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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-11 06:30:11
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 555
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
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dadiiiiiqaweq/test1
|
dadiiiiiqaweq
| 2022-11-11T09:21:35Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-11-11T09:21:35Z |
---
license: creativeml-openrail-m
---
|
Terence3927/testpyramidsrnd
|
Terence3927
| 2022-11-11T08:56:50Z | 7 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2022-11-11T08:56:41Z |
---
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: Terence3927/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
sd-concepts-library/obama-based-on-xi
|
sd-concepts-library
| 2022-11-11T08:45:43Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-10-30T14:45:22Z |
---
license: mit
---
### obama_based_on_xi on Stable Diffusion
This is the `<obama>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
reza-aditya/q-Taxi-v3
|
reza-aditya
| 2022-11-11T08:32:14Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-11T08:32:06Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="reza-aditya/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"])
```
|
geek1024/prompt-extend
|
geek1024
| 2022-11-11T08:09:54Z | 106 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-11T07:21:00Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: prompt-extend
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. -->
# prompt-extend
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1502
## 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: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.3823 | 0.35 | 100 | 4.2083 |
| 3.72 | 0.69 | 200 | 3.2991 |
| 3.1185 | 1.04 | 300 | 2.8394 |
| 2.7284 | 1.39 | 400 | 2.5546 |
| 2.4932 | 1.74 | 500 | 2.3679 |
| 2.3408 | 2.08 | 600 | 2.2430 |
| 2.1997 | 2.43 | 700 | 2.1748 |
| 2.1631 | 2.78 | 800 | 2.1502 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
reza-aditya/q-FrozenLake-v1-4x4-noSlippery
|
reza-aditya
| 2022-11-11T07:52:03Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-11T07:18:21Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
Colab: https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit2/unit2.ipynb
## Usage
```python
model = load_from_hub(repo_id="reza-aditya/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
BearlyWorkingYT/OPT-125M-Kaggle-Creepypasta
|
BearlyWorkingYT
| 2022-11-11T07:39:02Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"opt",
"text-generation",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-11T07:19:36Z |
---
license: other
widget:
- text: "There was a ghost"
example_title: "First Prompt used in video"
- text: "I was playing Terraria but then"
example_title: "Second prompt used in video"
inference:
parameters:
temperature: 0.6
repetition_penalty: 1.15
min_length: 128
max_length: 468
---
This is the model trained for this video:
https://www.youtube.com/watch?v=OEPL5Tm3mmQ
Due to hardware limitations, I trained this model with only a batch size of 2. (I know this isn't ideal).
The quality of the model may be affected.
After training was complete, the best model according to a hold-out set was used.
This model was trained using a filtered version of this dataset:
https://www.kaggle.com/datasets/thomaskonstantin/3500-popular-creepypastas
This dataset had a lot of blank entries and missing text.
Please subscribe to my YouTube Channel for bad quality videos and poorly trained models.
https://www.youtube.com/channel/UCLXxfueCPZRZnyGFWJ07uqA
|
amitjohn007/roberta-base-finetuned-squad
|
amitjohn007
| 2022-11-11T07:26:14Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"question-answering",
"generated_from_keras_callback",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-11-11T06:35:49Z |
---
license: cc-by-4.0
tags:
- generated_from_keras_callback
model-index:
- name: amitjohn007/roberta-base-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# amitjohn007/roberta-base-finetuned-squad
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4173
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16608, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.7396 | 0 |
| 0.5461 | 1 |
| 0.4173 | 2 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.2
|
NineArtsDragon/bert-finetuned-ner2
|
NineArtsDragon
| 2022-11-11T05:03:39Z | 124 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-11T04:08:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner2
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-ner2
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: 0.0015
- Precision: 0.9709
- Recall: 0.9904
- F1: 0.9806
- Accuracy: 0.9995
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 108 | 0.0035 | 0.9565 | 0.9876 | 0.9718 | 0.9990 |
| No log | 2.0 | 216 | 0.0019 | 0.9628 | 0.9904 | 0.9764 | 0.9994 |
| No log | 3.0 | 324 | 0.0016 | 0.9764 | 0.9904 | 0.9834 | 0.9995 |
| No log | 4.0 | 432 | 0.0017 | 0.9736 | 0.9866 | 0.9801 | 0.9995 |
| 0.0166 | 5.0 | 540 | 0.0015 | 0.9709 | 0.9904 | 0.9806 | 0.9995 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Dev003x/IvaneNovelForTraining
|
Dev003x
| 2022-11-11T03:43:05Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2022-10-26T17:30:15Z |
---
license: bigscience-openrail-m
---
|
kakaobrain/coyo-align-b7-base
|
kakaobrain
| 2022-11-11T03:42:56Z | 0 | 0 | null |
[
"align",
"clip",
"en",
"dataset:kakaobrain/coyo-700m",
"arxiv:2102.05918",
"license:apache-2.0",
"region:us"
] | null | 2022-11-09T07:13:12Z |
---
language:
- en
tags:
- align
- clip
license: apache-2.0
datasets:
- kakaobrain/coyo-700m
inference: false
---
# Model Details
This is an unofficial implementation of [ALIGN](https://arxiv.org/abs/2102.05918) trained on [COYO-700M](https://github.com/kakaobrain/coyo-dataset). The official ALIGN is trained on its dataset of 1.8B samples. That dataset is not released to the public. Instead, we trained our implementation of ALIGN model on [COYO-700M](https://github.com/kakaobrain/coyo-dataset).
It's developed by Kakao Brain to validate the performance of COYO-700M dataset on a large-scale model.
The training took about 8 days on TPU V3-512.
## Model Date
April 2022
## Model Type
This is dual encoder model where
- image encoder is using EfficientNet-B7 architecture
- text encoder is using BERT-base architecture
# Training data
This model is trained on [COYO-700M](https://github.com/kakaobrain/coyo-dataset) dataset.
# Evaluation results
| | Dataset | ImageNet | Flickr30k | | MsCOCO | |
|----------------------------------|:----------:|:--------:|:---------:|:-------:|:-------:|:-------:|
| | | KNN | I2T R@1 | T2I R@1 | I2T R@1 | T2I R@1 |
| ALIGN-L2-Large(Google) | ALIGN 1.8B | 76.4 | 88.6 | 75.7 | 58.6 | 45.6 |
| ALIGN-B7-Base(Google) | ALIGN 1.8B | 69.3 | - | - | 55.4 | 41.7 |
| COYO-ALIGN-B7-Base(Kakao Brain) | COYO-700M | 68.6 | 88.1 | 73.2 | 61.2 | 43.1 |
|
alextoyment/ppo-LunarLander-v2
|
alextoyment
| 2022-11-11T03:03:46Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-11T03:03:14Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 165.65 +/- 21.36
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
42MARU/ko-ctc-kenlm-spelling-only-wiki
|
42MARU
| 2022-11-11T03:01:14Z | 0 | 1 |
kenlm
|
[
"kenlm",
"audio",
"automatic-speech-recognition",
"text2text-generation",
"ko",
"dataset:korean-wiki",
"license:apache-2.0",
"region:us"
] |
text2text-generation
| 2022-11-10T02:15:35Z |
---
language:
- ko # Example: fr
license: apache-2.0 # Example: apache-2.0 or any license from https://hf.co/docs/hub/repositories-licenses
library_name: kenlm # Optional. Example: keras or any library from https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Libraries.ts
tags:
- audio
- automatic-speech-recognition
- text2text-generation
datasets:
- korean-wiki
---
# ko-ctc-kenlm-spelling-only-wiki
## Table of Contents
- [ko-ctc-kenlm-spelling-only-wiki](#ko-ctc-kenlm-spelling-only-wiki)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
## Model Details
- **Model Description** <br />
- 음향 모델을 위한 N-gram Base의 LM으로 자소별 단어기반으로 만들어졌으며, KenLM으로 학습되었습니다. 해당 모델은 [ko-spelling-wav2vec2-conformer-del-1s](https://huggingface.co/42MARU/ko-spelling-wav2vec2-conformer-del-1s)과 사용하십시오. <br />
- HuggingFace Transformers Style로 불러와 사용할 수 있도록 처리했습니다. <br />
- pyctcdecode lib을 이용해서도 바로 사용가능합니다. <br />
- data는 wiki korean을 사용했습니다. <br />
spelling vocab data에 없는 문장은 전부 제거하여, 오히려 LM으로 Outlier가 발생할 소요를 최소화 시켰습니다. <br />
해당 모델은 **철자전사** 기준의 데이터로 학습된 모델입니다. (숫자와 영어는 각 표기법을 따름) <br />
- **Developed by:** TADev (@lIlBrother)
- **Language(s):** Korean
- **License:** apache-2.0
## How to Get Started With the Model
```python
import librosa
from pyctcdecode import build_ctcdecoder
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForCTC,
AutoTokenizer,
Wav2Vec2ProcessorWithLM,
)
from transformers.pipelines import AutomaticSpeechRecognitionPipeline
audio_path = ""
# 모델과 토크나이저, 예측을 위한 각 모듈들을 불러옵니다.
model = AutoModelForCTC.from_pretrained("42MARU/ko-spelling-wav2vec2-conformer-del-1s")
feature_extractor = AutoFeatureExtractor.from_pretrained("42MARU/ko-spelling-wav2vec2-conformer-del-1s")
tokenizer = AutoTokenizer.from_pretrained("42MARU/ko-spelling-wav2vec2-conformer-del-1s")
processor = Wav2Vec2ProcessorWithLM("42MARU/ko-ctc-kenlm-spelling-only-wiki")
# 실제 예측을 위한 파이프라인에 정의된 모듈들을 삽입.
asr_pipeline = AutomaticSpeechRecognitionPipeline(
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
decoder=processor.decoder,
device=-1,
)
# 음성파일을 불러오고 beamsearch 파라미터를 특정하여 예측을 수행합니다.
raw_data, _ = librosa.load(audio_path, sr=16000)
kwargs = {"decoder_kwargs": {"beam_width": 100}}
pred = asr_pipeline(inputs=raw_data, **kwargs)["text"]
# 모델이 자소 분리 유니코드 텍스트로 나오므로, 일반 String으로 변환해줄 필요가 있습니다.
result = unicodedata.normalize("NFC", pred)
print(result)
# 안녕하세요 123 테스트입니다.
```
|
42MARU/ko-ctc-kenlm-42maru-only-wiki
|
42MARU
| 2022-11-11T02:44:16Z | 0 | 0 |
kenlm
|
[
"kenlm",
"audio",
"automatic-speech-recognition",
"text2text-generation",
"ko",
"dataset:korean-wiki",
"license:apache-2.0",
"region:us"
] |
text2text-generation
| 2022-11-10T02:15:58Z |
---
language:
- ko # Example: fr
license: apache-2.0 # Example: apache-2.0 or any license from https://hf.co/docs/hub/repositories-licenses
library_name: kenlm # Optional. Example: keras or any library from https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Libraries.ts
tags:
- audio
- automatic-speech-recognition
- text2text-generation
datasets:
- korean-wiki
---
# ko-ctc-kenlm-42maru-only-wiki
## Table of Contents
- [ko-ctc-kenlm-42maru-only-wiki](#ko-ctc-kenlm-42maru-only-wiki)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
## Model Details
- **Model Description** <br />
- 음향 모델을 위한 N-gram Base의 LM으로 자소별 단어기반으로 만들어졌으며, KenLM으로 학습되었습니다. 해당 모델은 [ko-42maru-wav2vec2-conformer-del-1s](https://huggingface.co/42MARU/ko-42maru-wav2vec2-conformer-del-1s)과 사용하십시오. <br />
- HuggingFace Transformers Style로 불러와 사용할 수 있도록 처리했습니다. <br />
- pyctcdecode lib을 이용해서도 바로 사용가능합니다. <br />
- data는 wiki korean을 사용했습니다. <br />
42maru vocab data에 없는 문장은 전부 제거하여, 오히려 LM으로 Outlier가 발생할 소요를 최소화 시켰습니다. <br />
해당 모델은 **음성전사를 자체 커스텀한 42maru** 기준의 데이터로 학습된 모델입니다. (숫자와 영어는 한글 표기법을 따름) <br />
- **Developed by:** TADev (@lIlBrother)
- **Language(s):** Korean
- **License:** apache-2.0
## How to Get Started With the Model
```python
import librosa
from pyctcdecode import build_ctcdecoder
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForCTC,
AutoTokenizer,
Wav2Vec2ProcessorWithLM,
)
from transformers.pipelines import AutomaticSpeechRecognitionPipeline
audio_path = ""
# 모델과 토크나이저, 예측을 위한 각 모듈들을 불러옵니다.
model = AutoModelForCTC.from_pretrained("42MARU/ko-42maru-wav2vec2-conformer-del-1s")
feature_extractor = AutoFeatureExtractor.from_pretrained("42MARU/ko-42maru-wav2vec2-conformer-del-1s")
tokenizer = AutoTokenizer.from_pretrained("42MARU/ko-42maru-wav2vec2-conformer-del-1s")
processor = Wav2Vec2ProcessorWithLM.from_pretrained("42MARU/ko-ctc-kenlm-42maru-only-wiki")
# 실제 예측을 위한 파이프라인에 정의된 모듈들을 삽입.
asr_pipeline = AutomaticSpeechRecognitionPipeline(
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
decoder=processor.decoder,
device=-1,
)
# 음성파일을 불러오고 beamsearch 파라미터를 특정하여 예측을 수행합니다.
raw_data, _ = librosa.load(audio_path, sr=16000)
kwargs = {"decoder_kwargs": {"beam_width": 100}}
pred = asr_pipeline(inputs=raw_data, **kwargs)["text"]
# 모델이 자소 분리 유니코드 텍스트로 나오므로, 일반 String으로 변환해줄 필요가 있습니다.
result = unicodedata.normalize("NFC", pred)
print(result)
# 안녕하세요 하나둘셋 테스트입니다.
```
|
jjjj-j/distilbert-base-uncased-finetuned-cola
|
jjjj-j
| 2022-11-11T02:39:27Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-06T22:10:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0884
- Matthews Correlation: 0.2439
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| No log | 1.0 | 23 | 1.1535 | 0.0 |
| No log | 2.0 | 46 | 1.1430 | 0.0 |
| No log | 3.0 | 69 | 1.1438 | 0.0 |
| No log | 4.0 | 92 | 1.0995 | 0.1890 |
| No log | 5.0 | 115 | 1.1155 | 0.0509 |
| No log | 6.0 | 138 | 1.0881 | 0.1554 |
| No log | 7.0 | 161 | 1.1095 | 0.2136 |
| No log | 8.0 | 184 | 1.0884 | 0.2439 |
| No log | 9.0 | 207 | 1.1145 | 0.2155 |
| No log | 10.0 | 230 | 1.1092 | 0.1897 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
shafin/chemical-bert-uncased-finetuned-cust-c1-cust
|
shafin
| 2022-11-11T02:39:18Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-11-11T00:16:08Z |
---
tags:
- generated_from_trainer
model-index:
- name: chemical-bert-uncased-finetuned-cust-c1-cust
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. -->
# chemical-bert-uncased-finetuned-cust-c1-cust
This model is a fine-tuned version of [shafin/chemical-bert-uncased-finetuned-cust](https://huggingface.co/shafin/chemical-bert-uncased-finetuned-cust) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5420
## 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: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.96 | 1.0 | 63 | 1.6719 |
| 1.7095 | 2.0 | 126 | 1.5305 |
| 1.5634 | 3.0 | 189 | 1.2972 |
| 1.4785 | 4.0 | 252 | 1.3354 |
| 1.3991 | 5.0 | 315 | 1.2542 |
| 1.3482 | 6.0 | 378 | 1.1870 |
| 1.2984 | 7.0 | 441 | 1.1844 |
| 1.2589 | 8.0 | 504 | 1.1262 |
| 1.1762 | 9.0 | 567 | 1.1176 |
| 1.1724 | 10.0 | 630 | 1.0312 |
| 1.1222 | 11.0 | 693 | 1.0113 |
| 1.1021 | 12.0 | 756 | 1.0518 |
| 1.0646 | 13.0 | 819 | 1.0433 |
| 1.0273 | 14.0 | 882 | 0.9634 |
| 1.0187 | 15.0 | 945 | 0.9299 |
| 0.9854 | 16.0 | 1008 | 0.9458 |
| 0.9799 | 17.0 | 1071 | 0.9733 |
| 0.95 | 18.0 | 1134 | 0.9169 |
| 0.934 | 19.0 | 1197 | 0.9246 |
| 0.907 | 20.0 | 1260 | 0.8939 |
| 0.8974 | 21.0 | 1323 | 0.8575 |
| 0.8749 | 22.0 | 1386 | 0.8513 |
| 0.8526 | 23.0 | 1449 | 0.8089 |
| 0.8359 | 24.0 | 1512 | 0.8600 |
| 0.8292 | 25.0 | 1575 | 0.8517 |
| 0.8263 | 26.0 | 1638 | 0.8293 |
| 0.8033 | 27.0 | 1701 | 0.7747 |
| 0.7999 | 28.0 | 1764 | 0.8169 |
| 0.7778 | 29.0 | 1827 | 0.7981 |
| 0.7574 | 30.0 | 1890 | 0.7457 |
| 0.7581 | 31.0 | 1953 | 0.7504 |
| 0.7404 | 32.0 | 2016 | 0.7637 |
| 0.7332 | 33.0 | 2079 | 0.7902 |
| 0.7314 | 34.0 | 2142 | 0.7185 |
| 0.7209 | 35.0 | 2205 | 0.7534 |
| 0.6902 | 36.0 | 2268 | 0.7334 |
| 0.6973 | 37.0 | 2331 | 0.7069 |
| 0.687 | 38.0 | 2394 | 0.6820 |
| 0.6658 | 39.0 | 2457 | 0.7155 |
| 0.6697 | 40.0 | 2520 | 0.7149 |
| 0.6584 | 41.0 | 2583 | 0.7413 |
| 0.6638 | 42.0 | 2646 | 0.7245 |
| 0.6282 | 43.0 | 2709 | 0.7177 |
| 0.6418 | 44.0 | 2772 | 0.6653 |
| 0.6323 | 45.0 | 2835 | 0.7715 |
| 0.6256 | 46.0 | 2898 | 0.7269 |
| 0.6109 | 47.0 | 2961 | 0.6744 |
| 0.6133 | 48.0 | 3024 | 0.6816 |
| 0.595 | 49.0 | 3087 | 0.6969 |
| 0.6058 | 50.0 | 3150 | 0.6965 |
| 0.5961 | 51.0 | 3213 | 0.6988 |
| 0.587 | 52.0 | 3276 | 0.6727 |
| 0.5861 | 53.0 | 3339 | 0.6327 |
| 0.5758 | 54.0 | 3402 | 0.6538 |
| 0.5692 | 55.0 | 3465 | 0.6612 |
| 0.567 | 56.0 | 3528 | 0.5989 |
| 0.5514 | 57.0 | 3591 | 0.6776 |
| 0.5526 | 58.0 | 3654 | 0.6440 |
| 0.556 | 59.0 | 3717 | 0.6682 |
| 0.5476 | 60.0 | 3780 | 0.6254 |
| 0.536 | 61.0 | 3843 | 0.6239 |
| 0.526 | 62.0 | 3906 | 0.6606 |
| 0.532 | 63.0 | 3969 | 0.6565 |
| 0.5189 | 64.0 | 4032 | 0.6586 |
| 0.5075 | 65.0 | 4095 | 0.6286 |
| 0.5131 | 66.0 | 4158 | 0.6646 |
| 0.498 | 67.0 | 4221 | 0.6486 |
| 0.4979 | 68.0 | 4284 | 0.6313 |
| 0.4885 | 69.0 | 4347 | 0.6419 |
| 0.4875 | 70.0 | 4410 | 0.6313 |
| 0.4904 | 71.0 | 4473 | 0.6602 |
| 0.4712 | 72.0 | 4536 | 0.6200 |
| 0.4798 | 73.0 | 4599 | 0.5912 |
| 0.4802 | 74.0 | 4662 | 0.6001 |
| 0.4704 | 75.0 | 4725 | 0.6303 |
| 0.4709 | 76.0 | 4788 | 0.5871 |
| 0.465 | 77.0 | 4851 | 0.6344 |
| 0.4651 | 78.0 | 4914 | 0.6030 |
| 0.4501 | 79.0 | 4977 | 0.5998 |
| 0.4584 | 80.0 | 5040 | 0.5926 |
| 0.4651 | 81.0 | 5103 | 0.6134 |
| 0.438 | 82.0 | 5166 | 0.6254 |
| 0.448 | 83.0 | 5229 | 0.6260 |
| 0.4295 | 84.0 | 5292 | 0.5866 |
| 0.434 | 85.0 | 5355 | 0.5740 |
| 0.4261 | 86.0 | 5418 | 0.5691 |
| 0.4312 | 87.0 | 5481 | 0.6243 |
| 0.4289 | 88.0 | 5544 | 0.5781 |
| 0.4255 | 89.0 | 5607 | 0.6226 |
| 0.4254 | 90.0 | 5670 | 0.5538 |
| 0.4231 | 91.0 | 5733 | 0.5874 |
| 0.4107 | 92.0 | 5796 | 0.6054 |
| 0.4082 | 93.0 | 5859 | 0.5898 |
| 0.4144 | 94.0 | 5922 | 0.5826 |
| 0.4225 | 95.0 | 5985 | 0.5501 |
| 0.3964 | 96.0 | 6048 | 0.5886 |
| 0.3972 | 97.0 | 6111 | 0.5831 |
| 0.4165 | 98.0 | 6174 | 0.5164 |
| 0.4024 | 99.0 | 6237 | 0.5714 |
| 0.4013 | 100.0 | 6300 | 0.5734 |
| 0.3933 | 101.0 | 6363 | 0.5727 |
| 0.3821 | 102.0 | 6426 | 0.5985 |
| 0.3904 | 103.0 | 6489 | 0.5571 |
| 0.3965 | 104.0 | 6552 | 0.5837 |
| 0.3789 | 105.0 | 6615 | 0.5989 |
| 0.3733 | 106.0 | 6678 | 0.5405 |
| 0.3907 | 107.0 | 6741 | 0.6059 |
| 0.3794 | 108.0 | 6804 | 0.5602 |
| 0.3689 | 109.0 | 6867 | 0.5590 |
| 0.3603 | 110.0 | 6930 | 0.5886 |
| 0.3747 | 111.0 | 6993 | 0.5294 |
| 0.3667 | 112.0 | 7056 | 0.5759 |
| 0.3754 | 113.0 | 7119 | 0.5821 |
| 0.3676 | 114.0 | 7182 | 0.5653 |
| 0.3524 | 115.0 | 7245 | 0.5537 |
| 0.3624 | 116.0 | 7308 | 0.5523 |
| 0.3527 | 117.0 | 7371 | 0.5799 |
| 0.3588 | 118.0 | 7434 | 0.6346 |
| 0.3539 | 119.0 | 7497 | 0.5116 |
| 0.3553 | 120.0 | 7560 | 0.5716 |
| 0.3483 | 121.0 | 7623 | 0.5721 |
| 0.3625 | 122.0 | 7686 | 0.5393 |
| 0.3354 | 123.0 | 7749 | 0.5800 |
| 0.3392 | 124.0 | 7812 | 0.5389 |
| 0.344 | 125.0 | 7875 | 0.5455 |
| 0.3451 | 126.0 | 7938 | 0.5428 |
| 0.3374 | 127.0 | 8001 | 0.5580 |
| 0.3428 | 128.0 | 8064 | 0.5339 |
| 0.3386 | 129.0 | 8127 | 0.5447 |
| 0.3318 | 130.0 | 8190 | 0.5738 |
| 0.3388 | 131.0 | 8253 | 0.5667 |
| 0.3335 | 132.0 | 8316 | 0.5407 |
| 0.3383 | 133.0 | 8379 | 0.5679 |
| 0.3299 | 134.0 | 8442 | 0.5846 |
| 0.327 | 135.0 | 8505 | 0.5511 |
| 0.3354 | 136.0 | 8568 | 0.5649 |
| 0.32 | 137.0 | 8631 | 0.5358 |
| 0.3265 | 138.0 | 8694 | 0.5528 |
| 0.319 | 139.0 | 8757 | 0.5926 |
| 0.3304 | 140.0 | 8820 | 0.5531 |
| 0.3191 | 141.0 | 8883 | 0.5379 |
| 0.3298 | 142.0 | 8946 | 0.5468 |
| 0.3134 | 143.0 | 9009 | 0.5623 |
| 0.3186 | 144.0 | 9072 | 0.5162 |
| 0.3179 | 145.0 | 9135 | 0.5570 |
| 0.3175 | 146.0 | 9198 | 0.5379 |
| 0.3051 | 147.0 | 9261 | 0.5437 |
| 0.312 | 148.0 | 9324 | 0.5301 |
| 0.3093 | 149.0 | 9387 | 0.5393 |
| 0.3227 | 150.0 | 9450 | 0.5531 |
| 0.3125 | 151.0 | 9513 | 0.5794 |
| 0.3162 | 152.0 | 9576 | 0.5677 |
| 0.3006 | 153.0 | 9639 | 0.5668 |
| 0.3011 | 154.0 | 9702 | 0.5797 |
| 0.3208 | 155.0 | 9765 | 0.5450 |
| 0.3048 | 156.0 | 9828 | 0.5465 |
| 0.3092 | 157.0 | 9891 | 0.5358 |
| 0.3125 | 158.0 | 9954 | 0.5043 |
| 0.3083 | 159.0 | 10017 | 0.5321 |
| 0.3 | 160.0 | 10080 | 0.5526 |
| 0.2968 | 161.0 | 10143 | 0.5324 |
| 0.3068 | 162.0 | 10206 | 0.5471 |
| 0.3129 | 163.0 | 10269 | 0.5575 |
| 0.3061 | 164.0 | 10332 | 0.5796 |
| 0.2943 | 165.0 | 10395 | 0.5544 |
| 0.2967 | 166.0 | 10458 | 0.5422 |
| 0.2959 | 167.0 | 10521 | 0.5149 |
| 0.2987 | 168.0 | 10584 | 0.5685 |
| 0.3045 | 169.0 | 10647 | 0.5176 |
| 0.2975 | 170.0 | 10710 | 0.5044 |
| 0.2948 | 171.0 | 10773 | 0.5264 |
| 0.3 | 172.0 | 10836 | 0.5174 |
| 0.2967 | 173.0 | 10899 | 0.5658 |
| 0.2873 | 174.0 | 10962 | 0.4988 |
| 0.2939 | 175.0 | 11025 | 0.5512 |
| 0.2954 | 176.0 | 11088 | 0.5139 |
| 0.301 | 177.0 | 11151 | 0.6007 |
| 0.2948 | 178.0 | 11214 | 0.5167 |
| 0.2898 | 179.0 | 11277 | 0.5443 |
| 0.2869 | 180.0 | 11340 | 0.5544 |
| 0.2973 | 181.0 | 11403 | 0.5644 |
| 0.2985 | 182.0 | 11466 | 0.5153 |
| 0.2904 | 183.0 | 11529 | 0.5561 |
| 0.2872 | 184.0 | 11592 | 0.5610 |
| 0.2894 | 185.0 | 11655 | 0.5511 |
| 0.297 | 186.0 | 11718 | 0.5408 |
| 0.2904 | 187.0 | 11781 | 0.5574 |
| 0.2818 | 188.0 | 11844 | 0.5182 |
| 0.2873 | 189.0 | 11907 | 0.5425 |
| 0.2973 | 190.0 | 11970 | 0.5198 |
| 0.2913 | 191.0 | 12033 | 0.5119 |
| 0.2931 | 192.0 | 12096 | 0.5585 |
| 0.2859 | 193.0 | 12159 | 0.5368 |
| 0.2853 | 194.0 | 12222 | 0.5274 |
| 0.294 | 195.0 | 12285 | 0.5685 |
| 0.2885 | 196.0 | 12348 | 0.5581 |
| 0.295 | 197.0 | 12411 | 0.4987 |
| 0.2807 | 198.0 | 12474 | 0.5168 |
| 0.289 | 199.0 | 12537 | 0.5284 |
| 0.2893 | 200.0 | 12600 | 0.5420 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
huggingtweets/queenofbithynia
|
huggingtweets
| 2022-11-11T00:37:45Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/queenofbithynia/1668126937466/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1010627358879932416/0xVVQg3X_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">the needle-felted head of joyce carol oates</div>
<div style="text-align: center; font-size: 14px;">@queenofbithynia</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 the needle-felted head of joyce carol oates.
| Data | the needle-felted head of joyce carol oates |
| --- | --- |
| Tweets downloaded | 3186 |
| Retweets | 1 |
| Short tweets | 64 |
| Tweets kept | 3121 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1pdmfti8/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 @queenofbithynia's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/hmbsp4tx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/hmbsp4tx/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/queenofbithynia')
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)
|
robertbogdon/model_tuning_mindalle9_jsy6zj-labels-classification
|
robertbogdon
| 2022-11-11T00:34:27Z | 0 | 0 |
sklearn
|
[
"sklearn",
"tabular-classification",
"baseline-trainer",
"license:apache-2.0",
"region:us"
] |
tabular-classification
| 2022-11-11T00:34:25Z |
---
license: apache-2.0
library_name: sklearn
tags:
- tabular-classification
- baseline-trainer
---
## Baseline Model trained on model_tuning_mindalle9_jsy6zj to apply classification on labels
**Metrics of the best model:**
accuracy 0.735922
recall_macro 0.631737
precision_macro 0.440117
f1_macro 0.457940
Name: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000), dtype: float64
**See model plot below:**
<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-3" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float ... free_string useless
temperatures False False ... False False
superconditions True False ... False False
is_megas False False ... False False
feature_0 True False ... False False
feature_1 True False ... False False
... ... ... ... ... ...
feature_763 True False ... False False
feature_764 True False ... False False
feature_765 True False ... False False
feature_766 True False ... False False
feature_767 True False ... False False[771 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float ... free_string useless
temperatures False False ... False False
superconditions True False ... False False
is_megas False False ... False False
feature_0 True False ... False False
feature_1 True False ... False False
... ... ... ... ... ...
feature_763 True False ... False False
feature_764 True False ... False False
feature_765 True False ... False False
feature_766 True False ... False False
feature_767 True False ... False False[771 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float ... free_string useless
temperatures False False ... False False
superconditions True False ... False False
is_megas False False ... False False
feature_0 True False ... False False
feature_1 True False ... False False
... ... ... ... ... ...
feature_763 True False ... False False
feature_764 True False ... False False
feature_765 True False ... False False
feature_766 True False ... False False
feature_767 True False ... False False[771 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-9" type="checkbox" ><label for="sk-estimator-id-9" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)</pre></div></div></div></div></div></div></div>
**Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain).
**Logs of training** including the models tried in the process can be found in logs.txt
|
robertbogdon/model_tuning_mindalleeu83oz7r-labels-classification
|
robertbogdon
| 2022-11-11T00:29:29Z | 0 | 0 |
sklearn
|
[
"sklearn",
"tabular-classification",
"baseline-trainer",
"license:apache-2.0",
"region:us"
] |
tabular-classification
| 2022-11-11T00:29:26Z |
---
license: apache-2.0
library_name: sklearn
tags:
- tabular-classification
- baseline-trainer
---
## Baseline Model trained on model_tuning_mindalleeu83oz7r to apply classification on labels
**Metrics of the best model:**
accuracy 0.732672
recall_macro 0.630156
precision_macro 0.439732
f1_macro 0.455558
Name: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000), dtype: float64
**See model plot below:**
<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float ... free_string useless
temperatures False False ... False False
superconditions True False ... False False
is_megas False False ... False False
feature_0 True False ... False False
feature_1 True False ... False False
... ... ... ... ... ...
feature_763 True False ... False False
feature_764 True False ... False False
feature_765 True False ... False False
feature_766 True False ... False False
feature_767 True False ... False False[771 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float ... free_string useless
temperatures False False ... False False
superconditions True False ... False False
is_megas False False ... False False
feature_0 True False ... False False
feature_1 True False ... False False
... ... ... ... ... ...
feature_763 True False ... False False
feature_764 True False ... False False
feature_765 True False ... False False
feature_766 True False ... False False
feature_767 True False ... False False[771 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float ... free_string useless
temperatures False False ... False False
superconditions True False ... False False
is_megas False False ... False False
feature_0 True False ... False False
feature_1 True False ... False False
... ... ... ... ... ...
feature_763 True False ... False False
feature_764 True False ... False False
feature_765 True False ... False False
feature_766 True False ... False False
feature_767 True False ... False False[771 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)</pre></div></div></div></div></div></div></div>
**Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain).
**Logs of training** including the models tried in the process can be found in logs.txt
|
EhtashamNQ/mt5-small-finetuned-amazon-en-es
|
EhtashamNQ
| 2022-11-11T00:24:55Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-10T16:33:31Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: EhtashamNQ/mt5-small-finetuned-amazon-en-es
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. -->
# EhtashamNQ/mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.5396
- Validation Loss: 2.8061
- Epoch: 5
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 5208, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 20.5300 | 7.1775 | 0 |
| 5.5220 | 3.7545 | 1 |
| 3.4137 | 3.5929 | 2 |
| 2.9827 | 3.0892 | 3 |
| 2.7228 | 2.8718 | 4 |
| 2.5396 | 2.8061 | 5 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.2
|
evelynerhuan/distilbert-base-uncased-model-1
|
evelynerhuan
| 2022-11-11T00:05:42Z | 120 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-11-10T23:31:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-model-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-model-1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6472
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.0114 | 1.0 | 554 | 1.9485 |
| 1.6658 | 2.0 | 1108 | 1.6325 |
| 1.2555 | 3.0 | 1662 | 1.6071 |
| 1.038 | 4.0 | 2216 | 1.6472 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Berdy96/finetuning-sentiment-model-3000-samples
|
Berdy96
| 2022-11-10T23:27:14Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-09T22:36:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.88
- name: F1
type: f1
value: 0.880794701986755
---
<!-- 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: 0.3118
- Accuracy: 0.88
- F1: 0.8808
## 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 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
evelynerhuan/distilbert-base-uncased-original-finetuned-squad
|
evelynerhuan
| 2022-11-10T22:29:41Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-11-10T22:01:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-original-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-original-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6427
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.965 | 1.0 | 554 | 1.8076 |
| 1.6215 | 2.0 | 1108 | 1.6230 |
| 1.298 | 3.0 | 1662 | 1.6427 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
AlekseyKorshuk/dalio-all-io-1.3b-3-epoch
|
AlekseyKorshuk
| 2022-11-10T21:12:47Z | 97 | 0 |
transformers
|
[
"transformers",
"pytorch",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:AlekseyKorshuk/dalio-all-io",
"license:other",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-10T20:52:55Z |
---
license: other
tags:
- generated_from_trainer
datasets:
- AlekseyKorshuk/dalio-all-io
metrics:
- accuracy
model-index:
- name: dalio-all-io-1.3b-3-epoch
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: AlekseyKorshuk/dalio-all-io
type: AlekseyKorshuk/dalio-all-io
metrics:
- name: Accuracy
type: accuracy
value: 0.05841094794583167
---
<!-- 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. -->
# dalio-all-io-1.3b-3-epoch
This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the AlekseyKorshuk/dalio-all-io dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3008
- Accuracy: 0.0584
## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6543 | 0.03 | 1 | 2.6113 | 0.0513 |
| 2.6077 | 0.07 | 2 | 2.6113 | 0.0513 |
| 2.5964 | 0.1 | 3 | 2.5605 | 0.0519 |
| 2.7302 | 0.14 | 4 | 2.5234 | 0.0526 |
| 2.7004 | 0.17 | 5 | 2.5078 | 0.0529 |
| 2.5681 | 0.21 | 6 | 2.4941 | 0.0532 |
| 2.6404 | 0.24 | 7 | 2.4883 | 0.0534 |
| 2.5325 | 0.28 | 8 | 2.4805 | 0.0536 |
| 2.7205 | 0.31 | 9 | 2.4746 | 0.0536 |
| 2.5149 | 0.34 | 10 | 2.4648 | 0.0533 |
| 2.5017 | 0.38 | 11 | 2.4512 | 0.0535 |
| 2.7026 | 0.41 | 12 | 2.4395 | 0.0539 |
| 2.5259 | 0.45 | 13 | 2.4316 | 0.0543 |
| 2.563 | 0.48 | 14 | 2.4219 | 0.0546 |
| 2.5679 | 0.52 | 15 | 2.4141 | 0.0550 |
| 2.3701 | 0.55 | 16 | 2.4082 | 0.0551 |
| 2.4739 | 0.59 | 17 | 2.4082 | 0.0551 |
| 2.481 | 0.62 | 18 | 2.4023 | 0.0548 |
| 2.5795 | 0.66 | 19 | 2.3945 | 0.0549 |
| 2.4902 | 0.69 | 20 | 2.3867 | 0.0549 |
| 2.4509 | 0.72 | 21 | 2.3809 | 0.0551 |
| 2.6052 | 0.76 | 22 | 2.3730 | 0.0553 |
| 2.3323 | 0.79 | 23 | 2.3633 | 0.0555 |
| 2.5994 | 0.83 | 24 | 2.3555 | 0.0556 |
| 2.3347 | 0.86 | 25 | 2.3477 | 0.0556 |
| 2.421 | 0.9 | 26 | 2.3398 | 0.0559 |
| 2.5337 | 0.93 | 27 | 2.3359 | 0.0560 |
| 2.4102 | 0.97 | 28 | 2.3320 | 0.0563 |
| 2.4309 | 1.0 | 29 | 2.3262 | 0.0564 |
| 1.9305 | 1.03 | 30 | 2.3223 | 0.0564 |
| 1.8601 | 1.07 | 31 | 2.3203 | 0.0567 |
| 1.8682 | 1.1 | 32 | 2.3281 | 0.0564 |
| 1.8657 | 1.14 | 33 | 2.3535 | 0.0564 |
| 2.063 | 1.17 | 34 | 2.3398 | 0.0567 |
| 1.6443 | 1.21 | 35 | 2.3242 | 0.0568 |
| 1.7592 | 1.24 | 36 | 2.3164 | 0.0569 |
| 1.8981 | 1.28 | 37 | 2.3105 | 0.0569 |
| 1.9379 | 1.31 | 38 | 2.3047 | 0.0573 |
| 1.6008 | 1.34 | 39 | 2.3027 | 0.0574 |
| 1.595 | 1.38 | 40 | 2.3027 | 0.0575 |
| 1.7096 | 1.41 | 41 | 2.3027 | 0.0575 |
| 1.7245 | 1.45 | 42 | 2.3027 | 0.0576 |
| 1.795 | 1.48 | 43 | 2.3008 | 0.0577 |
| 1.7241 | 1.52 | 44 | 2.3008 | 0.0576 |
| 1.6356 | 1.55 | 45 | 2.2988 | 0.0576 |
| 1.77 | 1.59 | 46 | 2.2969 | 0.0576 |
| 1.6675 | 1.62 | 47 | 2.2930 | 0.0577 |
| 1.6929 | 1.66 | 48 | 2.2910 | 0.0577 |
| 1.6635 | 1.69 | 49 | 2.2910 | 0.0576 |
| 1.6093 | 1.72 | 50 | 2.2910 | 0.0578 |
| 1.7362 | 1.76 | 51 | 2.2891 | 0.0580 |
| 1.7015 | 1.79 | 52 | 2.2852 | 0.0581 |
| 1.9515 | 1.83 | 53 | 2.2812 | 0.0582 |
| 1.6494 | 1.86 | 54 | 2.2773 | 0.0580 |
| 1.7522 | 1.9 | 55 | 2.2734 | 0.0580 |
| 1.7369 | 1.93 | 56 | 2.2676 | 0.0581 |
| 1.6528 | 1.97 | 57 | 2.2637 | 0.0581 |
| 1.51 | 2.0 | 58 | 2.2617 | 0.0583 |
| 1.4579 | 2.03 | 59 | 2.2637 | 0.0585 |
| 1.2645 | 2.07 | 60 | 2.2695 | 0.0585 |
| 1.2424 | 2.1 | 61 | 2.2773 | 0.0584 |
| 1.2117 | 2.14 | 62 | 2.2891 | 0.0584 |
| 1.4059 | 2.17 | 63 | 2.3008 | 0.0580 |
| 1.328 | 2.21 | 64 | 2.3145 | 0.0581 |
| 1.3436 | 2.24 | 65 | 2.3281 | 0.0580 |
| 1.389 | 2.28 | 66 | 2.3379 | 0.0580 |
| 1.2127 | 2.31 | 67 | 2.3398 | 0.0580 |
| 1.3645 | 2.34 | 68 | 2.3418 | 0.0581 |
| 1.3389 | 2.38 | 69 | 2.3379 | 0.0581 |
| 1.2549 | 2.41 | 70 | 2.3320 | 0.0581 |
| 1.2193 | 2.45 | 71 | 2.3281 | 0.0582 |
| 1.3617 | 2.48 | 72 | 2.3223 | 0.0583 |
| 1.2336 | 2.52 | 73 | 2.3184 | 0.0583 |
| 1.179 | 2.55 | 74 | 2.3145 | 0.0583 |
| 1.2468 | 2.59 | 75 | 2.3125 | 0.0583 |
| 1.3325 | 2.62 | 76 | 2.3086 | 0.0583 |
| 1.1471 | 2.66 | 77 | 2.3066 | 0.0583 |
| 1.3123 | 2.69 | 78 | 2.3066 | 0.0583 |
| 1.3285 | 2.72 | 79 | 2.3047 | 0.0585 |
| 1.3232 | 2.76 | 80 | 2.3027 | 0.0584 |
| 1.1228 | 2.79 | 81 | 2.3027 | 0.0584 |
| 1.3524 | 2.83 | 82 | 2.3027 | 0.0584 |
| 1.2042 | 2.86 | 83 | 2.3027 | 0.0583 |
| 1.3588 | 2.9 | 84 | 2.3008 | 0.0583 |
| 1.2982 | 2.93 | 85 | 2.3008 | 0.0584 |
| 1.4373 | 2.97 | 86 | 2.3008 | 0.0585 |
| 1.3562 | 3.0 | 87 | 2.3008 | 0.0584 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AlekseyKorshuk/dalio-all-io-1.3b-2-epoch
|
AlekseyKorshuk
| 2022-11-10T20:45:37Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:AlekseyKorshuk/dalio-all-io",
"license:other",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-10T20:29:53Z |
---
license: other
tags:
- generated_from_trainer
datasets:
- AlekseyKorshuk/dalio-all-io
metrics:
- accuracy
model-index:
- name: dalio-all-io-1.3b-2-epoch
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: AlekseyKorshuk/dalio-all-io
type: AlekseyKorshuk/dalio-all-io
metrics:
- name: Accuracy
type: accuracy
value: 0.057553854065481976
---
<!-- 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. -->
# dalio-all-io-1.3b-2-epoch
This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the AlekseyKorshuk/dalio-all-io dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2949
- Accuracy: 0.0576
## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6543 | 0.03 | 1 | 2.6113 | 0.0513 |
| 2.6077 | 0.07 | 2 | 2.6113 | 0.0513 |
| 2.5964 | 0.1 | 3 | 2.5605 | 0.0519 |
| 2.7302 | 0.14 | 4 | 2.5234 | 0.0527 |
| 2.7002 | 0.17 | 5 | 2.5078 | 0.0529 |
| 2.5674 | 0.21 | 6 | 2.4941 | 0.0533 |
| 2.6399 | 0.24 | 7 | 2.4883 | 0.0534 |
| 2.533 | 0.28 | 8 | 2.4805 | 0.0536 |
| 2.7202 | 0.31 | 9 | 2.4746 | 0.0536 |
| 2.5137 | 0.34 | 10 | 2.4648 | 0.0534 |
| 2.499 | 0.38 | 11 | 2.4512 | 0.0536 |
| 2.7026 | 0.41 | 12 | 2.4414 | 0.0539 |
| 2.5254 | 0.45 | 13 | 2.4336 | 0.0543 |
| 2.5667 | 0.48 | 14 | 2.4238 | 0.0545 |
| 2.5715 | 0.52 | 15 | 2.4160 | 0.0548 |
| 2.3739 | 0.55 | 16 | 2.4102 | 0.0550 |
| 2.4756 | 0.59 | 17 | 2.4043 | 0.0549 |
| 2.4783 | 0.62 | 18 | 2.3984 | 0.0550 |
| 2.5665 | 0.66 | 19 | 2.3906 | 0.0549 |
| 2.4888 | 0.69 | 20 | 2.3906 | 0.0549 |
| 2.4476 | 0.72 | 21 | 2.3828 | 0.0550 |
| 2.604 | 0.76 | 22 | 2.375 | 0.0552 |
| 2.3416 | 0.79 | 23 | 2.3652 | 0.0554 |
| 2.6028 | 0.83 | 24 | 2.3555 | 0.0555 |
| 2.3425 | 0.86 | 25 | 2.3477 | 0.0558 |
| 2.4142 | 0.9 | 26 | 2.3398 | 0.0558 |
| 2.5317 | 0.93 | 27 | 2.3340 | 0.0559 |
| 2.4119 | 0.97 | 28 | 2.3301 | 0.0561 |
| 2.4048 | 1.0 | 29 | 2.3262 | 0.0563 |
| 1.9646 | 1.03 | 30 | 2.3242 | 0.0564 |
| 1.9233 | 1.07 | 31 | 2.3203 | 0.0563 |
| 1.9276 | 1.1 | 32 | 2.3203 | 0.0564 |
| 1.8702 | 1.14 | 33 | 2.3281 | 0.0565 |
| 2.0997 | 1.17 | 34 | 2.3340 | 0.0565 |
| 1.7943 | 1.21 | 35 | 2.3320 | 0.0568 |
| 1.8579 | 1.24 | 36 | 2.3242 | 0.0567 |
| 1.8844 | 1.28 | 37 | 2.3145 | 0.0568 |
| 1.9288 | 1.31 | 38 | 2.3086 | 0.0569 |
| 1.6616 | 1.34 | 39 | 2.3047 | 0.0570 |
| 1.6443 | 1.38 | 40 | 2.3047 | 0.0571 |
| 1.7616 | 1.41 | 41 | 2.3027 | 0.0572 |
| 1.7904 | 1.45 | 42 | 2.3027 | 0.0571 |
| 1.8762 | 1.48 | 43 | 2.3027 | 0.0573 |
| 1.6569 | 1.52 | 44 | 2.3027 | 0.0573 |
| 1.647 | 1.55 | 45 | 2.3027 | 0.0573 |
| 1.8168 | 1.59 | 46 | 2.3027 | 0.0574 |
| 1.7194 | 1.62 | 47 | 2.3027 | 0.0573 |
| 1.7667 | 1.66 | 48 | 2.3027 | 0.0572 |
| 1.7621 | 1.69 | 49 | 2.3027 | 0.0573 |
| 1.7269 | 1.72 | 50 | 2.3008 | 0.0573 |
| 1.7815 | 1.76 | 51 | 2.3008 | 0.0574 |
| 1.8318 | 1.79 | 52 | 2.2988 | 0.0574 |
| 1.9366 | 1.83 | 53 | 2.2988 | 0.0575 |
| 1.736 | 1.86 | 54 | 2.2969 | 0.0576 |
| 1.9984 | 1.9 | 55 | 2.2969 | 0.0575 |
| 1.7203 | 1.93 | 56 | 2.2949 | 0.0575 |
| 1.7391 | 1.97 | 57 | 2.2949 | 0.0576 |
| 1.6611 | 2.0 | 58 | 2.2949 | 0.0576 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
qiny17/distilbert-base-uncased-finetuned-squad-a4-q3
|
qiny17
| 2022-11-10T20:37:21Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-11-10T19:57:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad-a4-q3
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-a4-q3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7767
## 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: 17
- eval_batch_size: 17
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.8135 | 1.0 | 516 | 1.9304 |
| 1.4214 | 2.0 | 1032 | 1.7047 |
| 1.0682 | 3.0 | 1548 | 1.7341 |
| 0.8492 | 4.0 | 2064 | 1.7767 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
yunocchi/swin-large-patch4-window12-384-in22k-respirator
|
yunocchi
| 2022-11-10T20:08:26Z | 204 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-11-10T19:09:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-large-patch4-window12-384-in22k-respirator
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
<!-- 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. -->
# swin-large-patch4-window12-384-in22k-respirator
This model is a fine-tuned version of [microsoft/swin-large-patch4-window12-384-in22k](https://huggingface.co/microsoft/swin-large-patch4-window12-384-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4272
- Accuracy: 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: 5e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 5 | 0.9598 | 0.4074 |
| 0.9359 | 2.0 | 10 | 0.4272 | 1.0 |
| 0.9359 | 3.0 | 15 | 0.2660 | 0.8889 |
| 0.3813 | 4.0 | 20 | 0.1257 | 1.0 |
| 0.3813 | 5.0 | 25 | 0.1451 | 1.0 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
AlekseyKorshuk/dalio-synthetic-io-1.3b
|
AlekseyKorshuk
| 2022-11-10T19:34:46Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:AlekseyKorshuk/dalio-synthetic-io",
"license:other",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-10T13:52:53Z |
---
license: other
tags:
- generated_from_trainer
datasets:
- AlekseyKorshuk/dalio-synthetic-io
metrics:
- accuracy
model-index:
- name: dalio-synthetic-io-1.3b
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: AlekseyKorshuk/dalio-synthetic-io
type: AlekseyKorshuk/dalio-synthetic-io
metrics:
- name: Accuracy
type: accuracy
value: 0.06357949136406908
---
<!-- 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. -->
# dalio-synthetic-io-1.3b
This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the AlekseyKorshuk/dalio-synthetic-io dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4961
- Accuracy: 0.0636
## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6941 | 0.05 | 1 | 2.6543 | 0.0622 |
| 2.6914 | 0.11 | 2 | 2.6543 | 0.0622 |
| 2.6003 | 0.16 | 3 | 2.6016 | 0.0627 |
| 2.5603 | 0.21 | 4 | 2.5703 | 0.0626 |
| 2.606 | 0.26 | 5 | 2.5508 | 0.0629 |
| 2.5439 | 0.32 | 6 | 2.5449 | 0.0629 |
| 2.4449 | 0.37 | 7 | 2.5469 | 0.0629 |
| 2.5422 | 0.42 | 8 | 2.5469 | 0.0630 |
| 2.6101 | 0.47 | 9 | 2.5410 | 0.0632 |
| 2.4482 | 0.53 | 10 | 2.5352 | 0.0630 |
| 2.501 | 0.58 | 11 | 2.5293 | 0.0631 |
| 2.5967 | 0.63 | 12 | 2.5215 | 0.0634 |
| 2.4998 | 0.68 | 13 | 2.5137 | 0.0635 |
| 2.5957 | 0.74 | 14 | 2.5098 | 0.0636 |
| 2.5967 | 0.79 | 15 | 2.5039 | 0.0639 |
| 2.5022 | 0.84 | 16 | 2.5 | 0.0637 |
| 2.4314 | 0.89 | 17 | 2.4980 | 0.0637 |
| 2.6279 | 0.95 | 18 | 2.4961 | 0.0636 |
| 2.571 | 1.0 | 19 | 2.4961 | 0.0636 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
mohammedbriman/distilbert-base-uncased-finetuned-cola
|
mohammedbriman
| 2022-11-10T19:16:56Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-10T19:05:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.538017794243896
---
<!-- 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: 0.7303
- Matthews Correlation: 0.5380
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.2672 | 1.0 | 535 | 0.6988 | 0.4674 |
| 0.2414 | 2.0 | 1070 | 0.6748 | 0.4729 |
| 0.1682 | 3.0 | 1605 | 0.7303 | 0.5380 |
| 0.1415 | 4.0 | 2140 | 0.9895 | 0.4806 |
| 0.0971 | 5.0 | 2675 | 0.9933 | 0.5321 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
lmvasque/prompt-ls-pt-3
|
lmvasque
| 2022-11-10T19:11:44Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-11-08T14:06:17Z |
---
license: cc-by-4.0
---
## UoM&MMU at TSAR-2022 Shared Task - Prompt Learning for Lexical Simplification: prompt-ls-pt-3
We present **PromptLS**, a method for fine-tuning large pre-trained masked language models to perform the task of Lexical Simplification.
This model is part of a series of models presented at the [TSAR-2022 Shared Task](https://taln.upf.edu/pages/tsar2022-st/)
by the University of Manchester and Manchester Metropolitan University (UoM&MMU) Team in English, Spanish and Portuguese.
You can find more details about the project in our [GitHub](https://github.com/lmvasque/ls-prompt-tsar2022).
## Models
Our models were fine-tuned using prompt-learning for **Lexical Simplification**. These are the available models you can use (current model page in bold):
| Model Name | Run # | Language | Setting |
|----------------------------------------------------------------------|-------|:--------------:|---------------|
| [prompt-ls-en-1](https://huggingface.co/lmvasque/prompt-ls-en-1) | 1 | English | fine-tune |
| [prompt-ls-en-2](https://huggingface.co/lmvasque/prompt-ls-en-2) | 2 | English | fine-tune |
| [roberta-large](https://huggingface.co/roberta-large) | 3 | English | zero-shot |
| [prompt-ls-es-1](https://huggingface.co/lmvasque/prompt-ls-es-1) | 1 | Spanish | fine-tune |
| [prompt-ls-es-2](https://huggingface.co/lmvasque/prompt-ls-es-2) | 2 | Spanish | fine-tune |
| [prompt-ls-es-3](https://huggingface.co/lmvasque/prompt-ls-es-3) | 3 | Spanish | fine-tune |
| [prompt-ls-pt-1](https://huggingface.co/lmvasque/prompt-ls-pt-1) | 1 | Portuguese | fine-tune |
| [prompt-ls-pt-2](https://huggingface.co/lmvasque/prompt-ls-pt-2) | 2 | Portuguese | fine-tune |
| **[prompt-ls-pt-3](https://huggingface.co/lmvasque/prompt-ls-pt-3)** | **3** | **Portuguese** | **fine-tune** |
For the zero-shot setting, we used the original models with no further training. Links to these models are also updated in the table above.
## Results
We include the [official results](https://github.com/LaSTUS-TALN-UPF/TSAR-2022-Shared-Task/tree/main/results/official) from the competition test set as a reference. However, we encourage the users to also check our results in the development set, which show an increased performance for Spanish and Portuguese.
You can find more details in our [paper](https://drive.google.com/file/d/1x5dRxgcSGAaCCrjsgpCHnYek9G-TmZff/view?usp=share_link).
| Language | # | Model | Setting | Prompt1 | Prompt2 | w | k | Acc@1 | A@3 | M@3 | P@3 |
|------------|---|-------|--------------|---------|---------|---|---|-------|-----|-----|-------------|
| English | 1 | RoBERTa-L | fine-tune | simple | word | 5 | 5 | **0.6353** | **0.5308** | **0.4244** | **0.8739** |
| English | 2 | mBERT | multilingual | easier | word | 10 | 10 | 0.4959 | 0.4235 | 0.3273 | 0.7560 |
| English | 3 | RoBERTa-L | zero-shot | easier | word | 5 | - | 0.2654 | 0.268 | 0.1820 | 0.4906 |
| Spanish | 1 | BERTIN | fine-tune | sinónimo | fácil | - | 3 | 0.3451 | **0.2907** | **0.2238** | **0.5543** |
| Spanish | 2 | BERTIN | fine-tune | palabra | simple | - | 10 | 0.3614 | **0.2907**| 0.2225 | 0.538 |
| Spanish | 3 | BERTIN | fine-tune | sinónimo | fácil | 10 | 10 | **0.3668** | 0.269 | 0.2128 | 0.5326 |
| Portuguese | 1 | BR_BERTo | fine-tune | palavra | simples | - | 8 | **0.1711** | 0.1096 | 0.1011 | 0.2486 |
| Portuguese | 2 | BR_BERTo | fine-tune | sinônimo | fácil | - | 10 | 0.1363 | 0.0962 | 0.0944 | 0.2379 |
| Portuguese | 3 | BR_BERTo | fine-tune | sinônimo | simples | 5 | 10 | 0.1577 | **0.1283**| **0.1071**| **0.2834**|
## Citation
If you use our results and scripts in your research, please cite our work:
"[UoM&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification](https://drive.google.com/file/d/1x5dRxgcSGAaCCrjsgpCHnYek9G-TmZff/view?usp=share_link)".
```
@inproceedings{vasquez-rodriguez-etal-2022-prompt-ls,
title = "UoM\&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification",
author = "V{\'a}squez-Rodr{\'\i}guez, Laura and
Nguyen, Nhung T. H. and
Shardlow, Matthew and
Ananiadou, Sophia",
booktitle = "Shared Task on Text Simplification, Accessibility, and Readability (TSAR-2022), EMNLP 2022",
month = dec,
year = "2022",
}
```
|
lmvasque/prompt-ls-pt-1
|
lmvasque
| 2022-11-10T19:11:35Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-11-08T14:05:29Z |
---
license: cc-by-4.0
---
## UoM&MMU at TSAR-2022 Shared Task - Prompt Learning for Lexical Simplification: prompt-ls-pt-1
We present **PromptLS**, a method for fine-tuning large pre-trained masked language models to perform the task of Lexical Simplification.
This model is part of a series of models presented at the [TSAR-2022 Shared Task](https://taln.upf.edu/pages/tsar2022-st/)
by the University of Manchester and Manchester Metropolitan University (UoM&MMU) Team in English, Spanish and Portuguese.
You can find more details about the project in our [GitHub](https://github.com/lmvasque/ls-prompt-tsar2022).
## Models
Our models were fine-tuned using prompt-learning for **Lexical Simplification**. These are the available models you can use (current model page in bold):
| Model Name | Run # | Language | Setting |
|--------------------------------------------------------------------|----|:--------------:|-----------|
| [prompt-ls-en-1](https://huggingface.co/lmvasque/prompt-ls-en-1) | 1 | English | fine-tune |
| [prompt-ls-en-2](https://huggingface.co/lmvasque/prompt-ls-en-2) | 2 | English | fine-tune |
| [roberta-large](https://huggingface.co/roberta-large) | 3 | English | zero-shot |
| [prompt-ls-es-1](https://huggingface.co/lmvasque/prompt-ls-es-1) | 1 | Spanish | fine-tune |
| [prompt-ls-es-2](https://huggingface.co/lmvasque/prompt-ls-es-2) | 2 | Spanish | fine-tune |
| [prompt-ls-es-3](https://huggingface.co/lmvasque/prompt-ls-es-3) | 3 | Spanish | fine-tune |
| **[prompt-ls-pt-1](https://huggingface.co/lmvasque/prompt-ls-pt-1)** | **1** | **Portuguese** | **fine-tune** |
| [prompt-ls-pt-2](https://huggingface.co/lmvasque/prompt-ls-pt-2) | 2 | Portuguese | fine-tune |
| [prompt-ls-pt-3](https://huggingface.co/lmvasque/prompt-ls-pt-3) | 3 | Portuguese | fine-tune |
For the zero-shot setting, we used the original models with no further training. Links to these models are also updated in the table above.
## Results
We include the [official results](https://github.com/LaSTUS-TALN-UPF/TSAR-2022-Shared-Task/tree/main/results/official) from the competition test set as a reference. However, we encourage the users to also check our results in the development set, which show an increased performance for Spanish and Portuguese.
You can find more details in our [paper](https://drive.google.com/file/d/1x5dRxgcSGAaCCrjsgpCHnYek9G-TmZff/view?usp=share_link).
| Language | # | Model | Setting | Prompt1 | Prompt2 | w | k | Acc@1 | A@3 | M@3 | P@3 |
|------------|---|-------|--------------|---------|---------|---|---|-------|-----|-----|-------------|
| English | 1 | RoBERTa-L | fine-tune | simple | word | 5 | 5 | **0.6353** | **0.5308** | **0.4244** | **0.8739** |
| English | 2 | mBERT | multilingual | easier | word | 10 | 10 | 0.4959 | 0.4235 | 0.3273 | 0.7560 |
| English | 3 | RoBERTa-L | zero-shot | easier | word | 5 | - | 0.2654 | 0.268 | 0.1820 | 0.4906 |
| Spanish | 1 | BERTIN | fine-tune | sinónimo | fácil | - | 3 | 0.3451 | **0.2907** | **0.2238** | **0.5543** |
| Spanish | 2 | BERTIN | fine-tune | palabra | simple | - | 10 | 0.3614 | **0.2907**| 0.2225 | 0.538 |
| Spanish | 3 | BERTIN | fine-tune | sinónimo | fácil | 10 | 10 | **0.3668** | 0.269 | 0.2128 | 0.5326 |
| Portuguese | 1 | BR_BERTo | fine-tune | palavra | simples | - | 8 | **0.1711** | 0.1096 | 0.1011 | 0.2486 |
| Portuguese | 2 | BR_BERTo | fine-tune | sinônimo | fácil | - | 10 | 0.1363 | 0.0962 | 0.0944 | 0.2379 |
| Portuguese | 3 | BR_BERTo | fine-tune | sinônimo | simples | 5 | 10 | 0.1577 | **0.1283**| **0.1071**| **0.2834**|
## Citation
If you use our results and scripts in your research, please cite our work:
"[UoM&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification](https://drive.google.com/file/d/1x5dRxgcSGAaCCrjsgpCHnYek9G-TmZff/view?usp=share_link)".
```
@inproceedings{vasquez-rodriguez-etal-2022-prompt-ls,
title = "UoM\&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification",
author = "V{\'a}squez-Rodr{\'\i}guez, Laura and
Nguyen, Nhung T. H. and
Shardlow, Matthew and
Ananiadou, Sophia",
booktitle = "Shared Task on Text Simplification, Accessibility, and Readability (TSAR-2022), EMNLP 2022",
month = dec,
year = "2022",
}
```
|
lmvasque/prompt-ls-es-3
|
lmvasque
| 2022-11-10T19:11:28Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-11-08T14:03:09Z |
---
license: cc-by-4.0
---
## UoM&MMU at TSAR-2022 Shared Task - Prompt Learning for Lexical Simplification: prompt-ls-es-3
We present **PromptLS**, a method for fine-tuning large pre-trained masked language models to perform the task of Lexical Simplification.
This model is part of a series of models presented at the [TSAR-2022 Shared Task](https://taln.upf.edu/pages/tsar2022-st/)
by the University of Manchester and Manchester Metropolitan University (UoM&MMU) Team in English, Spanish and Portuguese.
You can find more details about the project in our [GitHub](https://github.com/lmvasque/ls-prompt-tsar2022).
## Models
Our models were fine-tuned using prompt-learning for **Lexical Simplification**. These are the available models you can use (current model page in bold):
| Model Name | Run # | Language | Setting |
|--------------------------------------------------------------------|----|:-----------:|-----------|
| [prompt-ls-en-1](https://huggingface.co/lmvasque/prompt-ls-en-1) | 1 | English | fine-tune |
| [prompt-ls-en-2](https://huggingface.co/lmvasque/prompt-ls-en-2) | 2 | English | fine-tune |
| [roberta-large](https://huggingface.co/roberta-large) | 3 | English | zero-shot |
| [prompt-ls-es-1](https://huggingface.co/lmvasque/prompt-ls-es-1) | 1 | Spanish | fine-tune |
| [prompt-ls-es-2](https://huggingface.co/lmvasque/prompt-ls-es-2) | 2 | Spanish | fine-tune |
| **[prompt-ls-es-3](https://huggingface.co/lmvasque/prompt-ls-es-3)** | **3** | **Spanish** | **fine-tune** |
| [prompt-ls-pt-1](https://huggingface.co/lmvasque/prompt-ls-pt-1) | 1 | Portuguese | fine-tune |
| [prompt-ls-pt-2](https://huggingface.co/lmvasque/prompt-ls-pt-2) | 2 | Portuguese | fine-tune |
| [prompt-ls-pt-3](https://huggingface.co/lmvasque/prompt-ls-pt-3) | 3 | Portuguese | fine-tune |
For the zero-shot setting, we used the original models with no further training. Links to these models are also updated in the table above.
## Results
We include the [official results](https://github.com/LaSTUS-TALN-UPF/TSAR-2022-Shared-Task/tree/main/results/official) from the competition test set as a reference. However, we encourage the users to also check our results in the development set, which show an increased performance for Spanish and Portuguese.
You can find more details in our [paper](https://drive.google.com/file/d/1x5dRxgcSGAaCCrjsgpCHnYek9G-TmZff/view?usp=share_link).
| Language | # | Model | Setting | Prompt1 | Prompt2 | w | k | Acc@1 | A@3 | M@3 | P@3 |
|------------|---|-------|--------------|---------|---------|---|---|-------|-----|-----|-------------|
| English | 1 | RoBERTa-L | fine-tune | simple | word | 5 | 5 | **0.6353** | **0.5308** | **0.4244** | **0.8739** |
| English | 2 | mBERT | multilingual | easier | word | 10 | 10 | 0.4959 | 0.4235 | 0.3273 | 0.7560 |
| English | 3 | RoBERTa-L | zero-shot | easier | word | 5 | - | 0.2654 | 0.268 | 0.1820 | 0.4906 |
| Spanish | 1 | BERTIN | fine-tune | sinónimo | fácil | - | 3 | 0.3451 | **0.2907** | **0.2238** | **0.5543** |
| Spanish | 2 | BERTIN | fine-tune | palabra | simple | - | 10 | 0.3614 | **0.2907**| 0.2225 | 0.538 |
| Spanish | 3 | BERTIN | fine-tune | sinónimo | fácil | 10 | 10 | **0.3668** | 0.269 | 0.2128 | 0.5326 |
| Portuguese | 1 | BR_BERTo | fine-tune | palavra | simples | - | 8 | **0.1711** | 0.1096 | 0.1011 | 0.2486 |
| Portuguese | 2 | BR_BERTo | fine-tune | sinônimo | fácil | - | 10 | 0.1363 | 0.0962 | 0.0944 | 0.2379 |
| Portuguese | 3 | BR_BERTo | fine-tune | sinônimo | simples | 5 | 10 | 0.1577 | **0.1283**| **0.1071**| **0.2834**|
## Citation
If you use our results and scripts in your research, please cite our work:
"[UoM&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification](https://drive.google.com/file/d/1x5dRxgcSGAaCCrjsgpCHnYek9G-TmZff/view?usp=share_link)".
```
@inproceedings{vasquez-rodriguez-etal-2022-prompt-ls,
title = "UoM\&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification",
author = "V{\'a}squez-Rodr{\'\i}guez, Laura and
Nguyen, Nhung T. H. and
Shardlow, Matthew and
Ananiadou, Sophia",
booktitle = "Shared Task on Text Simplification, Accessibility, and Readability (TSAR-2022), EMNLP 2022",
month = dec,
year = "2022",
}
```
|
yunocchi/swin-tiny-patch4-window7-224-finetuned-respirator
|
yunocchi
| 2022-11-10T19:03:05Z | 208 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-11-10T17:18:42Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-respirator
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9082397003745318
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-respirator
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2124
- Accuracy: 0.9082
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4872 | 0.98 | 37 | 0.2124 | 0.9082 |
| 0.4828 | 1.98 | 74 | 0.2124 | 0.9082 |
| 0.4772 | 2.98 | 111 | 0.2124 | 0.9082 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
nateraw/videomae-base-finetuned-ucf101
|
nateraw
| 2022-11-10T18:54:58Z | 158 | 1 |
transformers
|
[
"transformers",
"pytorch",
"videomae",
"video-classification",
"vision",
"en",
"arxiv:1910.09700",
"license:mit",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2022-11-10T18:46:46Z |
---
language: en
license: mit
library_name: transformers
tags:
- video-classification
- videomae
- vision
---
# Model Card for videomae-base-finetuned-ucf101
A [WandB report here](https://wandb.ai/nateraw/videomae-finetune-ucf101/reports/Fine-Tuning-VideoMAE-Base-on-UCF101--VmlldzoyOTUwMjk4) for metrics.
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Model Examination](#model-examination-optional)
7. [Environmental Impact](#environmental-impact)
8. [Technical Specifications](#technical-specifications-optional)
9. [Citation](#citation-optional)
10. [Glossary](#glossary-optional)
11. [More Information](#more-information-optional)
12. [Model Card Authors](#model-card-authors-optional)
13. [Model Card Contact](#model-card-contact)
14. [How To Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
VideoMAE Base model fine tuned on UCF101
- **Developed by:** [@nateraw](https://huggingface.co/nateraw)
- **Shared by [optional]:** [More Information Needed]
- **Model type:** fine-tuned
- **Language(s) (NLP):** en
- **License:** mit
- **Related Models [optional]:** [More Information Needed]
- **Parent Model [optional]:** [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base)
- **Resources for more information:** [More Information Needed]
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
This model can be used for Video Action Recognition
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations.
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
We sampled clips from the videos of 64 frames, then took a uniform sample of those frames to get 16 frame inputs for the model. During training, we used PyTorchVideo's [`MixVideo`](https://github.com/facebookresearch/pytorchvideo/blob/main/pytorchvideo/transforms/mix.py) to apply mixup/cutmix.
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
We only trained/evaluated one fold from the UCF101 annotations. Unlike in the VideoMAE paper, we did not perform inference over multiple crops/segments of validation videos, so the results are likely slightly lower than what you would get if you did that too.
- Eval Accuracy: 0.758209764957428
- Eval Accuracy Top 5: 0.8983050584793091
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
[More Information Needed]
# Model Card Authors [optional]
[@nateraw](https://huggingface.co/nateraw)
# Model Card Contact
[@nateraw](https://huggingface.co/nateraw)
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
from decord import VideoReader, cpu
import torch
import numpy as np
from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification
from huggingface_hub import hf_hub_download
np.random.seed(0)
def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
converted_len = int(clip_len * frame_sample_rate)
end_idx = np.random.randint(converted_len, seg_len)
start_idx = end_idx - converted_len
indices = np.linspace(start_idx, end_idx, num=clip_len)
indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
return indices
# video clip consists of 300 frames (10 seconds at 30 FPS)
file_path = hf_hub_download(
repo_id="nateraw/dino-clips", filename="archery.mp4", repo_type="space"
)
videoreader = VideoReader(file_path, num_threads=1, ctx=cpu(0))
# sample 16 frames
videoreader.seek(0)
indices = sample_frame_indices(clip_len=16, frame_sample_rate=4, seg_len=len(videoreader))
video = videoreader.get_batch(indices).asnumpy()
feature_extractor = VideoMAEFeatureExtractor.from_pretrained("nateraw/videomae-base-finetuned-ucf101")
model = VideoMAEForVideoClassification.from_pretrained("nateraw/videomae-base-finetuned-ucf101")
inputs = feature_extractor(list(video), return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 101 UCF101 classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])
```
</details>
|
robertbogdon/model_tuning_mindallee8kmcfjz-labels-classification
|
robertbogdon
| 2022-11-10T18:46:33Z | 0 | 0 |
sklearn
|
[
"sklearn",
"tabular-classification",
"baseline-trainer",
"license:apache-2.0",
"region:us"
] |
tabular-classification
| 2022-11-10T18:46:30Z |
---
license: apache-2.0
library_name: sklearn
tags:
- tabular-classification
- baseline-trainer
---
## Baseline Model trained on model_tuning_mindallee8kmcfjz to apply classification on labels
**Metrics of the best model:**
accuracy 0.806000
recall_macro 0.416887
precision_macro 0.391691
f1_macro 0.397991
Name: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000), dtype: float64
**See model plot below:**
<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float ... free_string useless
temperatures False False ... False False
superconditions True False ... False False
is_megas False False ... False False
feature_0 True False ... False False
feature_1 True False ... False False
... ... ... ... ... ...
feature_763 True False ... False False
feature_764 True False ... False False
feature_765 True False ... False False
feature_766 True False ... False False
feature_767 True False ... False False[771 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float ... free_string useless
temperatures False False ... False False
superconditions True False ... False False
is_megas False False ... False False
feature_0 True False ... False False
feature_1 True False ... False False
... ... ... ... ... ...
feature_763 True False ... False False
feature_764 True False ... False False
feature_765 True False ... False False
feature_766 True False ... False False
feature_767 True False ... False False[771 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float ... free_string useless
temperatures False False ... False False
superconditions True False ... False False
is_megas False False ... False False
feature_0 True False ... False False
feature_1 True False ... False False
... ... ... ... ... ...
feature_763 True False ... False False
feature_764 True False ... False False
feature_765 True False ... False False
feature_766 True False ... False False
feature_767 True False ... False False[771 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)</pre></div></div></div></div></div></div></div>
**Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain).
**Logs of training** including the models tried in the process can be found in logs.txt
|
Omerdor/dry_samples_train
|
Omerdor
| 2022-11-10T18:21:32Z | 3 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:imagefolder",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-11-10T14:50:19Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: imagefolder
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# dry_samples_train
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `imagefolder` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 4
- gradient_accumulation_steps: 3
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/Omerdor/dry_samples_train/tensorboard?#scalars)
|
Vested-Sigil/VanGO
|
Vested-Sigil
| 2022-11-10T17:54:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-11-10T17:51:20Z |
#!/usr/bin/env python3
from diffusers import DiffusionPipeline
import PIL
import requests
from io import BytesIO
import torch
def download_image(url):
response = requests.get(url)
return PIL.Image.open(BytesIO(response.content)).convert("RGB")
pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_mega", torch_dtype=torch.float16, revision="fp16")
pipe.to("cuda")
pipe.enable_attention_slicing()
### Text-to-Image
images = pipe.text2img("An astronaut riding a horse").images
### Image-to-Image
init_image = download_image("https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg")
prompt = "A fantasy landscape, trending on artstation"
images = pipe.img2img(prompt=prompt, init_image=init_image, strength=0.75, guidance_scale=7.5).images
### Inpainting
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
init_image = download_image(img_url).resize((512, 512))
mask_image = download_image(mask_url).resize((512, 512))
prompt = "a cat sitting on a bench"
images = pipe.inpaint(prompt=prompt, init_image=init_image, mask_image=mask_image, strength=0.75).images
|
huggingtweets/googlepoetics
|
huggingtweets
| 2022-11-10T17:53:15Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-10T17:52:16Z |
---
language: en
thumbnail: http://www.huggingtweets.com/googlepoetics/1668102791580/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/378800000152236311/e364d2a13dab35a8b65c9decf71ae134_400x400.jpeg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Google Poetics</div>
<div style="text-align: center; font-size: 14px;">@googlepoetics</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 Google Poetics.
| Data | Google Poetics |
| --- | --- |
| Tweets downloaded | 1569 |
| Retweets | 9 |
| Short tweets | 35 |
| Tweets kept | 1525 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2re8zf12/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 @googlepoetics's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/cwwobqqi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/cwwobqqi/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/googlepoetics')
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)
|
nielsr/detr-table-structure-recognition
|
nielsr
| 2022-11-10T17:22:16Z | 216 | 1 |
transformers
|
[
"transformers",
"pytorch",
"detr",
"object-detection",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2022-09-06T14:36:57Z |
Hi,
Please don't use this model anymore, it only worked for a specific branch of mine.
From now on it's recommended to use https://huggingface.co/microsoft/table-transformer-structure-recognition from Transformers.
Thanks, have a great day
|
yunocchi/swin-tiny-patch4-window7-224-finetuned-eurosat
|
yunocchi
| 2022-11-10T16:57:04Z | 204 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-11-10T16:52:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.48148148148148145
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0629
- Accuracy: 0.4815
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 2 | 1.0629 | 0.4815 |
| No log | 2.0 | 4 | 1.0387 | 0.4815 |
| No log | 3.0 | 6 | 1.0107 | 0.4815 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
google/deeplabv3_mobilenet_v2_1.0_513
|
google
| 2022-11-10T16:28:13Z | 870 | 7 |
transformers
|
[
"transformers",
"pytorch",
"mobilenet_v2",
"vision",
"image-segmentation",
"dataset:pascal-voc",
"arxiv:1801.04381",
"arxiv:1802.02611",
"license:other",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2022-11-10T16:05:57Z |
---
license: other
tags:
- vision
- image-segmentation
datasets:
- pascal-voc
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-2.jpg
example_title: Cat
---
# MobileNetV2 with DeepLabV3+
MobileNet V2 model pre-trained on PASCAL VOC at resolution 513x513. It was introduced in [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in [this repository](https://github.com/tensorflow/models/tree/master/research/deeplab).
Disclaimer: The team releasing MobileNet V2 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
From the [original README](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md):
> MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices [...] MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.
The model in this repo adds a [DeepLabV3+](https://arxiv.org/abs/1802.02611) head to the MobileNetV2 backbone for semantic segmentation.
## Intended uses & limitations
You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=mobilenet_v2) to look for fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
from transformers import AutoImageProcessor, AutoModelForSemanticSegmentation
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
preprocessor = AutoImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")
model = AutoModelForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")
inputs = preprocessor(images=image, return_tensors="pt")
outputs = model(**inputs)
predicted_mask = preprocessor.post_process_semantic_segmentation(outputs)
```
Currently, both the feature extractor and model support PyTorch.
### BibTeX entry and citation info
```bibtex
@inproceedings{deeplabv3plus2018,
title={Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
author={Liang-Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam},
booktitle={ECCV},
year={2018}
}
```
|
RafaelEiji/jurisbert-base-classify
|
RafaelEiji
| 2022-11-10T16:19:29Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-10T12:49:42Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: output
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output
This model is a fine-tuned version of [juridics/jurisbert-base-portuguese-uncased](https://huggingface.co/juridics/jurisbert-base-portuguese-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4838
- Accuracy: 0.7176
## 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: 24
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
### Training results
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.12.0+cu116
- Datasets 2.6.1
- Tokenizers 0.13.1
|
sid321axn/minilm-finetuned-emotionclassification
|
sid321axn
| 2022-11-10T16:16:40Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-10T05:46:34Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: minilm-finetuned-emotionclassification
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. -->
# minilm-finetuned-emotionclassification
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0554
- F1 Score: 0.6732
## Model description
The base model used is Microsoft MiniLM-L12-H384-uncased which is finetuned on [GoEmotions dataset](https://huggingface.co/datasets/go_emotions) available on huggingface.
With this model, you can classify emotions in English text data. The model predicts 10 basic emotions:
1) anger 🤬
2) love ❤️
3) fear 😨
4) joy 😀
5) excitement 😄
6) sadness 😭
7) surprise 😲
8) gratitude 😊
9) curiosity 🤔
10 caring
## Intended uses & limitations
The model can be used to detect emotions from text/ documents which can be used for analysis contextual emotional analysis of the documents
## Training and evaluation data
The dataset used for Training and Evaluation is [GoEmotions dataset](https://huggingface.co/datasets/go_emotions)
and in this, we have used 10 emotion variables.
{0:'sadness',1:'joy',2:'love',3:'anger',4:'fear',5:'surprise',6:'excitement',7:'gratitude',8:'curiosity',9:'caring'}
## How to use the model
Here is how to use this model to extract the emotions from the given text in PyTorch:
```python
>>> from transformers import pipeline
>>> model_ckpt ="sid321axn/minilm-finetuned-emotionclassification"
>>> pipe = pipeline("text-classification",model=model_ckpt)
>>> pipe("I am really excited about second part of Brahmastra Movie")
[{'label': 'excitement', 'score': 0.7849715352058411}]
```
## Training procedure
The training we have done by following this [video](https://www.youtube.com/watch?v=u--UVvH-LIQ) on Youtube by huggingface
### 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 | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1659 | 1.0 | 539 | 1.1419 | 0.6347 |
| 1.0719 | 2.0 | 1078 | 1.0789 | 0.6589 |
| 0.9893 | 3.0 | 1617 | 1.0537 | 0.6666 |
| 0.9296 | 4.0 | 2156 | 1.0366 | 0.6729 |
| 0.8763 | 5.0 | 2695 | 1.0359 | 0.6774 |
| 0.8385 | 6.0 | 3234 | 1.0484 | 0.6693 |
| 0.8085 | 7.0 | 3773 | 1.0478 | 0.6758 |
| 0.7842 | 8.0 | 4312 | 1.0488 | 0.6741 |
| 0.7608 | 9.0 | 4851 | 1.0538 | 0.6749 |
| 0.7438 | 10.0 | 5390 | 1.0554 | 0.6732 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
pinxi/bloom-1b7-igpt3
|
pinxi
| 2022-11-10T15:15:18Z | 171 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bloom",
"text-generation",
"license:bigscience-openrail-m",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-10T15:01:43Z |
---
license: bigscience-openrail-m
---
Bloom-1b7 model finetuned on InstructGPT3 generated data for email actionable points extraction
|
pinxi/bloom-560m-bloom
|
pinxi
| 2022-11-10T15:02:31Z | 172 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bloom",
"text-generation",
"license:bigscience-openrail-m",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-10T14:56:38Z |
---
license: bigscience-openrail-m
---
Bloom-560m model finetuned on Bloom-175b generated data for email actionable points extraction
|
Matthijs/mobilenet_v1_0.75_192
|
Matthijs
| 2022-11-10T14:20:14Z | 237 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mobilenet_v1",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:1704.04861",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-22T12:07:44Z |
---
license: other
tags:
- vision
- image-classification
datasets:
- imagenet-1k
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
---
# MobileNet V1
MobileNet V1 model pre-trained on ImageNet-1k at resolution 192x192. It was introduced in [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Howard et al, and first released in [this repository](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md).
Disclaimer: The team releasing MobileNet V1 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
From the [original README](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md):
> MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices [...] MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=mobilenet_v1) 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 MobileNetV1FeatureExtractor, MobileNetV1ForImageClassification
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = MobileNetV1FeatureExtractor.from_pretrained("Matthijs/mobilenet_v1_1.0_224")
model = MobileNetV1ForImageClassification.from_pretrained("Matthijs/mobilenet_v1_1.0_224")
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Note: This model actually predicts 1001 classes, the 1000 classes from ImageNet plus an extra “background” class (index 0).
Currently, both the feature extractor and model support PyTorch.
|
Matthijs/mobilenet_v1_1.0_224
|
Matthijs
| 2022-11-10T14:20:00Z | 3,887 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mobilenet_v1",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:1704.04861",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-22T12:05:41Z |
---
license: other
tags:
- vision
- image-classification
datasets:
- imagenet-1k
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
---
# MobileNet V1
MobileNet V1 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Howard et al, and first released in [this repository](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md).
Disclaimer: The team releasing MobileNet V1 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
From the [original README](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md):
> MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices [...] MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=mobilenet_v1) 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 MobileNetV1FeatureExtractor, MobileNetV1ForImageClassification
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = MobileNetV1FeatureExtractor.from_pretrained("Matthijs/mobilenet_v1_1.0_224")
model = MobileNetV1ForImageClassification.from_pretrained("Matthijs/mobilenet_v1_1.0_224")
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Note: This model actually predicts 1001 classes, the 1000 classes from ImageNet plus an extra “background” class (index 0).
Currently, both the feature extractor and model support PyTorch.
|
135790ESERESERESEr/Salem
|
135790ESERESERESEr
| 2022-11-10T14:15:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-11-10T14:14:18Z |
---
license: openrail
---paint(saelam)
|
hou/translation-model
|
hou
| 2022-11-10T14:02:41Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"m2m_100",
"text2text-generation",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-10T08:06:08Z |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: NLLB-alt-cv-bleu-40
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. -->
# NLLB-alt-cv-bleu-40
This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7734
- Bleu: 30.3568
- Gen Len: 50.699
## 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
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 2.1031 | 1.0 | 1380 | 1.6725 | 6.1774 | 56.292 |
| 1.4925 | 2.0 | 2760 | 1.3799 | 8.9414 | 54.966 |
| 1.1976 | 3.0 | 4140 | 1.2417 | 10.7993 | 55.32 |
| 0.9973 | 4.0 | 5520 | 1.1744 | 13.7633 | 51.225 |
| 0.8305 | 5.0 | 6900 | 1.1461 | 14.8273 | 51.723 |
| 0.6918 | 6.0 | 8280 | 1.1261 | 16.02 | 50.83 |
| 0.5823 | 7.0 | 9660 | 1.1556 | 17.8129 | 49.93 |
| 0.4934 | 8.0 | 11040 | 1.1567 | 19.3213 | 50.647 |
| 0.4049 | 9.0 | 12420 | 1.1794 | 21.712 | 50.722 |
| 0.3342 | 10.0 | 13800 | 1.2320 | 22.2956 | 50.552 |
| 0.2747 | 11.0 | 15180 | 1.2749 | 24.3631 | 50.443 |
| 0.2281 | 12.0 | 16560 | 1.2996 | 25.4046 | 52.437 |
| 0.1849 | 13.0 | 17940 | 1.3378 | 26.5399 | 50.361 |
| 0.153 | 14.0 | 19320 | 1.3709 | 27.0563 | 51.077 |
| 0.1256 | 15.0 | 20700 | 1.4128 | 27.8781 | 51.129 |
| 0.1103 | 16.0 | 22080 | 1.4354 | 28.6894 | 51.974 |
| 0.0893 | 17.0 | 23460 | 1.4859 | 28.0852 | 52.005 |
| 0.0778 | 18.0 | 24840 | 1.4973 | 28.9053 | 50.803 |
| 0.0683 | 19.0 | 26220 | 1.5294 | 29.2219 | 50.845 |
| 0.0592 | 20.0 | 27600 | 1.5576 | 29.1227 | 51.051 |
| 0.0505 | 21.0 | 28980 | 1.5885 | 29.4121 | 50.376 |
| 0.0441 | 22.0 | 30360 | 1.6028 | 29.5531 | 51.946 |
| 0.0397 | 23.0 | 31740 | 1.6254 | 29.3607 | 50.811 |
| 0.0361 | 24.0 | 33120 | 1.6374 | 29.5197 | 51.166 |
| 0.0323 | 25.0 | 34500 | 1.6423 | 29.7589 | 51.335 |
| 0.0288 | 26.0 | 35880 | 1.6630 | 29.6029 | 51.036 |
| 0.0257 | 27.0 | 37260 | 1.6800 | 29.4437 | 50.623 |
| 0.0235 | 28.0 | 38640 | 1.6887 | 29.9344 | 50.797 |
| 0.0201 | 29.0 | 40020 | 1.7096 | 30.1522 | 50.694 |
| 0.018 | 30.0 | 41400 | 1.7223 | 30.1291 | 50.425 |
| 0.0163 | 31.0 | 42780 | 1.7282 | 29.8131 | 51.114 |
| 0.0148 | 32.0 | 44160 | 1.7299 | 29.9721 | 50.851 |
| 0.0133 | 33.0 | 45540 | 1.7463 | 30.0369 | 50.477 |
| 0.0122 | 34.0 | 46920 | 1.7514 | 30.0663 | 51.133 |
| 0.0112 | 35.0 | 48300 | 1.7508 | 30.0451 | 50.736 |
| 0.0099 | 36.0 | 49680 | 1.7631 | 30.0576 | 50.62 |
| 0.0087 | 37.0 | 51060 | 1.7683 | 30.1648 | 50.874 |
| 0.0083 | 38.0 | 52440 | 1.7750 | 30.2558 | 50.667 |
| 0.0076 | 39.0 | 53820 | 1.7757 | 30.3551 | 50.886 |
| 0.0076 | 40.0 | 55200 | 1.7734 | 30.3568 | 50.699 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.10.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Narsil/layoutlm-funsd
|
Narsil
| 2022-11-10T13:52:40Z | 54 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlm",
"token-classification",
"generated_from_trainer",
"endpoints-template",
"object-detection",
"dataset:funsd",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2022-11-10T11:35:09Z |
---
tags:
- generated_from_trainer
- endpoints-template
library_name: transformers
pipeline_tag: object-detection
widget:
- src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"
example_title: invoice
- src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/contract.jpeg"
example_title: contract
datasets:
- funsd
model-index:
- name: layoutlm-funsd
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. -->
# layoutlm-funsd
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0045
- Answer: {'precision': 0.7348314606741573, 'recall': 0.8084054388133498, 'f1': 0.7698646262507357, 'number': 809}
- Header: {'precision': 0.44285714285714284, 'recall': 0.5210084033613446, 'f1': 0.47876447876447875, 'number': 119}
- Question: {'precision': 0.8211009174311926, 'recall': 0.8403755868544601, 'f1': 0.8306264501160092, 'number': 1065}
- Overall Precision: 0.7599
- Overall Recall: 0.8083
- Overall F1: 0.7866
- Overall Accuracy: 0.8106
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- 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: 15
- mixed_precision_training: Native AMP
## Deploy Model with Inference Endpoints
Before we can get started, make sure you meet all of the following requirements:
1. An Organization/User with an active plan and *WRITE* access to the model repository.
2. Can access the UI: [https://ui.endpoints.huggingface.co](https://ui.endpoints.huggingface.co/endpoints)
### 1. Deploy LayoutLM and Send requests
In this tutorial, you will learn how to deploy a [LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm) to [Hugging Face Inference Endpoints](https://huggingface.co/inference-endpoints) and how you can integrate it via an API into your products.
This tutorial is not covering how you create the custom handler for inference. If you want to learn how to create a custom Handler for Inference Endpoints, you can either checkout the [documentation](https://huggingface.co/docs/inference-endpoints/guides/custom_handler) or go through [“Custom Inference with Hugging Face Inference Endpoints”](https://www.philschmid.de/custom-inference-handler)
We are going to deploy [philschmid/layoutlm-funsd](https://huggingface.co/philschmid/layoutlm-funsd) which implements the following `handler.py`
```python
from typing import Dict, List, Any
from transformers import LayoutLMForTokenClassification, LayoutLMv2Processor
import torch
from subprocess import run
# install tesseract-ocr and pytesseract
run("apt install -y tesseract-ocr", shell=True, check=True)
run("pip install pytesseract", shell=True, check=True)
# helper function to unnormalize bboxes for drawing onto the image
def unnormalize_box(bbox, width, height):
return [
width * (bbox[0] / 1000),
height * (bbox[1] / 1000),
width * (bbox[2] / 1000),
height * (bbox[3] / 1000),
]
# set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class EndpointHandler:
def __init__(self, path=""):
# load model and processor from path
self.model = LayoutLMForTokenClassification.from_pretrained(path).to(device)
self.processor = LayoutLMv2Processor.from_pretrained(path)
def __call__(self, data: Dict[str, bytes]) -> Dict[str, List[Any]]:
"""
Args:
data (:obj:):
includes the deserialized image file as PIL.Image
"""
# process input
image = data.pop("inputs", data)
# process image
encoding = self.processor(image, return_tensors="pt")
# run prediction
with torch.inference_mode():
outputs = self.model(
input_ids=encoding.input_ids.to(device),
bbox=encoding.bbox.to(device),
attention_mask=encoding.attention_mask.to(device),
token_type_ids=encoding.token_type_ids.to(device),
)
predictions = outputs.logits.softmax(-1)
# post process output
result = []
for item, inp_ids, bbox in zip(
predictions.squeeze(0).cpu(), encoding.input_ids.squeeze(0).cpu(), encoding.bbox.squeeze(0).cpu()
):
label = self.model.config.id2label[int(item.argmax().cpu())]
if label == "O":
continue
score = item.max().item()
text = self.processor.tokenizer.decode(inp_ids)
bbox = unnormalize_box(bbox.tolist(), image.width, image.height)
result.append({"label": label, "score": score, "text": text, "bbox": bbox})
return {"predictions": result}
```
### 2. Send HTTP request using Python
Hugging Face Inference endpoints can directly work with binary data, this means that we can directly send our image from our document to the endpoint. We are going to use `requests` to send our requests. (make your you have it installed `pip install requests`)
```python
import json
import requests as r
import mimetypes
ENDPOINT_URL="" # url of your endpoint
HF_TOKEN="" # organization token where you deployed your endpoint
def predict(path_to_image:str=None):
with open(path_to_image, "rb") as i:
b = i.read()
headers= {
"Authorization": f"Bearer {HF_TOKEN}",
"Content-Type": mimetypes.guess_type(path_to_image)[0]
}
response = r.post(ENDPOINT_URL, headers=headers, data=b)
return response.json()
prediction = predict(path_to_image="path_to_your_image.png")
print(prediction)
# {'predictions': [{'label': 'I-ANSWER', 'score': 0.4823932945728302, 'text': '[CLS]', 'bbox': [0.0, 0.0, 0.0, 0.0]}, {'label': 'B-HEADER', 'score': 0.992474377155304, 'text': 'your', 'bbox': [1712.529, 181.203, 1859.949, 228.88799999999998]},
```
### 3. Draw result on image
To get a better understanding of what the model predicted you can also draw the predictions on the provided image.
```python
from PIL import Image, ImageDraw, ImageFont
# draw results on image
def draw_result(path_to_image,result):
image = Image.open(path_to_image)
label2color = {
"B-HEADER": "blue",
"B-QUESTION": "red",
"B-ANSWER": "green",
"I-HEADER": "blue",
"I-QUESTION": "red",
"I-ANSWER": "green",
}
# draw predictions over the image
draw = ImageDraw.Draw(image)
font = ImageFont.load_default()
for res in result:
draw.rectangle(res["bbox"], outline="black")
draw.rectangle(res["bbox"], outline=label2color[res["label"]])
draw.text((res["bbox"][0] + 10, res["bbox"][1] - 10), text=res["label"], fill=label2color[res["label"]], font=font)
return image
draw_result("path_to_your_image.png", prediction["predictions"])
```
|
toanbui1991/distilbert-base-uncased-finetuned-squad
|
toanbui1991
| 2022-11-10T13:39:29Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-11-09T03:01:51Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: toanbui1991/distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# toanbui1991/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.5101
- Train End Logits Accuracy: 0.6065
- Train Start Logits Accuracy: 0.5692
- Validation Loss: 1.1679
- Validation End Logits Accuracy: 0.6823
- Validation Start Logits Accuracy: 0.6523
- 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': 2e-05, 'decay_steps': 11064, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.5101 | 0.6065 | 0.5692 | 1.1679 | 0.6823 | 0.6523 | 0 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.10.0
- Datasets 2.6.1
- Tokenizers 0.13.2
|
huggingtweets/barkmeta-lb22_sus-nft_god
|
huggingtweets
| 2022-11-10T13:18:55Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-10T13:00:51Z |
---
language: en
thumbnail: http://www.huggingtweets.com/barkmeta-lb22_sus-nft_god/1668086330381/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1579110344420622342/QzePSc2g_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1489268127565324291/ZQK5RoFg_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1559936197564268551/WXSx0leh_400x400.png')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">LB22 & NFT God & Bark❓</div>
<div style="text-align: center; font-size: 14px;">@barkmeta-lb22_sus-nft_god</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 LB22 & NFT God & Bark❓.
| Data | LB22 | NFT God | Bark❓ |
| --- | --- | --- | --- |
| Tweets downloaded | 1220 | 3250 | 3250 |
| Retweets | 467 | 20 | 285 |
| Short tweets | 381 | 165 | 1868 |
| Tweets kept | 372 | 3065 | 1097 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3vq9v8ck/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 @barkmeta-lb22_sus-nft_god's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ixknti18) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ixknti18/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/barkmeta-lb22_sus-nft_god')
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)
|
Matthijs/deeplabv3_mobilenet_v2_1.0_513
|
Matthijs
| 2022-11-10T12:50:21Z | 296 | 1 |
transformers
|
[
"transformers",
"pytorch",
"coreml",
"mobilenet_v2",
"vision",
"image-segmentation",
"dataset:pascal-voc",
"arxiv:1801.04381",
"arxiv:1802.02611",
"license:other",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2022-06-28T11:16:06Z |
---
license: other
tags:
- vision
- image-segmentation
datasets:
- pascal-voc
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-2.jpg
example_title: Cat
---
# MobileNetV2 with DeepLabV3+
MobileNet V2 model pre-trained on PASCAL VOC at resolution 513x513. It was introduced in [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in [this repository](https://github.com/tensorflow/models/tree/master/research/deeplab).
Disclaimer: The team releasing MobileNet V2 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
From the [original README](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md):
> MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices [...] MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.
The model in this repo adds a [DeepLabV3+](https://arxiv.org/abs/1802.02611) head to the MobileNetV2 backbone for semantic segmentation.
## Intended uses & limitations
You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=mobilenet_v2) to look for fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
from transformers import MobileNetV2FeatureExtractor, MobileNetV2ForSemanticSegmentation
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = MobileNetV2FeatureExtractor.from_pretrained("Matthijs/deeplabv3_mobilenet_v2_1.0_513")
model = MobileNetV2ForSemanticSegmentation.from_pretrained("Matthijs/deeplabv3_mobilenet_v2_1.0_513")
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_mask = logits.argmax(1).squeeze(0)
```
Currently, both the feature extractor and model support PyTorch.
### BibTeX entry and citation info
```bibtex
@inproceedings{deeplabv3plus2018,
title={Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
author={Liang-Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam},
booktitle={ECCV},
year={2018}
}
```
|
Matthijs/mobilenet_v2_1.4_224
|
Matthijs
| 2022-11-10T12:48:00Z | 1,146 | 0 |
transformers
|
[
"transformers",
"pytorch",
"coreml",
"mobilenet_v2",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:1801.04381",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-06-27T13:32:28Z |
---
license: other
tags:
- vision
- image-classification
datasets:
- imagenet-1k
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
---
# MobileNet V2
MobileNet V2 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in [this repository](https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet).
Disclaimer: The team releasing MobileNet V2 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
From the [original README](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md):
> MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices [...] MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.
The checkpoints are named **mobilenet\_v2\_*depth*\_*size***, for example **mobilenet\_v2\_1.0\_224**, where **1.0** is the depth multiplier and **224** is the resolution of the input images the model was trained on.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=mobilenet_v2) 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 MobileNetV2FeatureExtractor, MobileNetV2ForImageClassification
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = MobileNetV2FeatureExtractor.from_pretrained("Matthijs/mobilenet_v2_1.0_224")
model = MobileNetV2ForImageClassification.from_pretrained("Matthijs/mobilenet_v2_1.0_224")
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Note: This model actually predicts 1001 classes, the 1000 classes from ImageNet plus an extra “background” class (index 0).
Currently, both the feature extractor and model support PyTorch.
### BibTeX entry and citation info
```bibtex
@inproceedings{mobilenetv22018,
title={MobileNetV2: Inverted Residuals and Linear Bottlenecks},
author={Mark Sandler and Andrew Howard and Menglong Zhu and Andrey Zhmoginov and Liang-Chieh Chen},
booktitle={CVPR},
year={2018}
}
```
|
Vsevolod/company-names-similarity-sentence-transformer
|
Vsevolod
| 2022-11-10T12:44:01Z | 648 | 16 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-10-24T11:15:41Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1222 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.WeightedRandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 122.1875,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
huggingtweets/sbe_sus
|
huggingtweets
| 2022-11-10T12:41:46Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-10T12:20:40Z |
---
language: en
thumbnail: http://www.huggingtweets.com/sbe_sus/1668084101960/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1579111637973336071/MkdCeTeX_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">sberto.eth 📈</div>
<div style="text-align: center; font-size: 14px;">@sbe_sus</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 sberto.eth 📈.
| Data | sberto.eth 📈 |
| --- | --- |
| Tweets downloaded | 1273 |
| Retweets | 648 |
| Short tweets | 221 |
| Tweets kept | 404 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1rwjbirb/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 @sbe_sus's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ejp5m2v) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ejp5m2v/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/sbe_sus')
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)
|
Galeros/Reinforce-pong0001
|
Galeros
| 2022-11-10T12:41:31Z | 0 | 0 | null |
[
"Pong-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-10T12:41:19Z |
---
tags:
- Pong-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pong0001
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-PLE-v0
type: Pong-PLE-v0
metrics:
- type: mean_reward
value: -16.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
wakio/dummy-model
|
wakio
| 2022-11-10T12:22:03Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"camembert",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-11-10T11:54:52Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: dummy-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. -->
# dummy-model
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) 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 4.24.0
- TensorFlow 2.9.2
- Tokenizers 0.13.2
|
ahmadmwali/finetuning-sentiment-hausa-4
|
ahmadmwali
| 2022-11-10T12:14:24Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-10T11:39:58Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-hausa-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-hausa-4
This model is a fine-tuned version of [castorini/afriberta_large](https://huggingface.co/castorini/afriberta_large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3976
- Accuracy: 0.8848
- F1: 0.8850
## 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: 3
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.8.2+cu111
- Datasets 2.6.1
- Tokenizers 0.13.2
|
luanngo/evjvqa_mt5_vit_16
|
luanngo
| 2022-11-10T11:04:55Z | 101 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-07T09:04:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: evjvqa_mt5_vit_16
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. -->
# evjvqa_mt5_vit_16
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2997
- F1: 0.4194
- Bleu4: 0.3783
- Mean Pred Len: 14.85
- Mean Label Len: 15.25
## 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: 40
- eval_batch_size: 40
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 80
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Bleu4 | Mean Pred Len | Mean Label Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------------:|:--------------:|
| 15.7375 | 0.07 | 20 | 9.6637 | 0.0771 | 0.0567 | 10.75 | 15.25 |
| 15.7459 | 0.15 | 40 | 10.0761 | 0.0784 | 0.0754 | 11.5 | 15.25 |
| 15.456 | 0.22 | 60 | 9.5077 | 0.0574 | 0.0595 | 11.35 | 15.25 |
| 15.3725 | 0.3 | 80 | 9.5230 | 0.0589 | 0.0436 | 11.45 | 15.25 |
| 14.9377 | 0.37 | 100 | 8.6082 | 0.079 | 0.0725 | 12.2 | 15.25 |
| 14.5629 | 0.45 | 120 | 9.3522 | 0.0851 | 0.0704 | 12.35 | 15.25 |
| 14.2505 | 0.52 | 140 | 8.0656 | 0.0666 | 0.0473 | 11.85 | 15.25 |
| 13.4648 | 0.6 | 160 | 7.5456 | 0.0783 | 0.054 | 10.4 | 15.25 |
| 13.055 | 0.67 | 180 | 7.0022 | 0.0607 | 0.0529 | 10.2 | 15.25 |
| 12.2861 | 0.75 | 200 | 6.6263 | 0.0704 | 0.0677 | 10.4 | 15.25 |
| 11.8459 | 0.82 | 220 | 6.1817 | 0.0849 | 0.0802 | 11.15 | 15.25 |
| 10.9808 | 0.9 | 240 | 5.6607 | 0.0779 | 0.053 | 11.65 | 15.25 |
| 10.0039 | 0.97 | 260 | 5.3278 | 0.0867 | 0.0619 | 10.55 | 15.25 |
| 8.819 | 1.05 | 280 | 4.5316 | 0.1154 | 0.1346 | 9.45 | 15.25 |
| 7.5032 | 1.12 | 300 | 3.7815 | 0.1355 | 0.1159 | 9.75 | 15.25 |
| 6.1347 | 1.2 | 320 | 3.0172 | 0.1807 | 0.1546 | 9.85 | 15.25 |
| 4.8126 | 1.27 | 340 | 2.6729 | 0.2177 | 0.1978 | 9.35 | 15.25 |
| 4.1824 | 1.35 | 360 | 2.3100 | 0.3017 | 0.3567 | 11.3 | 15.25 |
| 3.6456 | 1.42 | 380 | 2.2327 | 0.3029 | 0.3605 | 11.4 | 15.25 |
| 3.3865 | 1.5 | 400 | 2.0704 | 0.316 | 0.3167 | 13.15 | 15.25 |
| 3.2078 | 1.57 | 420 | 2.0376 | 0.3027 | 0.2856 | 13.5 | 15.25 |
| 3.0357 | 1.65 | 440 | 1.9508 | 0.3207 | 0.3404 | 13.1 | 15.25 |
| 2.9388 | 1.72 | 460 | 1.9042 | 0.3872 | 0.3665 | 13.5 | 15.25 |
| 2.7807 | 1.8 | 480 | 1.8595 | 0.3954 | 0.3692 | 13.65 | 15.25 |
| 2.7234 | 1.87 | 500 | 1.8956 | 0.3871 | 0.3484 | 14.2 | 15.25 |
| 2.6417 | 1.95 | 520 | 1.7809 | 0.4406 | 0.3592 | 15.85 | 15.25 |
| 2.5189 | 2.02 | 540 | 1.7255 | 0.4242 | 0.3844 | 14.8 | 15.25 |
| 2.4075 | 2.1 | 560 | 1.7226 | 0.4378 | 0.4022 | 14.55 | 15.25 |
| 2.3158 | 2.17 | 580 | 1.6749 | 0.46 | 0.4313 | 14.7 | 15.25 |
| 2.3145 | 2.25 | 600 | 1.6850 | 0.4229 | 0.3525 | 15.75 | 15.25 |
| 2.2615 | 2.32 | 620 | 1.6651 | 0.4618 | 0.3666 | 16.65 | 15.25 |
| 2.1983 | 2.4 | 640 | 1.6409 | 0.4101 | 0.3297 | 15.1 | 15.25 |
| 2.1365 | 2.47 | 660 | 1.6350 | 0.4317 | 0.3728 | 15.4 | 15.25 |
| 2.1286 | 2.55 | 680 | 1.6045 | 0.389 | 0.3352 | 14.95 | 15.25 |
| 2.1301 | 2.62 | 700 | 1.5884 | 0.4391 | 0.3679 | 15.55 | 15.25 |
| 2.1368 | 2.7 | 720 | 1.5702 | 0.415 | 0.3352 | 15.4 | 15.25 |
| 2.0449 | 2.77 | 740 | 1.5415 | 0.4215 | 0.366 | 14.7 | 15.25 |
| 2.0286 | 2.85 | 760 | 1.5434 | 0.406 | 0.3291 | 15.35 | 15.25 |
| 2.0126 | 2.92 | 780 | 1.5358 | 0.389 | 0.3033 | 15.0 | 15.25 |
| 1.9923 | 3.0 | 800 | 1.4857 | 0.4471 | 0.3605 | 15.85 | 15.25 |
| 1.8807 | 3.07 | 820 | 1.4665 | 0.4743 | 0.3717 | 15.95 | 15.25 |
| 1.8989 | 3.15 | 840 | 1.4760 | 0.3996 | 0.3502 | 14.8 | 15.25 |
| 1.8745 | 3.22 | 860 | 1.4294 | 0.3815 | 0.3258 | 15.2 | 15.25 |
| 1.9292 | 3.3 | 880 | 1.4454 | 0.4366 | 0.3694 | 15.6 | 15.25 |
| 1.8473 | 3.37 | 900 | 1.4205 | 0.4032 | 0.3523 | 15.65 | 15.25 |
| 1.8723 | 3.45 | 920 | 1.4080 | 0.4167 | 0.3609 | 15.5 | 15.25 |
| 1.8272 | 3.52 | 940 | 1.4069 | 0.3944 | 0.3734 | 14.45 | 15.25 |
| 1.8443 | 3.6 | 960 | 1.4088 | 0.409 | 0.3712 | 14.65 | 15.25 |
| 1.7956 | 3.67 | 980 | 1.3970 | 0.3848 | 0.3573 | 14.6 | 15.25 |
| 1.802 | 3.75 | 1000 | 1.3971 | 0.4116 | 0.3856 | 14.75 | 15.25 |
| 1.8154 | 3.82 | 1020 | 1.4013 | 0.4382 | 0.3731 | 14.85 | 15.25 |
| 1.7599 | 3.9 | 1040 | 1.4035 | 0.4106 | 0.3566 | 15.25 | 15.25 |
| 1.8375 | 3.97 | 1060 | 1.3992 | 0.4286 | 0.3594 | 15.6 | 15.25 |
| 1.739 | 4.04 | 1080 | 1.3955 | 0.4218 | 0.3686 | 15.1 | 15.25 |
| 1.7291 | 4.12 | 1100 | 1.3968 | 0.4702 | 0.4011 | 15.65 | 15.25 |
| 1.7279 | 4.19 | 1120 | 1.3743 | 0.4328 | 0.3668 | 15.5 | 15.25 |
| 1.7092 | 4.27 | 1140 | 1.3650 | 0.4321 | 0.3721 | 15.55 | 15.25 |
| 1.7002 | 4.34 | 1160 | 1.3413 | 0.3999 | 0.3669 | 15.25 | 15.25 |
| 1.7333 | 4.42 | 1180 | 1.3715 | 0.4459 | 0.3758 | 16.15 | 15.25 |
| 1.707 | 4.49 | 1200 | 1.3630 | 0.4173 | 0.3686 | 15.0 | 15.25 |
| 1.6815 | 4.57 | 1220 | 1.3326 | 0.4344 | 0.3755 | 15.1 | 15.25 |
| 1.7045 | 4.64 | 1240 | 1.3440 | 0.4083 | 0.3801 | 14.7 | 15.25 |
| 1.6511 | 4.72 | 1260 | 1.3361 | 0.3976 | 0.3722 | 14.7 | 15.25 |
| 1.682 | 4.79 | 1280 | 1.3314 | 0.3964 | 0.3707 | 14.85 | 15.25 |
| 1.6511 | 4.87 | 1300 | 1.3461 | 0.4081 | 0.3704 | 15.0 | 15.25 |
| 1.5936 | 4.94 | 1320 | 1.3362 | 0.4185 | 0.3667 | 15.15 | 15.25 |
| 1.6287 | 5.02 | 1340 | 1.3312 | 0.4296 | 0.374 | 14.85 | 15.25 |
| 1.6401 | 5.09 | 1360 | 1.3152 | 0.403 | 0.366 | 14.95 | 15.25 |
| 1.6093 | 5.17 | 1380 | 1.3316 | 0.3931 | 0.3689 | 14.75 | 15.25 |
| 1.6002 | 5.24 | 1400 | 1.3506 | 0.3948 | 0.3702 | 14.8 | 15.25 |
| 1.6245 | 5.32 | 1420 | 1.3344 | 0.401 | 0.3605 | 15.1 | 15.25 |
| 1.6005 | 5.39 | 1440 | 1.3310 | 0.4174 | 0.3698 | 15.1 | 15.25 |
| 1.5903 | 5.47 | 1460 | 1.3218 | 0.4156 | 0.3716 | 14.85 | 15.25 |
| 1.6016 | 5.54 | 1480 | 1.3219 | 0.4368 | 0.3984 | 14.8 | 15.25 |
| 1.6143 | 5.62 | 1500 | 1.3157 | 0.4094 | 0.3729 | 14.55 | 15.25 |
| 1.6082 | 5.69 | 1520 | 1.3109 | 0.4068 | 0.3778 | 14.9 | 15.25 |
| 1.5451 | 5.77 | 1540 | 1.3057 | 0.4056 | 0.3703 | 14.95 | 15.25 |
| 1.6312 | 5.84 | 1560 | 1.3055 | 0.4032 | 0.3656 | 14.85 | 15.25 |
| 1.5476 | 5.92 | 1580 | 1.3282 | 0.4154 | 0.3662 | 15.2 | 15.25 |
| 1.5758 | 5.99 | 1600 | 1.3205 | 0.4136 | 0.3623 | 15.2 | 15.25 |
| 1.598 | 6.07 | 1620 | 1.3200 | 0.4159 | 0.3675 | 14.9 | 15.25 |
| 1.567 | 6.14 | 1640 | 1.3359 | 0.4153 | 0.3699 | 14.7 | 15.25 |
| 1.5349 | 6.22 | 1660 | 1.3378 | 0.4036 | 0.3649 | 14.8 | 15.25 |
| 1.5536 | 6.29 | 1680 | 1.3374 | 0.4143 | 0.3691 | 14.85 | 15.25 |
| 1.5382 | 6.37 | 1700 | 1.3274 | 0.4052 | 0.38 | 14.65 | 15.25 |
| 1.5238 | 6.44 | 1720 | 1.3217 | 0.406 | 0.3674 | 14.9 | 15.25 |
| 1.5434 | 6.52 | 1740 | 1.3174 | 0.4096 | 0.3759 | 14.85 | 15.25 |
| 1.5326 | 6.59 | 1760 | 1.3134 | 0.4096 | 0.3759 | 14.85 | 15.25 |
| 1.5263 | 6.67 | 1780 | 1.3157 | 0.4104 | 0.3635 | 15.05 | 15.25 |
| 1.4775 | 6.74 | 1800 | 1.3197 | 0.4096 | 0.3759 | 14.85 | 15.25 |
| 1.5173 | 6.82 | 1820 | 1.3121 | 0.4167 | 0.3722 | 14.9 | 15.25 |
| 1.5304 | 6.89 | 1840 | 1.3240 | 0.4198 | 0.3818 | 14.7 | 15.25 |
| 1.5344 | 6.97 | 1860 | 1.3250 | 0.4135 | 0.3793 | 14.7 | 15.25 |
| 1.5392 | 7.04 | 1880 | 1.3187 | 0.4135 | 0.3793 | 14.7 | 15.25 |
| 1.5201 | 7.12 | 1900 | 1.3128 | 0.4143 | 0.3681 | 14.8 | 15.25 |
| 1.5139 | 7.19 | 1920 | 1.3072 | 0.4143 | 0.3654 | 14.95 | 15.25 |
| 1.4878 | 7.27 | 1940 | 1.3021 | 0.4143 | 0.3654 | 14.95 | 15.25 |
| 1.5123 | 7.34 | 1960 | 1.3041 | 0.4143 | 0.3681 | 14.8 | 15.25 |
| 1.4569 | 7.42 | 1980 | 1.3203 | 0.417 | 0.3712 | 14.8 | 15.25 |
| 1.4984 | 7.49 | 2000 | 1.3149 | 0.4198 | 0.3832 | 14.65 | 15.25 |
| 1.5187 | 7.57 | 2020 | 1.3102 | 0.4076 | 0.3818 | 14.7 | 15.25 |
| 1.5394 | 7.64 | 2040 | 1.3223 | 0.4176 | 0.3907 | 14.65 | 15.25 |
| 1.4602 | 7.72 | 2060 | 1.3102 | 0.4101 | 0.3686 | 14.9 | 15.25 |
| 1.4959 | 7.79 | 2080 | 1.3123 | 0.4178 | 0.3688 | 15.05 | 15.25 |
| 1.5462 | 7.87 | 2100 | 1.3083 | 0.4262 | 0.3692 | 15.1 | 15.25 |
| 1.4951 | 7.94 | 2120 | 1.2964 | 0.4301 | 0.3816 | 14.95 | 15.25 |
| 1.5016 | 8.01 | 2140 | 1.3078 | 0.4274 | 0.3784 | 14.9 | 15.25 |
| 1.4464 | 8.09 | 2160 | 1.3154 | 0.4178 | 0.3654 | 15.1 | 15.25 |
| 1.4654 | 8.16 | 2180 | 1.3070 | 0.4243 | 0.3702 | 15.0 | 15.25 |
| 1.4519 | 8.24 | 2200 | 1.2995 | 0.4339 | 0.3708 | 15.05 | 15.25 |
| 1.5098 | 8.31 | 2220 | 1.3051 | 0.4395 | 0.3903 | 14.75 | 15.25 |
| 1.4601 | 8.39 | 2240 | 1.3013 | 0.4376 | 0.3881 | 14.8 | 15.25 |
| 1.4693 | 8.46 | 2260 | 1.2981 | 0.4278 | 0.3871 | 14.8 | 15.25 |
| 1.5386 | 8.54 | 2280 | 1.3002 | 0.4112 | 0.3781 | 14.8 | 15.25 |
| 1.5115 | 8.61 | 2300 | 1.2994 | 0.4153 | 0.3806 | 14.9 | 15.25 |
| 1.5133 | 8.69 | 2320 | 1.2971 | 0.4236 | 0.385 | 14.85 | 15.25 |
| 1.4691 | 8.76 | 2340 | 1.2979 | 0.4321 | 0.3896 | 14.75 | 15.25 |
| 1.4548 | 8.84 | 2360 | 1.3054 | 0.4276 | 0.385 | 14.75 | 15.25 |
| 1.4816 | 8.91 | 2380 | 1.3029 | 0.4259 | 0.3857 | 14.7 | 15.25 |
| 1.4386 | 8.99 | 2400 | 1.2983 | 0.4196 | 0.3826 | 14.75 | 15.25 |
| 1.5242 | 9.06 | 2420 | 1.2958 | 0.421 | 0.3739 | 14.95 | 15.25 |
| 1.4824 | 9.14 | 2440 | 1.2939 | 0.4292 | 0.3827 | 14.9 | 15.25 |
| 1.5137 | 9.21 | 2460 | 1.2896 | 0.4213 | 0.3796 | 14.8 | 15.25 |
| 1.4634 | 9.29 | 2480 | 1.2934 | 0.4191 | 0.3855 | 14.85 | 15.25 |
| 1.4881 | 9.36 | 2500 | 1.2982 | 0.4134 | 0.3838 | 14.65 | 15.25 |
| 1.4185 | 9.44 | 2520 | 1.2995 | 0.4117 | 0.3795 | 14.65 | 15.25 |
| 1.3843 | 9.51 | 2540 | 1.3013 | 0.4217 | 0.3826 | 14.65 | 15.25 |
| 1.4563 | 9.59 | 2560 | 1.3005 | 0.4117 | 0.3795 | 14.65 | 15.25 |
| 1.461 | 9.66 | 2580 | 1.3008 | 0.4194 | 0.3783 | 14.85 | 15.25 |
| 1.47 | 9.74 | 2600 | 1.2999 | 0.4194 | 0.3783 | 14.85 | 15.25 |
| 1.4892 | 9.81 | 2620 | 1.2994 | 0.4196 | 0.3826 | 14.75 | 15.25 |
| 1.4503 | 9.89 | 2640 | 1.2992 | 0.4196 | 0.3826 | 14.75 | 15.25 |
| 1.4216 | 9.96 | 2660 | 1.2997 | 0.4194 | 0.3783 | 14.85 | 15.25 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.12.1
|
karolill/nb-bert-finetuned-on-imdb
|
karolill
| 2022-11-10T10:44:28Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-09T21:40:50Z |
---
license: mit
---
# NB-BERT fine-tuned on IMDB
## Description
This model is based on the pre-trained [NB-BERT-large model](https://huggingface.co/NbAiLab/nb-bert-large?text=P%C3%A5+biblioteket+kan+du+l%C3%A5ne+en+%5BMASK%5D.). It is a model for sentiment analysis. The idea behind this model was to check if a language model mostly pretrained on norwegian (with approximately 4% english) could learn a down stream Norwegian tasks when only seing English examples during fine-tuning.
## Data for fine-tuning
This model was fine-tuned on 1000 examples from the [IMDB train dataset](https://huggingface.co/datasets/imdb) that belonged to the screen category. The training lasted 3 epochs with a learning rate of 5e-5. The code used to create this model (and some additional models) can be found on [Github](https://github.com/Karolill/NB-BERT-fine-tuned-on-english).
|
karolill/nb-bert-finetuned-on-norec
|
karolill
| 2022-11-10T10:41:04Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-10T09:08:18Z |
---
license: mit
---
# NB-BERT fine-tuned on NoReC
## Description
This model is based on the pre-trained [NB-BERT-large model](https://huggingface.co/NbAiLab/nb-bert-large?text=P%C3%A5+biblioteket+kan+du+l%C3%A5ne+en+%5BMASK%5D.). It is a model for sentiment analysis.
## Data for fine-tuning
This model was fine-tuned on 1000 exemples from the [NoReC train dataset](https://github.com/ltgoslo/norec) that belonged to the screen category. The training lasted 3 epochs with a learning rate of 5e-5. The code used to create this model (and some additional models) can be found on [Github](https://github.com/Karolill/NB-BERT-fine-tuned-on-english).
|
Norod78/hebrew-gpt_neo-xl
|
Norod78
| 2022-11-10T10:38:56Z | 65 | 9 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"onnx",
"safetensors",
"gpt_neo",
"text-generation",
"he",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language: he
thumbnail: https://avatars1.githubusercontent.com/u/3617152?norod.jpg
widget:
- text: "עוד בימי קדם"
- text: "קוראים לי דורון ואני מעוניין ל"
- text: "קוראים לי איציק ואני חושב ש"
- text: "החתול שלך מאוד חמוד ו"
- text: "ובדרך ראינו שהגן"
license: mit
---
# hebrew-gpt_neo-xl
Hebrew text generation model based on [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo). Each was trained on a TPUv3-8 which was made avilable to me via the [TPU Research Cloud](https://sites.research.google/trc/) Program.
## Datasets
1. An assortment of various Hebrew corpuses - I have made it available [here](https://mega.nz/folder/CodSSA4R#4INvMes-56m_WUi7jQMbJQ)
2. oscar / unshuffled_deduplicated_he - [Homepage](https://oscar-corpus.com) | [Dataset Permalink](https://huggingface.co/datasets/viewer/?dataset=oscar&config=unshuffled_deduplicated_he)
The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.
3. CC100-Hebrew Dataset [Homepage](https://metatext.io/datasets/cc100-hebrew)
Created by Conneau & Wenzek et al. at 2020, the CC100-Hebrew This dataset is one of the 100 corpora of monolingual data that was processed from the January-December 2018 Commoncrawl snapshots from the CC-Net repository. The size of this corpus is 6.1G., in Hebrew language.
## Training Config
Available [here](https://github.com/Norod/hebrew-gpt_neo/tree/main/hebrew-gpt_neo-xl/configs) <BR>
## Usage
### Google Colab Notebook
Available [here ](https://colab.research.google.com/github/Norod/hebrew-gpt_neo/blob/main/hebrew-gpt_neo-xl/Norod78_hebrew_gpt_neo_xl_Colab.ipynb) <BR>
#### Simple usage sample code
```python
!pip install tokenizers==0.10.3 transformers==4.8.0
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-gpt_neo-xl")
model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-gpt_neo-xl", pad_token_id=tokenizer.eos_token_id)
prompt_text = "אני אוהב שוקולד ועוגות"
max_len = 512
sample_output_num = 3
seed = 1000
import numpy as np
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = 0 if torch.cuda.is_available()==False else torch.cuda.device_count()
print(f"device: {device}, n_gpu: {n_gpu}")
np.random.seed(seed)
torch.manual_seed(seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(seed)
model.to(device)
encoded_prompt = tokenizer.encode(
prompt_text, add_special_tokens=False, return_tensors="pt")
encoded_prompt = encoded_prompt.to(device)
if encoded_prompt.size()[-1] == 0:
input_ids = None
else:
input_ids = encoded_prompt
print("input_ids = " + str(input_ids))
if input_ids != None:
max_len += len(encoded_prompt[0])
if max_len > 2048:
max_len = 2048
print("Updated max_len = " + str(max_len))
stop_token = "<|endoftext|>"
new_lines = "\
\
\
"
sample_outputs = model.generate(
input_ids,
do_sample=True,
max_length=max_len,
top_k=50,
top_p=0.95,
num_return_sequences=sample_output_num
)
print(100 * '-' + "\
\t\tOutput\
" + 100 * '-')
for i, sample_output in enumerate(sample_outputs):
text = tokenizer.decode(sample_output, skip_special_tokens=True)
# Remove all text after the stop token
text = text[: text.find(stop_token) if stop_token else None]
# Remove all text after 3 newlines
text = text[: text.find(new_lines) if new_lines else None]
print("\
{}: {}".format(i, text))
print("\
" + 100 * '-')
```
|
Norod78/hebrew-gpt_neo-small
|
Norod78
| 2022-11-10T10:35:44Z | 400 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"onnx",
"safetensors",
"gpt_neo",
"text-generation",
"he",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language: he
thumbnail: https://avatars1.githubusercontent.com/u/3617152?norod.jpg
widget:
- text: "עוד בימי קדם"
- text: "קוראים לי דורון ואני מעוניין ל"
- text: "קוראים לי איציק ואני חושב ש"
- text: "החתול שלך מאוד חמוד ו"
license: mit
---
# hebrew-gpt_neo-small
Hebrew text generation model based on [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo). Each was trained on a TPUv3-8 which was made avilable to me via the [TPU Research Cloud](https://sites.research.google/trc/) Program.
## Datasets
1. An assortment of various Hebrew corpuses - I have made it available [here](https://mega.nz/folder/CodSSA4R#4INvMes-56m_WUi7jQMbJQ)
2. oscar / unshuffled_deduplicated_he - [Homepage](https://oscar-corpus.com) | [Dataset Permalink](https://huggingface.co/datasets/viewer/?dataset=oscar&config=unshuffled_deduplicated_he)
The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.
3. CC100-Hebrew Dataset [Homepage](https://metatext.io/datasets/cc100-hebrew)
Created by Conneau & Wenzek et al. at 2020, the CC100-Hebrew This dataset is one of the 100 corpora of monolingual data that was processed from the January-December 2018 Commoncrawl snapshots from the CC-Net repository. The size of this corpus is 6.1G., in Hebrew language.
## Training Config
Available [here](https://github.com/Norod/hebrew-gpt_neo/tree/main/hebrew-gpt_neo-small/configs) <BR>
## Usage
### Google Colab Notebook
Available [here ](https://colab.research.google.com/github/Norod/hebrew-gpt_neo/blob/main/hebrew-gpt_neo-small/Norod78_hebrew_gpt_neo_small_Colab.ipynb) <BR>
#### Simple usage sample code
```python
!pip install tokenizers==0.10.2 transformers==4.6.0
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-gpt_neo-small")
model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-gpt_neo-small", pad_token_id=tokenizer.eos_token_id)
prompt_text = "אני אוהב שוקולד ועוגות"
max_len = 512
sample_output_num = 3
seed = 1000
import numpy as np
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = 0 if torch.cuda.is_available()==False else torch.cuda.device_count()
print(f"device: {device}, n_gpu: {n_gpu}")
np.random.seed(seed)
torch.manual_seed(seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(seed)
model.to(device)
encoded_prompt = tokenizer.encode(
prompt_text, add_special_tokens=False, return_tensors="pt")
encoded_prompt = encoded_prompt.to(device)
if encoded_prompt.size()[-1] == 0:
input_ids = None
else:
input_ids = encoded_prompt
print("input_ids = " + str(input_ids))
if input_ids != None:
max_len += len(encoded_prompt[0])
if max_len > 2048:
max_len = 2048
print("Updated max_len = " + str(max_len))
stop_token = "<|endoftext|>"
new_lines = "\n\n\n"
sample_outputs = model.generate(
input_ids,
do_sample=True,
max_length=max_len,
top_k=50,
top_p=0.95,
num_return_sequences=sample_output_num
)
print(100 * '-' + "\n\t\tOutput\n" + 100 * '-')
for i, sample_output in enumerate(sample_outputs):
text = tokenizer.decode(sample_output, skip_special_tokens=True)
# Remove all text after the stop token
text = text[: text.find(stop_token) if stop_token else None]
# Remove all text after 3 newlines
text = text[: text.find(new_lines) if new_lines else None]
print("\n{}: {}".format(i, text))
print("\n" + 100 * '-')
```
|
facebook/maskformer-swin-base-ade
|
facebook
| 2022-11-10T10:22:19Z | 3,410 | 11 |
transformers
|
[
"transformers",
"pytorch",
"maskformer",
"vision",
"image-segmentation",
"dataset:scene_parse_150",
"arxiv:2107.06278",
"license:other",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2022-03-02T23:29:05Z |
---
license: other
tags:
- vision
- image-segmentation
datasets:
- scene_parse_150
widget:
- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg
example_title: House
- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg
example_title: Castle
---
# MaskFormer
MaskFormer model trained on ADE20k semantic segmentation (base-sized version, Swin backbone). It was introduced in the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) and first released in [this repository](https://github.com/facebookresearch/MaskFormer/blob/da3e60d85fdeedcb31476b5edd7d328826ce56cc/mask_former/modeling/criterion.py#L169).
Disclaimer: The team releasing MaskFormer did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
MaskFormer addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation.
## Intended uses & limitations
You can use this particular checkpoint for semantic segmentation. See the [model hub](https://huggingface.co/models?search=maskformer) to look for other
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
from PIL import Image
import requests
url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-base-ade")
inputs = feature_extractor(images=image, return_tensors="pt")
model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-ade")
outputs = model(**inputs)
# model predicts class_queries_logits of shape `(batch_size, num_queries)`
# and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
class_queries_logits = outputs.class_queries_logits
masks_queries_logits = outputs.masks_queries_logits
# you can pass them to feature_extractor for postprocessing
# we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs)
predicted_semantic_map = feature_extractor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/maskformer).
|
ArtbyArbi/picbixex
|
ArtbyArbi
| 2022-11-10T10:00:15Z | 33 | 0 |
diffusers
|
[
"diffusers",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2022-11-10T09:57:15Z |
---
license: mit
---
### PicBixex on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
#### model by ArbiCreatesArt
This your the Stable Diffusion model fine-tuned the PicBixex concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt(s)`: **PicBixex**
You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
PicBixex
.jpg)
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.jpg)
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|
naverpapago/garnet
|
naverpapago
| 2022-11-10T09:33:03Z | 0 | 2 |
pytorch
|
[
"pytorch",
"Scene Text Removal",
"Image to Image",
"arxiv:2210.07489",
"license:apache-2.0",
"region:us"
] | null | 2022-11-08T02:01:55Z |
---
license: apache-2.0
tags:
- Scene Text Removal
- Image to Image
library_name: pytorch
---
### GaRNet
This is text-removal model that introduced in the paper below and first released at [this page](https://github.com/naver/garnet). \
[The Surprisingly Straightforward Scene Text Removal Method With Gated Attention and Region of Interest Generation: A Comprehensive Prominent Model Analysis](https://arxiv.org/abs/2210.07489). \
Hyeonsu Lee, Chankyu Choi \
Naver Corp. \
In ECCV 2022.
### Model description
GaRNet is a generator that create non-text image with given image and coresponding text box mask. It consists of convolution encoder and decoder. The encoder consists of residual block with attention module called Gated Attention.
Gated Attention module has two Spatial attention branch. Each attention branch finds text stroke or its surrounding regions. The module adjusts the weight of these two domains by trainable parameters.
The model was trained in PatchGAN manner with Region-of-Interest Generation. \
The discriminator is consists of convolution encoder. Given an image, it determines whether each patch, which indicates text-box regions, is real or fake.
All loss functions treat non-textbox regions as 'don't care'.
### Intended uses & limitations
This model can be used for areas that require the process of erasing text from an image, such as concealment private information, text editing.\
You can use the raw model or pre-trained model.\
Note that pre-trained model was trained in both Synthetic and SCUT_EnsText dataset. And the SCUT-EnsText dataset can only be used for non-commercial research purposes.
### How to use
You can use inference code in [this page](https://github.com/naver/garnet).
### BibTeX entry and citation info
```
@inproceedings{lee2022surprisingly,
title={The Surprisingly Straightforward Scene Text Removal Method with Gated Attention and Region of Interest Generation: A Comprehensive Prominent Model Analysis},
author={Lee, Hyeonsu and Choi, Chankyu},
booktitle={European Conference on Computer Vision},
pages={457--472},
year={2022},
organization={Springer}
}
```
|
alexionby/clip-l-roberta-finetuned
|
alexionby
| 2022-11-10T09:05:37Z | 81 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vision-text-dual-encoder",
"feature-extraction",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-11-10T07:16:25Z |
---
tags:
- generated_from_trainer
model-index:
- name: clip-l-roberta-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clip-l-roberta-finetuned
This model is a fine-tuned version of [./clip-l-roberta](https://huggingface.co/./clip-l-roberta) 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: 22
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.2
|
CompVis/stable-diffusion-v-1-2-original
|
CompVis
| 2022-11-10T08:54:27Z | 0 | 13 | null |
[
"stable-diffusion",
"text-to-image",
"arxiv:2112.10752",
"arxiv:2103.00020",
"arxiv:2205.11487",
"arxiv:2207.12598",
"arxiv:1910.09700",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2022-08-10T12:40:54Z |
---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
extra_gated_prompt: |-
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
extra_gated_heading: Please read the LICENSE to access this model
---
# Stable Diffusion v1 Model Card
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
The **Stable-Diffusion-v-1-2** checkpoint was initialized with the weights of the [Stable-Diffusion-v-1-1](https:/steps/huggingface.co/CompVis/stable-diffusion-v-1-1-original)
checkpoint and subsequently fine-tuned on 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`.
For more information, please refer to [Training](#training).
#### Download the weights
- [sd-v1-2.ckpt](https://huggingface.co/CompVis/stable-diffusion-v-1-2-original/resolve/main/sd-v1-2.ckpt)
- [sd-v1-2-full-ema.ckpt](https://huggingface.co/CompVis/stable-diffusion-v-1-2-original/resolve/main/sd-v1-2-full-ema.ckpt)
This weights are intended to be used with the original [CompVis Stable Diffusion codebase](https://github.com/CompVis/stable-diffusion). If you are looking for the model to use with the D🧨iffusers library, [come here](https://huggingface.co/CompVis/stable-diffusion-v1-2).
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and
tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
and is not fit for product use without additional safety mechanisms and
considerations.
- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-2B (en) and subsets thereof (see next section)
**Training Procedure**
Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through a ViT-L/14 text-encoder.
- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet.
We currently provide three checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`,
which were trained as follows,
- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 2
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
steps show the relative improvements of the checkpoints:
Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 150000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
## Citation
```bibtex
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
```
*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
|
CompVis/stable-diffusion-v-1-1-original
|
CompVis
| 2022-11-10T08:54:14Z | 0 | 17 | null |
[
"stable-diffusion",
"text-to-image",
"arxiv:2112.10752",
"arxiv:2103.00020",
"arxiv:2205.11487",
"arxiv:2207.12598",
"arxiv:1910.09700",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2022-08-09T19:36:42Z |
---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
extra_gated_prompt: |-
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
extra_gated_heading: Please read the LICENSE to access this model
---
# Stable Diffusion v1 Model Card
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
The **Stable-Diffusion-v-1-1** was trained on 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en), followed by
194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). For more information, please refer to [Training](#training).
#### Download the weights
- [sd-v1-1.ckpt](https://huggingface.co/CompVis/stable-diffusion-v-1-1-original/resolve/main/sd-v1-1.ckpt)
- [sd-v1-1-full-ema.ckpt](https://huggingface.co/CompVis/stable-diffusion-v-1-1-original/resolve/main/sd-v1-1-full-ema.ckpt)
This weights are intended to be used with the original [CompVis Stable Diffusion codebase](https://github.com/CompVis/stable-diffusion). If you are looking for the model to use with the D🧨iffusers library, [come here](https://huggingface.co/CompVis/stable-diffusion-v1-1).
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and
tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
and is not fit for product use without additional safety mechanisms and
considerations.
- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-2B (en) and subsets thereof (see next section)
**Training Procedure**
Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through a ViT-L/14 text-encoder.
- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet.
We currently provide three checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`,
which were trained as follows,
- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 2
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
steps show the relative improvements of the checkpoints:
Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 150000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
## Citation
```bibtex
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
```
*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
|
csukuangfj/wenet-english-model
|
csukuangfj
| 2022-11-10T08:47:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-11-10T08:37:30Z |
# Introduction
Models in this file are downloaded from
<https://github.com/wenet-e2e/wenet/releases/download/v2.0.1/en.tar.gz>
```bash
wget https://github.com/wenet-e2e/wenet/releases/download/v2.0.1/en.tar.gz
tar xvf en.tar.gz --strip-components=1
rm en.tar.gz
```
|
ntsema/wav2vec2-xlsr-53-espeak-cv-ft-sah2-ntsema-colab
|
ntsema
| 2022-11-10T08:25:33Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:audiofolder",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-09T07:30:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-xlsr-53-espeak-cv-ft-sah2-ntsema-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Wer
type: wer
value: 0.3295938104448743
---
<!-- 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-xlsr-53-espeak-cv-ft-sah2-ntsema-colab
This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3586
- Wer: 0.3296
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4128 | 5.71 | 400 | 0.4462 | 0.5733 |
| 0.2344 | 11.43 | 800 | 0.3489 | 0.3969 |
| 0.1181 | 17.14 | 1200 | 0.3470 | 0.3602 |
| 0.0837 | 22.85 | 1600 | 0.3608 | 0.3451 |
| 0.0645 | 28.57 | 2000 | 0.3586 | 0.3296 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
vent42/test
|
vent42
| 2022-11-10T08:22:44Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2022-11-10T08:22:44Z |
---
license: bigscience-openrail-m
---
|
ahujaniharika95/distilbert-base-uncased-finetuned-squad
|
ahujaniharika95
| 2022-11-10T08:18:34Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-12T06:14:07Z |
---
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.
## 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: 1
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
Kohei1201/distilbert-base-uncased-finetuned-cola
|
Kohei1201
| 2022-11-10T07:48:32Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-10T06:43:42Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5567273065308361
---
<!-- 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: 0.8434
- Matthews Correlation: 0.5567
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5224 | 1.0 | 535 | 0.5360 | 0.4275 |
| 0.3498 | 2.0 | 1070 | 0.5205 | 0.5078 |
| 0.2383 | 3.0 | 1605 | 0.6466 | 0.5318 |
| 0.1739 | 4.0 | 2140 | 0.7723 | 0.5532 |
| 0.1276 | 5.0 | 2675 | 0.8434 | 0.5567 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.10.2
- Datasets 2.6.1
- Tokenizers 0.13.2
|
libok/test
|
libok
| 2022-11-10T06:57:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-11-10T06:56:42Z |
a robot reading the book and playing the piano
|
TimePlan/q-FrozenLake-v1-4x4-noSlippery
|
TimePlan
| 2022-11-10T06:54:48Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-10T06:54:40Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="TimePlan/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
NoCrypt/momocha-mix
|
NoCrypt
| 2022-11-10T06:49:03Z | 0 | 19 | null |
[
"stable-diffusion",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2022-11-10T06:39:29Z |
---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
inference: false
---
# Momocha mix models
Scrapped from [chenyfan's sharepoint](https://cyfan-my.sharepoint.com/:f:/g/personal/chenyfan_cyfan_onmicrosoft_com/EilOWB40m3ZJn6ahczIUIs4B6v0XvizO5YorOhG_5eYSUw?e=ZyP7qE)
Example output:
|
Terence3927/q-Taxi-v3
|
Terence3927
| 2022-11-10T06:20:54Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-10T06:20:45Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Terence3927/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"])
```
|
Terence3927/q-FrozenLake-v1-4x4-noSlippery
|
Terence3927
| 2022-11-10T06:12:39Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-10T06:08:58Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Terence3927/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Yocel1/whisper-small-fr
|
Yocel1
| 2022-11-10T05:29:29Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-09T16:31:12Z |
---
language:
- hi
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Fr - Joss
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0 FR
type: mozilla-foundation/common_voice_11_0
args: 'config: fr, split: test'
metrics:
- name: Wer
type: wer
value: 24.03653329331678
---
<!-- 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. -->
# Whisper Small Fr - Joss
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 FR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4212
- Wer: 24.0365
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.3803 | 0.99 | 1000 | 0.3992 | 23.9465 |
| 0.2214 | 1.99 | 2000 | 0.3902 | 22.8108 |
| 0.0986 | 2.98 | 3000 | 0.4028 | 22.4459 |
| 0.0478 | 3.98 | 4000 | 0.4212 | 24.0365 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Terence3927/ppo-LunarLander-v2-optuna
|
Terence3927
| 2022-11-10T05:17:58Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-10T05:17:35Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 275.24 +/- 24.12
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
xu1998hz/sescore_english_coco
|
xu1998hz
| 2022-11-10T03:59:19Z | 0 | 1 | null |
[
"region:us"
] | null | 2022-11-05T01:45:07Z |
SEScore for COCO Image Captioning
|
lerma3/xlm-roberta-base-finetuned-panx-de
|
lerma3
| 2022-11-10T03:50:53Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-09T20:34:16Z |
---
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: 0.8648740833380706
---
<!-- 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: 0.1365
- F1: 0.8649
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 |
| 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 |
| 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
zhangfx7/deberta-base-finetuned-cola
|
zhangfx7
| 2022-11-10T02:43:42Z | 101 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-10T02:22:29Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: deberta-base-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.0
---
<!-- 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-base-finetuned-cola
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6187
- Matthews Correlation: 0.0
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.6187 | 1.0 | 535 | 0.6187 | 0.0 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
42MARU/ko-42maru-wav2vec2-conformer-del-1s
|
42MARU
| 2022-11-10T02:33:57Z | 81 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2-conformer",
"automatic-speech-recognition",
"audio",
"ko",
"dataset:KsponSpeech",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-10-31T07:50:05Z |
---
language:
- ko # Example: fr
license: apache-2.0 # Example: apache-2.0 or any license from https://hf.co/docs/hub/repositories-licenses
library_name: transformers # Optional. Example: keras or any library from https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Libraries.ts
tags:
- audio
- automatic-speech-recognition
datasets:
- KsponSpeech
metrics:
- wer # Example: wer. Use metric id from https://hf.co/metrics
---
# ko-42maru-wav2vec2-conformer-del-1s
## Table of Contents
- [ko-42maru-wav2vec2-conformer-del-1s](#ko-42maru-wav2vec2-conformer-del-1s)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Evaluation](#evaluation)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
## Model Details
- **Model Description:**
해당 모델은 wav2vec2-conformer base architecture에 scratch pre-training 되었습니다. <br />
Wav2Vec2ConformerForCTC를 이용하여 KsponSpeech에 대한 Fine-Tuning 모델입니다. <br />
- Dataset use [AIHub KsponSpeech](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=123) <br />
Datasets는 해당 Data를 전처리하여 임의로 만들어 사용하였습니다. <br />
del-1s의 의미는 1초 이하의 데이터 필터링을 의미합니다. <br />
해당 모델은 **음성전사를 자체 커스텀한 42maru** 기준의 데이터로 학습된 모델입니다. (숫자와 영어는 한글 표기법을 따름) <br />
- **Developed by:** TADev (@lIlBrother, @ddobokki, @jp42maru)
- **Language(s):** Korean
- **License:** apache-2.0
- **Parent Model:** See the [wav2vec2-conformer](https://huggingface.co/docs/transformers/model_doc/wav2vec2-conformer) for more information about the pre-trained base model. (해당 모델은 wav2vec2-conformer base architecture에 scratch pre-training 되었습니다.)
## Evaluation
Just using `load_metric("wer")` and `load_metric("wer")` in huggingface `datasets` library <br />
## How to Get Started With the Model
KenLM과 혼용된 Wav2Vec2ProcessorWithLM 예제를 보시려면 [42maru-kenlm 예제](https://huggingface.co/42MARU/ko-ctc-kenlm-42maru-only-wiki)를 참고하세요
```python
import librosa
from pyctcdecode import build_ctcdecoder
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForCTC,
AutoTokenizer,
Wav2Vec2ProcessorWithLM,
)
from transformers.pipelines import AutomaticSpeechRecognitionPipeline
audio_path = ""
# 모델과 토크나이저, 예측을 위한 각 모듈들을 불러옵니다.
model = AutoModelForCTC.from_pretrained("42MARU/ko-42maru-wav2vec2-conformer-del-1s")
feature_extractor = AutoFeatureExtractor.from_pretrained("42MARU/ko-42maru-wav2vec2-conformer-del-1s")
tokenizer = AutoTokenizer.from_pretrained("42MARU/ko-42maru-wav2vec2-conformer-del-1s")
beamsearch_decoder = build_ctcdecoder(
labels=list(tokenizer.encoder.keys()),
kenlm_model_path=None,
)
processor = Wav2Vec2ProcessorWithLM(
feature_extractor=feature_extractor, tokenizer=tokenizer, decoder=beamsearch_decoder
)
# 실제 예측을 위한 파이프라인에 정의된 모듈들을 삽입.
asr_pipeline = AutomaticSpeechRecognitionPipeline(
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
decoder=processor.decoder,
device=-1,
)
# 음성파일을 불러오고 beamsearch 파라미터를 특정하여 예측을 수행합니다.
raw_data, _ = librosa.load(audio_path, sr=16000)
kwargs = {"decoder_kwargs": {"beam_width": 100}}
pred = asr_pipeline(inputs=raw_data, **kwargs)["text"]
# 모델이 자소 분리 유니코드 텍스트로 나오므로, 일반 String으로 변환해줄 필요가 있습니다.
result = unicodedata.normalize("NFC", pred)
print(result)
# 안녕하세요 하나둘셋 테스트입니다.
```
*Beam-100 Result (WER)*:
| "clean" | "other" |
| ------- | ------- |
| 21.52 | 25.72 |
|
undertheseanlp/vietnamese-ner-v1.4.0a2
|
undertheseanlp
| 2022-11-10T02:29:43Z | 389 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"vi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-10T02:16:40Z |
---
license: apache-2.0
language: vi
---
|
irfan-noordin/segformer-b0-finetuned-segments-sidewalk-oct-22
|
irfan-noordin
| 2022-11-10T02:23:44Z | 157 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:other",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2022-11-09T06:58:03Z |
---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-sidewalk-oct-22
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. -->
# segformer-b0-finetuned-segments-sidewalk-oct-22
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9249
- Mean Iou: 0.1675
- Mean Accuracy: 0.2109
- Overall Accuracy: 0.7776
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.8631
- Accuracy Flat-sidewalk: 0.9423
- Accuracy Flat-crosswalk: 0.0
- Accuracy Flat-cyclinglane: 0.4704
- Accuracy Flat-parkingdriveway: 0.1421
- Accuracy Flat-railtrack: 0.0
- Accuracy Flat-curb: 0.0061
- Accuracy Human-person: 0.0
- Accuracy Human-rider: 0.0
- Accuracy Vehicle-car: 0.8937
- Accuracy Vehicle-truck: 0.0
- Accuracy Vehicle-bus: 0.0
- Accuracy Vehicle-tramtrain: 0.0
- Accuracy Vehicle-motorcycle: 0.0
- Accuracy Vehicle-bicycle: 0.0
- Accuracy Vehicle-caravan: 0.0
- Accuracy Vehicle-cartrailer: 0.0
- Accuracy Construction-building: 0.9143
- Accuracy Construction-door: 0.0
- Accuracy Construction-wall: 0.0055
- Accuracy Construction-fenceguardrail: 0.0
- Accuracy Construction-bridge: 0.0
- Accuracy Construction-tunnel: nan
- Accuracy Construction-stairs: 0.0
- Accuracy Object-pole: 0.0
- Accuracy Object-trafficsign: 0.0
- Accuracy Object-trafficlight: 0.0
- Accuracy Nature-vegetation: 0.9291
- Accuracy Nature-terrain: 0.8710
- Accuracy Sky: 0.9207
- Accuracy Void-ground: 0.0
- Accuracy Void-dynamic: 0.0
- Accuracy Void-static: 0.0
- Accuracy Void-unclear: 0.0
- Iou Unlabeled: nan
- Iou Flat-road: 0.6127
- Iou Flat-sidewalk: 0.8192
- Iou Flat-crosswalk: 0.0
- Iou Flat-cyclinglane: 0.4256
- Iou Flat-parkingdriveway: 0.1262
- Iou Flat-railtrack: 0.0
- Iou Flat-curb: 0.0061
- Iou Human-person: 0.0
- Iou Human-rider: 0.0
- Iou Vehicle-car: 0.6655
- Iou Vehicle-truck: 0.0
- Iou Vehicle-bus: 0.0
- Iou Vehicle-tramtrain: 0.0
- Iou Vehicle-motorcycle: 0.0
- Iou Vehicle-bicycle: 0.0
- Iou Vehicle-caravan: 0.0
- Iou Vehicle-cartrailer: 0.0
- Iou Construction-building: 0.5666
- Iou Construction-door: 0.0
- Iou Construction-wall: 0.0054
- Iou Construction-fenceguardrail: 0.0
- Iou Construction-bridge: 0.0
- Iou Construction-tunnel: nan
- Iou Construction-stairs: 0.0
- Iou Object-pole: 0.0
- Iou Object-trafficsign: 0.0
- Iou Object-trafficlight: 0.0
- Iou Nature-vegetation: 0.7875
- Iou Nature-terrain: 0.6912
- Iou Sky: 0.8218
- Iou Void-ground: 0.0
- Iou Void-dynamic: 0.0
- Iou Void-static: 0.0
- Iou Void-unclear: 0.0
## 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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:|
| 2.832 | 0.05 | 20 | 3.1768 | 0.0700 | 0.1095 | 0.5718 | nan | 0.1365 | 0.9472 | 0.0019 | 0.0006 | 0.0004 | 0.0 | 0.0205 | 0.0 | 0.0 | 0.2074 | 0.0 | 0.0 | 0.0 | 0.0017 | 0.0001 | 0.0 | 0.0 | 0.7360 | 0.0 | 0.0235 | 0.0050 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9559 | 0.0429 | 0.5329 | 0.0010 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1260 | 0.5906 | 0.0016 | 0.0006 | 0.0004 | 0.0 | 0.0175 | 0.0 | 0.0 | 0.2006 | 0.0 | 0.0 | 0.0 | 0.0003 | 0.0001 | 0.0 | 0.0 | 0.3729 | 0.0 | 0.0209 | 0.0044 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5778 | 0.0408 | 0.4932 | 0.0009 | 0.0 | 0.0 | 0.0 |
| 2.3224 | 0.1 | 40 | 2.4686 | 0.0885 | 0.1321 | 0.6347 | nan | 0.5225 | 0.9260 | 0.0005 | 0.0001 | 0.0006 | 0.0 | 0.0113 | 0.0 | 0.0 | 0.3738 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8191 | 0.0 | 0.0263 | 0.0012 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9649 | 0.0701 | 0.6434 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4240 | 0.6602 | 0.0005 | 0.0001 | 0.0006 | 0.0 | 0.0109 | 0.0 | 0.0 | 0.3292 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3962 | 0.0 | 0.0260 | 0.0011 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6019 | 0.0617 | 0.5862 | 0.0001 | 0.0 | 0.0 | 0.0 |
| 2.1961 | 0.15 | 60 | 1.9886 | 0.0988 | 0.1431 | 0.6500 | nan | 0.5168 | 0.9319 | 0.0 | 0.0001 | 0.0000 | 0.0 | 0.0032 | 0.0 | 0.0 | 0.5761 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8325 | 0.0 | 0.0132 | 0.0003 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9612 | 0.1260 | 0.7625 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3929 | 0.6721 | 0.0 | 0.0001 | 0.0000 | 0.0 | 0.0032 | 0.0 | 0.0 | 0.4609 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4375 | 0.0 | 0.0131 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6342 | 0.1108 | 0.6353 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2.2964 | 0.2 | 80 | 2.0597 | 0.1066 | 0.1503 | 0.6682 | nan | 0.6577 | 0.9207 | 0.0 | 0.0000 | 0.0002 | 0.0 | 0.0044 | 0.0 | 0.0 | 0.5257 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8466 | 0.0 | 0.0094 | 0.0001 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9526 | 0.2022 | 0.8392 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4276 | 0.7093 | 0.0 | 0.0000 | 0.0002 | 0.0 | 0.0044 | 0.0 | 0.0 | 0.4438 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4488 | 0.0 | 0.0093 | 0.0001 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6560 | 0.1833 | 0.7408 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.9751 | 0.25 | 100 | 1.7493 | 0.1186 | 0.1645 | 0.6944 | nan | 0.7604 | 0.9146 | 0.0 | 0.0004 | 0.0012 | 0.0 | 0.0016 | 0.0 | 0.0 | 0.7381 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8273 | 0.0 | 0.0026 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9636 | 0.3289 | 0.8909 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4904 | 0.7490 | 0.0 | 0.0004 | 0.0012 | 0.0 | 0.0016 | 0.0 | 0.0 | 0.5465 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4913 | 0.0 | 0.0026 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6542 | 0.2761 | 0.7004 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.7626 | 0.3 | 120 | 1.5608 | 0.1295 | 0.1752 | 0.7118 | nan | 0.8168 | 0.9102 | 0.0 | 0.0002 | 0.0025 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.8094 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8362 | 0.0 | 0.0030 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9492 | 0.5677 | 0.8861 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4958 | 0.7592 | 0.0 | 0.0002 | 0.0025 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.5680 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5095 | 0.0 | 0.0030 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7082 | 0.4878 | 0.7392 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.32 | 0.35 | 140 | 1.5048 | 0.1323 | 0.1797 | 0.7181 | nan | 0.7883 | 0.9260 | 0.0 | 0.0000 | 0.0037 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.8711 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8590 | 0.0 | 0.0022 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9128 | 0.7088 | 0.8576 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5141 | 0.7598 | 0.0 | 0.0000 | 0.0037 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.5287 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5016 | 0.0 | 0.0022 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7458 | 0.5602 | 0.7499 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.6464 | 0.4 | 160 | 1.3886 | 0.1342 | 0.1783 | 0.7217 | nan | 0.7859 | 0.9390 | 0.0 | 0.0 | 0.0059 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7401 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8508 | 0.0 | 0.0010 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9368 | 0.7223 | 0.9025 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5173 | 0.7561 | 0.0 | 0.0 | 0.0058 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5846 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5059 | 0.0 | 0.0010 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7366 | 0.5802 | 0.7401 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4757 | 0.45 | 180 | 1.3649 | 0.1367 | 0.1840 | 0.7255 | nan | 0.8587 | 0.9185 | 0.0 | 0.0001 | 0.0039 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8588 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8337 | 0.0 | 0.0014 | 0.0001 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9036 | 0.7809 | 0.9138 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5077 | 0.7693 | 0.0 | 0.0001 | 0.0039 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5980 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5264 | 0.0 | 0.0014 | 0.0001 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7521 | 0.6078 | 0.7438 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2.0018 | 0.5 | 200 | 1.3118 | 0.1353 | 0.1839 | 0.7242 | nan | 0.7797 | 0.9457 | 0.0 | 0.0029 | 0.0057 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8345 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8509 | 0.0 | 0.0018 | 0.0001 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8704 | 0.8688 | 0.9069 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5321 | 0.7602 | 0.0 | 0.0029 | 0.0057 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6060 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5276 | 0.0 | 0.0018 | 0.0001 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7133 | 0.5551 | 0.7593 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4636 | 0.55 | 220 | 1.2729 | 0.1330 | 0.1797 | 0.7249 | nan | 0.8619 | 0.9203 | 0.0 | 0.0015 | 0.0067 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8903 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8514 | 0.0 | 0.0031 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9447 | 0.5448 | 0.9040 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5249 | 0.7844 | 0.0 | 0.0015 | 0.0066 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5735 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5336 | 0.0 | 0.0031 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7136 | 0.4869 | 0.7613 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.1856 | 0.6 | 240 | 1.2551 | 0.1382 | 0.1828 | 0.7274 | nan | 0.7497 | 0.9518 | 0.0 | 0.0005 | 0.0048 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8893 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8153 | 0.0 | 0.0048 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9475 | 0.7597 | 0.9107 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5097 | 0.7477 | 0.0 | 0.0005 | 0.0047 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6172 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5527 | 0.0 | 0.0048 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7293 | 0.6250 | 0.7703 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4577 | 0.65 | 260 | 1.1862 | 0.1387 | 0.1848 | 0.7304 | nan | 0.8842 | 0.9065 | 0.0 | 0.0001 | 0.0024 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8566 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8632 | 0.0 | 0.0006 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9442 | 0.7313 | 0.9080 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5121 | 0.7833 | 0.0 | 0.0001 | 0.0024 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6297 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5381 | 0.0 | 0.0006 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7437 | 0.6199 | 0.7486 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0748 | 0.7 | 280 | 1.2000 | 0.1391 | 0.1846 | 0.7301 | nan | 0.7249 | 0.9690 | 0.0 | 0.0005 | 0.0064 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8909 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8656 | 0.0 | 0.0014 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8917 | 0.8362 | 0.9065 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5306 | 0.7403 | 0.0 | 0.0005 | 0.0063 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6223 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5491 | 0.0 | 0.0014 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7566 | 0.6061 | 0.7761 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.642 | 0.75 | 300 | 1.1452 | 0.1432 | 0.1880 | 0.7409 | nan | 0.8682 | 0.9389 | 0.0 | 0.0030 | 0.0062 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8605 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8759 | 0.0 | 0.0020 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9092 | 0.8515 | 0.8892 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5333 | 0.7905 | 0.0 | 0.0030 | 0.0062 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6393 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5418 | 0.0 | 0.0020 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7655 | 0.6551 | 0.7893 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.2166 | 0.8 | 320 | 1.1450 | 0.1388 | 0.1849 | 0.7391 | nan | 0.8516 | 0.9460 | 0.0 | 0.0043 | 0.0060 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.8944 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8803 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9283 | 0.6849 | 0.9071 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5584 | 0.7932 | 0.0 | 0.0043 | 0.0060 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.5844 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5259 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7548 | 0.5985 | 0.7549 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2.1346 | 0.85 | 340 | 1.1215 | 0.1428 | 0.1887 | 0.7411 | nan | 0.7956 | 0.9551 | 0.0 | 0.0145 | 0.0098 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.8646 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8884 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9131 | 0.8828 | 0.9024 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5611 | 0.7721 | 0.0 | 0.0145 | 0.0097 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.6313 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5405 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7563 | 0.6337 | 0.7917 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.8351 | 0.9 | 360 | 1.1012 | 0.1433 | 0.1896 | 0.7449 | nan | 0.8723 | 0.9432 | 0.0 | 0.0025 | 0.0114 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8822 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8662 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9213 | 0.8361 | 0.9201 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5472 | 0.7989 | 0.0 | 0.0025 | 0.0113 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6277 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5416 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7666 | 0.6674 | 0.7664 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.152 | 0.95 | 380 | 1.1045 | 0.1452 | 0.1891 | 0.7453 | nan | 0.8827 | 0.9332 | 0.0 | 0.0457 | 0.0124 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8396 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8848 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9399 | 0.7910 | 0.9107 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5462 | 0.7966 | 0.0 | 0.0457 | 0.0123 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6494 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5395 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7636 | 0.6627 | 0.7763 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.2062 | 1.0 | 400 | 1.0607 | 0.1469 | 0.1897 | 0.7482 | nan | 0.8192 | 0.9644 | 0.0 | 0.0944 | 0.0198 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8406 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8821 | 0.0 | 0.0006 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9193 | 0.8054 | 0.9137 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5772 | 0.7742 | 0.0 | 0.0941 | 0.0195 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6414 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5360 | 0.0 | 0.0006 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7740 | 0.6591 | 0.7710 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0116 | 1.05 | 420 | 1.0503 | 0.1493 | 0.1950 | 0.7554 | nan | 0.8686 | 0.9478 | 0.0 | 0.2033 | 0.0295 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9166 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8409 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9414 | 0.7667 | 0.9196 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5809 | 0.8022 | 0.0 | 0.1995 | 0.0287 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5916 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5517 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7628 | 0.6441 | 0.7652 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.009 | 1.1 | 440 | 1.0723 | 0.1529 | 0.1958 | 0.7553 | nan | 0.7797 | 0.9670 | 0.0 | 0.2214 | 0.0547 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.8978 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8927 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9274 | 0.8016 | 0.9176 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5898 | 0.7717 | 0.0 | 0.2157 | 0.0526 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.6389 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5499 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7760 | 0.6697 | 0.7818 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.1496 | 1.15 | 460 | 1.0417 | 0.1571 | 0.2017 | 0.7607 | nan | 0.7736 | 0.9645 | 0.0 | 0.3606 | 0.0669 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.8775 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8801 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9098 | 0.8906 | 0.9326 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6102 | 0.7737 | 0.0 | 0.3374 | 0.0634 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.6549 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5538 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7682 | 0.6437 | 0.7772 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4669 | 1.2 | 480 | 1.0161 | 0.1566 | 0.2024 | 0.7637 | nan | 0.8236 | 0.9531 | 0.0 | 0.3507 | 0.0584 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.9165 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8675 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9263 | 0.8597 | 0.9222 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6005 | 0.7983 | 0.0 | 0.3296 | 0.0556 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.6153 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5498 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7752 | 0.6654 | 0.7770 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.075 | 1.25 | 500 | 1.0124 | 0.1556 | 0.2000 | 0.7634 | nan | 0.8521 | 0.9499 | 0.0 | 0.3154 | 0.0410 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.8944 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8618 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9442 | 0.8133 | 0.9290 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5910 | 0.8068 | 0.0 | 0.2992 | 0.0394 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.6338 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5507 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7689 | 0.6697 | 0.7737 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.888 | 1.3 | 520 | 0.9797 | 0.1597 | 0.2028 | 0.7677 | nan | 0.8590 | 0.9472 | 0.0 | 0.3534 | 0.0469 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.8900 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8807 | 0.0 | 0.0005 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9379 | 0.8578 | 0.9187 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5908 | 0.8056 | 0.0 | 0.3311 | 0.0448 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.6598 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5676 | 0.0 | 0.0005 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7712 | 0.6912 | 0.8088 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.8099 | 1.35 | 540 | 0.9760 | 0.1589 | 0.2026 | 0.7678 | nan | 0.8526 | 0.9534 | 0.0 | 0.3370 | 0.0313 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.9235 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8862 | 0.0 | 0.0005 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9252 | 0.8551 | 0.9206 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5954 | 0.8014 | 0.0 | 0.3188 | 0.0303 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.6382 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5706 | 0.0 | 0.0005 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7830 | 0.6934 | 0.8122 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.1998 | 1.4 | 560 | 0.9815 | 0.1578 | 0.2030 | 0.7631 | nan | 0.8956 | 0.9250 | 0.0 | 0.3267 | 0.0461 | 0.0 | 0.0004 | 0.0 | 0.0 | 0.8929 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8956 | 0.0 | 0.0002 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9206 | 0.8669 | 0.9275 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5656 | 0.8136 | 0.0 | 0.3102 | 0.0440 | 0.0 | 0.0004 | 0.0 | 0.0 | 0.6574 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5524 | 0.0 | 0.0002 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7894 | 0.6940 | 0.7818 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.5591 | 1.45 | 580 | 0.9654 | 0.1618 | 0.2043 | 0.7698 | nan | 0.8198 | 0.9655 | 0.0 | 0.3715 | 0.0848 | 0.0 | 0.0003 | 0.0 | 0.0 | 0.8935 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8965 | 0.0 | 0.0013 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9146 | 0.8730 | 0.9198 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6182 | 0.7898 | 0.0 | 0.3467 | 0.0792 | 0.0 | 0.0003 | 0.0 | 0.0 | 0.6590 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5647 | 0.0 | 0.0013 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7871 | 0.6835 | 0.8101 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.861 | 1.5 | 600 | 0.9622 | 0.1607 | 0.2045 | 0.7689 | nan | 0.8163 | 0.9648 | 0.0 | 0.3780 | 0.0907 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.9187 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8714 | 0.0 | 0.0006 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9229 | 0.8485 | 0.9361 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6180 | 0.7903 | 0.0 | 0.3541 | 0.0844 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.6307 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5609 | 0.0 | 0.0006 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7854 | 0.6904 | 0.7884 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.8335 | 1.55 | 620 | 0.9569 | 0.1598 | 0.2050 | 0.7686 | nan | 0.8421 | 0.9561 | 0.0 | 0.3493 | 0.0928 | 0.0 | 0.0012 | 0.0 | 0.0 | 0.9261 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8753 | 0.0 | 0.0013 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9172 | 0.8688 | 0.9335 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6069 | 0.8031 | 0.0 | 0.3306 | 0.0860 | 0.0 | 0.0012 | 0.0 | 0.0 | 0.6123 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5618 | 0.0 | 0.0013 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7851 | 0.6911 | 0.7950 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.9988 | 1.6 | 640 | 0.9337 | 0.1611 | 0.2050 | 0.7711 | nan | 0.8595 | 0.9538 | 0.0 | 0.3512 | 0.0928 | 0.0 | 0.0006 | 0.0 | 0.0 | 0.8962 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8854 | 0.0 | 0.0004 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9281 | 0.8594 | 0.9367 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6062 | 0.8105 | 0.0 | 0.3310 | 0.0868 | 0.0 | 0.0006 | 0.0 | 0.0 | 0.6565 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5596 | 0.0 | 0.0004 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7819 | 0.6958 | 0.7880 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.966 | 1.65 | 660 | 0.9322 | 0.1612 | 0.2051 | 0.7707 | nan | 0.8706 | 0.9494 | 0.0 | 0.3470 | 0.0997 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.8905 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8722 | 0.0 | 0.0016 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9347 | 0.8652 | 0.9364 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5953 | 0.8136 | 0.0 | 0.3281 | 0.0922 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.6654 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5696 | 0.0 | 0.0016 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7756 | 0.6890 | 0.7885 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.2154 | 1.7 | 680 | 0.9373 | 0.1611 | 0.2048 | 0.7710 | nan | 0.8448 | 0.9577 | 0.0 | 0.3717 | 0.1010 | 0.0 | 0.0007 | 0.0 | 0.0 | 0.9173 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8613 | 0.0 | 0.0026 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9411 | 0.8371 | 0.9246 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6096 | 0.8056 | 0.0 | 0.3487 | 0.0930 | 0.0 | 0.0007 | 0.0 | 0.0 | 0.6272 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5696 | 0.0 | 0.0026 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7762 | 0.6911 | 0.7931 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.7979 | 1.75 | 700 | 0.9429 | 0.1622 | 0.2067 | 0.7717 | nan | 0.8496 | 0.9548 | 0.0 | 0.3821 | 0.1182 | 0.0 | 0.0013 | 0.0 | 0.0 | 0.9071 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8803 | 0.0 | 0.0043 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9202 | 0.8812 | 0.9204 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6104 | 0.8088 | 0.0 | 0.3583 | 0.1074 | 0.0 | 0.0013 | 0.0 | 0.0 | 0.6410 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5675 | 0.0 | 0.0043 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7784 | 0.6767 | 0.7994 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.8366 | 1.8 | 720 | 0.9379 | 0.1645 | 0.2075 | 0.7745 | nan | 0.8359 | 0.9580 | 0.0 | 0.4130 | 0.1275 | 0.0 | 0.0021 | 0.0 | 0.0 | 0.8998 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8704 | 0.0 | 0.0088 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.9450 | 0.8617 | 0.9251 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6227 | 0.8035 | 0.0 | 0.3850 | 0.1147 | 0.0 | 0.0021 | 0.0 | 0.0 | 0.6544 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5777 | 0.0 | 0.0088 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.7682 | 0.6867 | 0.8055 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0448 | 1.85 | 740 | 0.9419 | 0.1659 | 0.2087 | 0.7769 | nan | 0.8483 | 0.9532 | 0.0 | 0.4442 | 0.1387 | 0.0 | 0.0028 | 0.0 | 0.0 | 0.8986 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8865 | 0.0 | 0.0042 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9458 | 0.8442 | 0.9215 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6240 | 0.8122 | 0.0 | 0.4077 | 0.1237 | 0.0 | 0.0028 | 0.0 | 0.0 | 0.6529 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5700 | 0.0 | 0.0041 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7767 | 0.6938 | 0.8070 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.9737 | 1.9 | 760 | 0.9193 | 0.1664 | 0.2082 | 0.7772 | nan | 0.8420 | 0.9586 | 0.0 | 0.4353 | 0.1193 | 0.0 | 0.0010 | 0.0 | 0.0 | 0.9082 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8955 | 0.0 | 0.0079 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9385 | 0.8464 | 0.9190 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6232 | 0.8053 | 0.0 | 0.4022 | 0.1088 | 0.0 | 0.0010 | 0.0 | 0.0 | 0.6549 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5766 | 0.0 | 0.0079 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7843 | 0.7077 | 0.8180 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0716 | 1.95 | 780 | 0.9170 | 0.1672 | 0.2098 | 0.7785 | nan | 0.8434 | 0.9539 | 0.0 | 0.4671 | 0.1283 | 0.0 | 0.0037 | 0.0 | 0.0 | 0.9012 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8984 | 0.0 | 0.0058 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9398 | 0.8661 | 0.9157 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6242 | 0.8106 | 0.0 | 0.4232 | 0.1156 | 0.0 | 0.0037 | 0.0 | 0.0 | 0.6631 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5777 | 0.0 | 0.0057 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7811 | 0.6920 | 0.8223 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4144 | 2.0 | 800 | 0.9249 | 0.1675 | 0.2109 | 0.7776 | nan | 0.8631 | 0.9423 | 0.0 | 0.4704 | 0.1421 | 0.0 | 0.0061 | 0.0 | 0.0 | 0.8937 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9143 | 0.0 | 0.0055 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9291 | 0.8710 | 0.9207 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6127 | 0.8192 | 0.0 | 0.4256 | 0.1262 | 0.0 | 0.0061 | 0.0 | 0.0 | 0.6655 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5666 | 0.0 | 0.0054 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7875 | 0.6912 | 0.8218 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.6.1
- Tokenizers 0.12.1
|
ntsema/wav2vec2-xlsr-53-espeak-cv-ft-mhr2-ntsema-colab
|
ntsema
| 2022-11-10T01:46:14Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:audiofolder",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-10T00:13:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-xlsr-53-espeak-cv-ft-mhr2-ntsema-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Wer
type: wer
value: 0.7993311036789298
---
<!-- 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-xlsr-53-espeak-cv-ft-mhr2-ntsema-colab
This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7562
- Wer: 0.7993
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.5636 | 5.79 | 400 | 1.8357 | 1.0 |
| 1.6348 | 11.59 | 800 | 0.6797 | 0.8528 |
| 0.8624 | 17.39 | 1200 | 0.6651 | 0.8194 |
| 0.5248 | 23.19 | 1600 | 0.6892 | 0.7826 |
| 0.3328 | 28.98 | 2000 | 0.7562 | 0.7993 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.14.0.dev20221109+cu116
- Datasets 2.6.1
- Tokenizers 0.13.2
|
burakyldrm/wav2vec2-burak-new-300-v2-6
|
burakyldrm
| 2022-11-10T01:45:10Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-09T19:25:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-burak-new-300-v2-6
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-burak-new-300-v2-6
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3074
- Wer: 0.2340
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 151
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 6.3136 | 9.61 | 500 | 3.1262 | 1.0 |
| 1.8247 | 19.23 | 1000 | 0.4049 | 0.5065 |
| 0.5387 | 28.83 | 1500 | 0.2828 | 0.3462 |
| 0.3713 | 38.45 | 2000 | 0.2761 | 0.3125 |
| 0.293 | 48.08 | 2500 | 0.2872 | 0.3001 |
| 0.2436 | 57.68 | 3000 | 0.2912 | 0.2904 |
| 0.2116 | 67.3 | 3500 | 0.2910 | 0.2725 |
| 0.1859 | 76.91 | 4000 | 0.2937 | 0.2533 |
| 0.1731 | 86.53 | 4500 | 0.2985 | 0.2485 |
| 0.1569 | 96.15 | 5000 | 0.3022 | 0.2409 |
| 0.1471 | 105.76 | 5500 | 0.3070 | 0.2374 |
| 0.1385 | 115.38 | 6000 | 0.2954 | 0.2429 |
| 0.1289 | 124.99 | 6500 | 0.3016 | 0.2361 |
| 0.1268 | 134.61 | 7000 | 0.3000 | 0.2368 |
| 0.12 | 144.23 | 7500 | 0.3074 | 0.2340 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
sanchit-gandhi/whisper-medium-es-5k
|
sanchit-gandhi
| 2022-11-10T01:33:57Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"es",
"dataset:facebook/multilingual_librispeech",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-09T19:30:55Z |
---
language:
- es
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- facebook/multilingual_librispeech
metrics:
- wer
model-index:
- name: Whisper Small Es - Sanchit Gandhi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Multilingual LibriSpeech
type: facebook/multilingual_librispeech
args: 'config: es, split: test'
metrics:
- name: Wer
type: wer
value: 60.16226172047142
---
<!-- 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. -->
# Whisper Small Es - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Multilingual LibriSpeech dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2668
- Wer: 60.1623
## 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-08
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- 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
- training_steps: 2500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 2.2112 | 0.2 | 500 | 1.7394 | 61.1126 |
| 1.4913 | 0.4 | 1000 | 1.3758 | 62.8143 |
| 1.6651 | 0.6 | 1500 | 1.3100 | 61.3261 |
| 1.7031 | 0.8 | 2000 | 1.2752 | 60.5261 |
| 1.4289 | 1.0 | 2500 | 1.2668 | 60.1623 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.0
- Datasets 2.6.2.dev0
- Tokenizers 0.12.1
|
noahkim/KoBigBird-KoBart-News-Summarization
|
noahkim
| 2022-11-10T01:19:59Z | 107 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"summarization",
"news",
"ko",
"autotrain_compatible",
"region:us"
] |
summarization
| 2022-09-15T01:25:23Z |
---
language: ko
tags:
- summarization
- news
inference: false
model-index:
- name: KoBigBird-KoBart-News-Summarization
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# KoBigBird-KoBart-News-Summarization
This model is a fine-tuned version of [noahkim/KoBigBird-KoBart-News-Summarization](https://huggingface.co/noahkim/KoBigBird-KoBart-News-Summarization) on the [daekeun-ml/naver-news-summarization-ko](https://huggingface.co/datasets/daekeun-ml/naver-news-summarization-ko)
## Model description
<<20221110 Commit>>
<<KoBigBird-KoBart-News-Summarization 모델 설명>>
다중문서요약(Multi-Document-Summarization) Task를 위해서 KoBigBird 모델을 Encoder-Decoder모델을 만들어서 학습을 진행했습니다. KoBigBird를 Decoder로 쓰려고 했으나 오류가 생겨서 요약에 특화된 KoBART의 Decoder를 활용해서 모델을 생성했습니다.
프로젝트용으로 뉴스 요약 모델 특화된 모델을 만들기 위해 기존에 만들었던 KoBigBird-KoBart-News-Summarization 모델에 추가적으로 daekeun-ml님이 제공해주신 naver-news-summarization-ko 데이터셋으로 파인튜닝 했습니다.
현재 AI-HUB에서 제공하는 요약 데이터를 추가 학습 진행 예정입니다.
지속적으로 발전시켜 좋은 성능의 모델을 구현하겠습니다.
감사합니다.
실행환경
- Google Colab Pro
- CPU : Intel(R) Xeon(R) CPU @ 2.20GHz
- GPU : A100-SXM4-40GB
<pre><code>
# Python Code
from transformers import AutoTokenizer
from transformers import AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("noahkim/KoT5_news_summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("noahkim/KoT5_news_summarization")
</pre></code>
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.0748 | 1.0 | 1388 | 4.3067 |
| 3.8457 | 2.0 | 2776 | 4.2039 |
| 3.7459 | 3.0 | 4164 | 4.1433 |
| 3.6773 | 4.0 | 5552 | 4.1236 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
flamesbob/Steampunk_angel
|
flamesbob
| 2022-11-10T01:08:39Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-11-10T01:06:53Z |
---
license: creativeml-openrail-m
---
art by `Steampunk_angel` this style gives a steampunk look and feel with gears and sometimes mechanical wings to prompts.
License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:
You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here
|
flamesbob/Caster_style
|
flamesbob
| 2022-11-10T01:06:34Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-11-10T01:01:58Z |
---
license: creativeml-openrail-m
---
art by `caster_style` this style gives a lot of magical clothes, purple, and crystals, to prompts.
License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:
You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here
|
ntsema/wav2vec2-xlsr-53-espeak-cv-ft-mhr-ntsema-colab
|
ntsema
| 2022-11-10T00:12:21Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:audiofolder",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-07T17:02:23Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-xlsr-53-espeak-cv-ft-mhr-ntsema-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Wer
type: wer
value: 0.8127090301003345
---
<!-- 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-xlsr-53-espeak-cv-ft-mhr-ntsema-colab
This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7728
- Wer: 0.8127
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.8463 | 5.79 | 400 | 1.0428 | 0.9331 |
| 1.4576 | 11.59 | 800 | 0.6796 | 0.8495 |
| 0.8054 | 17.39 | 1200 | 0.7131 | 0.8227 |
| 0.4946 | 23.19 | 1600 | 0.7202 | 0.8194 |
| 0.3157 | 28.98 | 2000 | 0.7728 | 0.8127 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.14.0.dev20221107+cu116
- Datasets 2.6.1
- Tokenizers 0.13.2
|
malay-patel/bert-ww-finetuned-squad
|
malay-patel
| 2022-11-09T23:20:25Z | 60 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-11-09T07:19:23Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: malay-patel/bert-ww-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# malay-patel/bert-ww-finetuned-squad
This model is a fine-tuned version of [bert-large-cased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1766
- Train End Logits Accuracy: 0.9455
- Train Start Logits Accuracy: 0.9312
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16638, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:-----:|
| 0.5635 | 0.8374 | 0.7992 | 0 |
| 0.3369 | 0.8987 | 0.8695 | 1 |
| 0.1766 | 0.9455 | 0.9312 | 2 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.2
|
model-attribution-challenge/gpt2-chinese-cluecorpussmall
|
model-attribution-challenge
| 2022-11-09T22:54:34Z | 109 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"zh",
"dataset:CLUECorpusSmall",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-09T20:16:03Z |
---
language: zh
datasets: CLUECorpusSmall
widget:
- text: "这是很久之前的事情了"
---
# Chinese GPT2 Model
## Model description
The model is used to generate Chinese texts. You can download the model either from the [GPT2-Chinese Github page](https://github.com/Morizeyao/GPT2-Chinese), or via HuggingFace from the link [gpt2-chinese-cluecorpussmall](https://huggingface.co/uer/gpt2-chinese-cluecorpussmall).
## How to use
You can use the model directly with a pipeline for text generation:
```python
>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-cluecorpussmall")
>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-cluecorpussmall")
>>> text_generator = TextGenerationPipeline(model, tokenizer)
>>> text_generator("这是很久之前的事情了", max_length=100, do_sample=True)
[{'generated_text': '这是很久之前的事情了 ， 我 曾 经 把 这 个 当 做 一 种 思 想 的 传 承 ， 或 者 是 人 生 的 回 顾 ， 当 时 我 们 是 一 个 刚 刚 加 入 的 时 候 就 想 要 加 入 他 们 ， 于 是 我 们 每 天 看 到 他 们 ， 加 上 他 们 的 各 种 不 可 思 议 的 行 为 ， 直 到 现 在 ， 我 们 的 人 生 才 完 整 起 来 。'}]
```
## Training data
[CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data.
## Training procedure
The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We pre-train 1,000,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 1024.
Stage1:
```
python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
--vocab_path models/google_zh_vocab.txt \
--dataset_path cluecorpussmall_lm_seq128_dataset.pt \
--seq_length 128 --processes_num 32 --data_processor lm
```
```
python3 pretrain.py --dataset_path cluecorpussmall_lm_seq128_dataset.pt \
--vocab_path models/google_zh_vocab.txt \
--config_path models/gpt2/config.json \
--output_model_path models/cluecorpussmall_gpt2_seq128_model.bin \
--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
--total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \
--learning_rate 1e-4 --batch_size 64
```
Stage2:
```
python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
--vocab_path models/google_zh_vocab.txt \
--dataset_path cluecorpussmall_lm_seq1024_dataset.pt \
--seq_length 1024 --processes_num 32 --data_processor lm
```
```
python3 pretrain.py --dataset_path cluecorpussmall_lm_seq1024_dataset.pt \
--vocab_path models/google_zh_vocab.txt \
--pretrained_model_path models/cluecorpussmall_gpt2_seq128_model.bin-1000000 \
--config_path models/gpt2/config.json \
--output_model_path models/cluecorpussmall_gpt2_seq1024_model.bin \
--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
--total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \
--learning_rate 5e-5 --batch_size 16
```
Finally, we convert the pre-trained model into Huggingface's format:
```
python3 scripts/convert_gpt2_from_uer_to_huggingface.py --input_model_path cluecorpussmall_gpt2_seq1024_model.bin-250000 \
--output_model_path pytorch_model.bin \
--layers_num 12
```
### BibTeX entry and citation info
```
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}
@article{zhao2019uer,
title={UER: An Open-Source Toolkit for Pre-training Models},
author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong},
journal={EMNLP-IJCNLP 2019},
pages={241},
year={2019}
}
```
|
gngpostalsrvc/BERiT_2000_enriched
|
gngpostalsrvc
| 2022-11-09T22:33:52Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-11-09T22:02:09Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: BERiT_2000_enriched
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. -->
# BERiT_2000_enriched
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.6052
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.786 | 0.19 | 500 | 6.6797 |
| 6.6441 | 0.39 | 1000 | 6.6574 |
| 6.6376 | 0.58 | 1500 | 6.6240 |
| 6.5951 | 0.77 | 2000 | 6.6291 |
| 6.6123 | 0.97 | 2500 | 6.6355 |
| 6.6028 | 1.16 | 3000 | 6.6084 |
| 6.5974 | 1.36 | 3500 | 6.5984 |
| 6.6104 | 1.55 | 4000 | 6.5775 |
| 6.6113 | 1.74 | 4500 | 6.6062 |
| 6.5895 | 1.94 | 5000 | 6.5931 |
| 6.6106 | 2.13 | 5500 | 6.6276 |
| 6.635 | 2.32 | 6000 | 6.5973 |
| 6.5694 | 2.52 | 6500 | 6.6021 |
| 6.612 | 2.71 | 7000 | 6.5882 |
| 6.5984 | 2.9 | 7500 | 6.6052 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
model-attribution-challenge/bert-base-chinese
|
model-attribution-challenge
| 2022-11-09T22:14:11Z | 161 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-11-09T20:14:16Z |
---
language: zh
---
# Bert-base-chinese
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
- **Model Description:**
This model has been pre-trained for Chinese, training and random input masking has been applied independently to word pieces (as in the original BERT paper).
- **Developed by:** HuggingFace team
- **Model Type:** Fill-Mask
- **Language(s):** Chinese
- **License:** [More Information needed]
- **Parent Model:** See the [BERT base uncased model](https://huggingface.co/bert-base-uncased) for more information about the BERT base model.
## Uses
#### Direct Use
This model can be used for masked language modeling
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## Training
#### Training Procedure
* **type_vocab_size:** 2
* **vocab_size:** 21128
* **num_hidden_layers:** 12
#### Training Data
[More Information Needed]
## Evaluation
#### Results
[More Information Needed]
## How to Get Started With the Model
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese")
model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese")
```
|
kartikpalani/eai-setfit-model3
|
kartikpalani
| 2022-11-09T22:13:11Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-11-09T22:13:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 3214 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 3214,
"warmup_steps": 322,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
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The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.