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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text-generation | transformers |
# 9APES DialoGPT Model | {"tags": ["conversational"]} | RarePizzaDog/Apes_Bot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-09T18:10:02+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 9APES DialoGPT Model | [
"# 9APES DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# 9APES DialoGPT Model"
] |
null | transformers |
# ECCO-BERT base model (cased)
A pretrained BERT model trained exclusively on the ECCO (Eighteenth Century Collections Online) dataset of digitized documents published during the 18th century in the United Kingdom. The model is equivalent in size to [bert-base-cased](https://huggingface.co/bert-base-cased). The model... | {"language": "en"} | TurkuNLP/eccobert-base-cased-v1 | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-09T18:11:30+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #en #endpoints_compatible #region-us
|
# ECCO-BERT base model (cased)
A pretrained BERT model trained exclusively on the ECCO (Eighteenth Century Collections Online) dataset of digitized documents published during the 18th century in the United Kingdom. The model is equivalent in size to bert-base-cased. The model is intended for fine-tuning on various ta... | [
"# ECCO-BERT base model (cased)\n\nA pretrained BERT model trained exclusively on the ECCO (Eighteenth Century Collections Online) dataset of digitized documents published during the 18th century in the United Kingdom. The model is equivalent in size to bert-base-cased. The model is intended for fine-tuning on vari... | [
"TAGS\n#transformers #pytorch #bert #pretraining #en #endpoints_compatible #region-us \n",
"# ECCO-BERT base model (cased)\n\nA pretrained BERT model trained exclusively on the ECCO (Eighteenth Century Collections Online) dataset of digitized documents published during the 18th century in the United Kingdom. The ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-wikihow_3epoch_b4_lr3e-5
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikihow"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-wikihow_3epoch_b4_lr3e-5", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wikihow", "ty... | Chikashi/t5-small-finetuned-wikihow_3epoch_b4_lr3e-5 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wikihow",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-09T18:16:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-wikihow\_3epoch\_b4\_lr3e-5
==============================================
This model is a fine-tuned version of t5-small on the wikihow dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4351
* Rouge1: 26.1071
* Rouge2: 9.3627
* Rougel: 22.0825
* Rougelsum: 25.4514
* Gen... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
text-generation | transformers | Test | {} | cbgbcbcg/DialoGPT-small-joshua | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-09T18:16:46+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Test | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- 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-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name"... | winegarj/distilbert-base-uncased-finetuned-sst2 | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-09T18:56:14+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #generated_from_trainer #dataset-glue #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-sst2
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2605
* Accuracy: 0.9071
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1024\n* eval\\_batch\\_size: 1024\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Tr... | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #text-classification #generated_from_trainer #dataset-glue #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were u... |
null | transformers | from transformers import AutoTokenizer, ROBERTAClassifier
tokenizer = AutoTokenizer.from_pretrained("ivalig94/Robertweet-large")
model = ROBERTAClassifier.from_pretrained("ivalig94/Robertweet-large") | {"license": "afl-3.0"} | ivalig94/Robertweet-large | null | [
"transformers",
"pytorch",
"roberta",
"license:afl-3.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-09T19:22:32+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #license-afl-3.0 #endpoints_compatible #region-us
| from transformers import AutoTokenizer, ROBERTAClassifier
tokenizer = AutoTokenizer.from_pretrained("ivalig94/Robertweet-large")
model = ROBERTAClassifier.from_pretrained("ivalig94/Robertweet-large") | [] | [
"TAGS\n#transformers #pytorch #roberta #license-afl-3.0 #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- 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-assertive-hillary
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-finetuned-assertive-hillary", "results": []}]} | michaellutz/bert-finetuned-assertive-hillary | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-09T20:17:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-finetuned-assertive-hillary
This model is a fine-tuned version of bert-base-cased 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 hyper... | [
"# bert-finetuned-assertive-hillary\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training pro... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-finetuned-assertive-hillary\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.",
"## Model descriptio... |
unconditional-image-generation | null |
# Generate fauvism still life image using FastGAN
## Model description
[FastGAN model](https://arxiv.org/abs/2101.04775) is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised d... | {"license": "mit", "tags": ["huggan", "gan", "unconditional-image-generation"], "datasets": ["huggan/few-shot-fauvism-still-life"]} | huggan/fastgan-few-shot-fauvism-still-life | null | [
"pytorch",
"huggan",
"gan",
"unconditional-image-generation",
"dataset:huggan/few-shot-fauvism-still-life",
"arxiv:2101.04775",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-09T22:55:11+00:00 | [
"2101.04775"
] | [] | TAGS
#pytorch #huggan #gan #unconditional-image-generation #dataset-huggan/few-shot-fauvism-still-life #arxiv-2101.04775 #license-mit #has_space #region-us
|
# Generate fauvism still life image using FastGAN
## Model description
FastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-en... | [
"# Generate fauvism still life image using FastGAN",
"## Model description\n\nFastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a ... | [
"TAGS\n#pytorch #huggan #gan #unconditional-image-generation #dataset-huggan/few-shot-fauvism-still-life #arxiv-2101.04775 #license-mit #has_space #region-us \n",
"# Generate fauvism still life image using FastGAN",
"## Model description\n\nFastGAN model is a Generative Adversarial Networks (GAN) training on a ... |
text-generation | transformers |
#Morty DialoGPT Model | {"tags": ["conversational"]} | iyedr8/DialoGPT-small-rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-09T23:14:28+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Morty DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
---
language:
- ne
tags:
- speech-to-text
---
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
process... | {} | shniranjan/wav2vec2-large-xlsr-300m-nepali | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-09T23:49:09+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #endpoints_compatible #has_space #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
---
language:
- ne
tags:
- speech-to-text
---
| [
"## Usage\nThe model can be used directly (without a language model) as follows:\n---\nlanguage:\n- ne\ntags:\n- speech-to-text\n---"
] | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #endpoints_compatible #has_space #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:\n---\nlanguage:\n- ne\ntags:\n- speech-to-text\n---"
] |
text-classification | transformers |
<!-- 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. -->
# DistilRoberta
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknow... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "accuracy", "f1"], "model-index": [{"name": "DistilRoberta", "results": []}]} | NoCaptain/DistilRoBERTa-C19-Vax-Fine-tuned | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-09T23:51:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DistilRoberta
=============
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1246
* Precision: 0.9633
* Accuracy: 0.9697
* F1: 0.9705
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-devices-sum-ver2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown datase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-devices-sum-ver2", "results": []}]} | Wizounovziki/t5-small-devices-sum-ver2 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-10T00:12:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-devices-sum-ver2
=========================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3679
* Rouge1: 90.6465
* Rouge2: 65.2833
* Rougel: 90.6707
* Rougelsum: 90.7313
* Gen Len: 4.4702
Model description
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-devices-sum-ver2
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-base-devices-sum-ver2", "results": []}]} | Wizounovziki/t5-base-devices-sum-ver2 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-10T00:47:47+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-base-devices-sum-ver2
========================
This model is a fine-tuned version of t5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1919
* Rouge1: 95.2959
* Rouge2: 72.5788
* Rougel: 95.292
* Rougelsum: 95.3437
* Gen Len: 4.5992
Model description
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
automatic-speech-recognition | transformers | ## Usage
The model can be used directly (without a language model) as follows:
---
language:
- ne
tags:
- speech-to-text
---
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
process... | {} | shniranjan/wav2vec2-large-xlsr-1b-nepali | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T01:20:23+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| ## Usage
The model can be used directly (without a language model) as follows:
---
language:
- ne
tags:
- speech-to-text
---
| [
"## Usage\nThe model can be used directly (without a language model) as follows:\n---\nlanguage:\n- ne\ntags:\n- speech-to-text\n---"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"## Usage\nThe model can be used directly (without a language model) as follows:\n---\nlanguage:\n- ne\ntags:\n- speech-to-text\n---"
] |
text2text-generation | transformers |
<!-- 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. -->
# jo0hnd0e/mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-s... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "jo0hnd0e/mt5-small-finetuned-amazon-en-es", "results": []}]} | jo0hnd0e/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-10T02:01:38+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| jo0hnd0e/mt5-small-finetuned-amazon-en-es
=========================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.9844
* Validation Loss: 3.3610
* Epoch: 7
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5.6e-05, 'decay\\_steps': 9672, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle'... | [
"TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam... |
null | null |
# TADNE (This Anime Does Not Exist) model
The original TADNE site is https://thisanimedoesnotexist.ai/.
## Original TensorFlow model
The original TADNE model is provided in [this site](https://www.gwern.net/Faces#tadne-download) under CC-0 license. ([Google Drive](https://drive.google.com/f... | {"license": "cc0-1.0", "tags": ["computer-vision", "image-generation", "anime"]} | public-data/TADNE | null | [
"computer-vision",
"image-generation",
"anime",
"license:cc0-1.0",
"region:us",
"has_space"
] | null | 2022-04-10T03:29:58+00:00 | [] | [] | TAGS
#computer-vision #image-generation #anime #license-cc0-1.0 #region-us #has_space
|
# TADNE (This Anime Does Not Exist) model
The original TADNE site is URL
## Original TensorFlow model
The original TADNE model is provided in this site under CC-0 license. (Google Drive)
## Model Conversion
The model in the 'models' directory is converted with the following repo:
URL
### Apply ... | [
"# TADNE (This Anime Does Not Exist) model\n\nThe original TADNE site is URL\n\n",
"## Original TensorFlow model\n\nThe original TADNE model is provided in this site under CC-0 license. (Google Drive)",
"## Model Conversion\n\nThe model in the 'models' directory is converted with the following r... | [
"TAGS\n#computer-vision #image-generation #anime #license-cc0-1.0 #region-us #has_space \n",
"# TADNE (This Anime Does Not Exist) model\n\nThe original TADNE site is URL\n\n",
"## Original TensorFlow model\n\nThe original TADNE model is provided in this site under CC-0 license. (Google Drive)",
... |
text-generation | transformers |
# DialoGPT Trained on the Speech of a Game Character
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://raw.githubusercontent.com/RuolinZheng08/twewy-discord-chatbot/main/gif-demo/icon.png"} | MEDT/ChatBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-10T03:38:15+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT Trained on the Speech of a Game Character
This is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.
I built a Discord AI chatbot based on this model. Check out my GitHub repo.
Chat with the model:
| [
"# DialoGPT Trained on the Speech of a Game Character\n\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.\n\nI built a Discord AI chatbot based on this model. Check out my GitHub repo.\n\nChat with th... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Trained on the Speech of a Game Character\n\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua fro... |
null | null | An image rotation detector trained to detect if an image is upside down or not
| {"license": "mit"} | Ayobami/UpsideDownDetector | null | [
"license:mit",
"region:us"
] | null | 2022-04-10T05:26:56+00:00 | [] | [] | TAGS
#license-mit #region-us
| An image rotation detector trained to detect if an image is upside down or not
| [] | [
"TAGS\n#license-mit #region-us \n"
] |
text-classification | transformers |
<!-- 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. -->
# TSC_SentimentA_IMDBAmznTSC_2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "TSC_SentimentA_IMDBAmznTSC_2", "results": []}]} | malcolm/TSC_SentimentA_IMDBAmznTSC_2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T06:59:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TSC_SentimentA_IMDBAmznTSC_2
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1985
- Accuracy: 0.9365
- F1: 0.9373
## Model description
More information needed
## Intended uses & limitations
More information n... | [
"# TSC_SentimentA_IMDBAmznTSC_2\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1985\n- Accuracy: 0.9365\n- F1: 0.9373",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TSC_SentimentA_IMDBAmznTSC_2\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the... |
fill-mask | transformers |
<!-- 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. -->
# distilroberta-base-1
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-1", "results": []}]} | uhlenbeckmew/distilroberta-base-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T07:52:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-1
====================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6634
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
text-generation | transformers |
<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/1279092409587163137/eN82... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/fitfounder/1649585355118/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/fitfounder | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-10T09:06:19+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Dan Go
@fitfounder
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
<!-- 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-all-squad_que_translated
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-all-squad_que_translated", "results": []}]} | krinal214/bert-all-squad_que_translated | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T09:36:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-all-squad\_que\_translated
===============================
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5174
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
token-classification | transformers |
<!-- 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-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | V3RX2000/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T09:46:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1380
* F1: 0.8591
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | vaariis/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T09:46:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2218
* Accuracy: 0.9205
* F1: 0.9208
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
question-answering | transformers |
<!-- 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-all-squad_ben_tel_context
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-all-squad_ben_tel_context", "results": []}]} | krinal214/bert-all-squad_ben_tel_context | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T10:23:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-all-squad\_ben\_tel\_context
=================================
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5393
Model description
-----------------
More information needed
Intended uses & limita... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
text-classification | transformers |
## Paper
## [SEAD: SIMPLE ENSEMBLE AND KNOWLEDGE DISTILLATION FRAMEWORK FOR NATURAL LANGUAGE UNDERSTANDING](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63)
Aurthors: *Moyan Mei*, *Rohit Sroch*
## Abstract
With the widespread use of pre-trained language models (PLM), the... | {"language": ["en"], "license": "apache-2.0", "tags": ["SEAD"], "datasets": ["glue", "sst2"]} | course5i/SEAD-L-6_H-256_A-8-sst2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"SEAD",
"en",
"dataset:glue",
"dataset:sst2",
"arxiv:1910.01108",
"arxiv:1909.10351",
"arxiv:2002.10957",
"arxiv:1810.04805",
"arxiv:1804.07461",
"arxiv:1905.00537",
"license:apache-2.0",
"autotrain_compatible",... | null | 2022-04-10T10:33:09+00:00 | [
"1910.01108",
"1909.10351",
"2002.10957",
"1810.04805",
"1804.07461",
"1905.00537"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #SEAD #en #dataset-glue #dataset-sst2 #arxiv-1910.01108 #arxiv-1909.10351 #arxiv-2002.10957 #arxiv-1810.04805 #arxiv-1804.07461 #arxiv-1905.00537 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Paper
-----
SEAD: SIMPLE ENSEMBLE AND KNOWLEDGE DISTILLATION FRAMEWORK FOR NATURAL LANGUAGE UNDERSTANDING
---------------------------------------------------------------------------------------------
Aurthors: *Moyan Mei*, *Rohit Sroch*
Abstract
--------
With the widespread use of pre-trained language models (P... | [
"### Training hyperparameters\n\n\nPlease take a look at the 'training\\_args.bin' file",
"### Evaluation results",
"### Framework versions\n\n\n* Transformers >=4.8.0\n* Pytorch >=1.6.0\n* TensorFlow >=2.5.0\n* Flax >=0.3.5\n* Datasets >=1.10.2\n* Tokenizers >=0.11.6\n\n\nIf you use these models, please cite t... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #SEAD #en #dataset-glue #dataset-sst2 #arxiv-1910.01108 #arxiv-1909.10351 #arxiv-2002.10957 #arxiv-1810.04805 #arxiv-1804.07461 #arxiv-1905.00537 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperpar... |
text-classification | transformers |
## Paper
## [SEAD: SIMPLE ENSEMBLE AND KNOWLEDGE DISTILLATION FRAMEWORK FOR NATURAL LANGUAGE UNDERSTANDING](https://www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63)
Aurthors: *Moyan Mei*, *Rohit Sroch*
## Abstract
With the widespread use of pre-trained language models (PLM), the... | {"language": ["en"], "license": "apache-2.0", "tags": ["SEAD"], "datasets": ["glue", "sst2"]} | course5i/SEAD-L-6_H-384_A-12-sst2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"SEAD",
"en",
"dataset:glue",
"dataset:sst2",
"arxiv:1910.01108",
"arxiv:1909.10351",
"arxiv:2002.10957",
"arxiv:1810.04805",
"arxiv:1804.07461",
"arxiv:1905.00537",
"license:apache-2.0",
"autotrain_compatible",... | null | 2022-04-10T10:41:07+00:00 | [
"1910.01108",
"1909.10351",
"2002.10957",
"1810.04805",
"1804.07461",
"1905.00537"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #SEAD #en #dataset-glue #dataset-sst2 #arxiv-1910.01108 #arxiv-1909.10351 #arxiv-2002.10957 #arxiv-1810.04805 #arxiv-1804.07461 #arxiv-1905.00537 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Paper
-----
SEAD: SIMPLE ENSEMBLE AND KNOWLEDGE DISTILLATION FRAMEWORK FOR NATURAL LANGUAGE UNDERSTANDING
---------------------------------------------------------------------------------------------
Aurthors: *Moyan Mei*, *Rohit Sroch*
Abstract
--------
With the widespread use of pre-trained language models (P... | [
"### Training hyperparameters\n\n\nPlease take a look at the 'training\\_args.bin' file",
"### Evaluation results",
"### Framework versions\n\n\n* Transformers >=4.8.0\n* Pytorch >=1.6.0\n* TensorFlow >=2.5.0\n* Flax >=0.3.5\n* Datasets >=1.10.2\n* Tokenizers >=0.11.6\n\n\nIf you use these models, please cite t... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #SEAD #en #dataset-glue #dataset-sst2 #arxiv-1910.01108 #arxiv-1909.10351 #arxiv-2002.10957 #arxiv-1810.04805 #arxiv-1804.07461 #arxiv-1905.00537 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperpar... |
image-classification | transformers |
# Upside Down Classifier | {"datasets": ["cifar100"], "widget": [{"src": "https://huggingface.co/daveni/upside_down_classifier/resolve/main/meme_upside_down.jpg", "example_title": "Upside down example"}, {"src": "https://huggingface.co/daveni/upside_down_classifier/resolve/main/meme.jpg", "example_title": "Original example"}]} | daveni/upside_down_classifier | null | [
"transformers",
"pytorch",
"vit",
"image-classification",
"dataset:cifar100",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T10:42:17+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #image-classification #dataset-cifar100 #autotrain_compatible #endpoints_compatible #region-us
|
# Upside Down Classifier | [
"# Upside Down Classifier"
] | [
"TAGS\n#transformers #pytorch #vit #image-classification #dataset-cifar100 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Upside Down Classifier"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | V3RX2000/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T11:24:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2285
* Accuracy: 0.9245
* F1: 0.9247
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text2text-generation | transformers |
# Generating Declarative Statements from QA Pairs
There are already some rule-based models that can accomplish this task, but I haven't seen any transformer-based models that can do so. Therefore, I trained this model based on `Bart-base` to transform QA pairs into declarative statements.
I compared the this model w... | {"license": "afl-3.0"} | MarkS/bart-base-qa2d | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"arxiv:2112.03849",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T11:32:22+00:00 | [
"2112.03849"
] | [] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #arxiv-2112.03849 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| Generating Declarative Statements from QA Pairs
===============================================
There are already some rule-based models that can accomplish this task, but I haven't seen any transformer-based models that can do so. Therefore, I trained this model based on 'Bart-base' to transform QA pairs into declar... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #arxiv-2112.03849 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
<!-- 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. -->
# baseline_longformerv1
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longfo... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "baseline_longformerv1", "results": []}]} | brad1141/baseline_longformerv1 | null | [
"transformers",
"pytorch",
"tensorboard",
"longformer",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T11:37:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #longformer #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| baseline\_longformerv1
======================
This model is a fine-tuned version of allenai/longformer-base-4096 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7596
* Precision: 0.1333
* Recall: 0.15
* F1: 0.1400
* Accuracy: 0.1400
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #longformer #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* e... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | V3RX2000/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T11:45:09+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1667
* F1: 0.8582
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
question-answering | transformers |
<!-- 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/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | laampt/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T12:05:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Mode... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-fr", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.fr"}, "me... | V3RX2000/xlm-roberta-base-finetuned-panx-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T12:08:53+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-fr
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2651
* F1: 0.8355
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- 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. -->
# baseline_bertv3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknow... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "baseline_bertv3", "results": []}]} | brad1141/baseline_bertv3 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T12:09:44+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# baseline_bertv3
This model is a fine-tuned version of bert-base-uncased 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... | [
"# baseline_bertv3\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"###... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# baseline_bertv3\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information neede... |
token-classification | transformers |
<!-- 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. -->
# baseline_gptv1
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model descri... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "baseline_gptv1", "results": []}]} | brad1141/baseline_gptv1 | null | [
"transformers",
"pytorch",
"gpt2",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-10T12:18:14+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# baseline_gptv1
This model is a fine-tuned version of gpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyp... | [
"# baseline_gptv1\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hype... | [
"TAGS\n#transformers #pytorch #gpt2 #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# baseline_gptv1\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore information... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.it"}, "me... | V3RX2000/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T12:26:51+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-it
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2323
* F1: 0.8228
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-en", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.en"}, "me... | V3RX2000/xlm-roberta-base-finetuned-panx-en | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T12:43:43+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3925
* F1: 0.7075
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
token-classification | transformers |
<!-- 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-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-all", "results": []}]} | V3RX2000/xlm-roberta-base-finetuned-panx-all | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T13:00:32+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-all
===================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1759
* F1: 0.8527
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
question-answering | transformers |
<!-- 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-all-squad_all_translated
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-all-squad_all_translated", "results": []}]} | krinal214/bert-all-squad_all_translated | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T14:05:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-all-squad\_all\_translated
===============================
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5261
Model description
-----------------
More information needed
Intended uses & limitation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_bat... |
text-generation | transformers |
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<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/1487906000875180033/7mIn... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/gceh/1650662812216/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/gceh | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-10T15:27:37+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Geoff Evamy Hill
@gceh
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
----------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- 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. -->
# xtreme_s_xlsr_300m_fleurs_asr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "xtreme_s_xlsr_300m_fleurs_asr", "results": []}]} | anton-l/xtreme_s_xlsr_300m_fleurs_asr | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T16:26:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| xtreme\_s\_xlsr\_300m\_fleurs\_asr
==================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Cer: 0.3330
* Loss: 1.2864
* Wer: 0.8344
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 16\n* o... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8... |
null | null | ...
tags:
- conversational
.... | {} | Mandela/DialoGPT-small-DEADPOOLS | null | [
"region:us"
] | null | 2022-04-10T16:29:21+00:00 | [] | [] | TAGS
#region-us
| ...
tags:
- conversational
.... | [] | [
"TAGS\n#region-us \n"
] |
token-classification | transformers |
<!-- 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. -->
# syedyusufali/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "syedyusufali/bert-finetuned-ner", "results": []}]} | syedyusufali/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T16:30:01+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| syedyusufali/bert-finetuned-ner
===============================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0900
* Validation Loss: 0.1200
* Epoch: 2
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': '... |
token-classification | transformers |
<!-- 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-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | danhsf/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T16:35:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1380
* F1: 0.8591
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-robert... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | danhsf/xlm-roberta-base-finetuned-panx-de-fr | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T17:01:12+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1667
* F1: 0.8582
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n*... |
text-classification | transformers |
<!-- 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_duke_final
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "finetuning-sentiment-model_duke_final", "results": []}]} | dpazmino/finetuning-sentiment-model_duke_final | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T17:06:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model_duke_final
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4776
- F1: 0.8708
## Model description
More information needed
## Intended uses & limitations
More information needed
## ... | [
"# finetuning-sentiment-model_duke_final\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4776\n- F1: 0.8708",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore info... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model_duke_final\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt ach... |
text-classification | transformers |
<!-- 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 [cardiffnlp/twitter-roberta-base-sentiment](https:... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": []}]} | yshAggarwal/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T17:23:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| finetuning-sentiment-model-3000-samples
=======================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 4.9295
* Accuracy: 0.4568
* Precision: 0.3403
* Recall: 0.3408
* F1:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 0\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training ... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\... |
null | null |
# Upside Down Detector
The following project involves training an ML model that detects whether an input image is upside down or not. | {"license": "afl-3.0"} | zaryabmakram/upside-down-detector | null | [
"tensorboard",
"license:afl-3.0",
"region:us"
] | null | 2022-04-10T17:41:10+00:00 | [] | [] | TAGS
#tensorboard #license-afl-3.0 #region-us
|
# Upside Down Detector
The following project involves training an ML model that detects whether an input image is upside down or not. | [
"# Upside Down Detector\n\nThe following project involves training an ML model that detects whether an input image is upside down or not."
] | [
"TAGS\n#tensorboard #license-afl-3.0 #region-us \n",
"# Upside Down Detector\n\nThe following project involves training an ML model that detects whether an input image is upside down or not."
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | cj-mills/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T18:11:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4875
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
text-generation | transformers |
<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/1511852580216967169/b1Ai... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/graveyard_plots-hel_ql-witheredstrings/1649618186549/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/graveyard_plots-hel_ql-witheredstrings | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-10T18:15:53+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
GHANEM & Anthropos & darth hattie
@graveyard\_plots-hel\_ql-witheredstrings
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was develop... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | 1. Run app.py or gradio_spinalvgg.py
2. Input http://127.0.0.1:7860/
3. Select any picture in the images file, then click submit, Predicted values will appear on the right side | {} | shuoyingzhao/CSI5140FinalProject | null | [
"region:us"
] | null | 2022-04-10T18:42:43+00:00 | [] | [] | TAGS
#region-us
| 1. Run URL or gradio_spinalvgg.py
2. Input http://127.0.0.1:7860/
3. Select any picture in the images file, then click submit, Predicted values will appear on the right side | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
<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/1129935220260704256/RSmw... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/nordicshrew/1649628249290/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/nordicshrew | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-10T21:02:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
guelph’s finest poster
@nordicshrew
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training da... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- 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. -->
# local_dataset
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-... | {"language": ["pt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "model-index": [{"name": "local_dataset", "results": []}]} | tonyalves/local_dataset | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"pt",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-10T21:05:01+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #pt #license-apache-2.0 #endpoints_compatible #region-us
|
# local_dataset
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PT dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training p... | [
"# local_dataset\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PT dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #pt #license-apache-2.0 #endpoints_compatible #region-us \n",
"# local_dataset\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_V... |
text-generation | transformers |
<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/1480658144833515525/DS0A... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/s_m_frank/1649629685555/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/s_m_frank | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-10T21:27:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
cute junco observer
@s\_m\_frank
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-wikihow_3epoch_b8_lr3e-3
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikihow"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-wikihow_3epoch_b8_lr3e-3", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wikihow", "ty... | Chikashi/t5-small-finetuned-wikihow_3epoch_b8_lr3e-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wikihow",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-10T22:51:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-wikihow\_3epoch\_b8\_lr3e-3
==============================================
This model is a fine-tuned version of t5-small on the wikihow dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3163
* Rouge1: 27.1711
* Rouge2: 10.6296
* Rougel: 23.206
* Rougelsum: 26.4801
* Gen... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
null | null | # Link to Forgotten Realms Wiki dataset
https://huggingface.co/datasets/Akila/ForgottenRealmsWikiDataset
| {} | Akila/ForgottenRealmsFreeTextGenerator | null | [
"region:us"
] | null | 2022-04-10T23:34:45+00:00 | [] | [] | TAGS
#region-us
| # Link to Forgotten Realms Wiki dataset
URL
| [
"# Link to Forgotten Realms Wiki dataset\nURL"
] | [
"TAGS\n#region-us \n",
"# Link to Forgotten Realms Wiki dataset\nURL"
] |
text-classification | transformers | # Bad_text_classifier
## Model 소개
인터넷 상에 퍼져있는 여러 댓글, 채팅이 민감한 내용인지 아닌지를 판별하는 모델을 공개합니다. 해당 모델은 공개데이터를 사용해 label을 수정하고 데이터들을 합쳐 구성해 finetuning을 진행하였습니다. 해당 모델이 언제나 모든 문장을 정확히 판단이 가능한 것은 아니라는 점 양해해 주시면 감사드리겠습니다.
```
NOTE)
공개 데이터의 저작권 문제로 인해 모델 학습에 사용된 변형된 데이터는 공개 불가능하다는 점을 밝힙니다.
또한 해당 모델의 의견은 제 의견과 무관하다는 점을 미리 밝힙니다.
```
... | {} | JminJ/tunibElectra_base_Bad_Sentence_Classifier | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"arxiv:2003.10555",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T00:32:41+00:00 | [
"2003.10555"
] | [] | TAGS
#transformers #pytorch #electra #text-classification #arxiv-2003.10555 #autotrain_compatible #endpoints_compatible #region-us
| Bad\_text\_classifier
=====================
Model 소개
--------
인터넷 상에 퍼져있는 여러 댓글, 채팅이 민감한 내용인지 아닌지를 판별하는 모델을 공개합니다. 해당 모델은 공개데이터를 사용해 label을 수정하고 데이터들을 합쳐 구성해 finetuning을 진행하였습니다. 해당 모델이 언제나 모든 문장을 정확히 판단이 가능한 것은 아니라는 점 양해해 주시면 감사드리겠습니다.
Dataset
-------
### data label
* 0 : bad sentence
* 1 : not bad sentence
... | [
"### data label\n\n\n* 0 : bad sentence\n* 1 : not bad sentence",
"### 사용한 dataset\n\n\n* smilegate-ai/Korean Unsmile Dataset\n* kocohub/Korean HateSpeech Dataset",
"### dataset 가공 방법\n\n\n기존 이진 분류가 아니였던 두 데이터를 이진 분류 형태로 labeling을 다시 해준 뒤, Korean HateSpeech Dataset중 label 1(not bad sentence)만을 추려 가공된 Korean Uns... | [
"TAGS\n#transformers #pytorch #electra #text-classification #arxiv-2003.10555 #autotrain_compatible #endpoints_compatible #region-us \n",
"### data label\n\n\n* 0 : bad sentence\n* 1 : not bad sentence",
"### 사용한 dataset\n\n\n* smilegate-ai/Korean Unsmile Dataset\n* kocohub/Korean HateSpeech Dataset",
"### da... |
text-classification | transformers | # Bad_text_classifier
## Model 소개
인터넷 상에 퍼져있는 여러 댓글, 채팅이 민감한 내용인지 아닌지를 판별하는 모델을 공개합니다. 해당 모델은 공개데이터를 사용해 label을 수정하고 데이터들을 합쳐 구성해 finetuning을 진행하였습니다. 해당 모델이 언제나 모든 문장을 정확히 판단이 가능한 것은 아니라는 점 양해해 주시면 감사드리겠습니다.
```
NOTE)
공개 데이터의 저작권 문제로 인해 모델 학습에 사용된 변형된 데이터는 공개 불가능하다는 점을 밝힙니다.
또한 해당 모델의 의견은 제 의견과 무관하다는 점을 미리 밝힙니다.
```
... | {} | JminJ/koElectra_base_Bad_Sentence_Classifier | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"arxiv:2003.10555",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T00:32:53+00:00 | [
"2003.10555"
] | [] | TAGS
#transformers #pytorch #electra #text-classification #arxiv-2003.10555 #autotrain_compatible #endpoints_compatible #region-us
| Bad\_text\_classifier
=====================
Model 소개
--------
인터넷 상에 퍼져있는 여러 댓글, 채팅이 민감한 내용인지 아닌지를 판별하는 모델을 공개합니다. 해당 모델은 공개데이터를 사용해 label을 수정하고 데이터들을 합쳐 구성해 finetuning을 진행하였습니다. 해당 모델이 언제나 모든 문장을 정확히 판단이 가능한 것은 아니라는 점 양해해 주시면 감사드리겠습니다.
Dataset
-------
### data label
* 0 : bad sentence
* 1 : not bad sentence
... | [
"### data label\n\n\n* 0 : bad sentence\n* 1 : not bad sentence",
"### 사용한 dataset\n\n\n* smilegate-ai/Korean Unsmile Dataset\n* kocohub/Korean HateSpeech Dataset",
"### dataset 가공 방법\n\n\n기존 이진 분류가 아니였던 두 데이터를 이진 분류 형태로 labeling을 다시 해준 뒤, Korean HateSpeech Dataset중 label 1(not bad sentence)만을 추려 가공된 Korean Uns... | [
"TAGS\n#transformers #pytorch #electra #text-classification #arxiv-2003.10555 #autotrain_compatible #endpoints_compatible #region-us \n",
"### data label\n\n\n* 0 : bad sentence\n* 1 : not bad sentence",
"### 사용한 dataset\n\n\n* smilegate-ai/Korean Unsmile Dataset\n* kocohub/Korean HateSpeech Dataset",
"### da... |
text-classification | transformers |
<!-- 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. -->
# twitter-roberta-base-efl-hateval
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2021-124m](https://huggi... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "twitter-roberta-base-efl-hateval", "results": []}]} | ChrisZeng/twitter-roberta-base-efl-hateval | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T00:33:11+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| twitter-roberta-base-efl-hateval
================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2021-124m on the HatEval dataset.
It achieves the following results on the evaluation set:
* Accuracy: 0.7913
* F1: 0.7899
* Loss: 0.3683
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 32\n* eval\\_batch\\_si... |
null | transformers |
baikal-BERT-base
---
- model: bert-base
- vocab: bert-wordpiece, 30,000
- version: latest
| {"language": "ko", "datasets": ["\ud55c\uad6d\uc5b4 \uc704\ud0a4", "\uad6d\ub9bd\uad6d\uc5b4\uc6d0 \ubb38\uc5b4/\ub274\uc2a4 \ub370\uc774\ud130\uc14b"]} | baikal/bert-wp30 | null | [
"transformers",
"pytorch",
"ko",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T00:35:40+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #ko #endpoints_compatible #region-us
|
baikal-BERT-base
---
- model: bert-base
- vocab: bert-wordpiece, 30,000
- version: latest
| [] | [
"TAGS\n#transformers #pytorch #ko #endpoints_compatible #region-us \n"
] |
null | transformers |
ELECTRA-base
---
- model: electra-base-discriminator
- vocab: bert-wordpiece, 30,000
| {"language": "ko", "datasets": ["\ud55c\uad6d\uc5b4\uc704\ud0a4", "\uad6d\ub9bd\uad6d\uc5b4\uc6d0 \ubb38\uc5b4\ub370\uc774\ud130\uc14b"]} | baikal/electra-wp30 | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"ko",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T01:24:13+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #electra #pretraining #ko #endpoints_compatible #region-us
|
ELECTRA-base
---
- model: electra-base-discriminator
- vocab: bert-wordpiece, 30,000
| [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #ko #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- 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. -->
# electra-large-discriminator-nli-efl-hateval
This model is a fine-tuned version of [ynie/electra-large-discriminator-snli_mnli_fe... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "electra-large-discriminator-nli-efl-hateval", "results": []}]} | ChrisZeng/electra-large-discriminator-nli-efl-hateval | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T02:04:30+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| electra-large-discriminator-nli-efl-hateval
===========================================
This model is a fine-tuned version of ynie/electra-large-discriminator-snli\_mnli\_fever\_anli\_R1\_R2\_R3-nli on the None dataset.
It achieves the following results on the evaluation set:
* Accuracy: 0.798
* F1: 0.7968
* Loss: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #electra #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_siz... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codeparrot-ds-500sample-gpt-neo-10epoch
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/E... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "codeparrot-ds-500sample-gpt-neo-10epoch", "results": []}]} | Pavithra/codeparrot-ds-500sample-gpt-neo-10epoch | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T03:16:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# codeparrot-ds-500sample-gpt-neo-10epoch
This model is a fine-tuned version of EleutherAI/gpt-neo-125M on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.5456
- eval_runtime: 87.6603
- eval_samples_per_second: 149.817
- eval_steps_per_second: 4.689
- epoch: 2.97
- step: ... | [
"# codeparrot-ds-500sample-gpt-neo-10epoch\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-125M on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.5456\n- eval_runtime: 87.6603\n- eval_samples_per_second: 149.817\n- eval_steps_per_second: 4.689\n- epoch: 2.9... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# codeparrot-ds-500sample-gpt-neo-10epoch\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-125M on an unknown dataset.\nIt achiev... |
unconditional-image-generation | null |
# Generate paiting image using FastGAN
## Model description
[FastGAN model](https://arxiv.org/abs/2101.04775) is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminato... | {"license": "mit", "tags": ["huggan", "gan", "unconditional-image-generation"], "datasets": ["huggan/few-shot-art-painting"]} | huggan/fastgan-few-shot-painting | null | [
"pytorch",
"huggan",
"gan",
"unconditional-image-generation",
"dataset:huggan/few-shot-art-painting",
"arxiv:2101.04775",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-11T03:24:01+00:00 | [
"2101.04775"
] | [] | TAGS
#pytorch #huggan #gan #unconditional-image-generation #dataset-huggan/few-shot-art-painting #arxiv-2101.04775 #license-mit #has_space #region-us
|
# Generate paiting image using FastGAN
## Model description
FastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, the ... | [
"# Generate paiting image using FastGAN",
"## Model description\n\nFastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-enc... | [
"TAGS\n#pytorch #huggan #gan #unconditional-image-generation #dataset-huggan/few-shot-art-painting #arxiv-2101.04775 #license-mit #has_space #region-us \n",
"# Generate paiting image using FastGAN",
"## Model description\n\nFastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of h... |
audio-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-mirst500
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-b... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["mir_st500"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-mirst500", "results": []}]} | gary109/wav2vec2-base-mirst500 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:mir_st500",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T05:13:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-mir_st500 #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-mirst500
======================
This model is a fine-tuned version of facebook/wav2vec2-base on the /workspace/datasets/datasets/MIR\_ST500/MIR\_ST500\_AUDIO\_CLASSIFICATION.py dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8678
* Accuracy: 0.7017
Model description
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 1\n* seed: 0\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-mir_st500 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 728922203
- CO2 Emissions (in grams): 583.728921803621
## Validation Metrics
- Loss: 1.2922444343566895
- Rouge1: 54.3928
- Rouge2: 31.666
- RougeL: 50.3552
- RougeLsum: 50.3694
- Gen Len: 13.3425
## Usage
You can use cURL to access this mo... | {"language": "unk", "tags": "autotrain", "datasets": ["FabsCool/autotrain-data-T5Base1_1"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 583.728921803621} | FabsCool/autotrain-T5Base1_1-728922203 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"unk",
"dataset:FabsCool/autotrain-data-T5Base1_1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-11T05:19:13+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain #unk #dataset-FabsCool/autotrain-data-T5Base1_1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 728922203
- CO2 Emissions (in grams): 583.728921803621
## Validation Metrics
- Loss: 1.2922444343566895
- Rouge1: 54.3928
- Rouge2: 31.666
- RougeL: 50.3552
- RougeLsum: 50.3694
- Gen Len: 13.3425
## Usage
You can use cURL to access this mo... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 728922203\n- CO2 Emissions (in grams): 583.728921803621",
"## Validation Metrics\n\n- Loss: 1.2922444343566895\n- Rouge1: 54.3928\n- Rouge2: 31.666\n- RougeL: 50.3552\n- RougeLsum: 50.3694\n- Gen Len: 13.3425",
"## Usage\n\nYou can u... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain #unk #dataset-FabsCool/autotrain-data-T5Base1_1 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 728922203\n- CO... |
text-classification | transformers |
<!-- 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-l12-h384-sst2-distilled
This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](h... | {"tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "minilm-l12-h384-sst2-distilled", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": [{"type": "accuracy"... | philschmid/minilm-l12-h384-sst2-distilled | null | [
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T05:28:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us
| minilm-l12-h384-sst2-distilled
==============================
This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5417
* Accuracy: 0.9220
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001400785945474408\n* train\\_batch\\_size: 512\n* eval\\_batch\\_size: 512\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs:... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000140078594... |
null | null |
# MyModelName
## Model description
[Pix2pix Model](https://arxiv.org/abs/1611.07004) is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mappi... | {"license": "mit", "tags": ["huggan", "gan"], "datasets": ["huggan/edge2shoes"]} | huggan/pix2pix-edge2shoes | null | [
"pytorch",
"huggan",
"gan",
"dataset:huggan/edge2shoes",
"arxiv:1611.07004",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-11T05:54:26+00:00 | [
"1611.07004"
] | [] | TAGS
#pytorch #huggan #gan #dataset-huggan/edge2shoes #arxiv-1611.07004 #license-mit #has_space #region-us
|
# MyModelName
## Model description
Pix2pix Model is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply ... | [
"# MyModelName",
"## Model description\n\nPix2pix Model is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible... | [
"TAGS\n#pytorch #huggan #gan #dataset-huggan/edge2shoes #arxiv-1611.07004 #license-mit #has_space #region-us \n",
"# MyModelName",
"## Model description\n\nPix2pix Model is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the ma... |
sentence-similarity | sentence-transformers | <img src="https://public.3.basecamp.com/p/rs5XqmAuF1iEuW6U7nMHcZeY/upload/download/VL-NLP-short.png" alt="logo voicelab nlp" style="width:300px;"/>
# SHerbert - Polish SentenceBERT
SentenceBERT is a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meani... | {"language": ["pl"], "license": "cc-by-4.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "datasets": ["Wikipedia"], "pipeline_tag": "sentence-similarity", "widget": [{"source_sentence": "Uczenie maszynowe jest konsekwencj\u0105 rozwoju idei sztucznej inteligencji i metod jej wdra\u01... | Voicelab/sbert-base-cased-pl | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"pl",
"dataset:Wikipedia",
"arxiv:1908.10084",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T05:57:47+00:00 | [
"1908.10084"
] | [
"pl"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #pl #dataset-Wikipedia #arxiv-1908.10084 #license-cc-by-4.0 #endpoints_compatible #region-us
| <img src="https://public.3.URL alt="logo voicelab nlp" style="width:300px;"/>
SHerbert - Polish SentenceBERT
==============================
SentenceBERT is a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be ... | [] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #pl #dataset-Wikipedia #arxiv-1908.10084 #license-cc-by-4.0 #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
<!-- 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. -->
# Team-Gryffindor-distilbert-base-finetuned-NER-creditcardcontract-100epoch
This model is a fine-tuned version of [distilbert-base... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Team_Gryffindor_NER", "results": []}]} | timhbach/Team_Gryffindor_NER | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T06:08:50+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Team-Gryffindor-distilbert-base-finetuned-NER-creditcardcontract-100epoch
=========================================================================
This model is a fine-tuned version of distilbert-base-uncased on the Credit card agreement dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 11",
"### Train... | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
unconditional-image-generation | null |
# Generate shell image using FastGAN
## Model description
[FastGAN model](https://arxiv.org/abs/2101.04775) is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator ... | {"license": "mit", "tags": ["huggan", "gan", "unconditional-image-generation"], "datasets": ["huggan/few-shot-shells"]} | huggan/fastgan-few-shot-shells | null | [
"pytorch",
"huggan",
"gan",
"unconditional-image-generation",
"dataset:huggan/few-shot-shells",
"arxiv:2101.04775",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-11T06:30:56+00:00 | [
"2101.04775"
] | [] | TAGS
#pytorch #huggan #gan #unconditional-image-generation #dataset-huggan/few-shot-shells #arxiv-2101.04775 #license-mit #has_space #region-us
|
# Generate shell image using FastGAN
## Model description
FastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, the mo... | [
"# Generate shell image using FastGAN",
"## Model description\n\nFastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encod... | [
"TAGS\n#pytorch #huggan #gan #unconditional-image-generation #dataset-huggan/few-shot-shells #arxiv-2101.04775 #license-mit #has_space #region-us \n",
"# Generate shell image using FastGAN",
"## Model description\n\nFastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fide... |
text-classification | transformers |
<!-- 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. -->
# thesis-freeform
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
I... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "thesis-freeform", "results": []}]} | maretamasaeva/thesis-freeform | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T06:33:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| thesis-freeform
===============
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6933
* Accuracy: 0.4636
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_si... |
null | transformers |
# Randeng-Transformer-1.1B-Denoise
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
以语法纠错任务为微调目标的中文Transformer-XL。
Chinese Transformer-XL with a denoising task as the fine-tuning objective.
## 模型分类 Model Tax... | {"language": ["zh"], "license": "apache-2.0"} | IDEA-CCNL/Randeng-Transformer-1.1B-Denoise | null | [
"transformers",
"pytorch",
"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T07:00:32+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #zh #arxiv-2209.02970 #license-apache-2.0 #endpoints_compatible #region-us
| Randeng-Transformer-1.1B-Denoise
================================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
以语法纠错任务为微调目标的中文Transformer-XL。
Chinese Transformer-XL with a denoising task as the fine-tuning objective.
模型分类 Model Taxonomy
-------------------
... | [
"### 加载模型 Loading Models",
"### 使用示例 Usage Examples\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:"
] | [
"TAGS\n#transformers #pytorch #zh #arxiv-2209.02970 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### 加载模型 Loading Models",
"### 使用示例 Usage Examples\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引... |
text-classification | transformers |
# DistilBERT base uncased model for Short Question Answer Assessment
## Model description
DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only,... | {"language": "en", "license": "apache-2.0", "datasets": ["Short Question Answer Assessment Dataset"], "library": "transformers", "other": "distilbert"} | Giyaseddin/distilbert-base-uncased-finetuned-short-answer-assessment | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T08:03:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DistilBERT base uncased model for Short Question Answer Assessment
==================================================================
Model description
-----------------
DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
self-supervised fashion, using the ... | [
"### How to use\n\n\nYou can use this model directly with a :",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fairly neutral, this model can have biased\npredictions. It also inherits some of\nthe bias of its teacher model.\n\n\nThis bias will also affect a... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a :",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized a... |
text-classification | transformers |
# DistilRoBERTa base model for Short Question Answer Assessment
## Model description
The pre-trained model is a distilled version of the [RoBERTa-base model](https://huggingface.co/roberta-base). It follows the same training procedure as [DistilBERT](https://huggingface.co/distilbert-base-uncased).
The code for the ... | {"language": "en", "license": "apache-2.0", "datasets": ["Short Question Answer Assessment Dataset"], "library": "transformers", "other": "distilroberta"} | Giyaseddin/distilroberta-base-finetuned-short-answer-assessment | null | [
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"text-classification",
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"arxiv:1806.02847",
"license:apache-2.0",
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"region:us"
] | null | 2022-04-11T08:03:47+00:00 | [
"1806.02847"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #en #arxiv-1806.02847 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DistilRoBERTa base model for Short Question Answer Assessment
=============================================================
Model description
-----------------
The pre-trained model is a distilled version of the RoBERTa-base model. It follows the same training procedure as DistilBERT.
The code for the distillation ... | [
"### How to use\n\n\nYou can use this model directly with a :",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fairly neutral, this model can have biased\npredictions. It also inherits some of\nthe bias of its teacher model.\n\n\nThis bias will also affect a... | [
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"### How to use\n\n\nYou can use this model directly with a :",
"### Limitations and bias\n\n\nEven if the training data used for this model could be ... |
null | null |
# How to run locally from GitHub
- [ ] ```git clone https://github.com/majauhar/UpsideDownDetector.git```
- [ ] ```cd UpsideDownDetector```
- [ ] ```pip install -r requirements.txt```
- [ ] ```python main.py --epochs=<Integer> --pretrained=[True/False]```
| {"language": ["en"], "datasets": ["cifar10"]} | Jauhar/UpsideDownDetector | null | [
"en",
"dataset:cifar10",
"region:us"
] | null | 2022-04-11T08:09:54+00:00 | [] | [
"en"
] | TAGS
#en #dataset-cifar10 #region-us
|
# How to run locally from GitHub
- [ ]
- [ ]
- [ ]
- [ ]
| [
"# How to run locally from GitHub\n\n- [ ] \n- [ ] \n- [ ] \n- [ ]"
] | [
"TAGS\n#en #dataset-cifar10 #region-us \n",
"# How to run locally from GitHub\n\n- [ ] \n- [ ] \n- [ ] \n- [ ]"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-wikihow_3epoch_b8_lr3e-4
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikihow"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-wikihow_3epoch_b8_lr3e-4", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wikihow", "ty... | Chikashi/t5-small-finetuned-wikihow_3epoch_b8_lr3e-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wikihow",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-11T08:18:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-wikihow\_3epoch\_b8\_lr3e-4
==============================================
This model is a fine-tuned version of t5-small on the wikihow dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3136
* Rouge1: 27.3718
* Rouge2: 10.6235
* Rougel: 23.3396
* Rougelsum: 26.6889
* Ge... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-efficient-base-finetuned-1.2
This model is a fine-tuned version of [google/t5-efficient-base](https://huggingface.co/google/t... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-efficient-base-finetuned-1.2", "results": []}]} | aleksavega/t5-efficient-base-finetuned-1.2 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-11T08:53:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-efficient-base-finetuned-1.2
===============================
This model is a fine-tuned version of google/t5-efficient-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5294
* Rouge1: 62.691
* Rouge2: 55.9731
* Rougel: 60.9097
* Rougelsum: 61.4393
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 4662\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 16",
"### Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-finetuned-clinc
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-large-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "args": "plus"},... | optimum/roberta-large-finetuned-clinc | null | [
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"generated_from_trainer",
"dataset:clinc_oos",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T08:53:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-large-finetuned-clinc
=============================
This model is a fine-tuned version of roberta-large on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1574
* Accuracy: 0.9729
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... |
text-classification | transformers |
<!-- 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. -->
# MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-finetuned-clinc
This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384... | {"tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "... | optimum/MiniLMv2-L12-H384-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T09:27:35+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #model-index #autotrain_compatible #endpoints_compatible #region-us
| MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-finetuned-clinc
==============================================================
This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 1.52... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n*... |
text2text-generation | transformers |
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 730222226
- CO2 Emissions (in grams): 2986.6520132805163
## Validation Metrics
- Loss: 2.682709217071533
- Rouge1: 19.6069
- Rouge2: 7.3367
- RougeL: 19.2706
- RougeLsum: 19.286
- Gen Len: 5.5731
## Usage
You can use cURL to access this mod... | {"language": "en", "tags": "autotrain", "datasets": ["yogi/autotrain-data-amazon_text_sum"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 2986.6520132805163} | yogi/autotrain-amazon_text_sum-730222226 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"en",
"dataset:yogi/autotrain-data-amazon_text_sum",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-11T09:39:58+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain #en #dataset-yogi/autotrain-data-amazon_text_sum #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 730222226
- CO2 Emissions (in grams): 2986.6520132805163
## Validation Metrics
- Loss: 2.682709217071533
- Rouge1: 19.6069
- Rouge2: 7.3367
- RougeL: 19.2706
- RougeLsum: 19.286
- Gen Len: 5.5731
## Usage
You can use cURL to access this mod... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 730222226\n- CO2 Emissions (in grams): 2986.6520132805163",
"## Validation Metrics\n\n- Loss: 2.682709217071533\n- Rouge1: 19.6069\n- Rouge2: 7.3367\n- RougeL: 19.2706\n- RougeLsum: 19.286\n- Gen Len: 5.5731",
"## Usage\n\nYou can us... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain #en #dataset-yogi/autotrain-data-amazon_text_sum #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Summarization\n- Model ID: 730222226\n- C... |
fill-mask | transformers | # DistilBERT with word2vec token embeddings
This model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset with MLM for 1.37M steps (batch size 64). The token embeddings were ... | {} | vocab-transformers/distilbert-word2vec_256k-MLM_best | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-11T10:10:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| # DistilBERT with word2vec token embeddings
This model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset with MLM for 1.37M steps (batch size 64). The token embeddings were ... | [
"# DistilBERT with word2vec token embeddings\n\nThis model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.\n\nThen the model was trained on this dataset with MLM for 1.37M steps (batch size 64). The token embeddi... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# DistilBERT with word2vec token embeddings\n\nThis model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, ... |
fill-mask | transformers | # DistilBERT with 256k token embeddings
This model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset wit... | {} | vocab-transformers/distilbert-tokenizer_256k-MLM_best | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T10:14:12+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # DistilBERT with 256k token embeddings
This model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset wit... | [
"# DistilBERT with 256k token embeddings\n\nThis model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.\n\nThen the model was trained on this da... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilBERT with 256k token embeddings\n\nThis model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was tra... |
text-classification | transformers |
<!-- 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. -->
# MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc
This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384... | {"tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "... | optimum/MiniLMv2-L12-H384-distilled-finetuned-clinc | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T10:18:49+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #model-index #autotrain_compatible #endpoints_compatible #region-us
| MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc
==============================================================
This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.34... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Tr... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batc... |
image-classification | transformers |
<!-- 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-cifar10
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "base_model": "microsoft/swin-tiny-patch4-window7-224", "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-cifar10", "results": [{"task": {"type": "image-classification", "name": "Image Cla... | nielsr/swin-tiny-patch4-window7-224-finetuned-cifar10 | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T10:59:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #base_model-microsoft/swin-tiny-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-cifar10
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0690
* Accuracy: 0.9789
Model description
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #base_model-microsoft/swin-tiny-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hype... |
audio-to-audio | espnet |
## ESPnet2 ENH model
### `espnet/Wangyou_Zhang_chime4_enh_train_enh_dc_crn_mapping_snr_raw`
This model was trained by Wangyou Zhang using chime4 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/chime4/enh1
./run.sh --skip_data_prep ... | {"license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-to-audio"], "datasets": ["chime4"]} | espnet/Wangyou_Zhang_chime4_enh_train_enh_dc_crn_mapping_snr_raw | null | [
"espnet",
"audio",
"audio-to-audio",
"dataset:chime4",
"arxiv:1804.00015",
"arxiv:2011.03706",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-11T11:42:06+00:00 | [
"1804.00015",
"2011.03706"
] | [] | TAGS
#espnet #audio #audio-to-audio #dataset-chime4 #arxiv-1804.00015 #arxiv-2011.03706 #license-cc-by-4.0 #region-us
|
## ESPnet2 ENH model
### 'espnet/Wangyou_Zhang_chime4_enh_train_enh_dc_crn_mapping_snr_raw'
This model was trained by Wangyou Zhang using chime4 recipe in espnet.
### Demo: How to use in ESPnet2
## ENH config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ENH model",
"### 'espnet/Wangyou_Zhang_chime4_enh_train_enh_dc_crn_mapping_snr_raw'\n\nThis model was trained by Wangyou Zhang using chime4 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## ENH config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor... | [
"TAGS\n#espnet #audio #audio-to-audio #dataset-chime4 #arxiv-1804.00015 #arxiv-2011.03706 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ENH model",
"### 'espnet/Wangyou_Zhang_chime4_enh_train_enh_dc_crn_mapping_snr_raw'\n\nThis model was trained by Wangyou Zhang using chime4 recipe in espnet.",
"### Demo: Ho... |
image-classification | transformers |
<!-- 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. -->
# van-base-finetuned-eurosat-imgaug
This model is a fine-tuned version of [Visual-Attention-Network/van-base](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "base_model": "Visual-Attention-Network/van-base", "model-index": [{"name": "van-base-finetuned-eurosat-imgaug", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "da... | nielsr/van-base-finetuned-eurosat-imgaug | null | [
"transformers",
"pytorch",
"tensorboard",
"van",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"base_model:Visual-Attention-Network/van-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-11T11:46:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #van #image-classification #generated_from_trainer #dataset-image_folder #base_model-Visual-Attention-Network/van-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| van-base-finetuned-eurosat-imgaug
=================================
This model is a fine-tuned version of Visual-Attention-Network/van-base on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0379
* Accuracy: 0.9885
Model description
-----------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #van #image-classification #generated_from_trainer #dataset-image_folder #base_model-Visual-Attention-Network/van-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-mlm
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on ... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "test-mlm", "results": []}]} | ZZ99/tapt_nbme_deberta_v3_base | null | [
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T12:02:18+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta-v2 #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# test-mlm
This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0870
- Accuracy: 0.7576
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evalua... | [
"# test-mlm\n\nThis model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.0870\n- Accuracy: 0.7576",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #deberta-v2 #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# test-mlm\n\nThis model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- L... |
audio-to-audio | espnet |
## ESPnet2 ENH model
### `lichenda/Chenda_Li_wsj0_2mix_enh_dprnn_tasnet`
This model was trained by LiChenda using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).
Imported from [zenodo](https://zenodo.org/record/4688000).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 54919e2529d6... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-to-audio"], "datasets": ["wsj0_2mix"]} | lichenda/Chenda_Li_wsj0_2mix_enh_dprnn_tasnet | null | [
"espnet",
"audio",
"audio-to-audio",
"en",
"dataset:wsj0_2mix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-11T12:11:49+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #audio-to-audio #en #dataset-wsj0_2mix #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ENH model
-----------------
### 'lichenda/Chenda\_Li\_wsj0\_2mix\_enh\_dprnn\_tasnet'
This model was trained by LiChenda using wsj0\_2mix recipe in espnet.
Imported from zenodo.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Thu Apr 15 00:03:19 CST 2021'
* pytho... | [
"### 'lichenda/Chenda\\_Li\\_wsj0\\_2mix\\_enh\\_dprnn\\_tasnet'\n\n\nThis model was trained by LiChenda using wsj0\\_2mix recipe in espnet.\n\n\nImported from zenodo.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu Apr 15 00:03:19 CST 2021'\n* python ver... | [
"TAGS\n#espnet #audio #audio-to-audio #en #dataset-wsj0_2mix #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'lichenda/Chenda\\_Li\\_wsj0\\_2mix\\_enh\\_dprnn\\_tasnet'\n\n\nThis model was trained by LiChenda using wsj0\\_2mix recipe in espnet.\n\n\nImported from zenodo.",
"### Demo: How to use in ESP... |
audio-to-audio | espnet |
## ESPnet2 ENH model
### `espnet/Wangyou_Zhang_chime4_enh_train_enh_conv_tasnet_raw`
This model was trained by Wangyou Zhang using chime4 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/chime4/enh1
./run.sh --skip_data_prep false -... | {"license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-to-audio"], "datasets": ["chime4"]} | espnet/Wangyou_Zhang_chime4_enh_train_enh_conv_tasnet_raw | null | [
"espnet",
"audio",
"audio-to-audio",
"dataset:chime4",
"arxiv:1804.00015",
"arxiv:2011.03706",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-11T12:17:43+00:00 | [
"1804.00015",
"2011.03706"
] | [] | TAGS
#espnet #audio #audio-to-audio #dataset-chime4 #arxiv-1804.00015 #arxiv-2011.03706 #license-cc-by-4.0 #region-us
|
## ESPnet2 ENH model
### 'espnet/Wangyou_Zhang_chime4_enh_train_enh_conv_tasnet_raw'
This model was trained by Wangyou Zhang using chime4 recipe in espnet.
### Demo: How to use in ESPnet2
## ENH config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ENH model",
"### 'espnet/Wangyou_Zhang_chime4_enh_train_enh_conv_tasnet_raw'\n\nThis model was trained by Wangyou Zhang using chime4 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## ENH config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:... | [
"TAGS\n#espnet #audio #audio-to-audio #dataset-chime4 #arxiv-1804.00015 #arxiv-2011.03706 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ENH model",
"### 'espnet/Wangyou_Zhang_chime4_enh_train_enh_conv_tasnet_raw'\n\nThis model was trained by Wangyou Zhang using chime4 recipe in espnet.",
"### Demo: How to us... |
null | transformers |
# Nowcasting CNN
## Model description
3d conv model, that takes in different data streams
architecture is roughly
1. satellite image time series goes into many 3d convolution layers.
2. nwp time series goes into many 3d convolution layers.
3. Final convolutional layer goes to full co... | {"license": "mit", "tags": ["nowcasting", "forecasting", "timeseries", "remote-sensing"]} | openclimatefix/nowcasting_cnn | null | [
"transformers",
"pytorch",
"nowcasting",
"forecasting",
"timeseries",
"remote-sensing",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T12:32:35+00:00 | [] | [] | TAGS
#transformers #pytorch #nowcasting #forecasting #timeseries #remote-sensing #license-mit #endpoints_compatible #region-us
|
# Nowcasting CNN
## Model description
3d conv model, that takes in different data streams
architecture is roughly
1. satellite image time series goes into many 3d convolution layers.
2. nwp time series goes into many 3d convolution layers.
3. Final convolutional layer goes to full co... | [
"# Nowcasting CNN",
"## Model description\n\n3d conv model, that takes in different data streams\n\n architecture is roughly\n 1. satellite image time series goes into many 3d convolution layers.\n 2. nwp time series goes into many 3d convolution layers.\n 3. Final convolutional layer ... | [
"TAGS\n#transformers #pytorch #nowcasting #forecasting #timeseries #remote-sensing #license-mit #endpoints_compatible #region-us \n",
"# Nowcasting CNN",
"## Model description\n\n3d conv model, that takes in different data streams\n\n architecture is roughly\n 1. satellite image time series goes i... |
text-classification | transformers |
<!-- 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. -->
# Neuron conversation
# MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc
This model is a fine-tuned version of [nrei... | {"tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "... | optimum/neuron-MiniLMv2-L12-H384-distilled-finetuned-clinc | null | [
"transformers",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T12:38:35+00:00 | [] | [] | TAGS
#transformers #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Neuron conversation
# MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc
This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9389999
## Deploy/use Model
If you w... | [
"# Neuron conversation",
"# MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc\n\nThis model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the clinc_oos dataset.\nIt achieves the following results on the evaluation set:\n- Accuracy: 0.9389999",
"## Deploy/use ... | [
"TAGS\n#transformers #roberta #text-classification #generated_from_trainer #dataset-clinc_oos #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Neuron conversation",
"# MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc\n\nThis model is a fine-tuned version of nreimers/Mini... |
sentence-similarity | sentence-transformers |
# jegorkitskerkin/robbert-v2-dutch-base-mqa-finetuned
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.
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch... | {"language": "nl", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "robbert"], "datasets": ["clips/mqa"], "pipeline_tag": "sentence-similarity"} | jegormeister/robbert-v2-dutch-base-mqa-finetuned | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"robbert",
"nl",
"dataset:clips/mqa",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-11T12:40:02+00:00 | [] | [
"nl"
] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #robbert #nl #dataset-clips/mqa #endpoints_compatible #has_space #region-us
|
# jegorkitskerkin/robbert-v2-dutch-base-mqa-finetuned
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This model is a fine-tuned version of pdelobelle/robbert-v2-dutch-base. It was fine-tuned o... | [
"# jegorkitskerkin/robbert-v2-dutch-base-mqa-finetuned\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.\n\nThis model is a fine-tuned version of pdelobelle/robbert-v2-dutch-base. It was fine... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #robbert #nl #dataset-clips/mqa #endpoints_compatible #has_space #region-us \n",
"# jegorkitskerkin/robbert-v2-dutch-base-mqa-finetuned\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token C... | issifuamajeed/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T12:40:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0614
* Precision: 0.9228
* Recall: 0.9360
* F1: 0.9294
* Accuracy: 0.9833
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyp... |
audio-to-audio | espnet |
## ESPnet2 ENH model
### `espnet/Wangyou_Zhang_wsj0_2mix_enh_dc_crn_mapping_snr_raw`
This model was trained by Wangyou Zhang using chime4 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd egs2/chime4/enh1
./run.sh --skip_data_prep false -... | {"license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-to-audio"], "datasets": ["chime4"]} | espnet/Wangyou_Zhang_wsj0_2mix_enh_dc_crn_mapping_snr_raw | null | [
"espnet",
"audio",
"audio-to-audio",
"dataset:chime4",
"arxiv:1804.00015",
"arxiv:2011.03706",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-11T12:47:30+00:00 | [
"1804.00015",
"2011.03706"
] | [] | TAGS
#espnet #audio #audio-to-audio #dataset-chime4 #arxiv-1804.00015 #arxiv-2011.03706 #license-cc-by-4.0 #region-us
|
## ESPnet2 ENH model
### 'espnet/Wangyou_Zhang_wsj0_2mix_enh_dc_crn_mapping_snr_raw'
This model was trained by Wangyou Zhang using chime4 recipe in espnet.
### Demo: How to use in ESPnet2
## ENH config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ENH model",
"### 'espnet/Wangyou_Zhang_wsj0_2mix_enh_dc_crn_mapping_snr_raw'\n\nThis model was trained by Wangyou Zhang using chime4 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## ENH config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:... | [
"TAGS\n#espnet #audio #audio-to-audio #dataset-chime4 #arxiv-1804.00015 #arxiv-2011.03706 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ENH model",
"### 'espnet/Wangyou_Zhang_wsj0_2mix_enh_dc_crn_mapping_snr_raw'\n\nThis model was trained by Wangyou Zhang using chime4 recipe in espnet.",
"### Demo: How to us... |
null | transformers |
#cloudy model
This is the cloudy-model | {} | Ghost1/cloudy-model | null | [
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T13:20:20+00:00 | [] | [] | TAGS
#transformers #endpoints_compatible #region-us
|
#cloudy model
This is the cloudy-model | [] | [
"TAGS\n#transformers #endpoints_compatible #region-us \n"
] |
null | null | 8K wsj asr model imported from [zenodo](https://zenodo.org/record/4012264) | {} | lichenda/wsj_asr_train_asr_transformer_raw_char_8k | null | [
"region:us"
] | null | 2022-04-11T13:27:49+00:00 | [] | [] | TAGS
#region-us
| 8K wsj asr model imported from zenodo | [] | [
"TAGS\n#region-us \n"
] |
null | null |
This Repository includes the files required to run the `Computer Science Named Entity Recognition (CS-NER)` ORKG-NLP service.
Please check [this article](https://orkg-nlp-pypi.readthedocs.io/en/latest/services/services.html) for more details about the service. | {"license": "mit"} | orkg/orkgnlp-cs-ner-titles | null | [
"license:mit",
"region:us"
] | null | 2022-04-11T13:31:01+00:00 | [] | [] | TAGS
#license-mit #region-us
|
This Repository includes the files required to run the 'Computer Science Named Entity Recognition (CS-NER)' ORKG-NLP service.
Please check this article for more details about the service. | [] | [
"TAGS\n#license-mit #region-us \n"
] |
token-classification | transformers |
## About model
This model based on [cointegrated/LaBSE-en-ru](https://huggingface.co/cointegrated/LaBSE-en-ru).
And trained on [surdan/nerel_short](https://huggingface.co/datasets/surdan/nerel_short) dataset
You can find more info:
- How the model was trained [Train_model.ipynb](https://huggingface.co/surdan/LaBSE... | {"language": ["ru", "en"], "tasks": ["token-classification"]} | surdan/LaBSE_ner_nerel | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"ru",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-11T13:45:16+00:00 | [] | [
"ru",
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #ru #en #autotrain_compatible #endpoints_compatible #region-us
|
## About model
This model based on cointegrated/LaBSE-en-ru.
And trained on surdan/nerel_short dataset
You can find more info:
- How the model was trained Train_model.ipynb
- Example of usage model URL | [
"## About model\n\nThis model based on cointegrated/LaBSE-en-ru.\n\nAnd trained on surdan/nerel_short dataset\n\nYou can find more info:\n\n- How the model was trained Train_model.ipynb\n- Example of usage model URL"
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #ru #en #autotrain_compatible #endpoints_compatible #region-us \n",
"## About model\n\nThis model based on cointegrated/LaBSE-en-ru.\n\nAnd trained on surdan/nerel_short dataset\n\nYou can find more info:\n\n- How the model was trained Train_model.ipynb\n-... |
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