modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-07 06:34:03
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 544
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-07 06:33:46
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
fmcurti/A2C-LunarLander-v2
|
fmcurti
| 2022-05-05T17:34:36Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T17:09:52Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 17.50 +/- 120.65
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **A2C** Agent playing **LunarLander-v2**
This is a trained model of a **A2C** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
pjarbas312/ppo-LunarLander-v2
|
pjarbas312
| 2022-05-05T17:01:56Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T17:01:26Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 260.06 +/- 30.94
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
|
DarthGrogu/TEST2ppo-LunarLander-v2
|
DarthGrogu
| 2022-05-05T17:01:09Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T17:00:38Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 215.27 +/- 12.72
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-large-cnn-finetuned-roundup-3-2
|
theojolliffe
| 2022-05-05T16:52:43Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-05T16:04:49Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-3-2
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-large-cnn-finetuned-roundup-3-2
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2234
- Rouge1: 50.9324
- Rouge2: 30.5257
- Rougel: 32.2166
- Rougelsum: 47.9849
- Gen Len: 141.6562
## 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: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 258 | 1.2775 | 50.0638 | 30.3036 | 32.9555 | 47.3277 | 142.0 |
| 1.1818 | 2.0 | 516 | 1.2234 | 50.9324 | 30.5257 | 32.2166 | 47.9849 | 141.6562 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
hugoguh/ppo-LunarLander-v2
|
hugoguh
| 2022-05-05T16:30:25Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T16:29:58Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 248.25 +/- 18.55
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
|
AdmiralTaco/TEST2ppo-LunarLander-v2
|
AdmiralTaco
| 2022-05-05T16:16:21Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T16:15:57Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 277.71 +/- 13.90
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-large-cnn-finetuned-roundup-3-1
|
theojolliffe
| 2022-05-05T16:14:12Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-05T15:09:42Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: bart-large-cnn-finetuned-roundup-3-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-roundup-3-1
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 258 | 1.3238 | 50.228 | 29.5898 | 30.1054 | 47.1265 | 142.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Khalsuu/english-filipino-wav2vec2-l-xls-r-test-03
|
Khalsuu
| 2022-05-05T15:44:36Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:filipino_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-05T08:43:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- filipino_voice
model-index:
- name: english-filipino-wav2vec2-l-xls-r-test-03
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. -->
# english-filipino-wav2vec2-l-xls-r-test-03
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the filipino_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6932
- Wer: 0.3676
## 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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.3398 | 2.09 | 400 | 0.5733 | 0.6166 |
| 0.5087 | 4.19 | 800 | 0.5210 | 0.4775 |
| 0.344 | 6.28 | 1200 | 0.5284 | 0.5008 |
| 0.2745 | 8.38 | 1600 | 0.5195 | 0.4457 |
| 0.2153 | 10.47 | 2000 | 0.5820 | 0.4668 |
| 0.1797 | 12.57 | 2400 | 0.4915 | 0.4432 |
| 0.1513 | 14.66 | 2800 | 0.6316 | 0.4513 |
| 0.1355 | 16.75 | 3200 | 0.5328 | 0.4070 |
| 0.1204 | 18.85 | 3600 | 0.5800 | 0.4405 |
| 0.1062 | 20.94 | 4000 | 0.6887 | 0.4532 |
| 0.0931 | 23.04 | 4400 | 0.6184 | 0.4152 |
| 0.0821 | 25.13 | 4800 | 0.7413 | 0.4461 |
| 0.0733 | 27.23 | 5200 | 0.7160 | 0.4549 |
| 0.071 | 29.32 | 5600 | 0.7001 | 0.4048 |
| 0.0577 | 31.41 | 6000 | 0.7839 | 0.4309 |
| 0.051 | 33.51 | 6400 | 0.7764 | 0.4128 |
| 0.046 | 35.6 | 6800 | 0.6753 | 0.3875 |
| 0.0384 | 37.7 | 7200 | 0.7106 | 0.3856 |
| 0.0359 | 39.79 | 7600 | 0.6932 | 0.3676 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Ruth/gbert-large-germaner
|
Ruth
| 2022-05-05T15:39:45Z | 5 | 1 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"token-classification",
"de",
"dataset:germaner",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-05T14:17:46Z |
---
language:
- de
license: mit
datasets:
- germaner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: gbert-large-germaner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: germaner
type: germaner
args: default
metrics:
- name: precision
type: precision
value: 0.8693333333333333
- name: recall
type: recall
value: 0.885640362225097
- name: f1
type: f1
value: 0.8774110861903236
- name: accuracy
type: accuracy
value: 0.9784210744831022
---
<!-- 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. -->
# gbert-large-germaner
This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on the germaner dataset.
It achieves the following results on the evaluation set:
- precision: 0.8693
- recall: 0.8856
- f1: 0.8774
- accuracy: 0.9784
## 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:
- num_train_epochs: 5
- train_batch_size: 8
- eval_batch_size: 8
- learning_rate: 2e-05
- weight_decay_rate: 0.01
- num_warmup_steps: 0
- fp16: True
### Framework versions
- Transformers 4.18.0
- Datasets 1.18.0
- Tokenizers 0.12.1
|
jcranney/ppo2-LunarLander-v2
|
jcranney
| 2022-05-05T15:12:39Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T15:03:38Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 269.91 +/- 16.76
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
|
vuiseng9/roberta-l-squadv1.1
|
vuiseng9
| 2022-05-05T15:09:27Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-05T14:51:42Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: run05-roberta-large-squadv1.1-sl384-ds128-e2-tbs16
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. -->
# run05-roberta-large-squadv1.1-sl384-ds128-e2-tbs16
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
# Train
```bash
python run_qa.py \
--model_name_or_path roberta-large \
--dataset_name squad \
--do_eval \
--do_train \
--evaluation_strategy steps \
--eval_steps 500 \
--learning_rate 3e-5 \
--fp16 \
--num_train_epochs 2 \
--per_device_eval_batch_size 64 \
--per_device_train_batch_size 16 \
--max_seq_length 384 \
--doc_stride 128 \
--save_steps 1000 \
--logging_steps 1 \
--overwrite_output_dir \
--run_name $RUNID \
--output_dir $OUTDIR
```
# Eval
```bash
export CUDA_VISIBLE_DEVICES=0
MODEL=vuiseng9/roberta-l-squadv1.1
OUTDIR=eval-$(basename $MODEL)
WORKDIR=transformers/examples/pytorch/question-answering
cd $WORKDIR
nohup python run_qa.py \
--model_name_or_path $MODEL \
--dataset_name squad \
--do_eval \
--per_device_eval_batch_size 16 \
--max_seq_length 384 \
--doc_stride 128 \
--overwrite_output_dir \
--output_dir $OUTDIR 2>&1 | tee $OUTDIR/run.log &
```
```bash
eval_exact_match = 88.4674
eval_f1 = 94.3001
eval_samples = 10790
```
|
antgoldbloom/distilbert-rater
|
antgoldbloom
| 2022-05-05T14:45:54Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-05T14:22:55Z |
---
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
|
DeepRoller/rl-model
|
DeepRoller
| 2022-05-05T14:38:57Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T13:42:02Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 248.52 +/- 19.00
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
|
benjamin/gpt2-wechsel-uyghur
|
benjamin
| 2022-05-05T14:24:36Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"ug",
"arxiv:2112.06598",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-05T13:27:26Z |
---
language: ug
license: mit
---
# gpt2-wechsel-uyghur
Model trained with WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
See the code here: https://github.com/CPJKU/wechsel
And the paper here: https://arxiv.org/abs/2112.06598
## Performance
| Model | PPL |
|---|---|
| `gpt2-wechsel-sundanese` | **111.72** |
| `gpt2` (retrained from scratch) | 149.46 |
| Model | PPL |
|---|---|
| `gpt2-wechsel-scottish-gaelic` | **16.43** |
| `gpt2` (retrained from scratch) | 19.53 |
| Model | PPL |
|---|---|
| `gpt2-wechsel-uyghur` | **34.33** |
| `gpt2` (retrained from scratch) | 42.82 |
| Model | PPL |
|---|---|
| `gpt2-wechsel-malagasy` | **14.01** |
| `gpt2` (retrained from scratch) | 15.93 |
See our paper for details.
## Citation
Please cite WECHSEL as
```
@misc{minixhofer2021wechsel,
title={WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models},
author={Benjamin Minixhofer and Fabian Paischer and Navid Rekabsaz},
year={2021},
eprint={2112.06598},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
utsavnandi/LunarLander-v2_ppo-mlp-0505_02
|
utsavnandi
| 2022-05-05T13:39:28Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T13:38:50Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO-MLP
results:
- metrics:
- type: mean_reward
value: 212.27 +/- 22.31
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO-MLP** Agent playing **LunarLander-v2**
This is a trained model of a **PPO-MLP** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
moussaKam/AraBART
|
moussaKam
| 2022-05-05T13:17:29Z | 665 | 14 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"feature-extraction",
"summarization",
"bart",
"fill-mask",
"ar",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-09T10:05:16Z |
---
tags:
- summarization
- bart
language:
- ar
widget:
- text: Ψ¨ΩΨ±ΩΨͺ ΩΩ ΨΉΨ§Ψ΅Ω
Ψ© <mask>.
license: apache-2.0
pipeline_tag: "fill-mask"
---
AraBART is the first Arabic model in which the encoder and the decoder are pretrained end-to-end, based on BART. AraBART follows the architecture of BART-Base
which has 6 encoder and 6 decoder layers and 768 hidden dimensions. In total AraBART has 139M parameters.
AraBART achieves the best performance on multiple abstractive summarization datasets, outperforming strong baselines including a pretrained Arabic BERT-based models and multilingual mBART and mT5 models.
|
obrizum/all-mpnet-base-v2
|
obrizum
| 2022-05-05T12:38:54Z | 12 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-05-05T11:49:12Z |
---
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
license: apache-2.0
---
# all-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('obrizum/all-mpnet-base-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('obrizum/all-mpnet-base-v2')
model = AutoModel.from_pretrained('obrizum/all-mpnet-base-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# Normalize embeddings
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-mpnet-base-v2)
------
## Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We used the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model and fine-tuned in on a
1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developped this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developped this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
## Intended uses
Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
By default, input text longer than 384 word pieces is truncated.
## Training procedure
### Pre-training
We use the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. Please refer to the model card for more detailed information about the pre-training procedure.
### Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
#### Hyper parameters
We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
#### Training data
We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
| Dataset | Paper | Number of training tuples |
|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 |
| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| **Total** | | **1,170,060,424** |
|
arsenplus/TEST2ppo-LunarLander-v2
|
arsenplus
| 2022-05-05T12:20:53Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T11:21:45Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 221.25 +/- 20.09
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
|
dk-crazydiv/LunarLander-v2
|
dk-crazydiv
| 2022-05-05T11:48:19Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T05:13:22Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 296.60 +/- 16.78
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
|
Piumi/ScholarBERT
|
Piumi
| 2022-05-05T11:41:19Z | 0 | 0 | null |
[
"text-classification",
"region:us"
] |
text-classification
| 2022-05-05T10:41:19Z |
---
tags:
- text-classification
---
# Scintific article multilabel classification SciBERT Model
|
katta/PPO-LunarLander-v2
|
katta
| 2022-05-05T11:33:31Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T11:33:01Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO-mlp
results:
- metrics:
- type: mean_reward
value: 272.32 +/- 16.75
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO-mlp** Agent playing **LunarLander-v2**
This is a trained model of a **PPO-mlp** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
ceyda/RLcourse-ppo-LunarLanderv2
|
ceyda
| 2022-05-05T11:31:01Z | 6 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T10:54:33Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 271.05 +/- 22.76
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
|
nondevs/100k-ppo-LunarLander-v2
|
nondevs
| 2022-05-05T11:27:11Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T10:26:48Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 295.52 +/- 15.60
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
|
Gootter/autotrain-Bart_683-825526269
|
Gootter
| 2022-05-05T10:03:01Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain",
"unk",
"dataset:Gootter/autotrain-data-Bart_683",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-05T09:46:53Z |
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain π€"
datasets:
- Gootter/autotrain-data-Bart_683
co2_eq_emissions: 28.12268287254098
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 825526269
- CO2 Emissions (in grams): 28.12268287254098
## Validation Metrics
- Loss: 2.836289644241333
- Rouge1: 31.9867
- Rouge2: 10.3239
- RougeL: 21.0603
- RougeLsum: 30.0862
- Gen Len: 142.0
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Gootter/autotrain-Bart_683-825526269
```
|
adityay1221/cat.5.32
|
adityay1221
| 2022-05-05T09:58:36Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-05T09:57:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: cat.5.32
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. -->
# cat.5.32
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0293
- Bleu: 25.3811
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 121
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
|
DioLiu/distilroberta-base-wiki_shake_mask
|
DioLiu
| 2022-05-05T09:26:08Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-05T08:21:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-wiki_shake_mask
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. -->
# distilroberta-base-wiki_shake_mask
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4464
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6528 | 1.0 | 3015 | 2.5390 |
| 2.5536 | 2.0 | 6030 | 2.4558 |
| 2.5396 | 3.0 | 9045 | 2.4464 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Jezzarax/TEST2ppo-LunarLander-v2
|
Jezzarax
| 2022-05-05T08:49:26Z | 6 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T07:44:16Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 294.19 +/- 21.02
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
|
hfl/chinese-pert-large-mrc
|
hfl
| 2022-05-05T08:43:53Z | 23 | 10 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"bert",
"question-answering",
"zh",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-05T05:52:57Z |
---
language:
- zh
license: "apache-2.0"
---
## A Chinese MRC model built on Chinese PERT-large
**Please use `BertForQuestionAnswering` to load this model!**
This is a Chinese machine reading comprehension (MRC) model built on PERT-large and fine-tuned on a mixture of Chinese MRC datasets.
PERT is a pre-trained model based on permuted language model (PerLM) to learn text semantic information in a self-supervised manner without introducing the mask tokens [MASK]. It yields competitive results on in tasks such as reading comprehension and sequence labeling.
Results on Chinese MRC datasets (EM/F1):
(We report the checkpoint that has the best AVG score)
| | CMRC 2018 Dev | DRCD Dev | SQuAD-Zen Dev (Answerable) | AVG |
| :-------: | :-----------: | :-------: | :------------------------: | :-------: |
| PERT-large | 73.5/90.8 | 91.2/95.7 | 63.0/79.3 | 75.9/88.6 |
Please visit our GitHub repo for more information: https://github.com/ymcui/PERT
You may also be interested in,
Chinese Minority Languages CINO: https://github.com/ymcui/Chinese-Minority-PLM
Chinese MacBERT: https://github.com/ymcui/MacBERT
Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
|
mcditoos/PPO-LunarLander-v2
|
mcditoos
| 2022-05-05T07:12:33Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T07:11:57Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 233.04 +/- 17.51
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
|
DioLiu/distilbert-base-uncased-finetuned-sst2-shake-wiki
|
DioLiu
| 2022-05-05T06:39:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-05T05:17:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2-shake-wiki
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-sst2-shake-wiki
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0096
- Accuracy: 0.9994
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.001 | 1.0 | 5029 | 0.0120 | 0.9988 |
| 0.0017 | 2.0 | 10058 | 0.0028 | 0.9996 |
| 0.0 | 3.0 | 15087 | 0.0094 | 0.9992 |
| 0.0 | 4.0 | 20116 | 0.0091 | 0.9994 |
| 0.0 | 5.0 | 25145 | 0.0096 | 0.9994 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
YeRyeongLee/bert-base-uncased-finetuned-0505-2
|
YeRyeongLee
| 2022-05-05T06:29:23Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-05T05:39:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-finetuned-0505-2
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-0505-2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4277
- Accuracy: 0.9206
- F1: 0.9205
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 1373 | 0.3634 | 0.9025 | 0.9012 |
| No log | 2.0 | 2746 | 0.3648 | 0.9066 | 0.9060 |
| No log | 3.0 | 4119 | 0.3978 | 0.9189 | 0.9183 |
| No log | 4.0 | 5492 | 0.4277 | 0.9206 | 0.9205 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
jo0hnd0e/bert-finetuned-ner
|
jo0hnd0e
| 2022-05-05T06:10:51Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-21T06:03:50Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: jo0hnd0e/bert-finetuned-ner
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. -->
# jo0hnd0e/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0276
- Validation Loss: 0.0565
- 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': 2631, '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 |
|:----------:|:---------------:|:-----:|
| 0.1742 | 0.0636 | 0 |
| 0.0470 | 0.0551 | 1 |
| 0.0276 | 0.0565 | 2 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
SuperSecureHuman/Lunar-Landing-PPO
|
SuperSecureHuman
| 2022-05-05T05:56:49Z | 10 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T14:03:32Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 284.30 +/- 14.06
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
|
maesneako/gpt2-fr-eos-paco-cheese
|
maesneako
| 2022-05-05T04:47:13Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-05T04:29:18Z |
---
tags:
- generated_from_trainer
model-index:
- name: gpt2-fr-eos-paco-cheese
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-fr-eos-paco-cheese
This model is a fine-tuned version of [dbddv01/gpt2-french-small](https://huggingface.co/dbddv01/gpt2-french-small) 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: 1e-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
- lr_scheduler_warmup_steps: 100
- num_epochs: 65
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
YeRyeongLee/mental-bert-base-uncased-finetuned-0505
|
YeRyeongLee
| 2022-05-05T04:19:55Z | 42 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-05T03:29:42Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: mental-bert-base-uncased-finetuned-0505
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. -->
# mental-bert-base-uncased-finetuned-0505
This model is a fine-tuned version of [mental/mental-bert-base-uncased](https://huggingface.co/mental/mental-bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4195
- Accuracy: 0.9181
- F1: 0.9182
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 1373 | 0.2846 | 0.9124 | 0.9119 |
| No log | 2.0 | 2746 | 0.3468 | 0.9132 | 0.9129 |
| No log | 3.0 | 4119 | 0.3847 | 0.9189 | 0.9192 |
| No log | 4.0 | 5492 | 0.4195 | 0.9181 | 0.9182 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
schorndorfer/distilroberta-base-finetuned-wikitext2
|
schorndorfer
| 2022-05-05T04:09:37Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-05T03:45:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8347
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0853 | 1.0 | 2406 | 1.9214 |
| 1.986 | 2.0 | 4812 | 1.8799 |
| 1.9568 | 3.0 | 7218 | 1.8202 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
schorndorfer/distilgpt2-finetuned-wikitext2
|
schorndorfer
| 2022-05-05T03:42:12Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-05T03:09:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6425
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.76 | 1.0 | 2334 | 3.6658 |
| 3.6526 | 2.0 | 4668 | 3.6468 |
| 3.6004 | 3.0 | 7002 | 3.6425 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
vickyjm/ppo-LunarLander-v2
|
vickyjm
| 2022-05-05T03:08:38Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T01:41:28Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 214.15 +/- 72.82
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
|
dbarbedillo/ppo-LunarLander-v2
|
dbarbedillo
| 2022-05-05T02:41:24Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T02:40:54Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 296.33 +/- 19.27
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
|
heriosousa/ppo-LunarLander-v2
|
heriosousa
| 2022-05-05T01:58:24Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T00:32:16Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 242.77 +/- 18.06
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
|
magitz/ppo-LunarLander-v2-HFcourse
|
magitz
| 2022-05-05T01:06:58Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T01:05:35Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 188.35 +/- 88.74
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
|
TweebankNLP/bertweet-tb2_wnut17-ner
|
TweebankNLP
| 2022-05-05T00:23:17Z | 117 | 4 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"arxiv:2201.07281",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-04T16:50:37Z |
---
license: cc-by-nc-4.0
---
## Model Specification
- This is the **state-of-the-art Twitter NER model (with 74.35\% Entity-Level F1)** on Tweebank V2's NER benchmark (also called `Tweebank-NER`), trained on the corpus combining both Tweebank-NER and WNUT 17 training data.
- For more details about the `TweebankNLP` project, please refer to this [our paper](https://arxiv.org/pdf/2201.07281.pdf) and [github](https://github.com/social-machines/TweebankNLP) page.
- In the paper, it is referred as `HuggingFace-BERTweet (TB2+W17).`
## How to use the model
- **PRE-PROCESSING**: when you apply the model on tweets, please make sure that tweets are preprocessed by the [TweetTokenizer](https://github.com/VinAIResearch/BERTweet/blob/master/TweetNormalizer.py) to get the best performance.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("TweebankNLP/bertweet-tb2_wnut17-ner")
model = AutoModelForTokenClassification.from_pretrained("TweebankNLP/bertweet-tb2_wnut17-ner")
```
## References
If you use this repository in your research, please kindly cite [our paper](https://arxiv.org/pdf/2201.07281.pdf):
```bibtex
@article{jiang2022tweetnlp,
title={Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis},
author={Jiang, Hang and Hua, Yining and Beeferman, Doug and Roy, Deb},
journal={In Proceedings of the 13th Language Resources and Evaluation Conference (LREC)},
year={2022}
}
```
|
dalvarez/PPO-LunarLander-v2
|
dalvarez
| 2022-05-05T00:12:32Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-05T00:11:43Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 131.32 +/- 54.42
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
|
akkasayaz/ppo-LunarLander-v2
|
akkasayaz
| 2022-05-04T23:44:34Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T23:43:59Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 233.78 +/- 19.45
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
|
YeRyeongLee/bert-base-uncased-finetuned-small-0505
|
YeRyeongLee
| 2022-05-04T22:54:18Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-04T22:25:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-finetuned-small-0505
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-small-0505
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8649
- Accuracy: 0.1818
- F1: 0.1182
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 13 | 1.8337 | 0.1818 | 0.0559 |
| No log | 2.0 | 26 | 1.8559 | 0.2727 | 0.1414 |
| No log | 3.0 | 39 | 1.8488 | 0.1818 | 0.1010 |
| No log | 4.0 | 52 | 1.8649 | 0.1818 | 0.1182 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
HomayounSadri/bert-base-uncased-finetuned-squad
|
HomayounSadri
| 2022-05-04T22:40:18Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-04T18:45:32Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: HomayounSadri/bert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# HomayounSadri/bert-base-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6196
- Validation Loss: 1.0521
- 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': 16596, '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: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.3747 | 1.0166 | 0 |
| 0.8290 | 0.9963 | 1 |
| 0.6196 | 1.0521 | 2 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
mmangino/ppo-LunarLander-v2
|
mmangino
| 2022-05-04T20:24:48Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T20:24:21Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 282.72 +/- 23.16
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
|
utkusaglm/ppo-LunarLander-v1
|
utkusaglm
| 2022-05-04T20:23:28Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T20:17:22Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 295.94 +/- 13.13
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
|
elotech/ppo-LunarLander-v2
|
elotech
| 2022-05-04T19:50:20Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T19:26:09Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO_v2
results:
- metrics:
- type: mean_reward
value: 254.01 +/- 15.18
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO_v1** Agent playing **LunarLander-v2**
This is a trained model of a **PPO_v1** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
AndreyM/rl_course_luner_lander
|
AndreyM
| 2022-05-04T19:41:14Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T19:02:18Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 261.24 +/- 15.44
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-large-cnn-finetuned-roundup-2-4
|
theojolliffe
| 2022-05-04T19:31:38Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-04T17:32:49Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-2-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-roundup-2-4
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0908
- Rouge1: 51.9961
- Rouge2: 32.3963
- Rougel: 32.1774
- Rougelsum: 50.1033
- Gen Len: 141.0
## 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 167 | 1.2152 | 52.234 | 33.1104 | 33.308 | 49.5516 | 142.0 |
| No log | 2.0 | 334 | 1.1054 | 52.7096 | 33.4698 | 33.9595 | 49.8736 | 140.3333 |
| 1.0437 | 3.0 | 501 | 1.0796 | 51.699 | 32.4255 | 34.0294 | 49.5276 | 141.7143 |
| 1.0437 | 4.0 | 668 | 1.0908 | 51.9961 | 32.3963 | 32.1774 | 50.1033 | 141.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
nondevs/TEST2ppo-LunarLander-v2
|
nondevs
| 2022-05-04T19:31:18Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T18:50:42Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 270.00 +/- 22.67
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
|
Sami/PPO-LunarLander-v2
|
Sami
| 2022-05-04T19:10:11Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T17:45:52Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 292.63 +/- 17.52
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
|
Jaechang/ppo-LunarLander-v2
|
Jaechang
| 2022-05-04T19:08:59Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T17:56:16Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 210.82 +/- 19.82
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
|
SaiShashank1303/ch-1-ppo-LunarLander-v2
|
SaiShashank1303
| 2022-05-04T19:04:07Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T19:03:25Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo-LunarLander-v2
results:
- metrics:
- type: mean_reward
value: 203.94 +/- 26.92
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **ppo-LunarLander-v2** Agent playing **LunarLander-v2**
This is a trained model of a **ppo-LunarLander-v2** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
CWhy/given-ppo-LunarLander-v2
|
CWhy
| 2022-05-04T18:44:21Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T18:43:43Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 198.92 +/- 36.84
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
|
arkadip-maitra/ppo-LunarLander-v2
|
arkadip-maitra
| 2022-05-04T17:57:24Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T16:34:46Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 232.73 +/- 68.47
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
|
riteshhf/TEST1ppo-LunarLander-v2
|
riteshhf
| 2022-05-04T17:48:57Z | 0 | 2 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T17:48:25Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 267.56 +/- 15.74
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
|
huggingtweets/usmnt-zacksteffen_
|
huggingtweets
| 2022-05-04T17:19:08Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-04T17:18:29Z |
---
language: en
thumbnail: http://www.huggingtweets.com/usmnt-zacksteffen_/1651684743123/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/1410587808666955776/mWkKWw1U_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1509644465388105731/dErjQdWT_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>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">π€ AI CYBORG π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">USMNT & Zack Steffen</div>
<div style="text-align: center; font-size: 14px;">@usmnt-zacksteffen_</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 USMNT & Zack Steffen.
| Data | USMNT | Zack Steffen |
| --- | --- | --- |
| Tweets downloaded | 3250 | 3120 |
| Retweets | 600 | 869 |
| Short tweets | 215 | 523 |
| Tweets kept | 2435 | 1728 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/34uud8si/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 @usmnt-zacksteffen_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2wiyd3kq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2wiyd3kq/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/usmnt-zacksteffen_')
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)
|
jamm/ppo-LunarLander-v2
|
jamm
| 2022-05-04T16:42:01Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T16:41:30Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 281.88 +/- 14.38
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
|
MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2
|
MartinoMensio
| 2022-05-04T16:28:04Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T17:06:08Z |
---
language: es
license: mit
widget:
- text: "y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `w-m-vote-nonstrict-epoch-2`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'w-m-vote-nonstrict-epoch-2'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judΓos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.9680026173591614}, {'label': 'non-racist', 'score': 0.9936750531196594}]
```
For more details, see https://github.com/preyero/neatclass22
|
MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1
|
MartinoMensio
| 2022-05-04T16:27:31Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T17:01:40Z |
---
language: es
license: mit
widget:
- text: "y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `w-m-vote-nonstrict-epoch-1`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'w-m-vote-nonstrict-epoch-1'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judΓos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.8460916876792908}, {'label': 'non-racist', 'score': 0.9714874029159546}]
```
For more details, see https://github.com/preyero/neatclass22
|
Guillaume63/ppo-LunarLander-v2
|
Guillaume63
| 2022-05-04T16:27:19Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T16:26:48Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PP0
results:
- metrics:
- type: mean_reward
value: 223.27 +/- 26.13
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PP0** Agent playing **LunarLander-v2**
This is a trained model of a **PP0** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
MartinoMensio/racism-models-w-m-vote-strict-epoch-1
|
MartinoMensio
| 2022-05-04T16:24:13Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T16:53:35Z |
---
language: es
license: mit
widget:
- text: "y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `w-m-vote-strict-epoch-1`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'w-m-vote-strict-epoch-1'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judΓos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.9342454075813293}, {'label': 'non-racist', 'score': 0.7690662741661072}]
```
For more details, see https://github.com/preyero/neatclass22
|
MartinoMensio/racism-models-regression-w-m-vote-epoch-3
|
MartinoMensio
| 2022-05-04T16:21:40Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T16:21:04Z |
---
language: es
license: mit
widget:
- text: "y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `regression-w-m-vote-epoch-3`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
from transformers.pipelines import TextClassificationPipeline
class TextRegressionPipeline(TextClassificationPipeline):
"""
Class based on the TextClassificationPipeline from transformers.
The difference is that instead of being based on a classifier, it is based on a regressor.
You can specify the regression threshold when you call the pipeline or when you instantiate the pipeline.
"""
def __init__(self, **kwargs):
"""
Builds a new Pipeline based on regression.
regression_threshold: Optional(float). If None, the pipeline will simply output the score. If set to a specific value, the output will be both the score and the label.
"""
self.regression_threshold = kwargs.pop("regression_threshold", None)
super().__init__(**kwargs)
def __call__(self, *args, **kwargs):
"""
You can also specify the regression threshold when you call the pipeline.
regression_threshold: Optional(float). If None, the pipeline will simply output the score. If set to a specific value, the output will be both the score and the label.
"""
self.regression_threshold_call = kwargs.pop("regression_threshold", None)
result = super().__call__(*args, **kwargs)
return result
def postprocess(self, model_outputs, function_to_apply=None, return_all_scores=False):
outputs = model_outputs["logits"][0]
outputs = outputs.numpy()
scores = outputs
score = scores[0]
regression_threshold = self.regression_threshold
# override the specific threshold if it is specified in the call
if self.regression_threshold_call:
regression_threshold = self.regression_threshold_call
if regression_threshold:
return {"label": 'racist' if score > regression_threshold else 'non-racist', "score": score}
else:
return {"score": score}
model_name = 'regression-w-m-vote-epoch-3'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = TextRegressionPipeline(model=model, tokenizer=tokenizer)
texts = [
'y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judΓos controlan el mundo'
]
# just get the score of regression
print(pipe(texts))
# [{'score': 0.7393736}, {'score': 0.44301373}]
# or also specify a threshold to cut racist/non-racist
print(pipe(texts, regression_threshold=0.9))
# [{'label': 'non-racist', 'score': 0.7393736}, {'label': 'non-racist', 'score': 0.44301373}]
```
For more details, see https://github.com/preyero/neatclass22
|
MartinoMensio/racism-models-regression-w-m-vote-epoch-2
|
MartinoMensio
| 2022-05-04T16:20:44Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T16:18:45Z |
---
language: es
license: mit
widget:
- text: "y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `regression-w-m-vote-epoch-2`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
from transformers.pipelines import TextClassificationPipeline
class TextRegressionPipeline(TextClassificationPipeline):
"""
Class based on the TextClassificationPipeline from transformers.
The difference is that instead of being based on a classifier, it is based on a regressor.
You can specify the regression threshold when you call the pipeline or when you instantiate the pipeline.
"""
def __init__(self, **kwargs):
"""
Builds a new Pipeline based on regression.
regression_threshold: Optional(float). If None, the pipeline will simply output the score. If set to a specific value, the output will be both the score and the label.
"""
self.regression_threshold = kwargs.pop("regression_threshold", None)
super().__init__(**kwargs)
def __call__(self, *args, **kwargs):
"""
You can also specify the regression threshold when you call the pipeline.
regression_threshold: Optional(float). If None, the pipeline will simply output the score. If set to a specific value, the output will be both the score and the label.
"""
self.regression_threshold_call = kwargs.pop("regression_threshold", None)
result = super().__call__(*args, **kwargs)
return result
def postprocess(self, model_outputs, function_to_apply=None, return_all_scores=False):
outputs = model_outputs["logits"][0]
outputs = outputs.numpy()
scores = outputs
score = scores[0]
regression_threshold = self.regression_threshold
# override the specific threshold if it is specified in the call
if self.regression_threshold_call:
regression_threshold = self.regression_threshold_call
if regression_threshold:
return {"label": 'racist' if score > regression_threshold else 'non-racist', "score": score}
else:
return {"score": score}
model_name = 'regression-w-m-vote-epoch-2'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = TextRegressionPipeline(model=model, tokenizer=tokenizer)
texts = [
'y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judΓos controlan el mundo'
]
# just get the score of regression
print(pipe(texts))
# [{'score': 0.8367272}, {'score': 0.4402479}]
# or also specify a threshold to cut racist/non-racist
print(pipe(texts, regression_threshold=0.9))
# [{'label': 'non-racist', 'score': 0.8367272}, {'label': 'non-racist', 'score': 0.4402479}]
```
For more details, see https://github.com/preyero/neatclass22
|
MartinoMensio/racism-models-regression-w-m-vote-epoch-1
|
MartinoMensio
| 2022-05-04T16:18:39Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T16:15:44Z |
---
language: es
license: mit
widget:
- text: "y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `regression-w-m-vote-epoch-1`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
from transformers.pipelines import TextClassificationPipeline
class TextRegressionPipeline(TextClassificationPipeline):
"""
Class based on the TextClassificationPipeline from transformers.
The difference is that instead of being based on a classifier, it is based on a regressor.
You can specify the regression threshold when you call the pipeline or when you instantiate the pipeline.
"""
def __init__(self, **kwargs):
"""
Builds a new Pipeline based on regression.
regression_threshold: Optional(float). If None, the pipeline will simply output the score. If set to a specific value, the output will be both the score and the label.
"""
self.regression_threshold = kwargs.pop("regression_threshold", None)
super().__init__(**kwargs)
def __call__(self, *args, **kwargs):
"""
You can also specify the regression threshold when you call the pipeline.
regression_threshold: Optional(float). If None, the pipeline will simply output the score. If set to a specific value, the output will be both the score and the label.
"""
self.regression_threshold_call = kwargs.pop("regression_threshold", None)
result = super().__call__(*args, **kwargs)
return result
def postprocess(self, model_outputs, function_to_apply=None, return_all_scores=False):
outputs = model_outputs["logits"][0]
outputs = outputs.numpy()
scores = outputs
score = scores[0]
regression_threshold = self.regression_threshold
# override the specific threshold if it is specified in the call
if self.regression_threshold_call:
regression_threshold = self.regression_threshold_call
if regression_threshold:
return {"label": 'racist' if score > regression_threshold else 'non-racist', "score": score}
else:
return {"score": score}
model_name = 'regression-w-m-vote-epoch-1'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = TextRegressionPipeline(model=model, tokenizer=tokenizer)
texts = [
'y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judΓos controlan el mundo'
]
# just get the score of regression
print(pipe(texts))
# [{'score': 0.8378907}, {'score': 0.33399782}]
# or also specify a threshold to cut racist/non-racist
print(pipe(texts, regression_threshold=0.9))
# [{'label': 'non-racist', 'score': 0.8378907}, {'label': 'non-racist', 'score': 0.33399782}]
```
For more details, see https://github.com/preyero/neatclass22
|
MartinoMensio/racism-models-m-vote-nonstrict-epoch-2
|
MartinoMensio
| 2022-05-04T16:12:34Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T16:46:17Z |
---
language: es
license: mit
widget:
- text: "y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `m-vote-nonstrict-epoch-2`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'm-vote-nonstrict-epoch-2'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judΓos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.8650100827217102}, {'label': 'non-racist', 'score': 0.9674995541572571}]
```
For more details, see https://github.com/preyero/neatclass22
|
MartinoMensio/racism-models-m-vote-strict-epoch-3
|
MartinoMensio
| 2022-05-04T16:09:42Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T16:35:22Z |
---
language: es
license: mit
widget:
- text: "y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `m-vote-strict-epoch-3`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'm-vote-strict-epoch-3'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judΓos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.9929012656211853}, {'label': 'non-racist', 'score': 0.5616322159767151}]
```
For more details, see https://github.com/preyero/neatclass22
|
seriy21/ppo-LunarLander-v2
|
seriy21
| 2022-05-04T16:09:25Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T16:08:55Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 286.36 +/- 12.71
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
|
MartinoMensio/racism-models-m-vote-strict-epoch-2
|
MartinoMensio
| 2022-05-04T16:08:39Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T16:32:15Z |
---
language: es
license: mit
widget:
- text: "y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `m-vote-strict-epoch-2`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'm-vote-strict-epoch-2'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judΓos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.923829972743988}, {'label': 'non-racist', 'score': 0.8673009872436523}]
```
For more details, see https://github.com/preyero/neatclass22
|
MartinoMensio/racism-models-raw-label-epoch-4
|
MartinoMensio
| 2022-05-04T16:06:20Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T16:12:31Z |
---
language: es
license: mit
widget:
- text: "y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `raw-label-epoch-4`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'raw-label-epoch-4'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porquΓ© es lo que hay que hacer con los menas y con los adultos tambiΓ©n!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judΓos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.921501636505127}, {'label': 'non-racist', 'score': 0.9459075331687927}]
```
For more details, see https://github.com/preyero/neatclass22
|
NorbertRop/PPO-MlpPolicy-LunarLander-v2
|
NorbertRop
| 2022-05-04T15:13:59Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T15:11:17Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 234.34 +/- 20.06
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
|
LidarRL/TEST2ppo-LunarLander-v2
|
LidarRL
| 2022-05-04T15:10:24Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T14:20:45Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 204.65 +/- 31.76
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
|
dbmdz/flair-hipe-2022-ajmc-all
|
dbmdz
| 2022-05-04T13:43:34Z | 10 | 0 |
flair
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"multilingual",
"license:mit",
"region:us"
] |
token-classification
| 2022-04-29T07:26:42Z |
---
tags:
- flair
- token-classification
- sequence-tagger-model
language: multilingual
widget:
- text: "In editing the Fragments , I have availed myself of Mr . R . Ellis β acute remarks on them in the Cambridge Journal of Philology , Vol . IV , and that I am largely indebted , as every editor must now be , to the edition of the Tragic Fragments by A . Nauck , Leipzig , 1856 ."
- text: "459 . Skyros klang dem Athener etwa wie Pholegandros und Sikinos bei Solon Eleg . 1 , 4 , dem RΓΆmer Ulubrae , Butunti ."
- text: "Celles d β Ajax et des siens occupaient l ' extrΓͺme aile gauche , vers le promontoire RhΓ©tΓ©e , et confinaient tout Γ la fois au retranchement et Γ la mer ( // . XIT1 , 681 ; Heynce , excursns citΓ© ) ,"
license: mit
---
|
uhlenbeckmew/distilroberta-base-swift_shake
|
uhlenbeckmew
| 2022-05-04T13:25:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-04T13:07:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-swift_shake
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. -->
# distilroberta-base-swift_shake
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5309
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 334 | 2.5817 |
| 2.7363 | 2.0 | 668 | 2.4499 |
| 2.4584 | 3.0 | 1002 | 2.5309 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
jonfrank/xlm-roberta-base-finetuned-panx-de
|
jonfrank
| 2022-05-04T10:13:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-04T09:39:55Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8654425558524246
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1334
- F1: 0.8654
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2541 | 1.0 | 525 | 0.1596 | 0.8242 |
| 0.1284 | 2.0 | 1050 | 0.1360 | 0.8499 |
| 0.0827 | 3.0 | 1575 | 0.1334 | 0.8654 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Nijana/gpt-neo-1.3B-climate_change_tweets
|
Nijana
| 2022-05-04T10:12:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-05-02T11:35:45Z |
# A fine-tuned GPT-Neo Model for Tweet Generation
This model is a fine-tuned version of the 1.3B-parameter GPT-Neo model developed by EleutherAI. As the default GPT-Neo model did not receive any social media data during its pre-training, we fine-tuned it with tweets collected from Twitter from October to November 2021 related to climate change hashtags. The model received data in the format `<username> - <tweet>` We used an 80/20 train/test split, and to differentiate distinct tweets, we added a start-of-tweet and an end-of-tweet token to the training dataset.
To guide you in using this model, please consult the `gpt_neo_1.3B_twitter.ipynb` Jupyter Notebook file from this repository.
---
license: cc-by-3.0
---
|
waboucay/camembert-base-finetuned-xnli_fr-finetuned-nli-repnum_wl
|
waboucay
| 2022-05-04T09:31:42Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-04T09:28:44Z |
---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 73.3 | 73.3 |
| test | 69.4 | 69.4 |
|
nbhimte/tiny-bert-mnli-distilled
|
nbhimte
| 2022-05-04T07:14:17Z | 26 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-17T03:40:10Z |
---
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: tiny-bert-mnli-distilled
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5818644931227712
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-bert-mnli-distilled
It achieves the following results on the evaluation set:
- Loss: 1.5018
- Accuracy: 0.5819
- F1 score: 0.5782
- Precision score: 0.6036
- Metric recall: 0.5819
## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 32
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 score | Precision score | Metric recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:-------------:|
| 1.4475 | 1.0 | 614 | 1.4296 | 0.4521 | 0.4070 | 0.5621 | 0.4521 |
| 1.3354 | 2.0 | 1228 | 1.4320 | 0.4805 | 0.4579 | 0.5276 | 0.4805 |
| 1.2244 | 3.0 | 1842 | 1.4786 | 0.5699 | 0.5602 | 0.5865 | 0.5699 |
| 1.1416 | 4.0 | 2456 | 1.5018 | 0.5819 | 0.5782 | 0.6036 | 0.5819 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.9.1
- Datasets 2.1.0
- Tokenizers 0.11.6
|
huggingtweets/dril-nycguidovoice-senn_spud
|
huggingtweets
| 2022-05-04T01:55:26Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-04T01:44:12Z |
---
language: en
thumbnail: http://www.huggingtweets.com/dril-nycguidovoice-senn_spud/1651629321136/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/1510917391533830145/XW-zSFDJ_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1503095773059244036/xof9dI-A_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1387151448203358209/HKNuKY7L_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">π€ AI CYBORG π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">wint & Nick Mullen & Will Sennett</div>
<div style="text-align: center; font-size: 14px;">@dril-nycguidovoice-senn_spud</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 wint & Nick Mullen & Will Sennett.
| Data | wint | Nick Mullen | Will Sennett |
| --- | --- | --- | --- |
| Tweets downloaded | 3229 | 1007 | 3231 |
| Retweets | 486 | 71 | 314 |
| Short tweets | 300 | 41 | 631 |
| Tweets kept | 2443 | 895 | 2286 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3dcek2rh/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 @dril-nycguidovoice-senn_spud's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2f1xmo4s) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2f1xmo4s/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/dril-nycguidovoice-senn_spud')
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)
|
theojolliffe/bart-large-cnn-finetuned-roundup-64
|
theojolliffe
| 2022-05-04T00:41:04Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-03T21:34:00Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-64
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-large-cnn-finetuned-roundup-64
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4772
- Rouge1: 46.5444
- Rouge2: 27.4056
- Rougel: 29.6779
- Rougelsum: 44.0905
- 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 | 132 | 1.3213 | 48.3389 | 28.6641 | 31.4086 | 45.6679 | 142.0 |
| No log | 2.0 | 264 | 1.2325 | 48.798 | 29.3068 | 31.4329 | 45.7945 | 142.0 |
| No log | 3.0 | 396 | 1.2791 | 47.1449 | 27.3965 | 30.56 | 44.4704 | 142.0 |
| 0.9574 | 4.0 | 528 | 1.3134 | 46.2319 | 25.6249 | 28.7673 | 43.7555 | 140.3 |
| 0.9574 | 5.0 | 660 | 1.3187 | 46.7313 | 25.3467 | 29.3873 | 43.9495 | 142.0 |
| 0.9574 | 6.0 | 792 | 1.4271 | 48.1638 | 27.8874 | 30.5334 | 45.9944 | 142.0 |
| 0.9574 | 7.0 | 924 | 1.4876 | 46.7481 | 25.7259 | 29.7214 | 43.7042 | 140.5 |
| 0.3303 | 8.0 | 1056 | 1.5259 | 46.7075 | 26.0716 | 29.5521 | 43.7312 | 142.0 |
| 0.3303 | 9.0 | 1188 | 1.6223 | 48.012 | 27.2795 | 30.4989 | 45.4644 | 142.0 |
| 0.3303 | 10.0 | 1320 | 1.6842 | 48.0074 | 26.8831 | 29.3396 | 45.1937 | 142.0 |
| 0.3303 | 11.0 | 1452 | 1.7317 | 46.52 | 26.5152 | 29.5124 | 43.8797 | 142.0 |
| 0.1478 | 12.0 | 1584 | 1.8087 | 47.5887 | 27.0488 | 29.8569 | 44.7318 | 140.8 |
| 0.1478 | 13.0 | 1716 | 1.8263 | 46.1251 | 25.8576 | 30.1698 | 42.7228 | 142.0 |
| 0.1478 | 14.0 | 1848 | 1.9459 | 46.4034 | 25.7039 | 28.2542 | 43.7254 | 142.0 |
| 0.1478 | 15.0 | 1980 | 1.9539 | 44.4666 | 24.5827 | 27.7147 | 41.9769 | 142.0 |
| 0.0779 | 16.0 | 2112 | 1.9654 | 47.2267 | 26.4562 | 29.7352 | 44.0823 | 142.0 |
| 0.0779 | 17.0 | 2244 | 1.9580 | 48.5086 | 28.0294 | 30.8311 | 45.6336 | 142.0 |
| 0.0779 | 18.0 | 2376 | 2.0065 | 48.293 | 28.5678 | 30.0243 | 45.1384 | 142.0 |
| 0.0499 | 19.0 | 2508 | 1.9313 | 49.0549 | 28.9695 | 32.0711 | 46.3834 | 142.0 |
| 0.0499 | 20.0 | 2640 | 2.0176 | 47.0121 | 25.1606 | 29.0108 | 44.1556 | 142.0 |
| 0.0499 | 21.0 | 2772 | 2.0711 | 48.3754 | 28.2221 | 30.772 | 45.8547 | 140.95 |
| 0.0499 | 22.0 | 2904 | 2.0848 | 45.7392 | 25.254 | 29.0833 | 43.0381 | 142.0 |
| 0.0335 | 23.0 | 3036 | 2.0711 | 47.2931 | 27.4573 | 30.718 | 44.5932 | 142.0 |
| 0.0335 | 24.0 | 3168 | 2.1200 | 50.515 | 30.4253 | 33.7045 | 47.6158 | 142.0 |
| 0.0335 | 25.0 | 3300 | 2.1097 | 46.4737 | 26.3055 | 29.0148 | 43.2135 | 142.0 |
| 0.0335 | 26.0 | 3432 | 2.1695 | 46.9099 | 26.5227 | 29.7757 | 44.0613 | 142.0 |
| 0.0249 | 27.0 | 3564 | 2.1494 | 47.8319 | 27.6364 | 31.3593 | 45.065 | 141.95 |
| 0.0249 | 28.0 | 3696 | 2.1510 | 47.504 | 26.8971 | 31.7196 | 45.0328 | 142.0 |
| 0.0249 | 29.0 | 3828 | 2.1612 | 46.8789 | 27.266 | 30.1009 | 43.8248 | 142.0 |
| 0.0249 | 30.0 | 3960 | 2.1579 | 47.7012 | 27.7761 | 30.935 | 44.3686 | 142.0 |
| 0.018 | 31.0 | 4092 | 2.1981 | 48.4703 | 29.167 | 31.9815 | 45.8005 | 142.0 |
| 0.018 | 32.0 | 4224 | 2.2332 | 45.9512 | 25.8111 | 29.2467 | 42.9234 | 142.0 |
| 0.018 | 33.0 | 4356 | 2.1944 | 47.7189 | 28.1413 | 30.9692 | 44.9361 | 142.0 |
| 0.018 | 34.0 | 4488 | 2.2589 | 50.9687 | 32.3987 | 36.5644 | 48.3938 | 142.0 |
| 0.0132 | 35.0 | 4620 | 2.2269 | 47.8241 | 28.0442 | 31.5535 | 44.9394 | 142.0 |
| 0.0132 | 36.0 | 4752 | 2.2865 | 47.4383 | 27.0825 | 30.4109 | 44.194 | 142.0 |
| 0.0132 | 37.0 | 4884 | 2.3267 | 49.1786 | 29.6416 | 32.875 | 46.8821 | 142.0 |
| 0.0095 | 38.0 | 5016 | 2.2872 | 48.2085 | 28.3304 | 32.1473 | 45.3571 | 142.0 |
| 0.0095 | 39.0 | 5148 | 2.3340 | 46.6762 | 26.1637 | 29.0149 | 43.5923 | 142.0 |
| 0.0095 | 40.0 | 5280 | 2.3425 | 46.7561 | 26.1645 | 29.6337 | 43.6188 | 142.0 |
| 0.0095 | 41.0 | 5412 | 2.3111 | 49.4118 | 29.9761 | 33.4765 | 46.601 | 142.0 |
| 0.0076 | 42.0 | 5544 | 2.3892 | 45.3335 | 25.0161 | 28.4124 | 41.9873 | 142.0 |
| 0.0076 | 43.0 | 5676 | 2.3808 | 46.2506 | 26.4283 | 29.3841 | 42.7488 | 142.0 |
| 0.0076 | 44.0 | 5808 | 2.3825 | 45.6823 | 26.0048 | 29.5501 | 42.6475 | 142.0 |
| 0.0076 | 45.0 | 5940 | 2.3592 | 47.9127 | 26.7924 | 30.2353 | 44.791 | 142.0 |
| 0.0051 | 46.0 | 6072 | 2.4206 | 46.0415 | 27.0681 | 29.9602 | 43.1225 | 142.0 |
| 0.0051 | 47.0 | 6204 | 2.4214 | 48.1229 | 29.0913 | 31.1828 | 45.0022 | 142.0 |
| 0.0051 | 48.0 | 6336 | 2.4176 | 47.3825 | 27.7622 | 30.4138 | 43.9047 | 142.0 |
| 0.0051 | 49.0 | 6468 | 2.4137 | 48.2544 | 28.277 | 31.5548 | 45.6053 | 142.0 |
| 0.0041 | 50.0 | 6600 | 2.4384 | 49.6459 | 30.186 | 33.0059 | 47.0483 | 142.0 |
| 0.0041 | 51.0 | 6732 | 2.4433 | 47.7279 | 27.7857 | 30.2982 | 45.0842 | 142.0 |
| 0.0041 | 52.0 | 6864 | 2.4068 | 48.6047 | 28.1758 | 31.2744 | 45.8336 | 142.0 |
| 0.0041 | 53.0 | 6996 | 2.4362 | 48.7095 | 29.3335 | 31.9509 | 46.4161 | 142.0 |
| 0.003 | 54.0 | 7128 | 2.4307 | 48.836 | 29.6069 | 32.4004 | 46.1986 | 142.0 |
| 0.003 | 55.0 | 7260 | 2.4292 | 47.2945 | 26.7577 | 28.9719 | 43.8988 | 142.0 |
| 0.003 | 56.0 | 7392 | 2.4425 | 45.2261 | 25.6879 | 28.8129 | 42.6474 | 142.0 |
| 0.0024 | 57.0 | 7524 | 2.4386 | 47.967 | 28.5415 | 32.2049 | 45.5111 | 142.0 |
| 0.0024 | 58.0 | 7656 | 2.4528 | 47.5552 | 27.6397 | 30.9151 | 44.2627 | 142.0 |
| 0.0024 | 59.0 | 7788 | 2.4574 | 46.7821 | 27.3368 | 30.6334 | 44.0533 | 142.0 |
| 0.0024 | 60.0 | 7920 | 2.4659 | 47.3507 | 26.8371 | 30.4566 | 44.4452 | 142.0 |
| 0.0018 | 61.0 | 8052 | 2.4766 | 47.9847 | 28.2678 | 30.0664 | 45.0071 | 142.0 |
| 0.0018 | 62.0 | 8184 | 2.4682 | 46.8392 | 27.1275 | 30.144 | 43.6379 | 142.0 |
| 0.0018 | 63.0 | 8316 | 2.4754 | 45.6338 | 26.2812 | 29.4831 | 42.8744 | 142.0 |
| 0.0018 | 64.0 | 8448 | 2.4772 | 46.5444 | 27.4056 | 29.6779 | 44.0905 | 142.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ml4pubmed/albert-base-v2_pub_section
|
ml4pubmed
| 2022-05-04T00:09:08Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"en",
"dataset:pubmed",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-03T23:25:25Z |
---
language:
- en
datasets:
- pubmed
metrics:
- f1
pipeline_tag: text-classification
widget:
- text: "many pathogenic processes and diseases are the result of an erroneous activation of the complement cascade and a number of inhibitors of complement have thus been examined for anti-inflammatory actions."
example_title: "background example"
- text: "a total of 192 mi patients and 140 control persons were included."
example_title: "methods example"
- text: "mi patients had 18 % higher plasma levels of map44 (iqr 11-25 %) as compared to the healthy control group (p < 0. 001.)"
example_title: "results example"
- text: "the finding that a brief cb group intervention delivered by real-world providers significantly reduced mdd onset relative to both brochure control and bibliotherapy is very encouraging, although effects on continuous outcome measures were small or nonsignificant and approximately half the magnitude of those found in efficacy research, potentially because the present sample reported lower initial depression."
example_title: "conclusions example"
- text: "in order to understand and update the prevalence of myopia in taiwan, a nationwide survey was performed in 1995."
example_title: "objective example"
---
# albert-base-v2_pub_section
- original model file name: textclassifer_albert-base-v2_pubmed_full
- This is a fine-tuned checkpoint of `albert-base-v2` for document section text classification
- possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS,
## metadata
### training_parameters
- date_run: Apr-26-2022_t-04
- huggingface_tag: albert-base-v2
|
Lauler/sentiment-classifier
|
Lauler
| 2022-05-03T23:28:00Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-03T23:25:23Z |
## Sentiment classifier
Sentiment classifier for Swedish trained on ScandiSent dataset.
|
theojolliffe/bart-large-cnn-finetuned-roundup-32
|
theojolliffe
| 2022-05-03T21:24:20Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-03T19:23:27Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-32
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-large-cnn-finetuned-roundup-32
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2324
- Rouge1: 46.462
- Rouge2: 25.9506
- Rougel: 29.4584
- Rougelsum: 44.1863
- 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: 32
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 132 | 1.3139 | 48.8247 | 29.2173 | 31.7628 | 45.8992 | 142.0 |
| No log | 2.0 | 264 | 1.2287 | 47.9398 | 29.4061 | 30.9133 | 44.9142 | 140.9 |
| No log | 3.0 | 396 | 1.2676 | 49.2743 | 30.4469 | 32.8893 | 46.6208 | 142.0 |
| 0.9578 | 4.0 | 528 | 1.3218 | 47.315 | 26.7303 | 30.5007 | 44.7654 | 142.0 |
| 0.9578 | 5.0 | 660 | 1.3173 | 47.1476 | 25.9408 | 29.4257 | 44.4956 | 142.0 |
| 0.9578 | 6.0 | 792 | 1.4283 | 47.5836 | 27.1572 | 29.8553 | 44.8858 | 142.0 |
| 0.9578 | 7.0 | 924 | 1.5005 | 46.6839 | 26.2214 | 30.1895 | 43.8753 | 140.75 |
| 0.3306 | 8.0 | 1056 | 1.5316 | 47.7611 | 27.1105 | 30.8142 | 44.7598 | 142.0 |
| 0.3306 | 9.0 | 1188 | 1.6295 | 48.4416 | 27.6912 | 30.3409 | 45.317 | 142.0 |
| 0.3306 | 10.0 | 1320 | 1.6564 | 46.5751 | 27.2306 | 29.7265 | 43.7327 | 142.0 |
| 0.3306 | 11.0 | 1452 | 1.7471 | 47.9684 | 27.5739 | 30.7018 | 44.6852 | 141.75 |
| 0.145 | 12.0 | 1584 | 1.7700 | 47.9274 | 28.5129 | 31.129 | 45.1009 | 142.0 |
| 0.145 | 13.0 | 1716 | 1.8391 | 49.8091 | 30.1597 | 33.6004 | 47.2007 | 141.95 |
| 0.145 | 14.0 | 1848 | 1.9212 | 45.2195 | 25.033 | 27.4181 | 42.6161 | 142.0 |
| 0.145 | 15.0 | 1980 | 1.9267 | 48.4959 | 28.1 | 31.2796 | 46.2758 | 142.0 |
| 0.0723 | 16.0 | 2112 | 1.9130 | 47.0765 | 27.4929 | 30.6862 | 44.1458 | 142.0 |
| 0.0723 | 17.0 | 2244 | 1.9514 | 48.5354 | 28.4909 | 31.8966 | 45.7116 | 142.0 |
| 0.0723 | 18.0 | 2376 | 2.0064 | 47.9339 | 28.6862 | 32.4472 | 45.3704 | 142.0 |
| 0.042 | 19.0 | 2508 | 2.0210 | 48.3169 | 28.1579 | 30.2681 | 45.3831 | 141.3 |
| 0.042 | 20.0 | 2640 | 2.0377 | 46.8156 | 26.0122 | 28.817 | 43.9383 | 142.0 |
| 0.042 | 21.0 | 2772 | 2.0587 | 46.3813 | 27.3555 | 29.875 | 43.6605 | 142.0 |
| 0.042 | 22.0 | 2904 | 2.0695 | 45.6728 | 26.0639 | 29.5653 | 42.3772 | 142.0 |
| 0.025 | 23.0 | 3036 | 2.1617 | 46.7283 | 26.2082 | 28.52 | 43.3304 | 142.0 |
| 0.025 | 24.0 | 3168 | 2.1375 | 48.1347 | 28.3444 | 31.7509 | 45.4907 | 142.0 |
| 0.025 | 25.0 | 3300 | 2.1911 | 47.3358 | 27.1479 | 29.4923 | 44.0087 | 142.0 |
| 0.025 | 26.0 | 3432 | 2.1806 | 47.2218 | 26.8421 | 30.03 | 44.2417 | 142.0 |
| 0.0153 | 27.0 | 3564 | 2.1890 | 46.3745 | 27.0095 | 29.7274 | 43.3372 | 142.0 |
| 0.0153 | 28.0 | 3696 | 2.2235 | 50.1274 | 30.8817 | 32.8766 | 46.7486 | 141.5 |
| 0.0153 | 29.0 | 3828 | 2.2236 | 50.1785 | 30.8079 | 32.8886 | 46.9888 | 142.0 |
| 0.0153 | 30.0 | 3960 | 2.2312 | 46.7468 | 26.4272 | 30.1175 | 43.9132 | 142.0 |
| 0.0096 | 31.0 | 4092 | 2.2287 | 47.558 | 26.3933 | 29.9122 | 44.5752 | 142.0 |
| 0.0096 | 32.0 | 4224 | 2.2324 | 46.462 | 25.9506 | 29.4584 | 44.1863 | 142.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
SebastianS/distilbert-base-uncased-finetuned-imdb
|
SebastianS
| 2022-05-03T20:42:53Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-03T19:56:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0122
- eval_runtime: 27.9861
- eval_samples_per_second: 35.732
- eval_steps_per_second: 0.572
- epoch: 2.13
- step: 334
## 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: 3.0
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
stevemobs/bert-finetuned-squad-pytorch
|
stevemobs
| 2022-05-03T20:17:32Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-03T17:49:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad-pytorch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad-pytorch
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
BigSalmon/ConciseAndFormal
|
BigSalmon
| 2022-05-03T19:42:53Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-03T19:34:00Z |
how to start prompt:
```
wordy:
```
example:
```
wordy: the ndp has turned into the country's darling of the young.
```
output:
```
the ndp is youth-driven.
```
OR
```
informal english:
```
example:
```
informal english: corn fields are all across illinois, visible once you leave chicago.
```
output:
```
corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
```
|
theojolliffe/bart-large-cnn-finetuned-roundup-16
|
theojolliffe
| 2022-05-03T19:21:08Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-03T18:14:34Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-16
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-roundup-16
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8957
- Rouge1: 49.4097
- Rouge2: 29.3516
- Rougel: 31.527
- Rougelsum: 46.4241
- Gen Len: 141.9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- 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: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 132 | 1.3170 | 48.412 | 29.2017 | 31.6679 | 45.494 | 141.85 |
| No log | 2.0 | 264 | 1.2292 | 49.0133 | 29.6645 | 30.7612 | 46.1673 | 142.0 |
| No log | 3.0 | 396 | 1.2670 | 49.183 | 29.4104 | 31.573 | 46.7082 | 142.0 |
| 0.9596 | 4.0 | 528 | 1.3059 | 47.3854 | 26.6865 | 28.4666 | 44.4934 | 141.8 |
| 0.9596 | 5.0 | 660 | 1.3288 | 48.1189 | 26.9242 | 31.2938 | 45.3462 | 142.0 |
| 0.9596 | 6.0 | 792 | 1.4084 | 47.5713 | 26.7488 | 29.2959 | 45.1764 | 141.3 |
| 0.9596 | 7.0 | 924 | 1.5043 | 46.5407 | 26.0995 | 29.9007 | 43.9335 | 142.0 |
| 0.3369 | 8.0 | 1056 | 1.5115 | 49.6891 | 29.0514 | 32.33 | 46.9357 | 142.0 |
| 0.3369 | 9.0 | 1188 | 1.6131 | 47.5773 | 27.6348 | 30.5294 | 45.1151 | 142.0 |
| 0.3369 | 10.0 | 1320 | 1.6837 | 46.5699 | 26.3805 | 29.8581 | 43.5252 | 142.0 |
| 0.3369 | 11.0 | 1452 | 1.7874 | 47.1383 | 26.535 | 30.1724 | 44.2508 | 142.0 |
| 0.148 | 12.0 | 1584 | 1.7776 | 49.8061 | 30.1994 | 33.2405 | 47.6102 | 142.0 |
| 0.148 | 13.0 | 1716 | 1.8144 | 48.4451 | 28.2949 | 30.9026 | 45.6614 | 142.0 |
| 0.148 | 14.0 | 1848 | 1.8646 | 50.1964 | 30.4426 | 32.8156 | 47.4134 | 142.0 |
| 0.148 | 15.0 | 1980 | 1.8829 | 48.8129 | 29.2358 | 32.3247 | 46.2233 | 142.0 |
| 0.0726 | 16.0 | 2112 | 1.8957 | 49.4097 | 29.3516 | 31.527 | 46.4241 | 141.9 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
mak109/distilgpt2-finetuned-lyrics
|
mak109
| 2022-05-03T19:20:58Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-03T15:48:21Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: mak109/distilgpt2-finetuned-lyrics
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. -->
# mak109/distilgpt2-finetuned-lyrics
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.0226
- Validation Loss: 3.0275
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.2907 | 3.1500 | 0 |
| 3.1607 | 3.0962 | 1 |
| 3.1005 | 3.0664 | 2 |
| 3.0573 | 3.0430 | 3 |
| 3.0226 | 3.0275 | 4 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.6.3
- Datasets 2.1.0
- Tokenizers 0.12.1
|
laituan245/molt5-base-caption2smiles
|
laituan245
| 2022-05-03T18:08:45Z | 764 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2204.11817",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-03T04:08:16Z |
---
license: apache-2.0
---
This model can be used to generate a SMILES string from an input caption.
## Example Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-base-caption2smiles", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-base-caption2smiles')
input_text = 'The molecule is a monomethoxybenzene that is 2-methoxyphenol substituted by a hydroxymethyl group at position 4. It has a role as a plant metabolite. It is a member of guaiacols and a member of benzyl alcohols.'
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids, num_beams=5, max_length=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# The model will generate "COC1=C(C=CC(=C1)CCCO)O". The ground-truth is "COC1=C(C=CC(=C1)CO)O".
```
## Paper
For more information, please take a look at our paper.
Paper: [Translation between Molecules and Natural Language](https://arxiv.org/abs/2204.11817)
Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji*
|
laituan245/molt5-large-caption2smiles
|
laituan245
| 2022-05-03T18:08:19Z | 7,081 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2204.11817",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-03T15:58:10Z |
---
license: apache-2.0
---
This model can be used to generate a SMILES string from an input caption.
## Example Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-large-caption2smiles", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-large-caption2smiles')
input_text = 'The molecule is a monomethoxybenzene that is 2-methoxyphenol substituted by a hydroxymethyl group at position 4. It has a role as a plant metabolite. It is a member of guaiacols and a member of benzyl alcohols.'
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids, num_beams=5, max_length=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Paper
For more information, please take a look at our paper.
Paper: [Translation between Molecules and Natural Language](https://arxiv.org/abs/2204.11817)
Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji*
|
TehranNLP-org/electra-base-hateXplain
|
TehranNLP-org
| 2022-05-03T17:00:31Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"generated_from_trainer",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-30T12:51:26Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: SEED0042
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: HATEXPLAIN
type: ''
args: hatexplain
metrics:
- name: Accuracy
type: accuracy
value: 0.4162330905306972
---
<!-- 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. -->
# SEED0042
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the HATEXPLAIN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7667
- Accuracy: 0.4162
- Accuracy 0: 0.8145
- Accuracy 1: 0.1895
- Accuracy 2: 0.3084
## 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: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: not_parallel
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Accuracy 0 | Accuracy 1 | Accuracy 2 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:----------:|:----------:|
| No log | 1.0 | 481 | 0.7431 | 0.4152 | 0.7707 | 0.1805 | 0.3650 |
| No log | 2.0 | 962 | 0.7346 | 0.4152 | 0.8010 | 0.2190 | 0.2774 |
| No log | 3.0 | 1443 | 0.7667 | 0.4162 | 0.8145 | 0.1895 | 0.3084 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu113
- Datasets 2.1.0
- Tokenizers 0.11.6
|
mrm8488/data2vec-text-base-finetuned-stsb
|
mrm8488
| 2022-05-03T16:28:24Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"data2vec-text",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-03T15:51:59Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model-index:
- name: data2vec-text-base-finetuned-stsb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: stsb
metrics:
- name: Spearmanr
type: spearmanr
value: 0.8716633516590501
---
<!-- 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. -->
# data2vec-text-base-finetuned-stsb
This model is a fine-tuned version of [facebook/data2vec-text-base](https://huggingface.co/facebook/data2vec-text-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5530
- Pearson: 0.8732
- Spearmanr: 0.8717
## 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: 7.725353773731373e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 5
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|
| No log | 1.0 | 180 | 1.0650 | 0.8102 | 0.8380 |
| No log | 2.0 | 360 | 0.6211 | 0.8524 | 0.8497 |
| 0.9312 | 3.0 | 540 | 0.5917 | 0.8640 | 0.8642 |
| 0.9312 | 4.0 | 720 | 0.5672 | 0.8695 | 0.8686 |
| 0.9312 | 5.0 | 900 | 0.5530 | 0.8732 | 0.8717 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
facebook/data2vec-vision-base
|
facebook
| 2022-05-03T15:52:10Z | 664 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"data2vec-vision",
"image-feature-extraction",
"image-classification",
"vision",
"dataset:imagenet",
"dataset:imagenet-1k",
"arxiv:2202.03555",
"arxiv:2106.08254",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-04-14T08:08:12Z |
---
license: apache-2.0
tags:
- image-classification
- vision
datasets:
- imagenet
- imagenet-1k
---
# Data2Vec-Vision (base-sized model, pre-trained only)
BEiT model pre-trained in a self-supervised fashion on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/abs/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli and first released in [this repository](https://github.com/facebookresearch/data2vec_vision/tree/main/beit).
Disclaimer: The team releasing Facebook team did not write a model card for this model so this model card has been written by the Hugging Face team.
## Pre-Training method
For more information, please take a look at the [official paper](https://arxiv.org/abs/2202.03555).
## Abstract
*While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because
they were developed with a single modality in
mind. To get us closer to general self-supervised
learning, we present data2vec, a framework that
uses the same learning method for either speech,
NLP or computer vision. The core idea is to predict latent representations of the full input data
based on a masked view of the input in a selfdistillation setup using a standard Transformer architecture. Instead of predicting modality-specific
targets such as words, visual tokens or units of
human speech which are local in nature, data2vec
predicts contextualized latent representations that
contain information from the entire input. Experiments on the major benchmarks of speech
recognition, image classification, and natural language understanding demonstrate a new state of
the art or competitive performance to predominant approaches.*
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?other=data2vec-vision) to look for
fine-tuned versions on a task that interests you.
## Training data
The BEiT model was pretrained on [ImageNet-1k](http://www.image-net.org/), a dataset consisting of 1,2 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/microsoft/unilm/blob/master/beit/datasets.py).
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
For all pre-training related hyperparameters, we refer to the [original paper](https://arxiv.org/abs/2106.08254) and the [original codebase](https://github.com/facebookresearch/data2vec_vision/tree/main/beit)
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 1 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution. Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{https://doi.org/10.48550/arxiv.2202.03555,
doi = {10.48550/ARXIV.2202.03555},
url = {https://arxiv.org/abs/2202.03555},
author = {Baevski, Alexei and Hsu, Wei-Ning and Xu, Qiantong and Babu, Arun and Gu, Jiatao and Auli, Michael},
keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
|
soyasis/gpt2-finetuned-how-to-qa
|
soyasis
| 2022-05-03T15:32:40Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-01T13:10:42Z |
---
language: en
license: mit
---
# HowTo QA with GPT-2 base
GPT-2 English language model fine-tuned with Β±2.000 entries from WikiHow.
You can try it here: https://how-to-generator.herokuapp.com/
Input prompt should follow the following format:
`\n<|startoftext|>[WP] How to {text} \n[RESPONSE]`
Example:
`\n<|startoftext|>[WP] How to create a universe \n[RESPONSE]`
|
pietrolesci/t5v1_1-base-mnli_snli_anli
|
pietrolesci
| 2022-05-03T14:46:07Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-03T14:33:00Z |
## Overview
T5-Base v1.1 model trained to generate hypotheses given a premise and a label. Below the settings used to train it.
```yaml
Experiment configurations
βββ datasets
β βββ snli_train:
β dataset_name: snli
β dataset_config_name: null
β cache_dir: null
β input_fields:
β - premise
β - hypothesis
β target_field: label
β train_subset_names: null
β val_subset_names: validation
β test_subset_names: none
β train_val_split: null
β limit_train_samples: null
β limit_val_samples: null
β limit_test_samples: null
β sampling_kwargs:
β sampling_strategy: random
β seed: 42
β replace: false
β align_labels_with_mapping: null
β avoid_consistency_check: false
β predict_label_mapping: null
β anli_train:
β dataset_name: anli
β dataset_config_name: null
β cache_dir: null
β input_fields:
β - premise
β - hypothesis
β target_field: label
β train_subset_names:
β - train_r1
β - train_r2
β - train_r3
β val_subset_names:
β - dev_r1
β - dev_r2
β - dev_r3
β test_subset_names: none
β train_val_split: null
β limit_train_samples: null
β limit_val_samples: null
β limit_test_samples: null
β sampling_kwargs:
β sampling_strategy: random
β seed: 42
β replace: false
β align_labels_with_mapping: null
β avoid_consistency_check: false
β predict_label_mapping: null
β mnli_train:
β dataset_name: multi_nli
β dataset_config_name: null
β cache_dir: null
β input_fields:
β - premise
β - hypothesis
β target_field: label
β train_subset_names: null
β val_subset_names: validation_matched
β test_subset_names: none
β train_val_split: null
β limit_train_samples: null
β limit_val_samples: null
β limit_test_samples: null
β sampling_kwargs:
β sampling_strategy: random
β seed: 42
β replace: false
β align_labels_with_mapping: null
β avoid_consistency_check: false
β predict_label_mapping: null
β snli:
β dataset_name: snli
β dataset_config_name: null
β cache_dir: null
β input_fields:
β - premise
β - hypothesis
β target_field: label
β train_subset_names: none
β val_subset_names: none
β test_subset_names: null
β train_val_split: null
β limit_train_samples: null
β limit_val_samples: null
β limit_test_samples: null
β sampling_kwargs:
β sampling_strategy: random
β seed: 42
β replace: false
β align_labels_with_mapping: null
β avoid_consistency_check: false
β predict_label_mapping: null
β anli:
β dataset_name: anli
β dataset_config_name: null
β cache_dir: null
β input_fields:
β - premise
β - hypothesis
β target_field: label
β train_subset_names: none
β val_subset_names: none
β test_subset_names:
β - test_r1
β - test_r2
β - test_r3
β train_val_split: null
β limit_train_samples: null
β limit_val_samples: null
β limit_test_samples: null
β sampling_kwargs:
β sampling_strategy: random
β seed: 42
β replace: false
β align_labels_with_mapping: null
β avoid_consistency_check: false
β predict_label_mapping: null
β mnli:
β dataset_name: multi_nli
β dataset_config_name: null
β cache_dir: null
β input_fields:
β - premise
β - hypothesis
β target_field: label
β train_subset_names: none
β val_subset_names: none
β test_subset_names: validation_mismatched
β train_val_split: null
β limit_train_samples: null
β limit_val_samples: null
β limit_test_samples: null
β sampling_kwargs:
β sampling_strategy: random
β seed: 42
β replace: false
β align_labels_with_mapping: null
β avoid_consistency_check: false
β predict_label_mapping: null
β
βββ data
β βββ _target_: src.task.nli.data.NLIGenerationData.from_config
β main_dataset_name: null
β use_additional_as_test: null
β dataloader:
β batch_size: 96
β eval_batch_size: 96
β num_workers: 8
β pin_memory: true
β drop_last: false
β persistent_workers: false
β shuffle: true
β seed_dataloader: 42
β replacement: false
β processing:
β preprocessing_num_workers: 8
β preprocessing_batch_size: 1000
β load_from_cache_file: true
β padding: longest
β truncation: longest_first
β max_source_length: 128
β max_target_length: 128
β template: 'premise: $premise $label hypothesis: '
β tokenizer:
β _target_: transformers.AutoTokenizer.from_pretrained
β pretrained_model_name_or_path: pietrolesci/t5-v1_1-base_nli_gen
β use_fast: true
β
βββ task
β βββ optimizer:
β name: Adafactor
β lr: 0.001
β weight_decay: 0.0
β no_decay:
β - bias
β - LayerNorm.weight
β decay_rate: -0.8
β clip_threshold: 1.0
β relative_step: false
β scale_parameter: false
β warmup_init: false
β scheduler:
β name: constant_schedule
β model:
β model_name_or_path: pietrolesci/t5-v1_1-base_nli_gen
β checkpoint_path: null
β freeze: false
β seed_init_weight: 42
β _target_: src.task.nli.NLIGenerationTask.from_config
β generation:
β generation_max_length: 128
β generation_min_length: 3
β do_sample: true
β early_stopping: false
β num_beams: 1
β temperature: 1.0
β top_k: 50
β top_p: 0.95
β repetition_penalty: null
β length_penalty: null
β no_repeat_ngram_size: null
β encoder_no_repeat_ngram_size: null
β num_return_sequences: 1
β max_time: null
β max_new_tokens: null
β decoder_start_token_id: null
β use_cache: null
β num_beam_groups: null
β diversity_penalty: null
β
βββ trainer
β βββ _target_: pytorch_lightning.Trainer
β callbacks:
β lr_monitor:
β _target_: pytorch_lightning.callbacks.LearningRateMonitor
β logging_interval: step
β log_momentum: false
β model_checkpoint:
β _target_: pytorch_lightning.callbacks.ModelCheckpoint
β dirpath: ./checkpoints/
β filename: nli_generator_sma-epoch={epoch:02d}-val_loss={val/aggregat
β monitor: val/aggregated_loss
β mode: min
β verbose: false
β save_last: true
β save_top_k: 1
β auto_insert_metric_name: false
β save_on_train_epoch_end: false
β rich_model_summary:
β _target_: pytorch_lightning.callbacks.RichModelSummary
β max_depth: 1
β log_grad_norm:
β _target_: src.core.callbacks.LogGradNorm
β norm_type: 2
β group_separator: /
β only_total: true
β on_step: true
β on_epoch: false
β prog_bar: true
β log_generated_text:
β _target_: src.core.callbacks.GenerateAndLogText
β dirpath: ./generated_text
β type: generated_text
β pop_keys_after_logging: true
β on_train: false
β on_validation: false
β on_test: true
β log_to_wandb: true
β wandb_log_dataset_sizes:
β _target_: src.core.callbacks.WandbLogDatasetSizes
β logger:
β wandb:
β _target_: pytorch_lightning.loggers.WandbLogger
β project: nli_debiasing
β entity: team_brushino
β name: nli_generator_sma
β save_dir: ./
β offline: false
β log_model: false
β group: generator
β job_type: genearator_training
β tags:
β - nli_generator_sma
β - seed=42
β - seed_dataloader=42
β notes: nli_generator_sma_time=01-37-04
β enable_checkpointing: true
β enable_progress_bar: true
β enable_model_summary: true
β gradient_clip_val: 6
β gradient_clip_algorithm: null
β accelerator: gpu
β devices: auto
β gpus: null
β auto_select_gpus: true
β accumulate_grad_batches: 1
β max_epochs: 2
β min_epochs: 1
β max_steps: -1
β min_steps: null
β max_time: null
β num_sanity_val_steps: 2
β overfit_batches: 0.0
β fast_dev_run: false
β limit_train_batches: 1.0
β limit_val_batches: 1.0
β limit_test_batches: 1.0
β profiler: null
β detect_anomaly: false
β deterministic: false
β check_val_every_n_epoch: 1
β val_check_interval: 0.5
β log_every_n_steps: 1
β move_metrics_to_cpu: false
β
βββ training
βββ run_val_before_fit: false
run_val_after_fit: false
run_test_before_fit: false
run_test_after_fit: true
lr: 0.001
seed: 42
show_batch: false
batch_size: 96
eval_batch_size: 96
num_workers: 8
pin_memory: true
drop_last: false
persistent_workers: false
shuffle: true
seed_dataloader: 42
ignore_warnings: true
experiment_name: nli_generator_sma
```
|
PoloHuggingface/French_grammar_error_corrector
|
PoloHuggingface
| 2022-05-03T13:32:40Z | 102 | 6 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text2text generation",
"fr",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-02T15:45:49Z |
---
content:
language:
- fr
tags:
- text2text generation
widget:
- text: "improve grammar: Elle ne peux jamais aller au cinΓ©ma avec son amis"
example_title: "Grammar correction"
---
# Finetuned T5 on the french part of Lang-8 to automatically correct sentences.
Since the Lang-8 dataset contains really short sentences, the model does not generalize well with sentences larger than 10 words.
I'll upload soon the cleaned dataset that I've used for training.
|
sanchit-gandhi/flax-wav2vec2-2-bart-large-960h
|
sanchit-gandhi
| 2022-05-03T12:24:52Z | 3 | 0 |
transformers
|
[
"transformers",
"jax",
"speech-encoder-decoder",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-03T12:07:42Z |
2.5% WER on dev.clean: https://wandb.ai/sanchit-gandhi/flax-wav2vec2-2-bart-large-960h/runs/2lhazd5v
|
datauma/bert-finetuned-ner
|
datauma
| 2022-05-03T11:52:53Z | 7 | 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
| 2022-05-03T11:24:33Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9312510328871261
- name: Recall
type: recall
value: 0.9483338943116796
- name: F1
type: f1
value: 0.9397148336529643
- name: Accuracy
type: accuracy
value: 0.9855624889621475
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0630
- Precision: 0.9313
- Recall: 0.9483
- F1: 0.9397
- Accuracy: 0.9856
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.084 | 1.0 | 1756 | 0.0652 | 0.9203 | 0.9387 | 0.9294 | 0.9842 |
| 0.0387 | 2.0 | 3512 | 0.0589 | 0.9271 | 0.9504 | 0.9386 | 0.9853 |
| 0.0203 | 3.0 | 5268 | 0.0630 | 0.9313 | 0.9483 | 0.9397 | 0.9856 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.