0.9329 | 0.9478 | 0.9403 | 0.9855 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Berzemu/Coco | [] | null | {
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} | 0 | null | ---
license: other
tags:
- generated_from_trainer
model-index:
- name: opt-125m-finetuned-wikitext2
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. -->
# opt-125m-finetuned-wikitext2
This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3409
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.4123 | 1.0 | 2370 | 3.3621 |
| 3.2096 | 2.0 | 4740 | 3.3452 |
| 3.0822 | 3.0 | 7110 | 3.3409 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Bharathdamu/wav2vec2-large-xls-r-300m-hindi3-colab | [] | null | {
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} | 0 | 2022-06-28T03:36:59Z | ---
tags:
- conversational
---
# Koishi Komeiji DialoGPT Model |
Bia18/Beatriz | [] | null | {
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}
} | 0 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 563.00 +/- 159.85
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 vebie91 -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 vebie91
```
## 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', 6),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 2000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Biasface/DDDC | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
} | 14 | 2022-06-28T04:42:09Z | ---
language:
- zh
library_name: nemo
datasets:
- aishell_2
thumbnail: null
tags:
- automatic-speech-recognition
- speech
- audio
- CTC
- Citrinet
- pytorch
- NeMo
- hf-asr-leaderboard
- Riva
license: cc-by-4.0
model-index:
- name: stt_zh_citrinet_1024_gamma_0_25
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Dev iOS
type: aishell_2
config: ios
split: dev
args:
language: zh
metrics:
- name: Dev CER
type: cer
value: 4.8
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Test iOS
type: aishell_2
config: ios
split: test
args:
language: zh
metrics:
- name: Test CER
type: cer
value: 5.1
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Dev Android
type: aishell_2
config: android
split: dev
args:
language: zh
metrics:
- name: Dev CER
type: cer
value: 5.2
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Test Android
type: aishell_2
config: android
split: test
args:
language: zh
metrics:
- name: Test CER
type: cer
value: 5.5
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Dev Mic
type: aishell_2
config: mic
split: dev
args:
language: zh
metrics:
- name: Dev CER
type: cer
value: 5.2
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Test Mic
type: aishell_2
config: mic
split: test
args:
language: zh
metrics:
- name: Test CER
type: cer
value: 5.5
---
# NVIDIA Streaming Citrinet 1024 (zh)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [](#datasets)
| [](#deployment-with-nvidia-riva) |
This model utilizes a character encoding scheme, and transcribes text in the standard character set that is provided in the Aishell-2 Mandard Corpus.
It is a non-autoregressive "large" variant of Citrinet, with around 140 million parameters.
See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#citrinet) for complete architecture details.
It is also compatible with NVIDIA Riva for [production-grade server deployments](#deployment-with-nvidia-riva).
## Usage
The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest PyTorch version.
```
pip install nemo_toolkit['all']
```
### Automatically instantiate the model
```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained("nvidia/stt_zh_citrinet_1024_gamma_0_25")
```
### Transcribing using Python
First, let's get a sample of spoken Mandarin Chinese.
Then simply do:
```
asr_model.transcribe(['<Path of audio file(s)>'])
```
### Transcribing many audio files
```shell
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py
pretrained_name="nvidia/stt_zh_citrinet_1024_gamma_0_25"
audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
```
### Input
This model accepts 16000 kHz Mono-channel Audio (wav files) as input.
### Output
This model provides transcribed speech as a string for a given audio sample.
## Model Architecture
Citrinet model is a non-autoregressive model [1] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. You may find more info on the detail of this model here: [Citrinet Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#citrinet).
## Training
The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/citrinet/citrinet_1024.yaml).
The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
### Datasets
All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of several thousand hours of English speech:
- AIShell 2
Note: older versions of the model may have trained on smaller set of datasets.
## Performance
The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.
| Version | Tokenizer | Vocabulary Size | Dev iOS | Test iOS | Dev Android | Test Android | Dev Mic | Test Mic | Train Dataset |
|---------|-----------|-----------------|---------|----------|-------------|--------------|---------|----------|---------------|
| 1.0.0 | Character | 5000+ | 4.8 | 5.1 | 5.2 | 5.5 | 5.2 | 5.5 | AIShell 2 |
| | | | | | | | | | |
| | | | | | | | | | |
While deploying with [NVIDIA Riva](https://developer.nvidia.com/riva), you can combine this model with external language models to further improve WER. The WER(%) of the latest model with different language modeling techniques are reported in the following table.
## Limitations
Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
## Deployment with NVIDIA Riva
For the best real-time accuracy, latency, and throughput, deploy the model with [NVIDIA Riva](https://developer.nvidia.com/riva), an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, at the edge, and embedded.
Additionally, Riva provides:
* World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours
* Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization
* Streaming speech recognition, Kubernetes compatible scaling, and Enterprise-grade support
Check out [Riva live demo](https://developer.nvidia.com/riva#demos).
## References
- [1] [Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition](https://arxiv.org/abs/2104.01721)
- [2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)
- [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) |
BigSalmon/GPTT | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
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}
} | 9 | 2022-06-28T07:57:21Z | ---
license: afl-3.0
---
Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task. https://colab.research.google.com/drive/17WyqwdoRNnzImeik6wTRE5uuj9QQnkXA#scrollTo=nYtUtmyDFAqP |
BigSalmon/MrLincoln | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
],
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}
} | 7 | null | ---
pipeline_tag: fill-mask
tags:
- online social networks
- twitter
- spanish
language: es
license: apache-2.0
widget:
- text: "Las <mask> causan hipoxia."
example_title: "Mask filling"
---
Model BERTuit as presented in the [BERTuit: Understanding Spanish language in Twitter through a native transformer](https://arxiv.org/abs/2204.03465) article.
Before tokenization replace user tags and urls with "\<usr\>" and "\<url\>" respectively.
Tokenize text with base class RoBERTaTokenizer. |
BigSalmon/MrLincoln125MNeo | [
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
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}
} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: led-large-16384-finetuned-big_patent
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. -->
# led-large-16384-finetuned-big_patent
This model is a fine-tuned version of [robingeibel/led-large-16384-finetuned-big_patent](https://huggingface.co/robingeibel/led-large-16384-finetuned-big_patent) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.22.1
- TensorFlow 2.8.2
- Datasets 2.5.1
- Tokenizers 0.12.1
|
BigSalmon/Points | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
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"GPT2LMHeadModel"
],
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}
} | 13 | null | ---
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/959389610978742273/jfOMGQ1B_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">Greg Jackson</div>
<div style="text-align: center; font-size: 14px;">@g__j</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 Greg Jackson.
| Data | Greg Jackson |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 187 |
| Short tweets | 179 |
| Tweets kept | 2884 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2sl53oes/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 @g__j's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/stwh74do) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/stwh74do/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/g__j')
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)
|
BinksSachary/DialoGPT-small-shaxx | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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}
} | 12 | null | ---
license: apache-2.0
---
# Spanish Bert2Bert fine-tuned on Quora question pairs dataset
Fine-tuning of a [question generator model](https://huggingface.co/mrm8488/bert2bert-spanish-question-generation) into a paraphraser model using a poor-man's translation of the Quora question pairs dataset. It basically rephrases questions into similar questions. Non interrogative sentences are not handled very well.
- Original models: [mrm8488/bert2bert-spanish-question-generation](https://huggingface.co/mrm8488/bert2bert-spanish-question-generation?text=Manuel+vive+en+Murcia%2C+Espa%C3%B1a), which is based on [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) (?).
- Custom database: "Poor-man's" translation of duplicated questions in Quora (translated with [Helsinki-NLP/opus-mt-en-es](https://huggingface.co/Helsinki-NLP/opus-mt-en-es))
|
Brokette/projetCS | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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}
} | 4 | null | ---
license: mit
language:
- en
task_categories:
- fill-mask
task_ids:
- masked-language-modeling
pipeline_tag: fill-mask
widget:
- text: "M67 is one of the most studied [MASK] clusters."
example_title: "M67"
- text: "A solar twin is a star with [MASK] parameters and chemical composition very similar to our Sun."
example_title: "solar twin"
- text: "The dynamical evolution of planets close to their star is affected by [MASK] effects"
example_title: "dynamical evolution"
- text: "The Kepler satellite collected high-precision long-term and continuous light [MASK] for more than 100,000 solar-type stars"
example_title: "Kepler satellite"
- text: "The Local Group is composed of the Milky Way, the [MASK] Galaxy, and numerous smaller satellite galaxies."
example_title: "Local Group"
- text: "Cepheid variables are used to determine the [MASK] to galaxies in the local universe."
example_title: "Cepheid"
- text: "Jets are created and sustained by [MASK] of matter onto a compact massive object."
example_title: "Jets"
- text: "A single star of one solar mass will evolve into a [MASK] dwarf."
example_title: "single star"
- text: "The Very Large Array observes the sky at [MASK] wavelengths."
example_title: "Very Large Array"
- text: "Elements heavier than [MASK] are generated in supernovae explosions."
example_title: "Elements"
- text: "Spitzer was the first [MASK] to fly in an Earth-trailing orbit."
example_title: "Spitzer"
- text: "Galaxy [MASK] can occur when two (or more) galaxies collide"
example_title: "galaxies collide"
- text: "Dark [MASK] is a hypothetical form of matter thought to account for approximately 85% of the matter in the universe."
example_title: "hypothetical matter"
- text: "The cosmic microwave background (CMB, CMBR), in Big Bang cosmology, is electromagnetic radiation which is a remnant from an early stage of the [MASK]."
example_title: "CMBR"
- text: "The Local Group of galaxies is pulled toward The Great [MASK]."
example_title: "galaxies pulled"
- text: "The Moon is the only [MASK] of the Earth."
example_title: "Moon"
- text: "Galaxies are categorized according to their visual morphology as [MASK], spiral, or irregular."
example_title: "morphology"
- text: "Stars are made mostly of [MASK]."
example_title: "Stars moslyl"
- text: "Comet tails are created as comets approach the [MASK]."
example_title: "Comet tails"
- text: "Pluto is a dwarf [MASK] in the Kuiper Belt."
example_title: "Pluto"
- text: "The Large and Small Magellanic Clouds are irregular [MASK] galaxies and are two satellite galaxies of the Milky Way."
example_title: "Magellanic Clouds"
- text: "The Milky Way has a [MASK] black hole, Sagittarius A*, at its center."
example_title: "Milky Way"
- text: "Andromeda is the nearest large [MASK] to the Milky Way and is roughly its equal in mass."
example_title: "Andromeda"
- text: "The [MASK] medium is the gas and dust between stars."
example_title: "gast and dust"
---
# ***astroBERT: a language model for astrophysics***
This public repository contains the work of the [NASA/ADS](https://ui.adsabs.harvard.edu/) on building an NLP language model tailored to astrophysics, along with tutorials and miscellaneous related files.
This model is **cased** (it treats `ads` and `ADS` differently).
## astroBERT models
0. **Base model**: Pretrained model on English language using a masked language modeling (MLM) and next sentence prediction (NSP) objective. It was introduced in [this paper at ADASS 2021](https://arxiv.org/abs/2112.00590) and made public at ADASS 2022.
1. **NER-DEAL model**: This model adds a token classification head to the base model finetuned on the [DEAL@WIESP2022 named entity recognition](https://ui.adsabs.harvard.edu/WIESP/2022/SharedTasks) task. Must be loaded from the `revision='NER-DEAL'` branch (see tutorial 2).
2. **SciX Categorizer**: This model was finetuned to classify text into one of 7 categories of interest to SciX (Astronomy, Heliophysics, Planetary Science, Earth Science, NASA-funded Biophysics, Other Physics, Other, Text Garbage).
### Tutorials
0. [generate text embedding (for downstream tasks)](https://nbviewer.org/urls/huggingface.co/adsabs/astroBERT/raw/main/Tutorials/0_Embeddings.ipynb)
1. [use astroBERT for the Fill-Mask task](https://nbviewer.org/urls/huggingface.co/adsabs/astroBERT/raw/main/Tutorials/1_Fill-Mask.ipynb)
2. [make NER-DEAL predictions](https://nbviewer.org/urls/huggingface.co/adsabs/astroBERT/raw/main/Tutorials/2_NER_DEAL.ipynb)
3. [categorize texts for SciX](https://nbviewer.org/urls/huggingface.co/adsabs/astroBERT/raw/main/Tutorials/3_SciX_Categorizer.ipynb)
### BibTeX
```bibtex
@ARTICLE{2021arXiv211200590G,
author = {{Grezes}, Felix and {Blanco-Cuaresma}, Sergi and {Accomazzi}, Alberto and {Kurtz}, Michael J. and {Shapurian}, Golnaz and {Henneken}, Edwin and {Grant}, Carolyn S. and {Thompson}, Donna M. and {Chyla}, Roman and {McDonald}, Stephen and {Hostetler}, Timothy W. and {Templeton}, Matthew R. and {Lockhart}, Kelly E. and {Martinovic}, Nemanja and {Chen}, Shinyi and {Tanner}, Chris and {Protopapas}, Pavlos},
title = "{Building astroBERT, a language model for Astronomy \& Astrophysics}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language, Astrophysics - Instrumentation and Methods for Astrophysics},
year = 2021,
month = dec,
eid = {arXiv:2112.00590},
pages = {arXiv:2112.00590},
archivePrefix = {arXiv},
eprint = {2112.00590},
primaryClass = {cs.CL},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv211200590G},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
``` |
Brykee/DialoGPT-medium-Morty | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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"length_penalty": null,
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"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 10 | null | ---
license: other
tags:
- generated_from_trainer
model-index:
- name: opt-125m-wikitext2
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. -->
# opt-125m-wikitext2
This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3409
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.4123 | 1.0 | 2370 | 3.3621 |
| 3.2096 | 2.0 | 4740 | 3.3452 |
| 3.0822 | 3.0 | 7110 | 3.3409 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Buntan/xlm-roberta-base-finetuned-marc-en | [] | null | {
"architectures": null,
"model_type": null,
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},
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},
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},
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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}
}
} | 0 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.54 +/- 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="/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"])
```
|
CalvinHuang/mt5-small-finetuned-amazon-en-es | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 16 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 671.50 +/- 145.81
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 PoloHuggingface -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 PoloHuggingface
```
## 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)])
```
|
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