modelId stringlengths 4 111 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringlengths 5 30 ⌀ | author stringlengths 2 34 ⌀ | config null | securityStatus null | id stringlengths 4 111 | likes int64 0 9.53k | downloads int64 2 73.6M | library_name stringlengths 2 84 ⌀ | created timestamp[us] | card stringlengths 101 901k | card_len int64 101 901k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CeroShrijver/m3e-base-text-classification | 2023-06-24T10:32:07.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | CeroShrijver | null | null | CeroShrijver/m3e-base-text-classification | 0 | 2 | transformers | 2023-06-16T19:32:41 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: m3e-base-text-classification
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. -->
# m3e-ba... | 1,446 | [
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sam34738/mBERT | 2023-06-16T23:44:39.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | sam34738 | null | null | sam34738/mBERT | 0 | 2 | transformers | 2023-06-16T20:24:12 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: mbert
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. -->
# mber... | 1,473 | [
[
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mrjunos/depression-reddit-distilroberta-base | 2023-06-20T23:05:41.000Z | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"depression",
"reddit",
"generated_from_trainer",
"en",
"dataset:mrjunos/depression-reddit-cleaned",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | mrjunos | null | null | mrjunos/depression-reddit-distilroberta-base | 0 | 2 | transformers | 2023-06-17T01:45:38 | ---
license: apache-2.0
tags:
- text-classification
- depression
- reddit
- generated_from_trainer
datasets:
- mrjunos/depression-reddit-cleaned
metrics:
- accuracy
widget:
- text:
- >-
i just found out my boyfriend is depressed i really want to be there for him
but i feel like i ve only been saying the wrong... | 4,381 | [
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yo/tagger | 2023-06-18T08:56:18.000Z | [
"transformers",
"pytorch",
"tf",
"roberta",
"text-classification",
"en",
"dataset:cardiffnlp/tweet_topic_multi",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | yo | null | null | yo/tagger | 0 | 2 | transformers | 2023-06-17T11:59:17 | ---
language: en
widget:
- text: It is great to see athletes promoting awareness for climate change.
datasets:
- cardiffnlp/tweet_topic_multi
license: mit
metrics:
- f1
- accuracy
pipeline_tag: text-classification
---
# Lenster Tagger
<b>Labels</b>:
| <span style="font-weight:normal">0: arts\_&_culture</span... | 1,839 | [
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JvThunder/a2c-AntBulletEnv-v0 | 2023-07-20T09:02:28.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | JvThunder | null | null | JvThunder/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-06-17T19:07:25 | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
... | 791 | [
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arminmrm93/dqn-SpaceInvadersNoFrameskip-V4 | 2023-06-18T02:27:49.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | arminmrm93 | null | null | arminmrm93/dqn-SpaceInvadersNoFrameskip-V4 | 0 | 2 | stable-baselines3 | 2023-06-17T23:42:53 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
... | 2,764 | [
[
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edwardjjj/ppo-LunarLander-v2 | 2023-07-12T08:09:11.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | edwardjjj | null | null | edwardjjj/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-06-18T05:37:16 | ---
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
... | 784 | [
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antphb/DS-Chatbox-gpt2-vietnamese-V3 | 2023-06-19T11:13:12.000Z | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | antphb | null | null | antphb/DS-Chatbox-gpt2-vietnamese-V3 | 0 | 2 | transformers | 2023-06-18T07:40:12 | ---
tags:
- generated_from_trainer
model-index:
- name: DS-Chatbox-gpt2-vietnamese-V3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DS-Chatbox-gpt2-vietnames... | 1,835 | [
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MUmairAB/English_to_French_Translation_Transformer | 2023-06-19T18:46:14.000Z | [
"keras",
"region:us"
] | null | MUmairAB | null | null | MUmairAB/English_to_French_Translation_Transformer | 0 | 2 | keras | 2023-06-18T08:50:01 | ---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters ... | 840 | [
[
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... |
michaelfeil/ct2fast-e5-small | 2023-10-13T13:36:53.000Z | [
"sentence-transformers",
"bert",
"ctranslate2",
"int8",
"float16",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"en",
"arxiv:2212.03533",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | sentence-similarity | michaelfeil | null | null | michaelfeil/ct2fast-e5-small | 1 | 2 | sentence-transformers | 2023-06-18T11:41:56 | ---
tags:
- ctranslate2
- int8
- float16
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
model-index:
- name: e5-small
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: e... | 70,093 | [
[
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0.0... |
ManuD/speecht5_finetuned_voxpopuli_de | 2023-06-18T13:54:38.000Z | [
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | ManuD | null | null | ManuD/speecht5_finetuned_voxpopuli_de | 0 | 2 | transformers | 2023-06-18T11:52:20 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_de
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 comm... | 1,565 | [
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mazeinmouse/dqn-SpaceInvadersNoFrameskip-v | 2023-06-18T19:57:55.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | mazeinmouse | null | null | mazeinmouse/dqn-SpaceInvadersNoFrameskip-v | 0 | 2 | stable-baselines3 | 2023-06-18T19:57:10 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
... | 2,768 | [
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gevis1/distilbert-base-cased-finetuned-financial-csv-gevis1 | 2023-06-20T03:27:54.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | gevis1 | null | null | gevis1/distilbert-base-cased-finetuned-financial-csv-gevis1 | 0 | 2 | transformers | 2023-06-18T22:14:46 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-cased-finetuned-financial-csv-gevis1
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 thi... | 1,109 | [
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NasimB/gpt2_left_out_gutenberg | 2023-06-19T13:03:02.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | NasimB | null | null | NasimB/gpt2_left_out_gutenberg | 0 | 2 | transformers | 2023-06-19T09:06:05 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2_left_out_gutenberg
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. -->... | 3,211 | [
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IIC/XLM-R_Galen | 2023-06-19T11:05:59.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"beto",
"galen",
"es",
"license:mit",
"endpoints_compatible",
"region:us"
] | feature-extraction | IIC | null | null | IIC/XLM-R_Galen | 0 | 2 | transformers | 2023-06-19T10:51:30 | ---
language: es
tags:
- beto
- galen
license: mit
---
# XLM-R Galén
This is a third party reupload of the original XLM-R Galén model, available in [GitHub](https://github.com/guilopgar/ClinicalCodingTransformerES).
Please refer to the original publication for more information
## BibTeX entry and citation info
```... | 652 | [
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emresvd/u198 | 2023-06-19T14:05:39.000Z | [
"keras",
"region:us"
] | null | emresvd | null | null | emresvd/u198 | 0 | 2 | keras | 2023-06-19T14:05:37 | ---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters ... | 840 | [
[
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0.0002460... |
yo/locale-detector | 2023-06-19T14:23:41.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:common_language",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | yo | null | null | yo/locale-detector | 0 | 2 | transformers | 2023-06-19T14:14:56 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- common_language
metrics:
- accuracy
model-index:
- name: language-detection-fine-tuned-on-xlm-roberta-base
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: common_language
type: common_language... | 1,748 | [
[
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mun33b/dqn-SpaceInvadersNoFrameskip-v4 | 2023-06-19T18:14:14.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | mun33b | null | null | mun33b/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-06-19T15:53:18 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
... | 2,752 | [
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namedotpg/dqn-SpaceInvadersTraining | 2023-06-19T21:26:39.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | namedotpg | null | null | namedotpg/dqn-SpaceInvadersTraining | 0 | 2 | stable-baselines3 | 2023-06-19T21:26:01 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
... | 2,760 | [
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-0.0253143310546875,
... |
vladimirchabanov/mnist_decoder | 2023-06-20T13:30:39.000Z | [
"keras",
"region:us"
] | null | vladimirchabanov | null | null | vladimirchabanov/mnist_decoder | 0 | 2 | keras | 2023-06-20T13:24:14 | ---
library_name: keras
---
# Чать автоэнкодера (декодер) обученный на наборе данных mnist
Форма входа: `(49,)`
Форма выхода: `(28, 28, 1)`
Функция активации выходного слоя: `sigmoid` | 186 | [
[
-0.00678253173828125,
-0.0377197265625,
0.03741455078125,
0.0015249252319335938,
-0.04071044921875,
0.0101776123046875,
0.04254150390625,
0.0183258056640625,
0.050048828125,
0.0198822021484375,
-0.053009033203125,
-0.045379638671875,
-0.046142578125,
-0.0016... |
vladimirchabanov/fashion_mnist_decoder | 2023-06-20T13:32:45.000Z | [
"keras",
"region:us"
] | null | vladimirchabanov | null | null | vladimirchabanov/fashion_mnist_decoder | 0 | 2 | keras | 2023-06-20T13:32:29 | ---
library_name: keras
---
# Чать автоэнкодера (декодер) обученный на наборе данных fashion_mnist
Форма входа: `(49,)`
Форма выхода: `(28, 28, 1)`
Функция активации выходного слоя: `sigmoid` | 194 | [
[
-0.0019521713256835938,
-0.0419921875,
0.025726318359375,
0.00995635986328125,
-0.048248291015625,
0.015838623046875,
0.036651611328125,
-0.00025010108947753906,
0.0443115234375,
0.01149749755859375,
-0.064208984375,
-0.055206298828125,
-0.031402587890625,
-... |
kchen621/dqn-SpaceInvadersNoFrameskip-v4 | 2023-06-20T13:54:28.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | kchen621 | null | null | kchen621/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-06-20T13:53:48 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
... | 2,759 | [
[
-0.043365478515625,
-0.039154052734375,
0.0200347900390625,
0.0252532958984375,
-0.0109405517578125,
-0.017974853515625,
0.00994110107421875,
-0.01275634765625,
0.012969970703125,
0.0233917236328125,
-0.0723876953125,
-0.035003662109375,
-0.02569580078125,
-... |
SotirisLegkas/Socratic-GODEL-instruct | 2023-06-20T14:54:20.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | SotirisLegkas | null | null | SotirisLegkas/Socratic-GODEL-instruct | 0 | 2 | transformers | 2023-06-20T13:54:02 | ---
pipeline_tag: text2text-generation
---
Instruction: given a context, reply as in a Socratic dialogue. | 105 | [
[
0.021728515625,
-0.046966552734375,
0.0299224853515625,
0.018646240234375,
-0.030181884765625,
-0.01247406005859375,
-0.002147674560546875,
0.018402099609375,
0.01352691650390625,
0.0645751953125,
-0.059295654296875,
-0.007228851318359375,
-0.0233917236328125,
... |
venomdenom/MarkModel | 2023-06-20T15:56:31.000Z | [
"keras",
"dataset:mnist",
"region:us"
] | null | venomdenom | null | null | venomdenom/MarkModel | 0 | 2 | keras | 2023-06-20T14:34:00 | ---
datasets:
- mnist
metrics:
- accuracy
library_name: keras
---
## Задание:
Дан датасет mnist по входному изображению определить цифру;
## Общее количество обучаемых параметров: 269,322
## Используемые алгоритмы:
adam_optimizer - алгоритм оптимизации
sparse_categorical_crossentropy - категориальна... | 732 | [
[
-0.0308380126953125,
-0.058441162109375,
0.01064300537109375,
0.0075225830078125,
-0.031402587890625,
0.024505615234375,
0.007038116455078125,
-0.003833770751953125,
0.034332275390625,
-0.01311492919921875,
-0.057647705078125,
-0.049957275390625,
-0.053253173828... |
IlyaHtuePav/ForExam | 2023-06-20T17:48:18.000Z | [
"keras",
"region:us"
] | null | IlyaHtuePav | null | null | IlyaHtuePav/ForExam | 0 | 2 | keras | 2023-06-20T14:59:21 | ---
library_name: keras
---
Текст задания: "1. Дан датасет mnist по входному изображению определить цифру"
1. Данная модель нейросети предназначена для распознавания цифр.
2. Изображение послойной архитектуры НС: рисунок ниже.
3. Общее количество обучаемых параметров НС: рисунок ниже.
4. Алгоритм оптимизации: A... | 823 | [
[
-0.0285797119140625,
-0.04937744140625,
0.01160430908203125,
0.0175933837890625,
-0.043182373046875,
0.0019741058349609375,
0.009796142578125,
-0.01251983642578125,
0.04931640625,
-0.005279541015625,
-0.051727294921875,
-0.0452880859375,
-0.04241943359375,
0... |
Maksimk04/Digits_autoencoder_mnist | 2023-06-20T17:04:16.000Z | [
"keras",
"dataset:mnist",
"region:us"
] | null | Maksimk04 | null | null | Maksimk04/Digits_autoencoder_mnist | 0 | 2 | keras | 2023-06-20T15:00:59 | ---
datasets:
- mnist
---
Данная НС, по сути, является вариационным автоэнкодером (VAE), принимающая на вход изображение 28х28,
возвращая измененное изображение той же самой цифры.
Структура модели:
Общее количество параметров составляет 249247 (124233 для энкодера и 125014 для декодера)
В качестве алгор... | 1,187 | [
[
-0.028900146484375,
-0.032318115234375,
0.0298614501953125,
0.005374908447265625,
-0.0361328125,
-0.0109100341796875,
0.01187896728515625,
-0.0040130615234375,
0.048431396484375,
0.0033473968505859375,
-0.04119873046875,
-0.0548095703125,
-0.051544189453125,
... |
jxssx/autoencoder | 2023-06-20T16:40:13.000Z | [
"keras",
"region:us"
] | null | jxssx | null | null | jxssx/autoencoder | 0 | 2 | keras | 2023-06-20T15:05:31 | Данная нейронная сеть восстанавливает входное изображение из "скрытого" состояния. Таким образом, на выходе получается новое изображение.
Алгоритм оптимизации: Adam.
Функция ошибки выглядит так:
def loss(y, z):
y = K.reshape(y, shape = (batch_size, 28*28))
z = K.reshape(z, shape = (batch_size, 28*28... | 592 | [
[
-0.01239013671875,
-0.044097900390625,
0.03460693359375,
0.003856658935546875,
-0.0306549072265625,
-0.022003173828125,
0.00577545166015625,
0.00939178466796875,
0.050750732421875,
0.022857666015625,
-0.0660400390625,
-0.041168212890625,
-0.0309600830078125,
... |
Elvis120/95points | 2023-06-20T15:30:22.000Z | [
"keras",
"region:us"
] | null | Elvis120 | null | null | Elvis120/95points | 0 | 2 | keras | 2023-06-20T15:25:38 | ---
library_name: keras
---
# Моя модель для распознавания цифр
Натренирована на наборе данных mnist
навания цифр | 128 | [
[
-0.00965118408203125,
-0.051849365234375,
0.0159454345703125,
0.003971099853515625,
-0.0562744140625,
0.04150390625,
0.0282440185546875,
0.01212310791015625,
0.06866455078125,
0.028167724609375,
-0.032379150390625,
-0.044464111328125,
-0.054168701171875,
-0.... |
IIC/mdeberta-v3-base-caresA | 2023-06-20T15:54:52.000Z | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"biomedical",
"clinical",
"spanish",
"mdeberta-v3-base",
"es",
"dataset:chizhikchi/CARES",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | IIC | null | null | IIC/mdeberta-v3-base-caresA | 0 | 2 | transformers | 2023-06-20T15:27:49 | ---
language: es
tags:
- biomedical
- clinical
- spanish
- mdeberta-v3-base
license: mit
datasets:
- "chizhikchi/CARES"
metrics:
- f1
model-index:
- name: IIC/mdeberta-v3-base-caresA
results:
- task:
type: multi-label-classification
dataset:
name: Cares Area
type: chizhikchi/CARES
split... | 1,158 | [
[
-0.0287628173828125,
-0.021942138671875,
0.0428466796875,
0.03265380859375,
-0.04803466796875,
-0.0247344970703125,
0.00926971435546875,
-0.018707275390625,
0.021728515625,
0.03466796875,
-0.058441162109375,
-0.04345703125,
-0.05023193359375,
-0.014953613281... |
CyberTea/neuro5_fashion_mnist | 2023-06-20T20:09:15.000Z | [
"keras",
"region:us"
] | null | CyberTea | null | null | CyberTea/neuro5_fashion_mnist | 0 | 2 | keras | 2023-06-20T15:34:05 | # Распознавание класса изображений на датасете mnist.
# Задача НС
Модель распознаёт к какому классу из 3 (0 - одежда, 1 - обувь, 2 - сумка) относится изображение
## Изображение послойной архитектуры:
## Общее количество обучаемых параметров
Обучаемых параметров: 16... | 934 | [
[
-0.024810791015625,
-0.039154052734375,
0.0188446044921875,
0.0174407958984375,
-0.0394287109375,
0.0019969940185546875,
0.016082763671875,
-0.01303863525390625,
0.03240966796875,
-0.00676727294921875,
-0.038787841796875,
-0.03741455078125,
-0.047821044921875,
... |
IIC/xlm-roberta-large-caresA | 2023-06-20T15:39:00.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"biomedical",
"clinical",
"spanish",
"xlm-roberta-large",
"es",
"dataset:chizhikchi/CARES",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | IIC | null | null | IIC/xlm-roberta-large-caresA | 0 | 2 | transformers | 2023-06-20T15:35:16 | ---
language: es
tags:
- biomedical
- clinical
- spanish
- xlm-roberta-large
license: mit
datasets:
- "chizhikchi/CARES"
metrics:
- f1
model-index:
- name: IIC/xlm-roberta-large-caresA
results:
- task:
type: multi-label-classification
dataset:
name: Cares Area
type: chizhikchi/CARES
spl... | 1,163 | [
[
-0.023193359375,
-0.0478515625,
0.058837890625,
0.01203155517578125,
-0.034454345703125,
-0.04022216796875,
-0.0022430419921875,
-0.0169830322265625,
0.005558013916015625,
0.046173095703125,
-0.054443359375,
-0.042938232421875,
-0.052947998046875,
-0.0090026... |
Elvis120/95point | 2023-06-20T16:05:20.000Z | [
"keras",
"region:us"
] | null | Elvis120 | null | null | Elvis120/95point | 0 | 2 | keras | 2023-06-20T15:37:36 | ---
library_name: keras
---
# Моя модель для распознавания цифр и определения остатка от деления этой цифры на 2
# Описание задачи
Цель данной нейронной сети состоит в определении остатка от деления цифры на 2 по входному изображению из набора данных MNIST.
# Послойная архитектура нейронной сети
# Обще... | 910 | [
[
-0.023956298828125,
-0.035552978515625,
0.0216217041015625,
0.01500701904296875,
-0.04949951171875,
0.0302886962890625,
0.0169677734375,
-0.0232086181640625,
0.039459228515625,
0.0024547576904296875,
-0.03863525390625,
-0.03851318359375,
-0.05517578125,
-0.0... |
IIC/BETO_Galen-caresA | 2023-08-02T06:23:15.000Z | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"biomedical",
"clinical",
"spanish",
"BETO_Galen",
"es",
"dataset:chizhikchi/CARES",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | IIC | null | null | IIC/BETO_Galen-caresA | 0 | 2 | transformers | 2023-06-20T15:39:02 | ---
language: es
tags:
- biomedical
- clinical
- spanish
- BETO_Galen
license: mit
datasets:
- "chizhikchi/CARES"
metrics:
- f1
model-index:
- name: IIC/BETO_Galen-caresA
results:
- task:
type: multi-label-classification
dataset:
name: Cares Area
type: chizhikchi/CARES
split: test
m... | 1,135 | [
[
-0.0149993896484375,
-0.036865234375,
0.0501708984375,
0.01427459716796875,
-0.04315185546875,
-0.039825439453125,
0.01641845703125,
-0.0091552734375,
0.01186370849609375,
0.029998779296875,
-0.044769287109375,
-0.040374755859375,
-0.03558349609375,
-0.02151... |
Yandexxxx/zachet_python | 2023-06-20T17:04:05.000Z | [
"keras",
"region:us"
] | null | Yandexxxx | null | null | Yandexxxx/zachet_python | 0 | 2 | keras | 2023-06-20T16:13:33 | ---
library_name: keras
---
Модель для распознования цифр, которая выдает результат %2 от чисел, натренерованна на наборе данных mnist
Общее количество обучаемых параметров НС мы узнаем с помощью .summary и их число равно 209 826
.summary выводит сводку модели машинного обучения, созданной в рамках... | 1,443 | [
[
-0.03662109375,
-0.035247802734375,
0.0298614501953125,
0.002532958984375,
-0.033905029296875,
-0.0014867782592773438,
0.0085296630859375,
-0.0175018310546875,
0.037811279296875,
0.00887298583984375,
-0.03216552734375,
-0.049072265625,
-0.038482666015625,
-0... |
Dugoss/qwerty | 2023-06-20T17:30:10.000Z | [
"keras",
"region:us"
] | null | Dugoss | null | null | Dugoss/qwerty | 0 | 2 | keras | 2023-06-20T16:23:31 | Построили модель и натренировали ее на большей части данных с цифрами так, чтобы можно было передавать модели фотографии с цифрами размером 28×28 пикселей и получать на выходе значение этой цифры.
Для построения модели использовали обычные полносвязанные слои с разным количеством узлов. В качестве фу... | 1,573 | [
[
-0.0382080078125,
-0.023773193359375,
0.033447265625,
0.01009368896484375,
-0.034942626953125,
-0.01114654541015625,
0.004871368408203125,
-0.01132965087890625,
0.0309600830078125,
0.007965087890625,
-0.046600341796875,
-0.0413818359375,
-0.038818359375,
-0.... |
Andrey13rasfasf/task | 2023-06-20T17:08:20.000Z | [
"keras",
"region:us"
] | null | Andrey13rasfasf | null | null | Andrey13rasfasf/task | 0 | 2 | keras | 2023-06-20T16:25:39 | ---
library_name: keras
---
Характеристики НС:
Архитектура: автоэнкодер имеет два скрытых слоя, первый из которых имеет 128 нейронов, а второй слой имеет 64 нейрона. Выходной слой имеет 784 нейрона, которые соответствуют размеру исходного изображения MNIST.
Функции активации: автоэнкодер использует "ReLU" функцию акт... | 1,087 | [
[
-0.040313720703125,
-0.03118896484375,
0.021636962890625,
0.014862060546875,
-0.037750244140625,
0.00489044189453125,
0.01235198974609375,
-0.0140228271484375,
0.03411865234375,
0.0076751708984375,
-0.0300750732421875,
-0.0570068359375,
-0.03985595703125,
0.... |
Andysoeasy/fashion_detects | 2023-06-20T16:48:53.000Z | [
"keras",
"region:us"
] | null | Andysoeasy | null | null | Andysoeasy/fashion_detects | 0 | 2 | keras | 2023-06-20T16:35:34 | ---
library_name: keras
---
# Модель распознавания изображений.
Обучена на наборе данных fashion_mnist
Модель нейронной сети выполняет задачу предсказания образов, на основе чего делается вывод - какой это именно элемент: одежда, обувь или сумка.
Структура модели
Общее количество обучающих параметро... | 811 | [
[
-0.01776123046875,
-0.045013427734375,
0.0172271728515625,
0.00388336181640625,
-0.048492431640625,
0.0184173583984375,
0.01259613037109375,
-0.0154876708984375,
0.05340576171875,
-0.01039886474609375,
-0.06329345703125,
-0.065185546875,
-0.035308837890625,
... |
SaiderNN/Task | 2023-06-20T19:43:33.000Z | [
"keras",
"region:us"
] | null | SaiderNN | null | null | SaiderNN/Task | 0 | 2 | keras | 2023-06-20T16:51:25 | # Модель восстановления изображения
ИНС - автоэнкодер, на вход которой подается изображение размером 28*28.
Задача ИНС - сжать изображение и восстановить его.
Общее количество обучаемых параметров НС: 4,385
Используемый алгоритм оптимизации: Adamax , функция ошибки: mse
Размеры датасетов:
тренировочный - 48000 и... | 735 | [
[
-0.0265655517578125,
-0.04376220703125,
0.034881591796875,
0.00670623779296875,
-0.03765869140625,
0.00614166259765625,
0.0223846435546875,
-0.00934600830078125,
0.0298919677734375,
0.0023441314697265625,
-0.04327392578125,
-0.0516357421875,
-0.045166015625,
... |
Piun/Zachet | 2023-06-20T17:43:53.000Z | [
"keras",
"region:us"
] | null | Piun | null | null | Piun/Zachet | 0 | 2 | keras | 2023-06-20T17:16:33 | # Модель распознавания изображений.
Обучена на наборе данных mnist
Модель нейронной сети выполняет задачу предсказания цифр, на основе чего выводится остаток от деления данной цифры на 3.
Структура модели
Общее количество обучающих параметров - 111,146.
Алгоритм оптимизации - adam
Функция ошиб... | 739 | [
[
-0.023895263671875,
-0.0479736328125,
0.0209197998046875,
0.00925445556640625,
-0.0391845703125,
0.019073486328125,
0.00826263427734375,
-0.007457733154296875,
0.056640625,
-0.01079559326171875,
-0.04931640625,
-0.057861328125,
-0.045928955078125,
0.00128936... |
Bobiiii/FinalNumRemindByThree | 2023-06-20T19:45:41.000Z | [
"keras",
"region:us"
] | null | Bobiiii | null | null | Bobiiii/FinalNumRemindByThree | 0 | 2 | keras | 2023-06-20T17:27:02 | # Описание модели
Модель принимает цифры на основе датасета `mnist` определяет число и выводит остаток от деления этого числа на 3.
Модель состоит из двух частей.
Первая распознает число и передает это значение в вторую часть модели.
Вторая делит полученный результат число на три.
Выходной результат выглядит как м... | 1,891 | [
[
-0.031829833984375,
-0.042816162109375,
0.0276336669921875,
0.0218658447265625,
-0.03375244140625,
-0.01425933837890625,
0.0098114013671875,
-0.02337646484375,
0.04071044921875,
0.00933074951171875,
-0.052154541015625,
-0.032562255859375,
-0.0445556640625,
-... |
mariabashkeva/Exam | 2023-06-20T19:40:20.000Z | [
"keras",
"region:us"
] | null | mariabashkeva | null | null | mariabashkeva/Exam | 0 | 2 | keras | 2023-06-20T17:38:23 | 1. Описание задачи которую выполняет НС;
Дан датасет mnist постройте автоэнкодер принимающий на вход изображение цифры и
создающий её же изображение на выходе;
2. Изображение послойной архитектуры НС на которой указаны размеры слоя, функция
активации;
3. Общее количество обучаемых параметров НС;
... | 114,018 | [
[
-0.0673828125,
-0.06207275390625,
0.035736083984375,
-0.0016183853149414062,
-0.01546478271484375,
0.0004055500030517578,
0.0235748291015625,
-0.0281219482421875,
0.05181884765625,
0.036163330078125,
-0.020172119140625,
-0.026611328125,
-0.047332763671875,
0... |
Disskretnost/neuro9_ashion_mnist | 2023-06-20T18:06:29.000Z | [
"keras",
"region:us"
] | null | Disskretnost | null | null | Disskretnost/neuro9_ashion_mnist | 0 | 2 | keras | 2023-06-20T17:47:26 | # Распознавание класса изображений на датасете mnist.
# Задача НС
Генерация изображения похожего на предмет из набора fashion_mnist
## Изображение послойной архитектуры:
### Полная нейросеть:
### Encoder:
## Общее количество обучаемых параметров
Обучаемых параметров:... | 914 | [
[
-0.0304107666015625,
-0.032196044921875,
0.0104217529296875,
0.01488494873046875,
-0.051055908203125,
0.00562286376953125,
0.01117706298828125,
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0.035430908203125,
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-0.051971435546875,
-0.043792724609375,
-0.042846679687... |
Au3609/Exam | 2023-06-20T19:16:03.000Z | [
"keras",
"region:us"
] | null | Au3609 | null | null | Au3609/Exam | 0 | 2 | keras | 2023-06-20T17:53:51 | Дан датасет mnist по входному изображению определить цифру
Total params: 118,282
Используемый алгоритм оптимизации: Adam. Функция ошибки: разреженная категориальная кросс энтропия
LOSS
ACCURACY
 | 248 | [
[
-0.01529693603515625,
-0.04290771484375,
0.041656494140625,
0.00662994384765625,
-0.060638427734375,
0.005588531494140625,
0.0228729248046875,
0.0181732177734375,
0.05462646484375,
0.02142333984375,
-0.0433349609375,
-0.0701904296875,
-0.0582275390625,
-0.00... |
Aleksandra131325425/zachet_python_3 | 2023-06-20T18:12:33.000Z | [
"keras",
"region:us"
] | null | Aleksandra131325425 | null | null | Aleksandra131325425/zachet_python_3 | 0 | 2 | keras | 2023-06-20T17:55:45 | ---
library_name: keras
---
Модель для распознования цифр выдающая результаты %3 от чисел, которая была натренерованна на наборе данных mnist
Общее количество обучаемых параметров НС равно 209,826
В данной работе я воспользовалась функцией потерь categorical_crossentropy, которая использ... | 772 | [
[
-0.03173828125,
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0.027435302734375,
0.00476837158203125,
-0.036956787109375,
0.01187896728515625,
0.01480865478515625,
-0.00936126708984375,
0.03631591796875,
0.00597381591796875,
-0.0272369384765625,
-0.04718017578125,
-0.048614501953125,
-... |
msproper/PR6 | 2023-06-21T04:36:55.000Z | [
"keras",
"region:us"
] | null | msproper | null | null | msproper/PR6 | 0 | 2 | keras | 2023-06-20T18:07:32 | Дан датасет fashion_mnist и обученная нейронная сеть.
Использовал их для генерации изображения похожего на предмет из набора fashion_mnist .
Веса нейронной сети данной по заданию не должны быть изменены в процессе дообучения.
Оптимизатор использовал Adam, потери - среднеквадратичное
Total params: 54,699
![](загр... | 390 | [
[
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0.03387451171875,
0.013824462890625,
-0.060699462890625,
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0.0159454345703125,
-0.0228118896484375,
0.058441162109375,
0.0011777877807617188,
-0.05609130859375,
-0.0615234375,
-0.027191162109375,
0.0... |
ChilNik/PR_digits | 2023-06-21T11:23:24.000Z | [
"keras",
"code",
"dataset:mnist",
"region:us"
] | null | ChilNik | null | null | ChilNik/PR_digits | 0 | 2 | keras | 2023-06-20T18:17:48 | ---
datasets:
- mnist
library_name: keras
tags:
- code
---
Модель берет изображение (в данном случае из mnist) определяет цифру которая изображена, делит эту цифру на 2 и выводит остаток от деления.
Оптим... | 525 | [
[
-0.0170135498046875,
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0.0316162109375,
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-0.042236328125,
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0.01436614990234375,
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0.0615234375,
0.01043701171875,
-0.055145263671875,
-0.0399169921875,
-0.035125732421875,
0.000208... |
Neitha/fashion_mnist | 2023-06-20T19:16:49.000Z | [
"keras",
"region:us"
] | null | Neitha | null | null | Neitha/fashion_mnist | 0 | 2 | keras | 2023-06-20T18:38:09 | На этапе присоединения заданного декодера и энкодера была получена ошибка, решить которую за длительное время не получилось.
Код
input_dec = Input(shape=(49,))
x = input_dec
x = model.layers[1](input_dec)
x = model.layers[2](x)
decoded = Reshape((28, 28, 1))(x)
decoder = keras.Model(input_dec, decoded, name='de... | 1,768 | [
[
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0.0271148681640625,
0.00832366943359375,
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0.00830078125,
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0... |
Rage4/Gasilin_var8 | 2023-06-20T20:05:17.000Z | [
"keras",
"region:us"
] | null | Rage4 | null | null | Rage4/Gasilin_var8 | 0 | 2 | keras | 2023-06-20T19:14:31 | 1. Нейронная сеть генерирует цифры похожие на цифры из датасета mnist.
2. 
3. Общее количество обучаемых параметров НС: 54160
4. Используемый алгоритмы оптимизации и функция ошибки: adam и categorical_crossentropy.
5. Размеры тренировочного, валидационного и тестовог... | 607 | [
[
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... |
pln-fing-udelar/robertuito-HUHU-task1 | 2023-06-22T22:25:41.000Z | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | text-classification | pln-fing-udelar | null | null | pln-fing-udelar/robertuito-HUHU-task1 | 0 | 2 | transformers | 2023-06-20T20:13:45 | ---
tags:
- generated_from_keras_callback
model-index:
- name: robertuito-HUHU-task1
results: []
widget:
- text: "El español es un idioma muy hablado en el mundo."
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it... | 1,600 | [
[
-0.0302581787109375,
-0.0458984375,
0.021881103515625,
0.0108795166015625,
-0.039794921875,
-0.0214080810546875,
-0.019073486328125,
-0.031585693359375,
0.01526641845703125,
0.0240936279296875,
-0.055755615234375,
-0.0457763671875,
-0.064697265625,
-0.010421... |
emresvd/u203 | 2023-06-20T20:38:17.000Z | [
"keras",
"region:us"
] | null | emresvd | null | null | emresvd/u203 | 0 | 2 | keras | 2023-06-20T20:38:11 | ---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters ... | 841 | [
[
-0.037200927734375,
-0.03997802734375,
0.031890869140625,
0.0081634521484375,
-0.043243408203125,
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0.01097869873046875,
-0.0033969879150390625,
0.0204620361328125,
0.030517578125,
-0.04376220703125,
-0.05120849609375,
-0.040008544921875,
... |
dickreuter/poker-card-classification | 2023-06-20T20:58:10.000Z | [
"keras",
"poker-card-classification",
"pokerbot",
"region:us"
] | null | dickreuter | null | null | dickreuter/poker-card-classification | 1 | 2 | keras | 2023-06-20T20:54:16 | ---
library_name: keras
tags:
- poker-card-classification
- pokerbot
---
## 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 w... | 601 | [
[
-0.03021240234375,
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0.0220947265625,
0.0024738311767578125,
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0.0006575584411621094,
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0.016754150390625,
0.0216217041015625,
-0.034820556640625,
-0.052154541015625,
-0.03778076171875,
... |
akira225/deberta-v3-base-ECE | 2023-06-21T08:46:41.000Z | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"deberta-v3-base",
"deberta-v3",
"deberta",
"token-classification",
"emotion",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | token-classification | akira225 | null | null | akira225/deberta-v3-base-ECE | 0 | 2 | transformers | 2023-06-21T02:18:24 | ---
license: apache-2.0
language: en
tags:
- deberta-v3-base
- deberta-v3
- deberta
- token-classification
- emotion
library_name: transformers
pipeline_tag: token-classification
---
# Model Card for DeBERTa-v3-base-ECE
This is [DeBERTa-v3](https://huggingface.co/sileod/deberta-v3-base-tasksource-nli) fine-tuned for ... | 1,239 | [
[
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0.0027065277099609375,
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0.027740478515625,
0.012420654296875,
-0.0574951171875,
-0.053985595703125,
-0.05841064453125,
0.0... |
IIC/bert-base-spanish-wwm-cased-ctebmsp | 2023-07-18T07:10:29.000Z | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"biomedical",
"clinical",
"spanish",
"bert-base-spanish-wwm-cased",
"token-classification",
"es",
"dataset:lcampillos/ctebmsp",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | token-classification | IIC | null | null | IIC/bert-base-spanish-wwm-cased-ctebmsp | 0 | 2 | transformers | 2023-06-21T06:46:59 | ---
language: es
tags:
- biomedical
- clinical
- spanish
- bert-base-spanish-wwm-cased
license: cc-by-4.0
datasets:
- "lcampillos/ctebmsp"
metrics:
- f1
model-index:
- name: IIC/bert-base-spanish-wwm-cased-ctebmsp
results:
- task:
type: token-classification
dataset:
name: CT-EBM-SP (Clinical Trials... | 1,318 | [
[
-0.0283050537109375,
-0.04351806640625,
0.024200439453125,
0.03302001953125,
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0.0209808349609375,
0.037445068359375,
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-0.052001953125,
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-0.... |
IIC/mdeberta-v3-base-ctebmsp | 2023-06-21T06:54:01.000Z | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"biomedical",
"clinical",
"spanish",
"mdeberta-v3-base",
"token-classification",
"es",
"dataset:lcampillos/ctebmsp",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | token-classification | IIC | null | null | IIC/mdeberta-v3-base-ctebmsp | 0 | 2 | transformers | 2023-06-21T06:47:50 | ---
language: es
tags:
- biomedical
- clinical
- spanish
- mdeberta-v3-base
license: mit
datasets:
- "lcampillos/ctebmsp"
metrics:
- f1
model-index:
- name: IIC/mdeberta-v3-base-ctebmsp
results:
- task:
type: token-classification
dataset:
name: CT-EBM-SP (Clinical Trials for Evidence-based Medicine... | 1,272 | [
[
-0.0268096923828125,
-0.03466796875,
0.0396728515625,
0.0338134765625,
-0.03662109375,
-0.0221710205078125,
0.003582000732421875,
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0.027984619140625,
0.046295166015625,
-0.04461669921875,
-0.053009033203125,
-0.049652099609375,
-0.0034847... |
predictia/europe_reanalysis_downscaler_convbaseline | 2023-07-01T03:01:00.000Z | [
"transformers",
"pytorch",
"tensorboard",
"convbilinear",
"climate",
"super-resolution",
"image-to-image",
"es",
"en",
"dataset:openclimatefix/era5",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-to-image | predictia | null | null | predictia/europe_reanalysis_downscaler_convbaseline | 0 | 2 | transformers | 2023-06-21T08:01:26 | ---
license: apache-2.0
datasets:
- openclimatefix/era5
language:
- es
- en
metrics:
- mse
library_name: transformers
pipeline_tag: image-to-image
tags:
- climate
- transformers
- super-resolution
---
# Europe Reanalysis Super Resolution
The aim of the project is to create a Machine learning (ML) model that can gene... | 1,673 | [
[
-0.042266845703125,
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0.00411224365234375,
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0.02... |
IIC/bert-base-spanish-wwm-cased-distemist | 2023-08-30T07:26:01.000Z | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"biomedical",
"clinical",
"spanish",
"bert-base-spanish-wwm-cased",
"token-classification",
"es",
"dataset:bigbio/distemist",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | token-classification | IIC | null | null | IIC/bert-base-spanish-wwm-cased-distemist | 0 | 2 | transformers | 2023-06-21T09:25:32 | ---
language: es
tags:
- biomedical
- clinical
- spanish
- bert-base-spanish-wwm-cased
license: cc-by-4.0
datasets:
- "bigbio/distemist"
metrics:
- f1
model-index:
- name: IIC/bert-base-spanish-wwm-cased-distemist
results:
- task:
type: token-classification
dataset:
name: distemist
type: bigb... | 1,206 | [
[
-0.0478515625,
-0.04052734375,
0.024322509765625,
0.024658203125,
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0.007568359375,
0.01605224609375,
-0.06268310546875,
-0.0404052734375,
-0.053314208984375,
-0.0189666748046... |
pollner/distilhubert-finetuned-ravdess | 2023-06-21T12:36:48.000Z | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:xbgoose/ravdess",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | audio-classification | pollner | null | null | pollner/distilhubert-finetuned-ravdess | 2 | 2 | transformers | 2023-06-21T10:33:05 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xbgoose/ravdess
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-ravdess
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and co... | 1,975 | [
[
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0.0049285888671875,
0.00655364990234375,
-0.0171051025390625,
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-0.0029811859130859375,
-0.0153350830078125,
0.0095977783203125,
0.0210418701171875,
-0.051177978515625,
-0.044281005859375,
-0.05322265625,
... |
dg845/diffusers-ct_imagenet64 | 2023-09-01T07:27:08.000Z | [
"diffusers",
"generative model",
"unconditional image generation",
"arxiv:2303.01469",
"arxiv:1506.03365",
"arxiv:1512.00567",
"license:mit",
"diffusers:ConsistencyModelPipeline",
"region:us"
] | null | dg845 | null | null | dg845/diffusers-ct_imagenet64 | 0 | 2 | diffusers | 2023-06-21T11:08:15 | ---
license: mit
tags:
- generative model
- unconditional image generation
---
Consistency models are a new class of generative models introduced in ["Consistency Models"](https://arxiv.org/abs/2303.01469) ([paper](https://arxiv.org/pdf/2303.01469.pdf), [code](https://github.com/openai/consistency_models)) by Yang Song... | 9,397 | [
[
-0.02587890625,
-0.0277252197265625,
0.00946044921875,
0.0017747879028320312,
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0.0374755859375,
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-0.0236663818359375,
-0.0562744140625,
... |
Hollway/gpt2_finetune | 2023-06-29T20:24:47.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"zh",
"en",
"dataset:TigerResearch/tigerbot-zhihu-zh-10k",
"dataset:TigerResearch/tigerbot-book-qa-1k",
"dataset:TigerResearch/sft_zh",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | Hollway | null | null | Hollway/gpt2_finetune | 1 | 2 | transformers | 2023-06-21T11:34:27 | ---
language:
- zh
- en
license: mit
datasets:
- TigerResearch/tigerbot-zhihu-zh-10k
- TigerResearch/tigerbot-book-qa-1k
- TigerResearch/sft_zh
pipeline_tag: text-generation
---
# 中文文本生成
## 1 Usage
### 1.1 Initalization 初始化
!pip install transformers[torch]
```
from transformers import GPT2Tokenizer, GPT2LMHeadMode... | 1,789 | [
[
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... |
IIC/mdeberta-v3-base-livingner1 | 2023-06-21T15:28:01.000Z | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"biomedical",
"clinical",
"spanish",
"mdeberta-v3-base",
"token-classification",
"es",
"dataset:IIC/livingner1",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | token-classification | IIC | null | null | IIC/mdeberta-v3-base-livingner1 | 0 | 2 | transformers | 2023-06-21T15:06:45 | ---
language: es
tags:
- biomedical
- clinical
- spanish
- mdeberta-v3-base
license: mit
datasets:
- "IIC/livingner1"
metrics:
- f1
model-index:
- name: IIC/mdeberta-v3-base-livingner1
results:
- task:
type: token-classification
dataset:
name: livingner1
type: IIC/livingner1
split: test... | 1,158 | [
[
-0.039337158203125,
-0.0406494140625,
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0.0289306640625,
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-0.027191162109375,
-0.036895751953125,
-0.008705139... |
IIC/bert-base-spanish-wwm-cased-meddocan | 2023-06-21T15:41:33.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"biomedical",
"clinical",
"spanish",
"bert-base-spanish-wwm-cased",
"token-classification",
"es",
"dataset:bigbio/meddocan",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | token-classification | IIC | null | null | IIC/bert-base-spanish-wwm-cased-meddocan | 0 | 2 | transformers | 2023-06-21T15:40:42 | ---
language: es
tags:
- biomedical
- clinical
- spanish
- bert-base-spanish-wwm-cased
license: cc-by-4.0
datasets:
- "bigbio/meddocan"
metrics:
- f1
model-index:
- name: IIC/bert-base-spanish-wwm-cased-meddocan
results:
- task:
type: token-classification
dataset:
name: meddocan
type: bigbio/... | 1,202 | [
[
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0.041015625,
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-0.045684814453125,
-0.0404052734375,
-0.014907... |
IIC/mdeberta-v3-base-pharmaconer | 2023-06-21T16:11:42.000Z | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"biomedical",
"clinical",
"spanish",
"mdeberta-v3-base",
"token-classification",
"es",
"dataset:PlanTL-GOB-ES/pharmaconer",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | token-classification | IIC | null | null | IIC/mdeberta-v3-base-pharmaconer | 0 | 2 | transformers | 2023-06-21T16:09:43 | ---
language: es
tags:
- biomedical
- clinical
- spanish
- mdeberta-v3-base
license: mit
datasets:
- "PlanTL-GOB-ES/pharmaconer"
metrics:
- f1
model-index:
- name: IIC/mdeberta-v3-base-pharmaconer
results:
- task:
type: token-classification
dataset:
name: pharmaconer
type: PlanTL-GOB-ES/pharm... | 1,884 | [
[
-0.0209503173828125,
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0.01267242431640625,
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-0.037109375,
-0.0380859375,
-0.047119140625,
-0.002601... |
IIC/xlm-roberta-large-pharmaconer | 2023-06-26T07:27:29.000Z | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"biomedical",
"clinical",
"spanish",
"xlm-roberta-large",
"token-classification",
"es",
"dataset:PlanTL-GOB-ES/pharmaconer",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | token-classification | IIC | null | null | IIC/xlm-roberta-large-pharmaconer | 0 | 2 | transformers | 2023-06-21T16:15:06 | ---
language: es
tags:
- biomedical
- clinical
- spanish
- xlm-roberta-large
license: mit
datasets:
- "PlanTL-GOB-ES/pharmaconer"
metrics:
- f1
model-index:
- name: IIC/xlm-roberta-large-pharmaconer
results:
- task:
type: token-classification
dataset:
name: pharmaconer
type: PlanTL-GOB-ES/pha... | 1,888 | [
[
-0.01580810546875,
-0.03717041015625,
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0.0011301040649414062,
0.04803466796875,
-0.032501220703125,
-0.0408935546875,
-0.061309814453125,
... |
UnHolyTrinity/eng_quotes_model | 2023-06-22T05:20:22.000Z | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | UnHolyTrinity | null | null | UnHolyTrinity/eng_quotes_model | 0 | 2 | transformers | 2023-06-21T16:53:44 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: eng_quotes_model
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. -->
# eng_quotes_model
This mo... | 1,321 | [
[
-0.02484130859375,
-0.047271728515625,
0.0207061767578125,
0.00786590576171875,
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-0.0037288665771484375,
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0.023193359375,
-0.0513916015625,
-0.040740966796875,
-0.050506591796875,
... |
koreadaeil/my_awesome_model | 2023-06-24T14:15:51.000Z | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:rotten_tomatoes",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | koreadaeil | null | null | koreadaeil/my_awesome_model | 0 | 2 | transformers | 2023-06-21T19:11:38 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- rotten_tomatoes
metrics:
- accuracy
model-index:
- name: my_awesome_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: rotten_tomatoes
type: rotten_tomatoes
config: default
... | 1,714 | [
[
-0.0318603515625,
-0.0477294921875,
0.0234375,
0.00102996826171875,
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-0.0030040740966796875,
-0.00933074951171875,
0.0120391845703125,
0.0250244140625,
-0.0457763671875,
-0.050018310546875,
-0.05767822265625,
-0.01335906... |
agustinl/ppo-LunarLander-v2 | 2023-07-19T01:52:39.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | agustinl | null | null | agustinl/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-06-21T22:38:34 | ---
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
... | 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
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0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
aroot/mbart-finetuned-eng-guj | 2023-06-30T14:30:51.000Z | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | aroot | null | null | aroot/mbart-finetuned-eng-guj | 0 | 2 | transformers | 2023-06-22T00:44:05 | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mbart-finetuned-eng-guj
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. -->
# m... | 1,178 | [
[
-0.043853759765625,
-0.053375244140625,
0.01464080810546875,
0.0159759521484375,
-0.0269775390625,
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-0.0175933837890625,
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0.0095977783203125,
0.0235748291015625,
-0.054595947265625,
-0.03131103515625,
-0.044647216796875,
... |
NanoIsTrash/dqn-SpaceInvadersNoFrameskip-v4 | 2023-06-22T05:11:35.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | NanoIsTrash | null | null | NanoIsTrash/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-06-22T05:10:57 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
... | 2,768 | [
[
-0.04498291015625,
-0.040679931640625,
0.020263671875,
0.0227813720703125,
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0.0098114013671875,
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0.0208740234375,
-0.0716552734375,
-0.033294677734375,
-0.02508544921875,
-0.0026... |
rudzhehdehd/To_my_Love | 2023-06-22T08:40:50.000Z | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | rudzhehdehd | null | null | rudzhehdehd/To_my_Love | 0 | 2 | transformers | 2023-06-22T06:42:45 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: To_my_Love
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. -->
# To_my_Love
This model i... | 1,328 | [
[
-0.03497314453125,
-0.0416259765625,
0.01445770263671875,
0.018157958984375,
-0.02850341796875,
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0.0174102783203125,
-0.053558349609375,
-0.0380859375,
-0.05487060546875,
-... |
bandrocks/my_awesome_weeknd_clm-model | 2023-06-22T08:32:11.000Z | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | bandrocks | null | null | bandrocks/my_awesome_weeknd_clm-model | 0 | 2 | transformers | 2023-06-22T07:47:58 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: my_awesome_weeknd_clm-model
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. -->
# my_awesome_wee... | 1,343 | [
[
-0.040740966796875,
-0.0440673828125,
0.0221099853515625,
0.009002685546875,
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-0.00452423095703125,
-0.0228729248046875,
0.00955963134765625,
0.0276641845703125,
-0.061126708984375,
-0.050323486328125,
-0.047607421875,
-0.012... |
madiltalay/layoutlmv2-base-uncased_finetuned_docvqa | 2023-06-26T10:11:26.000Z | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"document-question-answering",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | document-question-answering | madiltalay | null | null | madiltalay/layoutlmv2-base-uncased_finetuned_docvqa | 0 | 2 | transformers | 2023-06-22T11:36:16 | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2-base-uncased_finetuned_docvqa
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 comme... | 3,580 | [
[
-0.037353515625,
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0.00897979736328125,
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0.0269775390625,
0.0275115966796875,
-0.045440673828125,
-0.04888916015625,
-0.0421142578125,
-0.021... |
HarshV9/finetuning-sentiment-model-8-labels | 2023-06-23T12:21:51.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | HarshV9 | null | null | HarshV9/finetuning-sentiment-model-8-labels | 0 | 2 | transformers | 2023-06-22T16:07:12 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: finetuning-sentiment-model-8-labels
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. -->
#... | 1,341 | [
[
-0.043914794921875,
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0.022491455078125,
-0.043701171875,
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0.00974273681640625,
0.019378662109375,
-0.047576904296875,
-0.0543212890625,
-0.055511474609375,
-0... |
bluemoonwj/movie_title_predictor | 2023-06-22T17:53:17.000Z | [
"transformers",
"pytorch",
"tensorboard",
"opt",
"text-generation",
"generated_from_trainer",
"license:other",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | bluemoonwj | null | null | bluemoonwj/movie_title_predictor | 0 | 2 | transformers | 2023-06-22T16:58:53 | ---
license: other
tags:
- generated_from_trainer
model-index:
- name: movie_title_predictor
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. -->
# movie_title_predic... | 1,359 | [
[
-0.0265045166015625,
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0.017242431640625,
-0.0027256011962890625,
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0.005184173583984375,
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0.0140380859375,
0.034759521484375,
-0.06597900390625,
-0.03857421875,
-0.04559326171875,
... |
battelle/FupBERT | 2023-09-05T16:43:16.000Z | [
"transformers",
"pytorch",
"FupBERT",
"feature-extraction",
"custom_code",
"license:gpl-2.0",
"has_space",
"region:us"
] | feature-extraction | battelle | null | null | battelle/FupBERT | 0 | 2 | transformers | 2023-06-22T17:47:56 | ---
license: gpl-2.0
---
# Model Card for FupBERT
A descriptor free approach to predicting fraction unbound in human plasma.
## Model Details
### Model Description
Chemical specific parameters are either measured _in vitro_ or estimated using quantitative
structure–activity relationship (QSAR) models. The existin... | 2,774 | [
[
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0.00235... |
valerio-unifei/ppo-Huggy | 2023-06-22T18:44:53.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | valerio-unifei | null | null | valerio-unifei/ppo-Huggy | 0 | 2 | ml-agents | 2023-06-22T18:44:46 | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (... | 1,324 | [
[
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... |
gaiamolinaro/dqn-SpaceInvadersNoFrameskip-v4 | 2023-06-23T04:37:52.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | gaiamolinaro | null | null | gaiamolinaro/dqn-SpaceInvadersNoFrameskip-v4 | 0 | 2 | stable-baselines3 | 2023-06-23T04:37:14 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
... | 2,771 | [
[
-0.043426513671875,
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0.019378662109375,
0.0257415771484375,
-0.01125335693359375,
-0.017608642578125,
0.01020050048828125,
-0.0135345458984375,
0.0129547119140625,
0.022064208984375,
-0.0718994140625,
-0.034271240234375,
-0.02508544921875,
-0... |
rahmas/abusive_content_identification | 2023-06-23T07:54:37.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | rahmas | null | null | rahmas/abusive_content_identification | 0 | 2 | transformers | 2023-06-23T07:47:11 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: abusive_content_identification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and comp... | 1,637 | [
[
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0.022918701171875,
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-0.01458740234375,
-0.016571044921875,
0.01390838623046875,
0.021026611328125,
-0.046783447265625,
-0.04559326171875,
-0.0499267578125,
... |
elsliew/autotrain-skillsync2-69166137722 | 2023-06-23T10:58:06.000Z | [
"transformers",
"pytorch",
"safetensors",
"deberta",
"text-classification",
"autotrain",
"en",
"dataset:elsliew/autotrain-data-skillsync2",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | text-classification | elsliew | null | null | elsliew/autotrain-skillsync2-69166137722 | 0 | 2 | transformers | 2023-06-23T10:56:13 | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain"
datasets:
- elsliew/autotrain-data-skillsync2
co2_eq_emissions:
emissions: 0.3593924337756782
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 69166137722
- CO2 Emissions (in grams... | 1,282 | [
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heon98/my_awesome_pokemon_model | 2023-06-23T13:50:10.000Z | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:pokemon-classification",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | heon98 | null | null | heon98/my_awesome_pokemon_model | 0 | 2 | transformers | 2023-06-23T11:40:02 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pokemon-classification
metrics:
- accuracy
model-index:
- name: my_awesome_pokemon_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: pokemon-classification
type: pokemon-classific... | 1,942 | [
[
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... |
4i-ai/BERT_disfluency_cls | 2023-08-25T08:09:58.000Z | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"disfluency identification",
"en",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | text-classification | 4i-ai | null | null | 4i-ai/BERT_disfluency_cls | 0 | 2 | transformers | 2023-06-23T14:27:16 | ---
license: cc-by-nc-sa-4.0
language:
- en
tags:
- disfluency identification
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This BERT model classifies a dialogue system's user utterance as fluent or disfluent.
## Model Details
### Model Description
<!-- Provide a longer... | 1,767 | [
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-0.038543701171875... |
michaelfeil/ct2fast-mpt-30b | 2023-06-28T22:14:21.000Z | [
"transformers",
"mpt",
"text-generation",
"ctranslate2",
"int8",
"float16",
"Composer",
"MosaicML",
"llm-foundry",
"StreamingDatasets",
"custom_code",
"dataset:allenai/c4",
"dataset:mc4",
"dataset:togethercomputer/RedPajama-Data-1T",
"dataset:bigcode/the-stack-dedup",
"dataset:allenai/... | text-generation | michaelfeil | null | null | michaelfeil/ct2fast-mpt-30b | 2 | 2 | transformers | 2023-06-23T15:55:16 | ---
license: apache-2.0
tags:
- ctranslate2
- int8
- float16
- Composer
- MosaicML
- llm-foundry
- StreamingDatasets
datasets:
- allenai/c4
- mc4
- togethercomputer/RedPajama-Data-1T
- bigcode/the-stack-dedup
- allenai/s2orc
inference: false
---
# # Fast-Inference with Ctranslate2
Speedup inference while reducing memor... | 13,748 | [
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dnzblgn/BERT_Text_Classification | 2023-06-23T18:09:17.000Z | [
"keras",
"region:us"
] | null | dnzblgn | null | null | dnzblgn/BERT_Text_Classification | 0 | 2 | keras | 2023-06-23T16:53:59 | ---
{}
---
# BERT Text Classification
This is a BERT-based text classification model trained on the "socialmedia-disaster-tweets" dataset. It performs sentiment analysis to classify tweets as "Relevant" or "Not Relevant" to a disaster event.
## Model Description
The model uses the BERT (Bidirectional Encoder Repres... | 2,387 | [
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... |
Xenova/deeplabv3-mobilevit-small | 2023-09-01T23:55:22.000Z | [
"transformers.js",
"onnx",
"mobilevit",
"image-segmentation",
"region:us"
] | image-segmentation | Xenova | null | null | Xenova/deeplabv3-mobilevit-small | 0 | 2 | transformers.js | 2023-06-23T18:47:06 | ---
library_name: transformers.js
pipeline_tag: image-segmentation
---
https://huggingface.co/apple/deeplabv3-mobilevit-small with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like... | 541 | [
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-0.0084381103... |
Xenova/deeplabv3-mobilevit-x-small | 2023-09-01T23:56:02.000Z | [
"transformers.js",
"onnx",
"mobilevit",
"image-segmentation",
"region:us"
] | image-segmentation | Xenova | null | null | Xenova/deeplabv3-mobilevit-x-small | 0 | 2 | transformers.js | 2023-06-23T18:47:10 | ---
library_name: transformers.js
pipeline_tag: image-segmentation
---
https://huggingface.co/apple/deeplabv3-mobilevit-x-small with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would li... | 543 | [
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Xenova/deeplabv3-mobilevit-xx-small | 2023-09-01T23:55:46.000Z | [
"transformers.js",
"onnx",
"mobilevit",
"image-segmentation",
"region:us"
] | image-segmentation | Xenova | null | null | Xenova/deeplabv3-mobilevit-xx-small | 0 | 2 | transformers.js | 2023-06-23T18:47:12 | ---
library_name: transformers.js
pipeline_tag: image-segmentation
---
https://huggingface.co/apple/deeplabv3-mobilevit-xx-small with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would l... | 544 | [
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-0.032440185546875,
-0.007... |
cardiffnlp/twitter-roberta-large-2022-154m-tweetner7-2020 | 2023-06-23T20:57:35.000Z | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | cardiffnlp | null | null | cardiffnlp/twitter-roberta-large-2022-154m-tweetner7-2020 | 0 | 2 | transformers | 2023-06-23T20:41:42 | ---
datasets:
- tner/tweetner7
metrics:
- f1
- precision
- recall
model-index:
- name: cardiffnlp/twitter-roberta-large-2022-154m-tweetner7-2020
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/tweetner7
type: tner/tweetner7
args: tner/twee... | 5,732 | [
[
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0.0200... |
cardiffnlp/twitter-roberta-base-2022-154m-tweetner7-2020 | 2023-06-23T20:54:51.000Z | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | cardiffnlp | null | null | cardiffnlp/twitter-roberta-base-2022-154m-tweetner7-2020 | 0 | 2 | transformers | 2023-06-23T20:41:43 | ---
datasets:
- tner/tweetner7
metrics:
- f1
- precision
- recall
model-index:
- name: cardiffnlp/twitter-roberta-base-2022-154m-tweetner7-2020
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/tweetner7
type: tner/tweetner7
args: tner/tweet... | 5,728 | [
[
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-0.044464111328125,
-0.04937744140625,
-0.05584716796875,
0.01... |
koreadaeil/my_awesome_model5 | 2023-06-24T07:43:16.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | koreadaeil | null | null | koreadaeil/my_awesome_model5 | 0 | 2 | transformers | 2023-06-24T07:41:58 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: my_awesome_model5
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: wnli
split: train[:635]
args: w... | 1,683 | [
[
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-0.045867919921875,
-0.046905517578125,
-0.057037353515625,
... |
chennaiai/my-hotdog-not-hotdog | 2023-06-24T08:39:55.000Z | [
"transformers",
"pytorch",
"tensorboard",
"coreml",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | image-classification | chennaiai | null | null | chennaiai/my-hotdog-not-hotdog | 0 | 2 | transformers | 2023-06-24T08:35:45 | ---
tags:
- image-classification
- huggingpics
metrics:
- accuracy
model-index:
- name: hotdog-not-hotdog
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.824999988079071
---
# hotdog-not-hotdog
Autogen... | 748 | [
[
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... |
SSSIN/my_segment_news_1 | 2023-06-24T11:18:18.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | SSSIN | null | null | SSSIN/my_segment_news_1 | 0 | 2 | transformers | 2023-06-24T11:06:29 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_segment_news_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. -->
... | 1,897 | [
[
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0.01678466796875,
-0.051513671875,
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-0.057769775390625,
... |
jeremyvictor/t5-v1_1-base-gramatika-e8-b16 | 2023-06-24T13:26:46.000Z | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | jeremyvictor | null | null | jeremyvictor/t5-v1_1-base-gramatika-e8-b16 | 0 | 2 | transformers | 2023-06-24T11:49:44 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-v1_1-base-gramatika-e8-b16
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 comm... | 10,787 | [
[
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0.0279541015625,
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-0.042755126953125,
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... |
romgrelier/drl_course_dqn | 2023-06-24T17:07:11.000Z | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | romgrelier | null | null | romgrelier/drl_course_dqn | 0 | 2 | stable-baselines3 | 2023-06-24T17:06:16 | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
... | 2,766 | [
[
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0.01055908203125,
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0.022216796875,
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-0.034423828125,
-0.0251617431640625,
-0... |
RogerioFreitas/whisper-medium-portuguese | 2023-06-24T18:39:22.000Z | [
"transformers",
"pytorch",
"jax",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"whisper-event",
"pt",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | RogerioFreitas | null | null | RogerioFreitas/whisper-medium-portuguese | 0 | 2 | transformers | 2023-06-24T17:42:08 | ---
language: pt
license: apache-2.0
tags:
- generated_from_trainer
- whisper-event
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: openai/whisper-medium
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: m... | 2,155 | [
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97jmlr/ppo-SnowballTarget | 2023-06-24T22:43:03.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 97jmlr | null | null | 97jmlr/ppo-SnowballTarget | 0 | 2 | ml-agents | 2023-06-24T22:42:57 | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Te... | 1,361 | [
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anas21/keras-dummy-sequential-demo | 2023-06-30T22:06:42.000Z | [
"keras",
"region:us"
] | null | anas21 | null | null | anas21/keras-dummy-sequential-demo | 0 | 2 | keras | 2023-06-24T23:14:55 | ---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters ... | 841 | [
[
-0.037200927734375,
-0.03997802734375,
0.03192138671875,
0.00814056396484375,
-0.043243408203125,
-0.017730712890625,
0.01097869873046875,
-0.0033893585205078125,
0.0204620361328125,
0.030548095703125,
-0.043731689453125,
-0.051177978515625,
-0.040008544921875,
... |
anas21/keras-dummy-functional-demo | 2023-06-25T09:07:24.000Z | [
"keras",
"region:us"
] | null | anas21 | null | null | anas21/keras-dummy-functional-demo | 0 | 2 | keras | 2023-06-24T23:19:07 | ---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters ... | 841 | [
[
-0.037200927734375,
-0.03997802734375,
0.03192138671875,
0.00814056396484375,
-0.043243408203125,
-0.017730712890625,
0.01097869873046875,
-0.0033893585205078125,
0.0204620361328125,
0.030548095703125,
-0.043731689453125,
-0.051177978515625,
-0.040008544921875,
... |
97jmlr/pyramids | 2023-06-24T23:32:30.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | 97jmlr | null | null | 97jmlr/pyramids | 0 | 2 | ml-agents | 2023-06-24T23:32:23 | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **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).
... | 1,327 | [
[
-0.041168212890625,
-0.03497314453125,
0.001468658447265625,
0.01450347900390625,
-0.01024627685546875,
0.012237548828125,
0.015960693359375,
-0.01519775390625,
0.033203125,
0.0299530029296875,
-0.040985107421875,
-0.05035400390625,
-0.029449462890625,
-0.01... |
Smaraa/bart-text-simplification_1e4_adafactor_newsela | 2023-06-25T17:52:49.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | Smaraa | null | null | Smaraa/bart-text-simplification_1e4_adafactor_newsela | 0 | 2 | transformers | 2023-06-25T11:51:17 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-text-simplification_1e4_adafactor_newsela
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... | 3,564 | [
[
-0.049072265625,
-0.047943115234375,
0.01396942138671875,
0.00531768798828125,
-0.0098724365234375,
-0.0005850791931152344,
-0.001209259033203125,
-0.00304412841796875,
0.054443359375,
0.0293426513671875,
-0.048004150390625,
-0.0487060546875,
-0.042755126953125,... |
AlexK-PL/speecht5_tts_fine-tuned_voxpopuli_nl | 2023-06-25T14:39:43.000Z | [
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"fine_tuned",
"generated_from_trainer",
"nl",
"dataset:facebook/voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | AlexK-PL | null | null | AlexK-PL/speecht5_tts_fine-tuned_voxpopuli_nl | 0 | 2 | transformers | 2023-06-25T12:16:58 | ---
language:
- nl
license: mit
tags:
- fine_tuned
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: SpeechT5 TTS Dutch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete i... | 1,582 | [
[
-0.032379150390625,
-0.041259765625,
-0.004489898681640625,
0.01525115966796875,
-0.0233917236328125,
-0.020172119140625,
-0.0177154541015625,
-0.0189056396484375,
-0.0004925727844238281,
0.0209808349609375,
-0.043060302734375,
-0.05224609375,
-0.050201416015625... |
nsanghi/distilhubert-finetuned-gtzan | 2023-07-01T15:25:51.000Z | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | audio-classification | nsanghi | null | null | nsanghi/distilhubert-finetuned-gtzan | 0 | 2 | transformers | 2023-06-25T14:44:02 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and comple... | 2,585 | [
[
-0.0406494140625,
-0.039093017578125,
0.0117340087890625,
0.0030498504638671875,
-0.013763427734375,
-0.01476287841796875,
-0.0028820037841796875,
-0.007843017578125,
0.027252197265625,
0.0198822021484375,
-0.05413818359375,
-0.05047607421875,
-0.049285888671875... |
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