modelId
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-29 18:27:06
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
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listlengths 1
4.05k
| pipeline_tag
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values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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huxxx657/roberta-base-finetuned-scrambled-squad-15
|
huxxx657
| 2022-05-10T21:13:58Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-10T19:13:39Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-finetuned-scrambled-squad-15
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-scrambled-squad-15
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8722
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8944 | 1.0 | 5590 | 1.8722 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
tjscollins/ppo-LunarLander-v2
|
tjscollins
| 2022-05-10T20:45:37Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-10T20:45:13Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 287.12 +/- 20.40
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
kosta-naumenko/ppo-LunarLander-v2-2
|
kosta-naumenko
| 2022-05-10T20:06:54Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-10T20:06:22Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 228.05 +/- 22.63
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
m-luebbers/mb-LunarLander-v1
|
m-luebbers
| 2022-05-10T19:17:16Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-10T19:16:46Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 224.96 +/- 73.06
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
huxxx657/roberta-base-finetuned-scrambled-squad-10
|
huxxx657
| 2022-05-10T19:05:14Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-10T17:05:40Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-finetuned-scrambled-squad-10
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-scrambled-squad-10
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7200
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7482 | 1.0 | 5532 | 1.7200 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Extred/TEST2ppo-LunarLander-v2-MlpLnLstmPolicy
|
Extred
| 2022-05-10T19:02:28Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-10T18:17:58Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 203.89 +/- 88.13
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Xuandong/HPD-TinyBERT-F128
|
Xuandong
| 2022-05-10T17:55:05Z | 33 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2203.07687",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-05-10T17:04:19Z |
---
license: apache-2.0
---
# HPD-TinyBERT-F128
This repository contains the pre-trained models for our paper [Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation](https://arxiv.org/abs/2203.07687). The sentence embedding model contains only 14M parameters and the model size is only 55MB.
## Overview
We propose **H**omomorphic **P**rojective **D**istillation (HPD) to learn compressed sentence embeddings. Our method augments a small Transformer encoder model with learnable projection layers to produce compact representations while mimicking a large pre-trained language model to retain the sentence representation quality.
## Details
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can be used for tasks like clustering or semantic search.
The teacher model is [`princeton-nlp/sup-simcse-roberta-large`](https://huggingface.co/princeton-nlp/sup-simcse-bert-base-uncased) and the student model is [`nreimers/TinyBERT_L-4_H-312_v2`](https://huggingface.co/nreimers/TinyBERT_L-4_H-312_v2).
## Usage
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
After installing the package, you can simply load our model
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('Xuandong/HPD-TinyBERT-F128')
```
Then you can use our model for **encoding sentences into embeddings**
```python
sentences = ['He plays guitar.', 'A street vendor is outside.']
sentence_embeddings = model.encode(sentences)
for sentence, embedding in zip(sentences, sentence_embeddings):
print("Sentence:", sentence)
print("Embedding:", embedding)
print("")
```
## Evaluation Results
We evaluate our model on semantic textual similarity (STS) tasks. The results are:
| STS12 | STS13 | STS14 | STS15 | STS16 | STS-B | SICK-R | Avg. |
|-------|-------|-------|-------|-------|--------------|-----------------|-------|
| 74.29 | 83.05 | 78.80 | 84.62 | 81.17 | 84.36 | 80.83 | 81.02 |
## Training
Please refer to the github repo (https://github.com/XuandongZhao/HPD) for the details about the training.
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 312, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 312, 'out_features': 128, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citation
Please cite our paper if you use HPD in your work:
```bibtex
@article{zhao2022compressing,
title={Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation},
author={Zhao, Xuandong and Yu, Zhiguo and Wu, Ming and Li, Lei},
journal={arXiv preprint arXiv:2203.07687},
year={2022}
}
```
|
Xuandong/HPD-MiniLM-F128
|
Xuandong
| 2022-05-10T17:54:43Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2203.07687",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-05-10T17:01:40Z |
---
license: apache-2.0
---
# HPD-MiniLM-F128
This repository contains the pre-trained models for our paper [Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation](https://arxiv.org/abs/2203.07687). The sentence embedding model contains only 23M parameters and the model size is only 87MB.
## Overview
We propose **H**omomorphic **P**rojective **D**istillation (HPD) to learn compressed sentence embeddings. Our method augments a small Transformer encoder model with learnable projection layers to produce compact representations while mimicking a large pre-trained language model to retain the sentence representation quality.
## Details
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can be used for tasks like clustering or semantic search.
The teacher model is [`princeton-nlp/sup-simcse-roberta-large`](https://huggingface.co/princeton-nlp/sup-simcse-bert-base-uncased) and the student model is [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased).
## Usage
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
After installing the package, you can simply load our model
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('Xuandong/HPD-MiniLM-F128')
```
Then you can use our model for **encoding sentences into embeddings**
```python
sentences = ['He plays guitar.', 'A street vendor is outside.']
sentence_embeddings = model.encode(sentences)
for sentence, embedding in zip(sentences, sentence_embeddings):
print("Sentence:", sentence)
print("Embedding:", embedding)
print("")
```
## Evaluation Results
We evaluate our model on semantic textual similarity (STS) tasks. The results are:
| STS12 | STS13 | STS14 | STS15 | STS16 | STS-B | SICK-R | Avg. |
|-------|-------|-------|-------|-------|--------------|-----------------|-------|
| 74.94 | 84.52 | 80.25 | 84.87 | 81.90 | 84.98 | 81.15 | 81.80 |
## Training
Please refer to the github repo (https://github.com/XuandongZhao/HPD) for the details about the training.
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 384, 'out_features': 128, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citation
Please cite our paper if you use HPD in your work:
```bibtex
@article{zhao2022compressing,
title={Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation},
author={Zhao, Xuandong and Yu, Zhiguo and Wu, Ming and Li, Lei},
journal={arXiv preprint arXiv:2203.07687},
year={2022}
}
```
|
cmcmorrow/distilbert-rater
|
cmcmorrow
| 2022-05-10T17:52:42Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-10T17:47:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-rater
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-rater
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
|
allenai/multicite-qa-qasper
|
allenai
| 2022-05-10T17:48:30Z | 18 | 1 |
transformers
|
[
"transformers",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-05-10T12:04:24Z |
---
language: en
license: mit
---
# MultiCite: Multi-label Citation Intent Analysis as paper-level Q&A (NAACL 2022)
This model has been trained on the data available here: https://github.com/allenai/multicite.
|
paultimothymooney/distilbert-rater
|
paultimothymooney
| 2022-05-10T17:40:47Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-10T16:11:45Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-rater
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-rater
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
|
husnu/wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_5
|
husnu
| 2022-05-10T17:22:15Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-10T13:23:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_5
This model is a fine-tuned version of [husnu/wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_4](https://huggingface.co/husnu/wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_4) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3439
- Wer: 0.3634
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1243 | 0.51 | 400 | 0.4312 | 0.4202 |
| 0.1956 | 1.02 | 800 | 0.4421 | 0.4498 |
| 0.1816 | 1.53 | 1200 | 0.4012 | 0.4285 |
| 0.1548 | 2.04 | 1600 | 0.3720 | 0.3845 |
| 0.1171 | 2.55 | 2000 | 0.3439 | 0.3634 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 2.1.0
- Tokenizers 0.10.3
|
datauma/mt5-small-finetuned-amazon-en-es
|
datauma
| 2022-05-10T16:52:35Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-04T04:07:58Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: datauma/mt5-small-finetuned-amazon-en-es
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# datauma/mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.2505
- Validation Loss: 3.4530
- Epoch: 7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 9672, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 11.9288 | 5.8713 | 0 |
| 6.6821 | 4.3246 | 1 |
| 5.6453 | 3.8715 | 2 |
| 5.0908 | 3.6368 | 3 |
| 4.7348 | 3.5496 | 4 |
| 4.5106 | 3.4939 | 5 |
| 4.3261 | 3.4659 | 6 |
| 4.2505 | 3.4530 | 7 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
anuragshas/wav2vec2-xls-r-300m-ur-cv9-with-lm
|
anuragshas
| 2022-05-10T16:51:19Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_9_0",
"generated_from_trainer",
"ur",
"dataset:mozilla-foundation/common_voice_9_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-04T14:27:44Z |
---
language:
- ur
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_9_0
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_9_0
metrics:
- wer
model-index:
- name: XLS-R-300M - Urdu
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_9_0
name: Common Voice 9
args: ur
metrics:
- type: wer
value: 23.750
name: Test WER
- name: Test CER
type: cer
value: 8.310
---
<!-- 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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - UR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4147
- Wer: 0.3172
- Cer: 0.1050
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 5108
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 3.2894 | 7.83 | 400 | 3.1501 | 1.0 | 1.0 |
| 1.8586 | 15.68 | 800 | 0.8871 | 0.6721 | 0.2402 |
| 1.3431 | 23.52 | 1200 | 0.5813 | 0.5502 | 0.1939 |
| 1.2052 | 31.37 | 1600 | 0.4956 | 0.4788 | 0.1665 |
| 1.1097 | 39.21 | 2000 | 0.4447 | 0.4143 | 0.1397 |
| 1.0528 | 47.06 | 2400 | 0.4439 | 0.3961 | 0.1333 |
| 0.9939 | 54.89 | 2800 | 0.4348 | 0.4014 | 0.1379 |
| 0.9441 | 62.74 | 3200 | 0.4236 | 0.3653 | 0.1223 |
| 0.913 | 70.58 | 3600 | 0.4309 | 0.3475 | 0.1157 |
| 0.8678 | 78.43 | 4000 | 0.4270 | 0.3337 | 0.1110 |
| 0.8414 | 86.27 | 4400 | 0.4158 | 0.3220 | 0.1070 |
| 0.817 | 94.12 | 4800 | 0.4185 | 0.3231 | 0.1072 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.1.1.dev0
- Tokenizers 0.12.1
|
Joiner/ppoLunarLanding-v2
|
Joiner
| 2022-05-10T16:44:09Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-10T16:43:26Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 126.84 +/- 80.67
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
anuragshas/wav2vec2-xls-r-300m-bn-cv9-with-lm
|
anuragshas
| 2022-05-10T16:17:38Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_9_0",
"generated_from_trainer",
"bn",
"dataset:mozilla-foundation/common_voice_9_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-06T03:54:55Z |
---
language:
- bn
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_9_0
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_9_0
metrics:
- wer
model-index:
- name: XLS-R-300M - Bengali
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_9_0
name: Common Voice 9
args: bn
metrics:
- type: wer
value: 20.150
name: Test WER
- name: Test CER
type: cer
value: 4.813
---
<!-- 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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - BN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2297
- Wer: 0.2850
- Cer: 0.0660
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 8692
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 3.675 | 2.3 | 400 | 3.5052 | 1.0 | 1.0 |
| 3.0446 | 4.6 | 800 | 2.2759 | 1.0052 | 0.5215 |
| 1.7276 | 6.9 | 1200 | 0.7083 | 0.6697 | 0.1969 |
| 1.5171 | 9.2 | 1600 | 0.5328 | 0.5733 | 0.1568 |
| 1.4176 | 11.49 | 2000 | 0.4571 | 0.5161 | 0.1381 |
| 1.343 | 13.79 | 2400 | 0.3910 | 0.4522 | 0.1160 |
| 1.2743 | 16.09 | 2800 | 0.3534 | 0.4137 | 0.1044 |
| 1.2396 | 18.39 | 3200 | 0.3278 | 0.3877 | 0.0959 |
| 1.2035 | 20.69 | 3600 | 0.3109 | 0.3741 | 0.0917 |
| 1.1745 | 22.99 | 4000 | 0.2972 | 0.3618 | 0.0882 |
| 1.1541 | 25.29 | 4400 | 0.2836 | 0.3427 | 0.0832 |
| 1.1372 | 27.59 | 4800 | 0.2759 | 0.3357 | 0.0812 |
| 1.1048 | 29.89 | 5200 | 0.2669 | 0.3284 | 0.0783 |
| 1.0966 | 32.18 | 5600 | 0.2678 | 0.3249 | 0.0775 |
| 1.0747 | 34.48 | 6000 | 0.2547 | 0.3134 | 0.0748 |
| 1.0593 | 36.78 | 6400 | 0.2491 | 0.3077 | 0.0728 |
| 1.0417 | 39.08 | 6800 | 0.2450 | 0.3012 | 0.0711 |
| 1.024 | 41.38 | 7200 | 0.2402 | 0.2956 | 0.0694 |
| 1.0106 | 43.68 | 7600 | 0.2351 | 0.2915 | 0.0681 |
| 1.0014 | 45.98 | 8000 | 0.2328 | 0.2896 | 0.0673 |
| 0.9999 | 48.28 | 8400 | 0.2318 | 0.2866 | 0.0667 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.1.1.dev0
- Tokenizers 0.12.1
|
joitandr/TEST2ppo-LunarLander-v2
|
joitandr
| 2022-05-10T15:13:17Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-10T15:12:47Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 249.46 +/- 20.60
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
akozlo/con_gpt_med
|
akozlo
| 2022-05-10T12:52:01Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-10T12:47:23Z |
---
tags:
- generated_from_trainer
model-index:
- name: con_gpt_med_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# con_gpt_med_model
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
hello
|
darshanz/occupation-prediction
|
darshanz
| 2022-05-10T11:59:28Z | 35 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-05-08T04:35:30Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: darshanz/occupaion-prediction
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# darshanz/occupation-prediction
This model is ViT base patch16. Which is pretrained on imagenet dataset, then trained on our custom dataset which is based on occupation prediction. This dataset contains facial images of Indian people which are labeled by occupation. This model predicts the occupation of a person from the facial image of a person. This model categorizes input facial images into 5 classes: Anchor, Athlete, Doctor, Professor, and Farmer. This model gives an accuracy of 84.43%.
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 70, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.4}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 1.0840 | 0.6156 | 0.8813 | 0.6843 | 0.75 | 0.9700 | 0 |
| 0.4686 | 0.8406 | 0.9875 | 0.5345 | 0.8100 | 0.9867 | 1 |
| 0.2600 | 0.9312 | 0.9953 | 0.4805 | 0.8333 | 0.9800 | 2 |
| 0.1515 | 0.9609 | 0.9969 | 0.5071 | 0.8267 | 0.9733 | 3 |
| 0.0746 | 0.9875 | 1.0 | 0.4853 | 0.8500 | 0.9833 | 4 |
| 0.0468 | 0.9953 | 1.0 | 0.5006 | 0.8433 | 0.9733 | 5 |
| 0.0378 | 0.9953 | 1.0 | 0.4967 | 0.8433 | 0.9800 | 6 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Tokenizers 0.12.1
|
runjivu/TEST2ppo-LunarLander-v2
|
runjivu
| 2022-05-10T11:14:17Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-10T11:13:50Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 254.09 +/- 18.39
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
ebonazza2910/model-1h
|
ebonazza2910
| 2022-05-10T11:13:54Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-10T09:45:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: model-1h
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model-1h
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8317
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 11.4106 | 1.24 | 10 | 7.1597 | 1.0 |
| 4.777 | 2.47 | 20 | 3.9782 | 1.0 |
| 3.6585 | 3.71 | 30 | 3.3961 | 1.0 |
| 3.3143 | 4.94 | 40 | 3.1481 | 1.0 |
| 3.3318 | 6.24 | 50 | 3.0596 | 1.0 |
| 3.1368 | 7.47 | 60 | 2.9751 | 1.0 |
| 3.1058 | 8.71 | 70 | 2.9510 | 1.0 |
| 3.0605 | 9.94 | 80 | 2.9479 | 1.0 |
| 3.2043 | 11.24 | 90 | 2.9270 | 1.0 |
| 3.0424 | 12.47 | 100 | 2.9349 | 1.0 |
| 3.0374 | 13.71 | 110 | 2.9316 | 1.0 |
| 3.0256 | 14.94 | 120 | 2.9165 | 1.0 |
| 3.1724 | 16.24 | 130 | 2.9076 | 1.0 |
| 3.0119 | 17.47 | 140 | 2.9034 | 1.0 |
| 2.9937 | 18.71 | 150 | 2.8812 | 1.0 |
| 2.9775 | 19.94 | 160 | 2.8674 | 1.0 |
| 3.0826 | 21.24 | 170 | 2.8147 | 1.0 |
| 2.8717 | 22.47 | 180 | 2.7212 | 1.0 |
| 2.7714 | 23.71 | 190 | 2.6149 | 0.9952 |
| 2.634 | 24.94 | 200 | 2.4611 | 0.9984 |
| 2.5637 | 26.24 | 210 | 2.2734 | 1.0 |
| 2.237 | 27.47 | 220 | 2.0705 | 1.0 |
| 2.0381 | 28.71 | 230 | 1.9216 | 1.0 |
| 1.8788 | 29.94 | 240 | 1.8317 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
|
mcsabai/huBert-fine-tuned-hungarian-squadv1
|
mcsabai
| 2022-05-10T10:59:53Z | 11 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"bert",
"question-answering",
"hu",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-27T12:35:44Z |
---
language: hu
thumbnail:
tags:
- question-answering
- bert
widget:
- text: "Melyik folyó szeli ketté Budapestet?"
context: "Magyarország fővárosát, Budapestet a Duna folyó szeli ketté. A XIX. században épült Lánchíd a dimbes-dombos budai oldalt köti össze a sík Pesttel. A Várdomb oldalában futó siklóval juthatunk fel a budai Óvárosba, ahol a Budapesti Történeti Múzeum egészen a római időkig visszavezetve mutatja be a városi életet. A Szentháromság tér ad otthont a XIII. századi Mátyás-templomnak és a Halászbástya lőtornyainak, amelyekből messzire ellátva gyönyörködhetünk a városban."
- text: "Mivel juthatunk fel az Óvárosba?"
context: "Magyarország fővárosát, Budapestet a Duna folyó szeli ketté. A XIX. században épült Lánchíd a dimbes-dombos budai oldalt köti össze a sík Pesttel. A Várdomb oldalában futó siklóval juthatunk fel a budai Óvárosba, ahol a Budapesti Történeti Múzeum egészen a római időkig visszavezetve mutatja be a városi életet. A Szentháromság tér ad otthont a XIII. századi Mátyás-templomnak és a Halászbástya lőtornyainak, amelyekből messzire ellátva gyönyörködhetünk a városban."
---
## MODEL DESCRIPTION
huBERT base model (cased) fine-tuned on SQuAD v1
- huBert model + Tokenizer: https://huggingface.co/SZTAKI-HLT/hubert-base-cc
- Hungarian SQUAD v1 dataset: Machine Translated SQuAD dataset (Google Translate API)
- This is a demo model. Date of publication: 2022.03.27.
## Model in action
- Fast usage with pipelines:
```python
from transformers import pipeline
qa_pipeline = pipeline(
"question-answering",
model="mcsabai/huBert-fine-tuned-hungarian-squadv1",
tokenizer="mcsabai/huBert-fine-tuned-hungarian-squadv1"
)
predictions = qa_pipeline({
'context': "Anita vagyok és Budapesten élek már több mint 4 éve.",
'question': "Hol lakik Anita?"
})
print(predictions)
# output:
# {'score': 0.9892364144325256, 'start': 16, 'end': 26, 'answer': 'Budapesten'}
```
|
osanseviero/TEST2ppo-LunarLander-v3
|
osanseviero
| 2022-05-10T10:41:13Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T09:38:06Z |
---
library_name: stable-baselines3
tags:
- MountainCar-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -97.87 +/- 143.38
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MountainCar-v0
type: MountainCar-v0
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
SreyanG-NVIDIA/bert-base-cased-finetuned-ner
|
SreyanG-NVIDIA
| 2022-05-10T10:05:34Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-10T09:56:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9325301204819277
- name: Recall
type: recall
value: 0.9374663556432801
- name: F1
type: f1
value: 0.9349917229654156
- name: Accuracy
type: accuracy
value: 0.9840466238888562
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0650
- Precision: 0.9325
- Recall: 0.9375
- F1: 0.9350
- Accuracy: 0.9840
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2346 | 1.0 | 878 | 0.0722 | 0.9168 | 0.9217 | 0.9192 | 0.9795 |
| 0.0483 | 2.0 | 1756 | 0.0618 | 0.9299 | 0.9370 | 0.9335 | 0.9837 |
| 0.0262 | 3.0 | 2634 | 0.0650 | 0.9325 | 0.9375 | 0.9350 | 0.9840 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
|
patrickvonplaten/wav2vec2-base-timit-demo-colab
|
patrickvonplaten
| 2022-05-10T09:38:48Z | 449 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4888
- Wer: 0.3392
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1134 | 4.0 | 500 | 0.4250 | 0.3626 |
| 0.1035 | 8.0 | 1000 | 0.4980 | 0.3650 |
| 0.0801 | 12.0 | 1500 | 0.5563 | 0.3632 |
| 0.0592 | 16.0 | 2000 | 0.6222 | 0.3607 |
| 0.0563 | 20.0 | 2500 | 0.4763 | 0.3457 |
| 0.0611 | 24.0 | 3000 | 0.4938 | 0.3489 |
| 0.0475 | 28.0 | 3500 | 0.4888 | 0.3392 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
etsymba/ppo-LunarLander-v2
|
etsymba
| 2022-05-10T09:26:45Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-10T09:23:14Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 208.93 +/- 53.16
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Pausaxo/ppo-LunarLander-v2
|
Pausaxo
| 2022-05-10T08:57:23Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-10T08:56:41Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 186.57 +/- 75.05
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
melodisease/ppo-LunarLander-v2
|
melodisease
| 2022-05-10T08:57:17Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-10T08:56:43Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 243.43 +/- 22.55
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
mrm8488/electricidad-base-finetuned-parmex
|
mrm8488
| 2022-05-10T08:18:19Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-10T07:56:42Z |
---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: electricidad-base-finetuned-parmex
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electricidad-base-finetuned-parmex
This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0372
- F1: 0.9764
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8.309269976237555e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 208 | 0.0377 | 0.9801 |
| No log | 2.0 | 416 | 0.0372 | 0.9764 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Theimisa/distilbert-base-uncased-aisera_texts-v3
|
Theimisa
| 2022-05-10T07:49:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-09T11:41:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-aisera_texts-v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-aisera_texts-v3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8106
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0183 | 1.0 | 3875 | 1.8913 |
| 1.9018 | 2.0 | 7750 | 1.8106 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
RicardFos/PPO-LunarLander-v2
|
RicardFos
| 2022-05-10T07:22:20Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-10T07:21:46Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 241.12 +/- 21.01
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
promsoft/ll2022-05-09-lunar4
|
promsoft
| 2022-05-10T06:43:14Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-10T06:13:26Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 252.15 +/- 22.31
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
ironbar/ppo-lunarlander-v2-local-train-bigger
|
ironbar
| 2022-05-10T05:32:57Z | 8 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-10T05:32:30Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 302.71 +/- 7.68
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
suicaokhoailang/gpt-neo-vi-comments-finetuned
|
suicaokhoailang
| 2022-05-10T05:19:54Z | 11 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-10T03:31:14Z |
---
license: mit
---
GPT-Neo-small for Vietnamese
Based on [NlpHUST/gpt-neo-vi-small](https://huggingface.co/NlpHUST/gpt-neo-vi-small), finetuned on dataset of [10m Facebook comments](https://github.com/binhvq/news-corpus)
|
kornosk/bert-political-election2020-twitter-mlm
|
kornosk
| 2022-05-10T04:45:45Z | 88 | 4 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"twitter",
"masked-token-prediction",
"election2020",
"politics",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: "en"
tags:
- twitter
- masked-token-prediction
- election2020
- politics
license: "gpl-3.0"
---
# Pre-trained BERT on Twitter US Political Election 2020
Pre-trained weights for [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
We use the initialized weights from BERT-base (uncased) or `bert-base-uncased`.
# Training Data
This model is pre-trained on over 5 million English tweets about the 2020 US Presidential Election.
# Training Objective
This model is initialized with BERT-base and trained with normal MLM objective.
# Usage
This pre-trained language model **can be fine-tunned to any downstream task (e.g. classification)**.
Please see the [official repository](https://github.com/GU-DataLab/stance-detection-KE-MLM) for more detail.
```python
from transformers import BertTokenizer, BertForMaskedLM, pipeline
import torch
# Choose GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Select mode path here
pretrained_LM_path = "kornosk/bert-political-election2020-twitter-mlm"
# Load model
tokenizer = BertTokenizer.from_pretrained(pretrained_LM_path)
model = BertForMaskedLM.from_pretrained(pretrained_LM_path)
# Fill mask
example = "Trump is the [MASK] of USA"
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)
# Use following line instead of the above one does not work.
# Huggingface have been updated, newer version accepts a string of model name instead.
fill_mask = pipeline('fill-mask', model=pretrained_LM_path, tokenizer=tokenizer)
outputs = fill_mask(example)
print(outputs)
# See embeddings
inputs = tokenizer(example, return_tensors="pt")
outputs = model(**inputs)
print(outputs)
# OR you can use this model to train on your downstream task!
# Please consider citing our paper if you feel this is useful :)
```
# Reference
- [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
# Citation
```bibtex
@inproceedings{kawintiranon2021knowledge,
title={Knowledge Enhanced Masked Language Model for Stance Detection},
author={Kawintiranon, Kornraphop and Singh, Lisa},
booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
year={2021},
publisher={Association for Computational Linguistics},
url={https://www.aclweb.org/anthology/2021.naacl-main.376}
}
```
|
Sounak/distilbert-finetuned
|
Sounak
| 2022-05-10T04:05:02Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-10T04:00:48Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Sounak/distilbert-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Sounak/distilbert-finetuned
This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.0422
- Validation Loss: 1.7343
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 468, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.9989 | 1.6524 | 0 |
| 1.3489 | 1.6702 | 1 |
| 1.0422 | 1.7343 | 2 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
kornosk/polibertweet-political-twitter-roberta-mlm-small
|
kornosk
| 2022-05-10T03:49:55Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"twitter",
"masked-token-prediction",
"bertweet",
"election2020",
"politics",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-10T03:41:49Z |
---
language: "en"
tags:
- twitter
- masked-token-prediction
- bertweet
- election2020
- politics
license: "gpl-3.0"
---
# This version is trained on a smaller data set.
See the full-size version at [PoliBERTweet](https://huggingface.co/kornosk/polibertweet-mlm).
# Citation
```bibtex
@inproceedings{kawintiranon2022polibertweet,
title = {PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter},
author = {Kawintiranon, Kornraphop and Singh, Lisa},
booktitle = {Proceedings of the Language Resources and Evaluation Conference},
year = {2022},
publisher = {European Language Resources Association}
}
```
|
ckiplab/bert-tiny-chinese
|
ckiplab
| 2022-05-10T03:28:12Z | 226 | 7 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"lm-head",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-10T02:53:57Z |
---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- lm-head
- bert
- zh
license: gpl-3.0
---
# CKIP BERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://github.com/ckiplab/ckip-transformers
## Contributers
- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
```
from transformers import (
BertTokenizerFast,
AutoModel,
)
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/bert-tiny-chinese')
```
For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
|
ckiplab/bert-tiny-chinese-ws
|
ckiplab
| 2022-05-10T03:28:12Z | 1,641 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-10T02:54:32Z |
---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- bert
- zh
license: gpl-3.0
---
# CKIP BERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://github.com/ckiplab/ckip-transformers
## Contributers
- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
```
from transformers import (
BertTokenizerFast,
AutoModel,
)
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/bert-tiny-chinese-ws')
```
For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
|
ckiplab/bert-base-chinese-ner
|
ckiplab
| 2022-05-10T03:28:12Z | 31,527 | 112 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- bert
- zh
license: gpl-3.0
---
# CKIP BERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://github.com/ckiplab/ckip-transformers
## Contributers
- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
```
from transformers import (
BertTokenizerFast,
AutoModel,
)
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/bert-base-chinese-ner')
```
For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
|
ckiplab/bert-tiny-chinese-ner
|
ckiplab
| 2022-05-10T03:28:12Z | 1,433 | 4 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-10T02:55:04Z |
---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- bert
- zh
license: gpl-3.0
---
# CKIP BERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://github.com/ckiplab/ckip-transformers
## Contributers
- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
```
from transformers import (
BertTokenizerFast,
AutoModel,
)
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/bert-tiny-chinese-ner')
```
For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
|
ckiplab/bert-tiny-chinese-pos
|
ckiplab
| 2022-05-10T03:28:12Z | 63 | 2 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-10T02:54:45Z |
---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- bert
- zh
license: gpl-3.0
---
# CKIP BERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://github.com/ckiplab/ckip-transformers
## Contributers
- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
```
from transformers import (
BertTokenizerFast,
AutoModel,
)
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/bert-tiny-chinese-pos')
```
For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
|
ckiplab/albert-tiny-chinese-ner
|
ckiplab
| 2022-05-10T03:28:10Z | 122 | 2 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- albert
- zh
license: gpl-3.0
---
# CKIP ALBERT Tiny Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://github.com/ckiplab/ckip-transformers
## Contributers
- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
```
from transformers import (
BertTokenizerFast,
AutoModel,
)
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/albert-tiny-chinese-ner')
```
For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
|
ckiplab/albert-base-chinese-pos
|
ckiplab
| 2022-05-10T03:28:09Z | 1,144 | 2 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- albert
- zh
license: gpl-3.0
---
# CKIP ALBERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://github.com/ckiplab/ckip-transformers
## Contributers
- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
```
from transformers import (
BertTokenizerFast,
AutoModel,
)
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/albert-base-chinese-pos')
```
For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
|
ckiplab/albert-base-chinese-ws
|
ckiplab
| 2022-05-10T03:28:09Z | 1,733 | 2 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- albert
- zh
license: gpl-3.0
---
# CKIP ALBERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://github.com/ckiplab/ckip-transformers
## Contributers
- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
```
from transformers import (
BertTokenizerFast,
AutoModel,
)
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/albert-base-chinese-ws')
```
For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
|
ckiplab/albert-base-chinese
|
ckiplab
| 2022-05-10T03:28:08Z | 1,117 | 12 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"fill-mask",
"lm-head",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- lm-head
- albert
- zh
license: gpl-3.0
---
# CKIP ALBERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://github.com/ckiplab/ckip-transformers
## Contributers
- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
```
from transformers import (
BertTokenizerFast,
AutoModel,
)
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/albert-base-chinese')
```
For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
|
ckiplab/albert-base-chinese-ner
|
ckiplab
| 2022-05-10T03:28:08Z | 2,295 | 14 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"token-classification",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- token-classification
- albert
- zh
license: gpl-3.0
---
# CKIP ALBERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://github.com/ckiplab/ckip-transformers
## Contributers
- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
```
from transformers import (
BertTokenizerFast,
AutoModel,
)
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/albert-base-chinese-ner')
```
For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
|
huxxx657/roberta-base-finetuned-squad-3
|
huxxx657
| 2022-05-10T01:09:48Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-09T22:50:17Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-finetuned-squad-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-squad-3
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8358
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8626 | 1.0 | 5536 | 0.8358 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
jayeshgar/ppo-LunarLander-v2
|
jayeshgar
| 2022-05-09T23:57:37Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-09T23:57:06Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 209.48 +/- 63.51
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
meln1k/ppo-LunarLander-v2
|
meln1k
| 2022-05-09T23:33:56Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-06T18:39:39Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 289.26 +/- 18.33
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
danielmaxwell/TEST2ppo-LunarLander-v2
|
danielmaxwell
| 2022-05-09T21:01:58Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-09T21:01:24Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 137.66 +/- 94.84
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
johko/ppo-lunarlander-v2
|
johko
| 2022-05-09T20:41:17Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-09T20:16:07Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 277.89 +/- 22.93
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
KenP/marian-finetuned-kde4-en-to-fr
|
KenP
| 2022-05-09T20:36:25Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-09T18:11:12Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: KenP/marian-finetuned-kde4-en-to-fr
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# KenP/marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6855
- Validation Loss: 0.8088
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 17733, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.0599 | 0.8835 | 0 |
| 0.7975 | 0.8254 | 1 |
| 0.6855 | 0.8088 | 2 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
umbertospazio/FirstPPO-LunarLander-v2
|
umbertospazio
| 2022-05-09T20:10:41Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-09T20:10:14Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -212.53 +/- 86.74
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
suppabob/TEST2ppo-LunarLander-v2
|
suppabob
| 2022-05-09T18:55:43Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-09T18:55:08Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 218.36 +/- 65.70
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
TinySuitStarfish/ppo-lunarlanderabhishek-v2
|
TinySuitStarfish
| 2022-05-09T18:02:18Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-09T18:01:42Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 169.97 +/- 15.25
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
promsoft/ll2022-05-09-lunar3
|
promsoft
| 2022-05-09T17:32:02Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-09T17:31:39Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 262.07 +/- 20.63
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
ysharma/distilbert-base-uncased-finetuned-emotions
|
ysharma
| 2022-05-09T17:10:14Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-09T16:29:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotions
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: F1
type: f1
value: 0.9331148494056558
---
<!-- 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-emotions
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1579
- Acc: 0.933
- F1: 0.9331
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.1723 | 1.0 | 250 | 0.1838 | 0.9315 | 0.9312 |
| 0.1102 | 2.0 | 500 | 0.1579 | 0.933 | 0.9331 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Joshwabail/lunar_lander_test
|
Joshwabail
| 2022-05-09T16:57:52Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-09T16:29:40Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -177.16 +/- 72.05
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
ansegura/ppo-LunarLander-v2-test-2
|
ansegura
| 2022-05-09T15:44:13Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-09T15:43:45Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 267.76 +/- 16.85
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
princeton-nlp/CoFi-MRPC-s60
|
princeton-nlp
| 2022-05-09T15:24:25Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2204.00408",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-09T15:19:52Z |
This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 60% sparsity on dataset MRPC. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the model for inference. Note that you would have to use the model class specified in our repository to load the model.
|
princeton-nlp/CoFi-CoLA-s95
|
princeton-nlp
| 2022-05-09T15:24:06Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2204.00408",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-09T15:20:55Z |
This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 95% sparsity on dataset CoLA. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the model for inference. Note that you would have to use the model class specified in our repository to load the model.
|
princeton-nlp/CoFi-CoLA-s60
|
princeton-nlp
| 2022-05-09T15:23:43Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2204.00408",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-09T15:20:20Z |
This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 60% sparsity on dataset CoLA. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the model for inference. Note that you would have to use the model class specified in our repository to load the model.
|
princeton-nlp/CoFi-RTE-s60
|
princeton-nlp
| 2022-05-09T15:23:20Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2204.00408",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-09T15:10:20Z |
This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 60% sparsity on dataset RTE. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the model for inference. Note that you would have to use the model class specified in our repository to load the model.
|
srini98/RLModel1
|
srini98
| 2022-05-09T15:04:19Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-09T15:03:27Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 97.96 +/- 73.06
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
guhuawuli/distilbert-base-uncased-finetuned-ner
|
guhuawuli
| 2022-05-09T15:03:24Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-09T13:28:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.8982049036777583
- name: Recall
type: recall
value: 0.9179997762613268
- name: F1
type: f1
value: 0.9079944674965422
- name: Accuracy
type: accuracy
value: 0.979427137115351
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0729
- Precision: 0.8982
- Recall: 0.9180
- F1: 0.9080
- Accuracy: 0.9794
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 220 | 0.1036 | 0.8607 | 0.8797 | 0.8701 | 0.9727 |
| No log | 2.0 | 440 | 0.0762 | 0.8912 | 0.9131 | 0.9020 | 0.9783 |
| 0.2005 | 3.0 | 660 | 0.0729 | 0.8982 | 0.9180 | 0.9080 | 0.9794 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0a0+3fd9dcf
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ansegura/ppo-LunarLander-v2-test-1
|
ansegura
| 2022-05-09T14:54:56Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-09T14:54:27Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 266.06 +/- 17.29
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
gigant/LunarLander-v2_PPO
|
gigant
| 2022-05-09T13:36:27Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-09T13:35:55Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 275.23 +/- 20.86
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
arimboux/ppo-LunarLander-v2
|
arimboux
| 2022-05-09T12:44:11Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-08T12:31:08Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 258.23 +/- 23.14
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
FollishBoi/ppo-LunarLander-v2_try2
|
FollishBoi
| 2022-05-09T12:11:55Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-09T12:11:20Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 244.64 +/- 58.94
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
deepgai/tweet_eval-sentiment-finetuned
|
deepgai
| 2022-05-09T10:46:47Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-08T19:20:19Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
model-index:
- name: tweet_eval-sentiment-finetuned
results:
- task:
name: Sentiment Analysis
type: sentiment-analysis
dataset:
name: tweeteval
type: tweeteval
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7099
- name: f1
type: f1
value: 0.7097
---
<!-- 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. -->
# tweet_eval-sentiment-finetuned
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the Tweet_Eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6532
- Accuracy: 0.744
- F1: 0.7437
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 128
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7491 | 1.0 | 357 | 0.6089 | 0.7345 | 0.7314 |
| 0.5516 | 2.0 | 714 | 0.5958 | 0.751 | 0.7516 |
| 0.4618 | 3.0 | 1071 | 0.6131 | 0.748 | 0.7487 |
| 0.4066 | 4.0 | 1428 | 0.6532 | 0.744 | 0.7437 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.9.1
- Datasets 2.1.0
- Tokenizers 0.12.1
|
jhoonk/bert-base-uncased-finetuned-swag
|
jhoonk
| 2022-05-09T10:41:40Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:swag",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2022-05-02T10:57:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- swag
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-swag
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-swag
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0337
- Accuracy: 0.7888
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7451 | 1.0 | 4597 | 0.5944 | 0.7696 |
| 0.3709 | 2.0 | 9194 | 0.6454 | 0.7803 |
| 0.1444 | 3.0 | 13791 | 1.0337 | 0.7888 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Theimisa/distilbert-base-uncased-aisera_texts
|
Theimisa
| 2022-05-09T09:49:59Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-05T12:29:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-aisera_texts
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-aisera_texts
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8283
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.0694 | 1.0 | 7790 | 1.9868 |
| 1.9054 | 2.0 | 15580 | 1.8646 |
| 1.8701 | 3.0 | 23370 | 1.8283 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Tokenizers 0.12.1
|
JacopoBandoni/BioBertRelationGenesDiseases
|
JacopoBandoni
| 2022-05-09T09:47:10Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-02T10:25:29Z |
---
license: afl-3.0
widget:
- text: "The case of a 72-year-old male with @DISEASE$ with poor insulin control (fasting hyperglycemia greater than 180 mg/dl) who had a long-standing polyuric syndrome is here presented. Hypernatremia and plasma osmolality elevated together with a low urinary osmolality led to the suspicion of diabetes insipidus, which was subsequently confirmed by the dehydration test and the administration of @GENE$ sc."
example_title: "Example 1"
- text: "Hypernatremia and plasma osmolality elevated together with a low urinary osmolality led to the suspicion of diabetes insipidus, which was subsequently confirmed by the dehydration test and the administration of @GENE$ sc. With 61% increase in the calculated urinary osmolarity one hour post desmopressin s.c., @DISEASE$ was diagnosed."
example_title: "Example 2"
---
The following is a fine-tuning of the BioBert models on the GAD dataset.
The model works by masking the gene string with "@GENE$" and the disease string with "@DISEASE$".
The output is a text classification that can either be:
- "LABEL0" if there is no relation
- "LABEL1" if there is a relation.
|
RajSang/pegasus-sports-titles
|
RajSang
| 2022-05-09T09:26:14Z | 16 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
widget:
- text: "Coutinho was just about to be introduced by Villa boss Gerrard midway through the second half when Bruno Fernandes slammed home
his second goal of the game off the underside of the bar. But the Brazilian proved the catalyst for a memorable response.
First he drove at the United defence, helping to create the space which Jacob Ramsey exploited to halve the deficit. Then Ramsey slid over an excellent
cross from the left which Raphael Varane was unable to intercept as he slid back, leaving Coutinho to finish into an empty net.
The goal brought celebrations at both ends of the pitch as Emiliano Martinez also went into the crowd in relief - it was the Argentine's horrible sixth-minute error that had gifted Fernandes the visitors' opener.
Given his background - with Liverpool, Barcelona and Bayern Munich - Coutinho is a bold loan signing by Villa, and underlines the pedigree of the man they appointed as manager in November.
Gerrard is not at Villa to learn how to avoid relegation.
His demands remain as high as they were as a player and Coutinho's arrival is an example of that.
Villa are a better team since Gerrard's arrival and, after a sluggish start against opponents they dominated but lost to in the FA Cup five days ago, they grew into the game.
The club's other newboy, Lucas Digne, was among those denied by United keeper David de Gea at the end of the first half - in unorthodox fashion, with his knees.
Ollie Watkins did not really test the Spain keeper when Villa broke after Edinson Cavani lost possession in his own half. However, Emi Buendia certainly did with a near-post header. Rooted to his line, De Gea's reactions were up to the job as he beat Buendia's effort away.
When De Gea produced more saves after half-time to deny Ramsey and Digne again, it appeared the image of the night for Villa would be midfielder Morgan Sanson kicking a drinks bottle in fury after his error in gifting Fred possession to set up Fernandes for the visitors' second had been followed immediately by his substitution.
However, as it was the prelude to Coutinho's arrival, it was the moment that changed the course of the game - and the acclaim for the Brazilian at the final whistle indicated Villa's fans are already firmly behind him."
language: en
---
<!-- 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-sports-titles
This model is a fine-tuned pegasus on some **sports news articles scraped from the internet. (For educational purposes only)**. The model can generate titles for sports articles. Try it out using the inference API.
## Model description
A Pegasus model tuned on generating scientific titles has been further fine-tuned to generate titles for sports articles. While training articles on **Tennis, Football (Soccer), Cricket , Athletics and Rugby** were used to generate titles. I experimented training the Tokenizer from scratch but it did not give good results compared to the pre-trained tokenizer.
## Usage
```python
from transformers import pipeline
#Feel free to play around with the generation parameters.
#Reduce the beam width for faster inference
#Note that the maximum length for the generated titles is 64
gen_kwargs = {"length_penalty": 0.6, "num_beams":4, "num_return_sequences": 4,"num_beam_groups":4,"diversity_penalty":2.0}
pipe = pipeline("summarization", model="RajSang/pegasus-sports-titles")
#Change the article according to your wish
article="""
Coutinho was just about to be introduced by Villa boss Gerrard midway through the second half when Bruno Fernandes slammed home
his second goal of the game off the underside of the bar. But the Brazilian proved the catalyst for a memorable response.
First he drove at the United defence, helping to create the space which Jacob Ramsey exploited to halve the deficit. Then Ramsey slid over an excellent
cross from the left which Raphael Varane was unable to intercept as he slid back, leaving Coutinho to finish into an empty net.
The goal brought celebrations at both ends of the pitch as Emiliano Martinez also went into the crowd in relief - it was the Argentine's horrible sixth-minute error that had gifted Fernandes the visitors' opener.
Given his background - with Liverpool, Barcelona and Bayern Munich - Coutinho is a bold loan signing by Villa, and underlines the pedigree of the man they appointed as manager in November.
Gerrard is not at Villa to learn how to avoid relegation.
His demands remain as high as they were as a player and Coutinho's arrival is an example of that.
Villa are a better team since Gerrard's arrival and, after a sluggish start against opponents they dominated but lost to in the FA Cup five days ago, they grew into the game.
The club's other newboy, Lucas Digne, was among those denied by United keeper David de Gea at the end of the first half - in unorthodox fashion, with his knees.
Ollie Watkins did not really test the Spain keeper when Villa broke after Edinson Cavani lost possession in his own half. However, Emi Buendia certainly did with a near-post header. Rooted to his line, De Gea's reactions were up to the job as he beat Buendia's effort away.
When De Gea produced more saves after half-time to deny Ramsey and Digne again, it appeared the image of the night for Villa would be midfielder Morgan Sanson kicking a drinks bottle in fury after his error in gifting Fred possession to set up Fernandes for the visitors' second had been followed immediately by his substitution.
However, as it was the prelude to Coutinho's arrival, it was the moment that changed the course of the game - and the acclaim for the Brazilian at the final whistle indicated Villa's fans are already firmly behind him.
"""
result=pipe(article, **gen_kwargs)[0]["summary_text"]
print(result)
''' Output
Title 1 :
Coutinho's arrival sparks Villa comeback
Title 2 :
Philippe Coutinho marked his debut for Aston Villa with a goal and an assist as Steven Gerrard's side came from two goals down to draw with Manchester United.
Title 3 :
Steven Gerrard's first game in charge of Aston Villa ended in a dramatic draw against Manchester United - but it was the arrival of Philippe Coutinho that marked the night.
Title 4 :
Liverpool loanee Philippe Coutinho marked his first appearance for Aston Villa with two goals as Steven Gerrard's side came from two goals down to draw 2-2.'''
```
## Training procedure
While training, **short titles were combined with the subtitles for the articles to improve the quality of the generated titles and the subtitles were removed from the main body of the articles.**
##Limitations
In rare cases, if the opening few lines of a passage/article are descriptive enough, the model often just copies these lines instead of looking for information further down the articles, which may not be conducive in some cases.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
### Training results
**Rouge1:38.2315**
**Rouge2: 18.6598**
**RougueL: 31.7393**
**RougeLsum: 31.7086**
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Cmepthbiu/deep_rl
|
Cmepthbiu
| 2022-05-09T09:22:41Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-09T09:09:06Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 203.88 +/- 20.92
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
madatnlp/gamza-bart-for-kormath
|
madatnlp
| 2022-05-09T09:17:11Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"bart",
"text2text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-09T08:19:07Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: madatnlp/gamza-bart-for-kormath
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# madatnlp/gamza-bart-for-kormath
This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1418
- Validation Loss: 0.3009
- Epoch: 29
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.4155 | 1.9300 | 0 |
| 1.4995 | 1.0293 | 1 |
| 1.0445 | 0.8365 | 2 |
| 0.8775 | 0.7569 | 3 |
| 0.8198 | 0.7778 | 4 |
| 0.7619 | 0.7430 | 5 |
| 0.7324 | 0.7259 | 6 |
| 0.7234 | 0.7214 | 7 |
| 0.6697 | 0.6819 | 8 |
| 0.6599 | 0.6673 | 9 |
| 0.6387 | 0.6433 | 10 |
| 0.6227 | 0.6651 | 11 |
| 0.6017 | 0.6128 | 12 |
| 0.5820 | 0.6430 | 13 |
| 0.5229 | 0.5611 | 14 |
| 0.4617 | 0.4675 | 15 |
| 0.4071 | 0.4463 | 16 |
| 0.3495 | 0.4213 | 17 |
| 0.3202 | 0.4103 | 18 |
| 0.2875 | 0.4477 | 19 |
| 0.2528 | 0.3244 | 20 |
| 0.2331 | 0.4037 | 21 |
| 0.2117 | 0.3041 | 22 |
| 0.1943 | 0.3069 | 23 |
| 0.1805 | 0.3385 | 24 |
| 0.2267 | 0.3347 | 25 |
| 0.2049 | 0.2993 | 26 |
| 0.1800 | 0.3792 | 27 |
| 0.1583 | 0.2905 | 28 |
| 0.1418 | 0.3009 | 29 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
theojolliffe/distilbart-cnn-arxiv-pubmed-v3-e8
|
theojolliffe
| 2022-05-09T08:48:07Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-09T07:16:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distilbart-cnn-arxiv-pubmed-v3-e8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbart-cnn-arxiv-pubmed-v3-e8
This model is a fine-tuned version of [theojolliffe/distilbart-cnn-arxiv-pubmed](https://huggingface.co/theojolliffe/distilbart-cnn-arxiv-pubmed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8329
- Rouge1: 53.3047
- Rouge2: 34.6219
- Rougel: 37.6148
- Rougelsum: 50.8973
- Gen Len: 141.8704
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 398 | 1.1211 | 50.4753 | 30.5417 | 33.192 | 48.1321 | 141.8704 |
| 1.3657 | 2.0 | 796 | 0.9944 | 52.2197 | 33.6109 | 35.9448 | 50.0028 | 141.6111 |
| 0.887 | 3.0 | 1194 | 0.9149 | 52.796 | 33.7683 | 36.4941 | 50.4514 | 141.5926 |
| 0.6548 | 4.0 | 1592 | 0.8725 | 52.5353 | 33.4019 | 36.4573 | 50.2506 | 142.0 |
| 0.6548 | 5.0 | 1990 | 0.8540 | 53.2987 | 34.6476 | 38.314 | 51.163 | 141.4815 |
| 0.504 | 6.0 | 2388 | 0.8395 | 52.7218 | 34.6524 | 37.9921 | 50.5185 | 141.5556 |
| 0.4006 | 7.0 | 2786 | 0.8342 | 53.2251 | 35.2702 | 38.3763 | 51.1958 | 141.6667 |
| 0.3314 | 8.0 | 3184 | 0.8329 | 53.3047 | 34.6219 | 37.6148 | 50.8973 | 141.8704 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
KushalRamaiya/ppo-LunarLander-v2
|
KushalRamaiya
| 2022-05-09T07:15:37Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-09T06:54:45Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 268.32 +/- 24.24
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
RustBucket/LunarLanderTest
|
RustBucket
| 2022-05-09T06:47:33Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-09T06:47:02Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 238.47 +/- 60.15
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
huggingtweets/jamesliao333
|
huggingtweets
| 2022-05-09T05:49:36Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-09T05:47:52Z |
---
language: en
thumbnail: http://www.huggingtweets.com/jamesliao333/1652075372352/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1522973288288333825/NhsZowLa_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">DON XMCA//素 Vitamin(RNG) 🦀 "MILLENNIUM 定制 Vision"</div>
<div style="text-align: center; font-size: 14px;">@jamesliao333</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from DON XMCA//素 Vitamin(RNG) 🦀 "MILLENNIUM 定制 Vision".
| Data | DON XMCA//素 Vitamin(RNG) 🦀 "MILLENNIUM 定制 Vision" |
| --- | --- |
| Tweets downloaded | 202 |
| Retweets | 37 |
| Short tweets | 16 |
| Tweets kept | 149 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ed1hlxcu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jamesliao333's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/mfrtr3lf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/mfrtr3lf/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jamesliao333')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/propertyexile
|
huggingtweets
| 2022-05-09T05:28:39Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-22T20:00:56Z |
---
language: en
thumbnail: http://www.huggingtweets.com/propertyexile/1652074114021/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1523442545153519616/mYJEJtEL_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Primo</div>
<div style="text-align: center; font-size: 14px;">@propertyexile</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Primo.
| Data | Primo |
| --- | --- |
| Tweets downloaded | 304 |
| Retweets | 37 |
| Short tweets | 26 |
| Tweets kept | 241 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1q8zni52/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @propertyexile's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1f85w6fy) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1f85w6fy/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/propertyexile')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/computerforever
|
huggingtweets
| 2022-05-09T05:19:58Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-09T05:19:20Z |
---
language: en
thumbnail: http://www.huggingtweets.com/computerforever/1652073594573/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1518444670266839045/38xr9OAd_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">computer sweetie</div>
<div style="text-align: center; font-size: 14px;">@computerforever</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from computer sweetie.
| Data | computer sweetie |
| --- | --- |
| Tweets downloaded | 2170 |
| Retweets | 48 |
| Short tweets | 313 |
| Tweets kept | 1809 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/9j3sj0ot/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @computerforever's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2iw1hcff) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2iw1hcff/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/computerforever')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
zzyzx0/PPO-LunarLander-v2
|
zzyzx0
| 2022-05-09T02:47:36Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-09T02:46:57Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 208.86 +/- 20.83
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
theojolliffe/bart-cnn-pubmed-arxiv-pubmed-v3-e64
|
theojolliffe
| 2022-05-09T02:03:17Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-08T18:50:49Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-cnn-pubmed-arxiv-pubmed-v3-e64
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-pubmed-arxiv-pubmed-v3-e64
This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0630
- Rouge1: 58.7
- Rouge2: 47.8042
- Rougel: 50.6967
- Rougelsum: 57.5543
- Gen Len: 142.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 64
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 398 | 0.9499 | 53.8396 | 34.0954 | 35.6734 | 51.3453 | 142.0 |
| 1.1219 | 2.0 | 796 | 0.8223 | 53.0414 | 33.3193 | 35.7448 | 50.1675 | 142.0 |
| 0.6681 | 3.0 | 1194 | 0.7689 | 53.6684 | 35.3651 | 37.7087 | 51.1441 | 142.0 |
| 0.4393 | 4.0 | 1592 | 0.7694 | 53.9066 | 35.3925 | 38.8917 | 51.6172 | 142.0 |
| 0.4393 | 5.0 | 1990 | 0.7597 | 54.0746 | 36.1026 | 39.1318 | 51.9272 | 142.0 |
| 0.2947 | 6.0 | 2388 | 0.8284 | 53.1168 | 34.7428 | 38.0573 | 50.9563 | 142.0 |
| 0.2016 | 7.0 | 2786 | 0.7951 | 55.7222 | 39.0458 | 42.5265 | 53.5359 | 142.0 |
| 0.1422 | 8.0 | 3184 | 0.7793 | 56.2376 | 40.3348 | 43.435 | 54.3228 | 142.0 |
| 0.1096 | 9.0 | 3582 | 0.8260 | 55.0372 | 39.0552 | 42.5403 | 53.0694 | 142.0 |
| 0.1096 | 10.0 | 3980 | 0.8397 | 53.849 | 37.519 | 40.674 | 52.1357 | 141.7037 |
| 0.0881 | 11.0 | 4378 | 0.8504 | 56.4835 | 41.0484 | 44.9407 | 54.3557 | 142.0 |
| 0.0693 | 12.0 | 4776 | 0.8285 | 55.7705 | 39.8585 | 43.722 | 53.7607 | 142.0 |
| 0.0572 | 13.0 | 5174 | 0.8327 | 57.932 | 43.5378 | 46.8233 | 55.8739 | 142.0 |
| 0.0461 | 14.0 | 5572 | 0.8720 | 57.6733 | 42.9742 | 45.8698 | 56.018 | 142.0 |
| 0.0461 | 15.0 | 5970 | 0.8723 | 57.6072 | 42.6946 | 45.2551 | 55.8486 | 142.0 |
| 0.0416 | 16.0 | 6368 | 0.8764 | 57.1973 | 43.1931 | 46.4492 | 55.3842 | 142.0 |
| 0.0343 | 17.0 | 6766 | 0.8638 | 57.4474 | 43.3544 | 46.3026 | 55.7863 | 142.0 |
| 0.03 | 18.0 | 7164 | 0.9234 | 57.9166 | 43.8551 | 46.6473 | 56.3895 | 142.0 |
| 0.0252 | 19.0 | 7562 | 0.9393 | 58.2908 | 45.2321 | 47.1398 | 56.6618 | 142.0 |
| 0.0252 | 20.0 | 7960 | 0.8966 | 59.2798 | 46.381 | 49.3514 | 57.6061 | 142.0 |
| 0.024 | 21.0 | 8358 | 0.9056 | 57.8409 | 44.2048 | 47.3329 | 56.2568 | 142.0 |
| 0.0195 | 22.0 | 8756 | 0.9424 | 57.551 | 44.6847 | 47.2771 | 56.2391 | 142.0 |
| 0.0182 | 23.0 | 9154 | 0.9361 | 59.1078 | 46.4704 | 49.4178 | 57.6796 | 142.0 |
| 0.0169 | 24.0 | 9552 | 0.9456 | 56.7966 | 43.3135 | 46.4208 | 55.4646 | 142.0 |
| 0.0169 | 25.0 | 9950 | 0.9867 | 59.5561 | 47.4638 | 50.0725 | 58.2388 | 141.8519 |
| 0.0147 | 26.0 | 10348 | 0.9727 | 58.2574 | 44.9904 | 47.2701 | 56.4274 | 142.0 |
| 0.0125 | 27.0 | 10746 | 0.9589 | 58.6792 | 45.8465 | 48.0781 | 57.0755 | 142.0 |
| 0.0117 | 28.0 | 11144 | 0.9635 | 59.1118 | 46.6614 | 50.0552 | 57.6153 | 142.0 |
| 0.0103 | 29.0 | 11542 | 0.9623 | 58.2517 | 45.6401 | 48.5888 | 56.7733 | 142.0 |
| 0.0103 | 30.0 | 11940 | 0.9752 | 59.0707 | 47.203 | 49.7992 | 57.6216 | 142.0 |
| 0.0096 | 31.0 | 12338 | 0.9610 | 57.6781 | 44.0504 | 47.6718 | 56.1201 | 142.0 |
| 0.0089 | 32.0 | 12736 | 0.9705 | 58.5592 | 45.7397 | 48.681 | 57.0302 | 142.0 |
| 0.008 | 33.0 | 13134 | 0.9989 | 58.1997 | 45.6345 | 48.2551 | 56.8571 | 141.7778 |
| 0.0075 | 34.0 | 13532 | 0.9880 | 57.9632 | 44.7845 | 47.8763 | 56.3979 | 142.0 |
| 0.0075 | 35.0 | 13930 | 1.0041 | 58.1316 | 46.2737 | 49.5986 | 56.8263 | 142.0 |
| 0.0061 | 36.0 | 14328 | 0.9923 | 58.4686 | 46.1735 | 49.1299 | 57.0331 | 142.0 |
| 0.0066 | 37.0 | 14726 | 1.0157 | 58.4277 | 45.6559 | 49.1739 | 56.8198 | 141.6481 |
| 0.0052 | 38.0 | 15124 | 1.0220 | 58.5166 | 46.3883 | 50.0964 | 57.0104 | 142.0 |
| 0.0049 | 39.0 | 15522 | 0.9949 | 59.3697 | 47.0609 | 50.2733 | 58.1388 | 142.0 |
| 0.0049 | 40.0 | 15920 | 1.0368 | 59.9537 | 48.4059 | 51.8185 | 58.8002 | 142.0 |
| 0.0039 | 41.0 | 16318 | 1.0228 | 58.2093 | 46.4807 | 49.54 | 56.9994 | 142.0 |
| 0.0041 | 42.0 | 16716 | 1.0218 | 57.6376 | 45.4951 | 49.003 | 56.4606 | 142.0 |
| 0.0035 | 43.0 | 17114 | 1.0381 | 57.2845 | 43.9593 | 46.779 | 55.6106 | 142.0 |
| 0.0059 | 44.0 | 17512 | 1.0316 | 58.5506 | 46.2111 | 49.4844 | 56.9506 | 142.0 |
| 0.0059 | 45.0 | 17910 | 1.0388 | 58.8383 | 47.6053 | 50.6187 | 57.7125 | 142.0 |
| 0.0028 | 46.0 | 18308 | 1.0068 | 59.3198 | 47.6888 | 50.2478 | 58.0 | 142.0 |
| 0.0028 | 47.0 | 18706 | 1.0446 | 58.8938 | 46.7524 | 49.5642 | 57.3659 | 142.0 |
| 0.0022 | 48.0 | 19104 | 1.0347 | 59.8253 | 48.3871 | 51.3949 | 58.5652 | 142.0 |
| 0.0024 | 49.0 | 19502 | 1.0294 | 60.655 | 50.2339 | 53.1662 | 59.3333 | 142.0 |
| 0.0024 | 50.0 | 19900 | 1.0225 | 58.5131 | 47.3009 | 50.1642 | 57.2287 | 142.0 |
| 0.0022 | 51.0 | 20298 | 1.0320 | 59.6101 | 47.4104 | 50.5291 | 58.075 | 142.0 |
| 0.0018 | 52.0 | 20696 | 1.0507 | 58.7957 | 46.8893 | 50.2996 | 57.3662 | 142.0 |
| 0.0015 | 53.0 | 21094 | 1.0599 | 58.9064 | 47.9433 | 51.3082 | 57.6871 | 142.0 |
| 0.0015 | 54.0 | 21492 | 1.0636 | 59.6607 | 48.5737 | 51.2361 | 58.333 | 142.0 |
| 0.0013 | 55.0 | 21890 | 1.0452 | 58.7026 | 46.5286 | 49.9672 | 57.2521 | 142.0 |
| 0.0012 | 56.0 | 22288 | 1.0418 | 58.9452 | 47.7209 | 50.657 | 57.7103 | 142.0 |
| 0.0011 | 57.0 | 22686 | 1.0578 | 58.485 | 46.0691 | 49.811 | 57.2591 | 142.0 |
| 0.0009 | 58.0 | 23084 | 1.0561 | 59.2268 | 48.1987 | 50.1948 | 57.8871 | 142.0 |
| 0.0009 | 59.0 | 23482 | 1.0548 | 59.6307 | 48.1778 | 50.9934 | 58.2098 | 142.0 |
| 0.0009 | 60.0 | 23880 | 1.0498 | 59.5054 | 48.8866 | 51.5977 | 58.1868 | 142.0 |
| 0.0008 | 61.0 | 24278 | 1.0583 | 60.0232 | 49.2518 | 52.2297 | 58.6774 | 142.0 |
| 0.0007 | 62.0 | 24676 | 1.0659 | 59.1755 | 48.4144 | 51.5157 | 58.0416 | 142.0 |
| 0.0007 | 63.0 | 25074 | 1.0622 | 59.1023 | 47.74 | 50.5188 | 57.9707 | 142.0 |
| 0.0007 | 64.0 | 25472 | 1.0630 | 58.7 | 47.8042 | 50.6967 | 57.5543 | 142.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ebonazza2910/model
|
ebonazza2910
| 2022-05-08T23:12:15Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-03T16:38:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2220
- Wer: 0.1301
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.9743 | 0.18 | 400 | 2.1457 | 1.0000 |
| 0.5747 | 0.36 | 800 | 0.3415 | 0.3456 |
| 0.3383 | 0.54 | 1200 | 0.2797 | 0.3095 |
| 0.2967 | 0.72 | 1600 | 0.2464 | 0.2568 |
| 0.2747 | 0.9 | 2000 | 0.2341 | 0.2466 |
| 0.2501 | 1.08 | 2400 | 0.2299 | 0.2317 |
| 0.2309 | 1.26 | 2800 | 0.2306 | 0.2328 |
| 0.2273 | 1.44 | 3200 | 0.2212 | 0.2375 |
| 0.225 | 1.62 | 3600 | 0.2193 | 0.2267 |
| 0.2204 | 1.8 | 4000 | 0.2157 | 0.2295 |
| 0.2256 | 1.98 | 4400 | 0.2165 | 0.2260 |
| 0.1941 | 2.17 | 4800 | 0.2105 | 0.2163 |
| 0.1925 | 2.35 | 5200 | 0.2098 | 0.2153 |
| 0.1925 | 2.53 | 5600 | 0.2120 | 0.2148 |
| 0.1952 | 2.71 | 6000 | 0.2063 | 0.2178 |
| 0.1971 | 2.89 | 6400 | 0.2100 | 0.2158 |
| 0.1888 | 3.07 | 6800 | 0.2131 | 0.2172 |
| 0.1702 | 3.25 | 7200 | 0.2155 | 0.2203 |
| 0.173 | 3.43 | 7600 | 0.2141 | 0.2254 |
| 0.174 | 3.61 | 8000 | 0.2017 | 0.2100 |
| 0.1802 | 3.79 | 8400 | 0.1998 | 0.2043 |
| 0.1717 | 3.97 | 8800 | 0.2070 | 0.2110 |
| 0.162 | 4.15 | 9200 | 0.2082 | 0.2157 |
| 0.154 | 4.33 | 9600 | 0.2163 | 0.2161 |
| 0.1598 | 4.51 | 10000 | 0.2070 | 0.2171 |
| 0.1576 | 4.69 | 10400 | 0.2034 | 0.2116 |
| 0.1601 | 4.87 | 10800 | 0.1990 | 0.2009 |
| 0.152 | 5.05 | 11200 | 0.1994 | 0.2039 |
| 0.1395 | 5.23 | 11600 | 0.2013 | 0.2046 |
| 0.1407 | 5.41 | 12000 | 0.2009 | 0.2022 |
| 0.1449 | 5.59 | 12400 | 0.1982 | 0.1961 |
| 0.1483 | 5.77 | 12800 | 0.2082 | 0.2054 |
| 0.1514 | 5.95 | 13200 | 0.1953 | 0.1985 |
| 0.138 | 6.13 | 13600 | 0.2046 | 0.1965 |
| 0.1322 | 6.31 | 14000 | 0.2076 | 0.1948 |
| 0.1372 | 6.5 | 14400 | 0.1968 | 0.1944 |
| 0.136 | 6.68 | 14800 | 0.1971 | 0.1963 |
| 0.1382 | 6.86 | 15200 | 0.2001 | 0.1990 |
| 0.1335 | 7.04 | 15600 | 0.2026 | 0.1935 |
| 0.1206 | 7.22 | 16000 | 0.1986 | 0.1938 |
| 0.1239 | 7.4 | 16400 | 0.2054 | 0.1919 |
| 0.1254 | 7.58 | 16800 | 0.1918 | 0.1939 |
| 0.1262 | 7.76 | 17200 | 0.1960 | 0.1947 |
| 0.126 | 7.94 | 17600 | 0.1932 | 0.1906 |
| 0.1169 | 8.12 | 18000 | 0.2037 | 0.1916 |
| 0.1142 | 8.3 | 18400 | 0.1999 | 0.1900 |
| 0.1151 | 8.48 | 18800 | 0.1920 | 0.1855 |
| 0.1121 | 8.66 | 19200 | 0.2007 | 0.1859 |
| 0.1135 | 8.84 | 19600 | 0.1932 | 0.1879 |
| 0.1158 | 9.02 | 20000 | 0.1916 | 0.1859 |
| 0.105 | 9.2 | 20400 | 0.1961 | 0.1831 |
| 0.1023 | 9.38 | 20800 | 0.1914 | 0.1791 |
| 0.1004 | 9.56 | 21200 | 0.1881 | 0.1787 |
| 0.1023 | 9.74 | 21600 | 0.1963 | 0.1817 |
| 0.1075 | 9.92 | 22000 | 0.1889 | 0.1861 |
| 0.103 | 10.1 | 22400 | 0.1975 | 0.1791 |
| 0.0952 | 10.28 | 22800 | 0.1979 | 0.1787 |
| 0.0957 | 10.46 | 23200 | 0.1922 | 0.1817 |
| 0.0966 | 10.65 | 23600 | 0.1953 | 0.1857 |
| 0.0997 | 10.83 | 24000 | 0.1902 | 0.1783 |
| 0.0981 | 11.01 | 24400 | 0.1959 | 0.1780 |
| 0.0868 | 11.19 | 24800 | 0.2056 | 0.1783 |
| 0.0905 | 11.37 | 25200 | 0.1958 | 0.1777 |
| 0.0892 | 11.55 | 25600 | 0.1935 | 0.1796 |
| 0.0891 | 11.73 | 26000 | 0.1968 | 0.1763 |
| 0.0888 | 11.91 | 26400 | 0.2043 | 0.1804 |
| 0.0842 | 12.09 | 26800 | 0.2043 | 0.1733 |
| 0.0828 | 12.27 | 27200 | 0.1964 | 0.1715 |
| 0.0827 | 12.45 | 27600 | 0.1991 | 0.1749 |
| 0.0844 | 12.63 | 28000 | 0.2014 | 0.1695 |
| 0.0837 | 12.81 | 28400 | 0.1973 | 0.1759 |
| 0.0872 | 12.99 | 28800 | 0.1975 | 0.1689 |
| 0.0778 | 13.17 | 29200 | 0.1979 | 0.1740 |
| 0.0759 | 13.35 | 29600 | 0.2093 | 0.1753 |
| 0.076 | 13.53 | 30000 | 0.1990 | 0.1731 |
| 0.0762 | 13.71 | 30400 | 0.2024 | 0.1690 |
| 0.0764 | 13.89 | 30800 | 0.2037 | 0.1709 |
| 0.0756 | 14.07 | 31200 | 0.2007 | 0.1716 |
| 0.0702 | 14.25 | 31600 | 0.2011 | 0.1680 |
| 0.0694 | 14.43 | 32000 | 0.2061 | 0.1683 |
| 0.0713 | 14.61 | 32400 | 0.2014 | 0.1687 |
| 0.0693 | 14.79 | 32800 | 0.1961 | 0.1658 |
| 0.071 | 14.98 | 33200 | 0.1921 | 0.1645 |
| 0.0659 | 15.16 | 33600 | 0.2079 | 0.1682 |
| 0.0659 | 15.34 | 34000 | 0.2046 | 0.1649 |
| 0.0685 | 15.52 | 34400 | 0.1994 | 0.1660 |
| 0.0663 | 15.7 | 34800 | 0.1970 | 0.1652 |
| 0.0678 | 15.88 | 35200 | 0.1961 | 0.1634 |
| 0.0644 | 16.06 | 35600 | 0.2141 | 0.1644 |
| 0.0596 | 16.24 | 36000 | 0.2098 | 0.1628 |
| 0.0629 | 16.42 | 36400 | 0.1969 | 0.1616 |
| 0.0598 | 16.6 | 36800 | 0.2026 | 0.1604 |
| 0.0628 | 16.78 | 37200 | 0.2050 | 0.1620 |
| 0.0616 | 16.96 | 37600 | 0.1958 | 0.1618 |
| 0.0538 | 17.14 | 38000 | 0.2093 | 0.1588 |
| 0.0573 | 17.32 | 38400 | 0.1995 | 0.1588 |
| 0.0555 | 17.5 | 38800 | 0.2077 | 0.1608 |
| 0.0555 | 17.68 | 39200 | 0.2036 | 0.1571 |
| 0.0578 | 17.86 | 39600 | 0.2045 | 0.1572 |
| 0.056 | 18.04 | 40000 | 0.2065 | 0.1593 |
| 0.0525 | 18.22 | 40400 | 0.2093 | 0.1580 |
| 0.0527 | 18.4 | 40800 | 0.2141 | 0.1585 |
| 0.0529 | 18.58 | 41200 | 0.2137 | 0.1585 |
| 0.0533 | 18.76 | 41600 | 0.2021 | 0.1558 |
| 0.0529 | 18.94 | 42000 | 0.2108 | 0.1535 |
| 0.05 | 19.12 | 42400 | 0.2114 | 0.1555 |
| 0.0479 | 19.31 | 42800 | 0.2091 | 0.1549 |
| 0.0509 | 19.49 | 43200 | 0.2145 | 0.1554 |
| 0.0486 | 19.67 | 43600 | 0.2061 | 0.1536 |
| 0.049 | 19.85 | 44000 | 0.2132 | 0.1548 |
| 0.0484 | 20.03 | 44400 | 0.2077 | 0.1523 |
| 0.0449 | 20.21 | 44800 | 0.2177 | 0.1529 |
| 0.0452 | 20.39 | 45200 | 0.2204 | 0.1517 |
| 0.0477 | 20.57 | 45600 | 0.2132 | 0.1517 |
| 0.048 | 20.75 | 46000 | 0.2119 | 0.1532 |
| 0.0469 | 20.93 | 46400 | 0.2109 | 0.1524 |
| 0.0439 | 21.11 | 46800 | 0.2118 | 0.1503 |
| 0.044 | 21.29 | 47200 | 0.2033 | 0.1474 |
| 0.0435 | 21.47 | 47600 | 0.2066 | 0.1485 |
| 0.0418 | 21.65 | 48000 | 0.2125 | 0.1491 |
| 0.0417 | 21.83 | 48400 | 0.2139 | 0.1487 |
| 0.0446 | 22.01 | 48800 | 0.2054 | 0.1493 |
| 0.039 | 22.19 | 49200 | 0.2179 | 0.1459 |
| 0.0414 | 22.37 | 49600 | 0.2118 | 0.1466 |
| 0.0394 | 22.55 | 50000 | 0.2104 | 0.1444 |
| 0.0381 | 22.73 | 50400 | 0.2095 | 0.1458 |
| 0.0382 | 22.91 | 50800 | 0.2193 | 0.1471 |
| 0.0391 | 23.09 | 51200 | 0.2143 | 0.1455 |
| 0.0365 | 23.27 | 51600 | 0.2198 | 0.1445 |
| 0.0368 | 23.46 | 52000 | 0.2151 | 0.1444 |
| 0.038 | 23.64 | 52400 | 0.2094 | 0.1439 |
| 0.038 | 23.82 | 52800 | 0.2137 | 0.1422 |
| 0.0374 | 24.0 | 53200 | 0.2180 | 0.1425 |
| 0.0352 | 24.18 | 53600 | 0.2207 | 0.1422 |
| 0.0343 | 24.36 | 54000 | 0.2269 | 0.1445 |
| 0.0353 | 24.54 | 54400 | 0.2222 | 0.1438 |
| 0.0348 | 24.72 | 54800 | 0.2224 | 0.1413 |
| 0.0342 | 24.9 | 55200 | 0.2146 | 0.1401 |
| 0.0337 | 25.08 | 55600 | 0.2246 | 0.1408 |
| 0.0327 | 25.26 | 56000 | 0.2161 | 0.1401 |
| 0.0339 | 25.44 | 56400 | 0.2212 | 0.1402 |
| 0.0324 | 25.62 | 56800 | 0.2203 | 0.1394 |
| 0.0319 | 25.8 | 57200 | 0.2145 | 0.1376 |
| 0.0317 | 25.98 | 57600 | 0.2147 | 0.1375 |
| 0.0302 | 26.16 | 58000 | 0.2213 | 0.1362 |
| 0.0309 | 26.34 | 58400 | 0.2218 | 0.1365 |
| 0.0308 | 26.52 | 58800 | 0.2167 | 0.1362 |
| 0.0294 | 26.7 | 59200 | 0.2169 | 0.1368 |
| 0.0297 | 26.88 | 59600 | 0.2163 | 0.1350 |
| 0.0289 | 27.06 | 60000 | 0.2188 | 0.1348 |
| 0.0284 | 27.24 | 60400 | 0.2172 | 0.1338 |
| 0.0278 | 27.42 | 60800 | 0.2230 | 0.1342 |
| 0.0283 | 27.6 | 61200 | 0.2233 | 0.1342 |
| 0.0292 | 27.79 | 61600 | 0.2238 | 0.1335 |
| 0.0286 | 27.97 | 62000 | 0.2218 | 0.1327 |
| 0.0262 | 28.15 | 62400 | 0.2220 | 0.1324 |
| 0.0274 | 28.33 | 62800 | 0.2182 | 0.1323 |
| 0.0279 | 28.51 | 63200 | 0.2170 | 0.1314 |
| 0.0269 | 28.69 | 63600 | 0.2228 | 0.1313 |
| 0.0264 | 28.87 | 64000 | 0.2209 | 0.1313 |
| 0.0254 | 29.05 | 64400 | 0.2224 | 0.1304 |
| 0.026 | 29.23 | 64800 | 0.2220 | 0.1302 |
| 0.0253 | 29.41 | 65200 | 0.2229 | 0.1304 |
| 0.0244 | 29.59 | 65600 | 0.2217 | 0.1298 |
| 0.025 | 29.77 | 66000 | 0.2223 | 0.1303 |
| 0.0255 | 29.95 | 66400 | 0.2220 | 0.1301 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
|
theojolliffe/bart-cnn-pubmed-arxiv-pubmed-v3-e12
|
theojolliffe
| 2022-05-08T23:01:48Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-08T20:57:25Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-cnn-pubmed-arxiv-pubmed-v3-e12
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-pubmed-arxiv-pubmed-v3-e12
This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8658
- Rouge1: 57.2678
- Rouge2: 43.347
- Rougel: 47.0854
- Rougelsum: 55.4167
- Gen Len: 142.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 1.2548 | 1.0 | 795 | 0.9154 | 53.4249 | 34.0377 | 36.4396 | 50.9884 | 141.8889 |
| 0.6994 | 2.0 | 1590 | 0.8213 | 54.7613 | 35.9428 | 38.3899 | 51.9527 | 142.0 |
| 0.5272 | 3.0 | 2385 | 0.7703 | 53.8561 | 35.4871 | 38.0502 | 51.131 | 141.8889 |
| 0.3407 | 4.0 | 3180 | 0.7764 | 53.9514 | 35.8553 | 39.1935 | 51.7005 | 142.0 |
| 0.2612 | 5.0 | 3975 | 0.7529 | 54.4056 | 36.2605 | 40.8003 | 52.0424 | 142.0 |
| 0.1702 | 6.0 | 4770 | 0.8105 | 54.2251 | 37.1441 | 41.2472 | 52.2803 | 142.0 |
| 0.1276 | 7.0 | 5565 | 0.8004 | 56.49 | 40.4009 | 44.018 | 54.2404 | 141.5556 |
| 0.0978 | 8.0 | 6360 | 0.7890 | 56.6339 | 40.9867 | 43.9603 | 54.4468 | 142.0 |
| 0.0711 | 9.0 | 7155 | 0.8285 | 56.0469 | 40.7758 | 44.1395 | 53.9668 | 142.0 |
| 0.0649 | 10.0 | 7950 | 0.8498 | 56.9873 | 42.4721 | 46.705 | 55.2188 | 142.0 |
| 0.0471 | 11.0 | 8745 | 0.8547 | 57.7898 | 43.4238 | 46.5868 | 56.0858 | 142.0 |
| 0.0336 | 12.0 | 9540 | 0.8658 | 57.2678 | 43.347 | 47.0854 | 55.4167 | 142.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
subhasisj/Ar-Mulitlingula-MiniLM
|
subhasisj
| 2022-05-08T21:26:17Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-08T19:19:57Z |
Ar-Mulitlingual-MiniLM
This model is a fine-tuned version of microsoft/Multilingual-MiniLM-L12-H384 on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 5e-05
train_batch_size: 24
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2
mixed_precision_training: Native AMP
Training results
Framework versions
Transformers 4.18.0
Pytorch 1.11.0+cu113
Tokenizers 0.12.1
|
subhasisj/Zh-Mulitlingual-MiniLM
|
subhasisj
| 2022-05-08T21:19:00Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-08T19:56:18Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: Zh-Mulitlingual-MiniLM
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Zh-Mulitlingual-MiniLM
This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
jecp97/trial-ppo-LunarLander-v2
|
jecp97
| 2022-05-08T20:28:36Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-08T16:22:10Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 206.72 +/- 58.57
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
leebaidyanathan/TEST2ppo-LunarLander-v2
|
leebaidyanathan
| 2022-05-08T20:28:32Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-08T20:28:01Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 223.51 +/- 38.67
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
syrios/lunarlanding
|
syrios
| 2022-05-08T20:18:08Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-08T16:47:46Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 260.47 +/- 35.08
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
sam999/t5-end2end-questions-generation
|
sam999
| 2022-05-08T20:01:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-08T01:16:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-end2end-questions-generation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-end2end-questions-generation
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6940
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0297 | 0.07 | 100 | 1.6940 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
huxxx657/roberta-base-finetuned-squad
|
huxxx657
| 2022-05-08T19:57:20Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-08T02:59:11Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: roberta-base-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-squad
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8152
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8557 | 1.0 | 8239 | 0.8152 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
SofyPreo/ppo-LunarLander-v2
|
SofyPreo
| 2022-05-08T18:45:10Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-08T17:42:02Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 259.34 +/- 20.02
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
|
LiYuan/Amazon-Cross-Encoder-Classification
|
LiYuan
| 2022-05-08T17:52:56Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-08T04:29:31Z |
---
license: afl-3.0
---
There are two types of Cross-Encoder models. One is the Cross-Encoder Regression model that we fine-tuned and mentioned in the previous section. Next, we have the Cross-Encoder Classification model. These two models are introduced in the same paper https://doi.org/10.48550/arxiv.1908.10084
Both models resolve the issue that the BERT model is too time-consuming and resource-consuming to train in pairwised sentences. These two model weights are initialized as the BERT and RoBERTa networks. We only need to fine-tune them, spending much less time to yield a comparable or even better sentence embedding. The below figure \ref{figure:5} shows the architecture of Cross-Encoder Classification.
Then we evaluated the model performance on the 2,000 held-out test set. We also got a test accuracy **46.05%** that is almost identical to the best validation accuracy, suggesting a good generalization model.
|
Kabutopusu/DialoGPT-medium-NITWMae
|
Kabutopusu
| 2022-05-08T17:39:42Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-08T16:18:15Z |
---
tags:
- conversational
---
# DialoGPT Model, Trained on dialogue from "Mae" in the game Night in the Woods
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("Kabutopusu/DialoGPT-medium-NITWMae")
model = AutoModelWithLMHead.from_pretrained("Kabutopusu/DialoGPT-medium-NITWMae")
# Let's chat for 4 lines
for step in range(4):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# print(new_user_input_ids)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.1,
temperature=1.2
)
# pretty print last ouput tokens from bot
print("Mae: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
|
PrajwalS/wav2vec2_custom_model_50
|
PrajwalS
| 2022-05-08T16:33:22Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-06T09:39:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2_custom_model_50
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2_custom_model_50
This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 1.18.4
- Tokenizers 0.11.6
|
GideonFr/PPO-LunarLander-v2-low-gamma
|
GideonFr
| 2022-05-08T16:00:31Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-08T15:59:53Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -34.75 +/- 121.94
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
huggingtweets/temapex
|
huggingtweets
| 2022-05-08T15:33:31Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-14T15:47:57Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1511150115582525442/9l-weW8Z_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ema Pex 🌠 ペクスえま</div>
<div style="text-align: center; font-size: 14px;">@temapex</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Ema Pex 🌠 ペクスえま.
| Data | Ema Pex 🌠 ペクスえま |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 446 |
| Short tweets | 259 |
| Tweets kept | 2540 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2qyw32m2/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @temapex's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3my4azzd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3my4azzd/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/temapex')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
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