modelId
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-14 06:27:53
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 519
values | tags
listlengths 1
4.05k
| pipeline_tag
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values | createdAt
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mrmoor/cti-bert-ner | mrmoor | 2022-10-23T19:17:18Z | 28 | 1 | transformers | [
"transformers",
"tf",
"tensorboard",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-10-23T18:33:27Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: mrmoor/cti-bert-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. -->
# mrmoor/cti-bert-ner
This model is a fine-tuned version of [mrmoor/cti-bert-mlm](https://huggingface.co/mrmoor/cti-bert-mlm) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1491
- Validation Loss: 0.3715
- Epoch: 6
## 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': 82800, '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 |
|:----------:|:---------------:|:-----:|
| 0.6883 | 0.5161 | 0 |
| 0.4567 | 0.4283 | 1 |
| 0.3420 | 0.3810 | 2 |
| 0.2688 | 0.3845 | 3 |
| 0.2144 | 0.3669 | 4 |
| 0.1788 | 0.3881 | 5 |
| 0.1491 | 0.3715 | 6 |
### Framework versions
- Transformers 4.23.1
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.1
|
pepa/roberta-base-fever | pepa | 2022-10-23T18:34:30Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:copenlu/fever_gold_evidence",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-10-23T18:33:10Z | ---
tags:
- generated_from_trainer
model-index:
- name: roberta-base-fever
results: []
datasets:
- copenlu/fever_gold_evidence
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-fever
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6929
- eval_p: 0.8856
- eval_r: 0.8851
- eval_f1: 0.8848
- eval_runtime: 44.4077
- eval_samples_per_second: 423.462
- eval_steps_per_second: 52.941
- step: 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: 1e-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: cosine
- num_epochs: 4
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.12.1
|
NikitaBaramiia/q-FrozenLake-v1-4x4-noSlippery | NikitaBaramiia | 2022-10-23T18:04:10Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-10-23T17:45:53Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="NikitaBaramiia/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
pepa/roberta-small-fever | pepa | 2022-10-23T17:53:38Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:copenlu/fever_gold_evidence",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-10-23T17:27:02Z | ---
tags:
- generated_from_trainer
model-index:
- name: roberta-small-fever
results: []
datasets:
- copenlu/fever_gold_evidence
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-small-fever
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6096
- eval_p: 0.8179
- eval_r: 0.8110
- eval_f1: 0.8104
- eval_runtime: 36.258
- eval_samples_per_second: 518.644
- eval_steps_per_second: 64.841
- step: 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 4
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.12.1
|
sd-concepts-library/xioboma | sd-concepts-library | 2022-10-23T17:51:13Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2022-10-23T17:51:03Z | ---
license: mit
---
### xioboma on Stable Diffusion
This is the `<xi-obama>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
patrickvonplaten/carol_model | patrickvonplaten | 2022-10-23T17:49:06Z | 0 | 1 | null | [
"license:mit",
"region:us"
]
| null | 2022-10-23T17:56:14Z | ---
license: mit
---
### Carol on Stable Diffusion
This is the `<carol>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`.
|
valhalla/SwinIR-real-sr-M-x2-GAN | valhalla | 2022-10-23T17:44:40Z | 2 | 0 | transformers | [
"transformers",
"jax",
"swin-ir",
"region:us"
]
| null | 2022-10-23T15:44:22Z | ---
tags:
- swin-ir
inference: false
--- |
valhalla/SwinIR-real-sr-M-x4-GAN | valhalla | 2022-10-23T17:44:30Z | 10 | 0 | transformers | [
"transformers",
"jax",
"swin-ir",
"region:us"
]
| null | 2022-10-23T15:44:38Z | ---
tags:
- swin-ir
inference: false
--- |
ivanwong/AnyML | ivanwong | 2022-10-23T17:38:38Z | 0 | 0 | null | [
"region:us"
]
| null | 2022-10-23T17:28:45Z | AnyML model is a YOLOv5 customized model. It is based on the Ultraistic YOLOv5. The based model was pre-trained with COCO with 80 classes.
|
ViktorDo/DistilBERT-WIKI_Life_Form_Finetuned | ViktorDo | 2022-10-23T17:25:08Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-10-23T17:01:10Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: DistilBERT-WIKI_Life_Form_Finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DistilBERT-WIKI_Life_Form_Finetuned
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: 0.4204
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5077 | 1.0 | 1175 | 0.4573 |
| 0.3693 | 2.0 | 2350 | 0.4196 |
| 0.2759 | 3.0 | 3525 | 0.4204 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
pepa/bigbird-roberta-base-snli | pepa | 2022-10-23T17:11:57Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"big_bird",
"text-classification",
"generated_from_trainer",
"dataset:snli",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-10-23T17:11:06Z | ---
tags:
- generated_from_trainer
datasets:
- snli
model-index:
- name: bigbird-roberta-base-snli
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. -->
# bigbird-roberta-base-snli
This model was trained from scratch on the snli dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2738
- eval_p: 0.9034
- eval_r: 0.9033
- eval_f1: 0.9033
- eval_runtime: 10.9262
- eval_samples_per_second: 899.126
- eval_steps_per_second: 56.195
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 4
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.12.1
|
hieuit7/wav2vec2-common_voice-vi-demo | hieuit7 | 2022-10-23T17:04:28Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"vi",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-10-23T15:48:50Z | ---
language:
- vi
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-common_voice-vi-demo
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-common_voice-vi-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - VI dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4768
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| No log | 7.67 | 100 | 5.9657 | 1.0 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu116
- Datasets 2.6.1
- Tokenizers 0.13.1
|
thothai/turkce-kufur-tespiti | thothai | 2022-10-23T16:55:48Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"license:afl-3.0",
"endpoints_compatible",
"region:us"
]
| null | 2022-10-23T16:45:09Z | ---
license: afl-3.0
---
# Thoth Ai, Türkçe hakaret ve küfürleri tespit etmek için oluşturulmuştur. Akademik projelerde kaynak gösterilmesi halinde kullanılabilir.
## Validation Metrics
- Loss: 0.230
- Accuracy: 0.936
- Macro F1: 0.927
- Micro F1: 0.936
- Weighted F1: 0.936
- Macro Precision: 0.929
- Micro Precision: 0.936
- Weighted Precision: 0.936
- Macro Recall: 0.925
- Micro Recall: 0.936
- Weighted Recall: 0.936
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("thothai/turkce-kufur-tespiti", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("thothai/turkce-kufur-tespiti", use_auth_token=True)
inputs = tokenizer("Merhaba", return_tensors="pt")
outputs = model(**inputs)
``` |
crumb/dalle-paint | crumb | 2022-10-23T16:22:50Z | 0 | 1 | null | [
"license:mit",
"region:us"
]
| null | 2022-10-23T16:22:37Z | ---
license: mit
---
### dalle-paint on Stable Diffusion
This is the `<dalle-paint>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
|
k4tel/bert-geolocation-prediction | k4tel | 2022-10-23T16:21:11Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
]
| null | 2022-10-23T13:10:50Z | ---
tags:
- generated_from_trainer
model-index:
- name: bert-geolocation-prediction
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-geolocation-prediction
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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
### Framework versions
- Transformers 4.23.1
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
|
situlla/ppo-LunarLander-v2 | situlla | 2022-10-23T16:00:09Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-10-20T12:49:56Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 292.79 +/- 16.63
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ddebnath/layoutlmv3-finetuned-cord_100 | ddebnath | 2022-10-23T15:37:39Z | 11 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:cord-layoutlmv3",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-10-23T14:42:28Z | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cord-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cord-layoutlmv3
type: cord-layoutlmv3
config: cord
split: train
args: cord
metrics:
- name: Precision
type: precision
value: 0.9485842026825634
- name: Recall
type: recall
value: 0.9528443113772455
- name: F1
type: f1
value: 0.9507094846900671
- name: Accuracy
type: accuracy
value: 0.9592529711375212
---
<!-- 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. -->
# layoutlmv3-finetuned-cord_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1978
- Precision: 0.9486
- Recall: 0.9528
- F1: 0.9507
- Accuracy: 0.9593
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.56 | 250 | 0.9543 | 0.7832 | 0.8166 | 0.7996 | 0.8226 |
| 1.3644 | 3.12 | 500 | 0.5338 | 0.8369 | 0.8683 | 0.8523 | 0.8824 |
| 1.3644 | 4.69 | 750 | 0.3658 | 0.8840 | 0.9072 | 0.8955 | 0.9232 |
| 0.3802 | 6.25 | 1000 | 0.3019 | 0.9156 | 0.9251 | 0.9203 | 0.9334 |
| 0.3802 | 7.81 | 1250 | 0.2833 | 0.9094 | 0.9237 | 0.9165 | 0.9346 |
| 0.2061 | 9.38 | 1500 | 0.2241 | 0.9377 | 0.9469 | 0.9423 | 0.9525 |
| 0.2061 | 10.94 | 1750 | 0.2282 | 0.9304 | 0.9409 | 0.9356 | 0.9474 |
| 0.1416 | 12.5 | 2000 | 0.2017 | 0.9509 | 0.9566 | 0.9537 | 0.9610 |
| 0.1416 | 14.06 | 2250 | 0.2006 | 0.9472 | 0.9536 | 0.9504 | 0.9614 |
| 0.1056 | 15.62 | 2500 | 0.1978 | 0.9486 | 0.9528 | 0.9507 | 0.9593 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Mhd/q-FrozenLake-v1-4x4-noSlippery | Mhd | 2022-10-23T15:21:59Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-10-23T15:21:53Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Mhd/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
pepa/deberta-v3-small-snli | pepa | 2022-10-23T14:31:53Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"dataset:snli",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-10-23T14:30:53Z | ---
tags:
- generated_from_trainer
datasets:
- snli
model-index:
- name: deberta-v3-small-snli
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. -->
# deberta-v3-small-snli
This model was trained from scratch on the snli dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2730
- eval_p: 0.9149
- eval_r: 0.9150
- eval_f1: 0.9149
- eval_runtime: 21.3166
- eval_samples_per_second: 460.86
- eval_steps_per_second: 28.804
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 4
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.12.1
|
pepa/roberta-small-snli | pepa | 2022-10-23T14:28:02Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:snli",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-10-23T14:27:19Z | ---
tags:
- generated_from_trainer
datasets:
- snli
model-index:
- name: roberta-small-snli
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-small-snli
This model was trained from scratch on the snli dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4118
- eval_p: 0.8492
- eval_r: 0.8469
- eval_f1: 0.8474
- eval_runtime: 17.312
- eval_samples_per_second: 567.467
- eval_steps_per_second: 35.467
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 4
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.12.1
|
theojolliffe/pegasus-cnn_dailymail-finetuned-roundup | theojolliffe | 2022-10-23T14:13:33Z | 12 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-10-23T11:23:16Z | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pegasus-cnn_dailymail-finetuned-roundup
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. -->
# pegasus-cnn_dailymail-finetuned-roundup
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9488
- Rouge1: 55.4754
- Rouge2: 39.1074
- Rougel: 40.822
- Rougelsum: 47.3045
- Gen Len: 126.7778
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 1.8482 | 1.0 | 795 | 1.3177 | 51.1349 | 32.0791 | 34.4191 | 41.2001 | 127.0 |
| 1.362 | 2.0 | 1590 | 1.1975 | 52.1955 | 33.7858 | 36.2998 | 42.5368 | 127.0 |
| 1.2847 | 3.0 | 2385 | 1.1299 | 53.3694 | 35.9817 | 39.2437 | 45.2062 | 127.0 |
| 1.1673 | 4.0 | 3180 | 1.0903 | 53.3629 | 35.173 | 37.7775 | 44.3446 | 126.8889 |
| 1.0943 | 5.0 | 3975 | 1.0525 | 54.6538 | 37.3115 | 39.5883 | 46.8043 | 127.0 |
| 1.0615 | 6.0 | 4770 | 1.0317 | 54.6794 | 36.7147 | 39.5731 | 45.8527 | 127.0 |
| 0.993 | 7.0 | 5565 | 1.0144 | 55.2425 | 38.3984 | 41.1723 | 47.266 | 126.8704 |
| 0.9705 | 8.0 | 6360 | 0.9993 | 55.3351 | 38.3237 | 40.4765 | 47.3272 | 127.0 |
| 0.9266 | 9.0 | 7155 | 0.9864 | 55.174 | 38.0535 | 40.3484 | 46.7058 | 126.0926 |
| 0.9181 | 10.0 | 7950 | 0.9713 | 54.822 | 37.8024 | 40.0583 | 46.4962 | 126.5 |
| 0.9053 | 11.0 | 8745 | 0.9690 | 55.6235 | 38.9253 | 40.6261 | 47.1602 | 127.0 |
| 0.8513 | 12.0 | 9540 | 0.9614 | 55.3525 | 38.9343 | 40.4011 | 46.9631 | 127.0 |
| 0.8436 | 13.0 | 10335 | 0.9565 | 56.1316 | 39.5794 | 41.4653 | 47.7626 | 127.0 |
| 0.8343 | 14.0 | 11130 | 0.9522 | 55.7155 | 38.9484 | 40.968 | 47.0273 | 126.7778 |
| 0.8308 | 15.0 | 11925 | 0.9502 | 55.7195 | 39.1268 | 40.7967 | 47.1869 | 127.0 |
| 0.8296 | 16.0 | 12720 | 0.9488 | 55.4754 | 39.1074 | 40.822 | 47.3045 | 126.7778 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Mozart-coder/BERT_newdata-6_tokenized | Mozart-coder | 2022-10-23T14:10:11Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-10-23T13:38:56Z | ---
tags:
- generated_from_trainer
model-index:
- name: BERT_newdata-6_tokenized
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_newdata-6_tokenized
This model is a fine-tuned version of [armheb/DNA_bert_6](https://huggingface.co/armheb/DNA_bert_6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0378
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0669 | 1.0 | 223 | 0.0373 |
| 0.0394 | 2.0 | 446 | 0.0398 |
| 0.0369 | 3.0 | 669 | 0.0371 |
| 0.0362 | 4.0 | 892 | 0.0358 |
| 0.0383 | 5.0 | 1115 | 0.0353 |
| 0.0365 | 6.0 | 1338 | 0.0378 |
| 0.0366 | 7.0 | 1561 | 0.0377 |
| 0.0373 | 8.0 | 1784 | 0.0359 |
| 0.0372 | 9.0 | 2007 | 0.0371 |
| 0.0377 | 10.0 | 2230 | 0.0357 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
adithya12/monkeypox-model-lin | adithya12 | 2022-10-23T13:45:38Z | 0 | 0 | keras | [
"keras",
"tf-keras",
"region:us"
]
| null | 2022-10-23T13:44:22Z | ---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| learning_rate | 0.0010000000474974513 |
| decay | 0.0 |
| beta_1 | 0.8999999761581421 |
| beta_2 | 0.9990000128746033 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> |
k4tel/bert-multilingial-geolocation-prediction | k4tel | 2022-10-23T12:55:25Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-10-22T12:58:09Z | ---
tags:
- generated_from_trainer
model-index:
- name: bert-multilingial-geolocation-prediction
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-multilingial-geolocation-prediction
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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
### Framework versions
- Transformers 4.23.1
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
|
pepa/roberta-base-snli | pepa | 2022-10-23T10:56:31Z | 54 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:snli",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-10-23T10:55:42Z | ---
tags:
- generated_from_trainer
datasets:
- snli
model-index:
- name: roberta-base-snli
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-snli
This model was trained from scratch on the snli dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2835
- eval_p: 0.9004
- eval_r: 0.9004
- eval_f1: 0.9004
- eval_runtime: 10.4036
- eval_samples_per_second: 944.286
- eval_steps_per_second: 59.018
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 4
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.12.1
|
pere/roberta-base-exp | pere | 2022-10-23T08:47:47Z | 1 | 0 | transformers | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
]
| null | 2022-10-21T15:40:20Z | # Roberta-base-xlm-exp
Just an experiment. Do not use. |
BigSalmon/InformalToFormalLincoln86Paraphrase | BigSalmon | 2022-10-23T04:48:33Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-10-23T01:57:39Z | data: https://github.com/BigSalmon2/InformalToFormalDataset
Text Generation Informal Formal
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln86Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln86Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
```
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: 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.
informal english:
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
- penny has practically no value
- should be taken out of circulation
- just as other coins have been in us history
- lost use
- value not enough
- to make environmental consequences worthy
text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
```
first: ( was complicit in / was involved in ).
antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ).
***
first: ( have no qualms about / see no issue with ).
antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ).
***
first: ( do not see eye to eye / disagree often ).
antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ).
***
first:
```
```
stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground.
***
languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo.
***
dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia.
***
embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons.
```
Infill / Infilling / Masking / Phrase Masking (Works pretty decently actually, especially when you use logprobs code from above):
```
his contention [blank] by the evidence [sep] was refuted [answer]
***
few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
the joy of sport is that no two games are alike. for every exhilarating experience, however, there is an interminable one. the national pastime, unfortunately, has a penchant for the latter. what begins as a summer evening at the ballpark can quickly devolve into a game of tedium. the primary culprit is the [blank] of play. from batters readjusting their gloves to fielders spitting on their mitts, the action is [blank] unnecessary interruptions. the sport's future is [blank] if these tendencies are not addressed [sep] plodding pace [answer] riddled with [answer] bleak [answer]
***
microsoft word's [blank] pricing [blank] competition [sep] unconscionable [answer] invites [answer]
***
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
Backwards
```
Essay Intro (National Parks):
text: tourists are at ease in the national parks, ( swept up in the beauty of their natural splendor ).
***
Essay Intro (D.C. Statehood):
washington, d.c. is a city of outsize significance, ( ground zero for the nation's political life / center stage for the nation's political machinations ).
```
```
topic: the Golden State Warriors.
characterization 1: the reigning kings of the NBA.
characterization 2: possessed of a remarkable cohesion.
characterization 3: helmed by superstar Stephen Curry.
characterization 4: perched atop the league’s hierarchy.
characterization 5: boasting a litany of hall-of-famers.
***
topic: emojis.
characterization 1: shorthand for a digital generation.
characterization 2: more versatile than words.
characterization 3: the latest frontier in language.
characterization 4: a form of self-expression.
characterization 5: quintessentially millennial.
characterization 6: reflective of a tech-centric world.
***
topic:
```
```
regular: illinois went against the census' population-loss prediction by getting more residents.
VBG: defying the census' prediction of population loss, illinois experienced growth.
***
regular: microsoft word’s high pricing increases the likelihood of competition.
VBG: extortionately priced, microsoft word is inviting competition.
***
regular:
```
```
source: badminton should be more popular in the US.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) games played with racquets are popular, (2) just look at tennis and ping pong, (3) but badminton underappreciated, (4) fun, fast-paced, competitive, (5) needs to be marketed more
text: the sporting arena is dominated by games that are played with racquets. tennis and ping pong, in particular, are immensely popular. somewhat curiously, however, badminton is absent from this pantheon. exciting, fast-paced, and competitive, it is an underappreciated pastime. all that it lacks is more effective marketing.
***
source: movies in theaters should be free.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) movies provide vital life lessons, (2) many venues charge admission, (3) those without much money
text: the lessons that movies impart are far from trivial. the vast catalogue of cinematic classics is replete with inspiring sagas of friendship, bravery, and tenacity. it is regrettable, then, that admission to theaters is not free. in their current form, the doors of this most vital of institutions are closed to those who lack the means to pay.
***
source:
```
```
in the private sector, { transparency } is vital to the business’s credibility. the { disclosure of information } can be the difference between success and failure.
***
the labor market is changing, with { remote work } now the norm. this { flexible employment } allows the individual to design their own schedule.
***
the { cubicle } is the locus of countless grievances. many complain that the { enclosed workspace } restricts their freedom of movement.
***
```
```
it would be natural to assume that americans, as a people whose ancestors { immigrated to this country }, would be sympathetic to those seeking to do likewise.
question: what does “do likewise” mean in the above context?
(a) make the same journey
(b) share in the promise of the american dream
(c) start anew in the land of opportunity
(d) make landfall on the united states
***
in the private sector, { transparency } is vital to the business’s credibility. this orientation can be the difference between success and failure.
question: what does “this orientation” mean in the above context?
(a) visible business practices
(b) candor with the public
(c) open, honest communication
(d) culture of accountability
```
```
example: suppose you are a teacher. further suppose you want to tell an accurate telling of history. then suppose a parent takes offense. they do so in the name of name of their kid. this happens a lot.
text: educators' responsibility to remain true to the historical record often clashes with the parent's desire to shelter their child from uncomfortable realities.
***
example: suppose you are a student at college. now suppose you have to buy textbooks. that is going to be worth hundreds of dollars. given how much you already spend on tuition, that is going to hard cost to bear.
text: the exorbitant cost of textbooks, which often reaches hundreds of dollars, imposes a sizable financial burden on the already-strapped college student.
```
```
<Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle>
***
<Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle>
```
```
accustomed to having its name uttered ______, harvard university is weathering a rare spell of reputational tumult
(a) in reverential tones
(b) with great affection
(c) in adulatory fashion
(d) in glowing terms
```
```
clarify: international ( {working together} / cooperation ) is called for when ( {issue go beyond lots of borders} / an issue transcends borders / a given matter has transnational implications ).
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
*Note* Of all the masking techniques, this one works the best.
```
<Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle>
***
<Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle>
```
```
essence: when someone's views are keeping within reasonable.
refine: the senator's voting record is ( moderate / centrist / pragmatic / balanced / fair-minded / even-handed ).
***
essence: when things are worked through in a petty way.
refine: the propensity of the u.s. congress to settle every dispute by way of ( mudslinging / bickering / demagoguery / name-calling / finger-pointing / vilification ) is appalling.
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
```
music before bedtime [makes for being able to relax] -> is a recipe for relaxation.
```
```
[people wanting entertainment love traveling new york city] -> travelers flock to new york city in droves, drawn to its iconic entertainment scene. [cannot blame them] -> one cannot fault them [broadway so fun] -> when it is home to such thrilling fare as Broadway.
```
```
in their ( ‖ when you are rushing because you want to get there on time ‖ / haste to arrive punctually / mad dash to be timely ), morning commuters are too rushed to whip up their own meal.
***
politicians prefer to author vague plans rather than ( ‖ when you can make a plan without many unknowns ‖ / actionable policies / concrete solutions ).
``` |
liujxing/distilgpt2-finetuned-wikitext2 | liujxing | 2022-10-23T02:54:18Z | 5 | 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 | 2022-10-23T02:06:47Z | ---
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: 3.6421
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7602 | 1.0 | 2334 | 3.6669 |
| 3.653 | 2.0 | 4668 | 3.6472 |
| 3.6006 | 3.0 | 7002 | 3.6421 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Dimitre/ddpm-butterflies-128 | Dimitre | 2022-10-23T00:53:58Z | 3 | 0 | diffusers | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
]
| null | 2022-10-22T23:08:26Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/Dimitre/ddpm-butterflies-128/tensorboard?#scalars)
|
Shaier/longformer_cosmos | Shaier | 2022-10-23T00:03:28Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"longformer",
"multiple-choice",
"generated_from_trainer",
"dataset:cosmos_qa",
"endpoints_compatible",
"region:us"
]
| multiple-choice | 2022-10-22T16:58:19Z | ---
tags:
- generated_from_trainer
datasets:
- cosmos_qa
model-index:
- name: longformer_cosmos
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. -->
# longformer_cosmos
This model was trained from scratch on the cosmos_qa 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 25
- total_train_batch_size: 100
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0
|
YusupB/YusupsFirstModel | YusupB | 2022-10-22T23:49:53Z | 0 | 0 | null | [
"region:us"
]
| null | 2022-10-22T22:16:05Z | ---
title: Fastai_pet_classifier
emoji: 📈
colorFrom: red
colorTo: green
sdk: gradio
app_file: app.py
pinned: false
---
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`sdk`: _string_
Can be either `gradio` or `streamlit`
`sdk_version` : _string_
Only applicable for `streamlit` SDK.
See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
`app_file`: _string_
Path to your main application file (which contains either `gradio` or `streamlit` Python code).
Path is relative to the root of the repository.
`pinned`: _boolean_
Whether the Space stays on top of your list. |
Meow412/finetuning-sentiment-model-w | Meow412 | 2022-10-22T21:57:01Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-10-22T01:17:19Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-w
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-w
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: 0.1824
- Accuracy: 0.9462
- F1: 0.9472
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Ghosteez/raiml | Ghosteez | 2022-10-22T17:53:31Z | 0 | 0 | fastai | [
"fastai",
"image-classification",
"region:us"
]
| image-classification | 2022-10-22T17:07:05Z | ---
tags:
- fastai
- image-classification
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
SergioVillanueva/autotrain-person-intruder-classification-1840363138 | SergioVillanueva | 2022-10-22T15:13:21Z | 36 | 0 | transformers | [
"transformers",
"pytorch",
"autotrain",
"vision",
"image-classification",
"dataset:SergioVillanueva/autotrain-data-person-intruder-classification",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-10-22T15:12:43Z | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- SergioVillanueva/autotrain-data-person-intruder-classification
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.5267790340228428
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1840363138
- CO2 Emissions (in grams): 0.5268
## Validation Metrics
- Loss: 0.464
- Accuracy: 0.818
- Precision: 0.778
- Recall: 1.000
- AUC: 1.000
- F1: 0.875 |
debbiesoon/bart_large_v2 | debbiesoon | 2022-10-22T13:05:39Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-10-22T01:30:08Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: distilbartv2
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. -->
# distilbartv2
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- label_smoothing_factor: 0.1
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
vvincentt/spanbert-finetuned-squadv2 | vvincentt | 2022-10-22T12:23:34Z | 12 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
]
| question-answering | 2022-10-20T13:56:58Z | ---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-finetuned-squadv2
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. -->
# spanbert-finetuned-squadv2
This model was trained from scratch 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 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
nghuyong/ernie-3.0-xbase-zh | nghuyong | 2022-10-22T11:01:58Z | 9,577 | 19 | transformers | [
"transformers",
"pytorch",
"ernie",
"fill-mask",
"zh",
"arxiv:2107.02137",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-10-22T10:57:03Z | ---
language: zh
---
# ERNIE-3.0-xbase-zh
## Introduction
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
More detail: https://arxiv.org/abs/2107.02137
## Released Model Info
This released pytorch model is converted from the officially released PaddlePaddle ERNIE model and
a series of experiments have been conducted to check the accuracy of the conversion.
- Official PaddlePaddle ERNIE repo:https://paddlenlp.readthedocs.io/zh/latest/model_zoo/transformers/ERNIE/contents.html
- Pytorch Conversion repo: https://github.com/nghuyong/ERNIE-Pytorch
## How to use
```Python
from transformers import BertTokenizer, ErnieModel
tokenizer = BertTokenizer.from_pretrained("nghuyong/ernie-3.0-xbase-zh")
model = ErnieModel.from_pretrained("nghuyong/ernie-3.0-xbase-zh")
```
## Citation
```bibtex
@article{sun2021ernie,
title={Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation},
author={Sun, Yu and Wang, Shuohuan and Feng, Shikun and Ding, Siyu and Pang, Chao and Shang, Junyuan and Liu, Jiaxiang and Chen, Xuyi and Zhao, Yanbin and Lu, Yuxiang and others},
journal={arXiv preprint arXiv:2107.02137},
year={2021}
}
```
|
Mozart-coder/BERT_full_data3-6_tokenized | Mozart-coder | 2022-10-22T10:27:34Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-10-22T09:53:16Z | ---
tags:
- generated_from_trainer
model-index:
- name: BERT_full_data3-6_tokenized
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_full_data3-6_tokenized
This model is a fine-tuned version of [armheb/DNA_bert_6](https://huggingface.co/armheb/DNA_bert_6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0370
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0649 | 1.0 | 284 | 0.0381 |
| 0.0401 | 2.0 | 568 | 0.0364 |
| 0.0374 | 3.0 | 852 | 0.0371 |
| 0.0371 | 4.0 | 1136 | 0.0352 |
| 0.0365 | 5.0 | 1420 | 0.0360 |
| 0.0353 | 6.0 | 1704 | 0.0375 |
| 0.0353 | 7.0 | 1988 | 0.0357 |
| 0.0364 | 8.0 | 2272 | 0.0349 |
| 0.0353 | 9.0 | 2556 | 0.0343 |
| 0.0356 | 10.0 | 2840 | 0.0345 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
bchaipats/distilbert-base-uncased-finetuned-ner | bchaipats | 2022-10-22T09:36:42Z | 12 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-10-22T09:10:38Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9247846255798542
- name: Recall
type: recall
value: 0.9366819554760041
- name: F1
type: f1
value: 0.9306952703829268
- name: Accuracy
type: accuracy
value: 0.9834622777892513
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0627
- Precision: 0.9248
- Recall: 0.9367
- F1: 0.9307
- Accuracy: 0.9835
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.245 | 1.0 | 878 | 0.0708 | 0.9130 | 0.9196 | 0.9163 | 0.9810 |
| 0.0538 | 2.0 | 1756 | 0.0636 | 0.9220 | 0.9350 | 0.9285 | 0.9827 |
| 0.0297 | 3.0 | 2634 | 0.0627 | 0.9248 | 0.9367 | 0.9307 | 0.9835 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.0
- Tokenizers 0.13.1
|
huggingtweets/ouvessvit | huggingtweets | 2022-10-22T09:34:51Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-10-22T09:33:50Z | ---
language: en
thumbnail: http://www.huggingtweets.com/ouvessvit/1666431286897/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/1539686183927795712/_V9skTmk_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Natalie Godec 🇺🇦</div>
<div style="text-align: center; font-size: 14px;">@ouvessvit</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 Natalie Godec 🇺🇦.
| Data | Natalie Godec 🇺🇦 |
| --- | --- |
| Tweets downloaded | 1043 |
| Retweets | 74 |
| Short tweets | 83 |
| Tweets kept | 886 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2yoysr8v/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 @ouvessvit's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3q5y5xzk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3q5y5xzk/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/ouvessvit')
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)
|
Mozart-coder/BERT_full-6_tokenized | Mozart-coder | 2022-10-22T09:22:10Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-10-21T07:30:07Z | ---
tags:
- generated_from_trainer
model-index:
- name: BERT_full-6_tokenized
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_full-6_tokenized
This model is a fine-tuned version of [armheb/DNA_bert_6](https://huggingface.co/armheb/DNA_bert_6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0362
## 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: 25
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0775 | 1.0 | 284 | 0.0411 |
| 0.0428 | 2.0 | 568 | 0.0393 |
| 0.0395 | 3.0 | 852 | 0.0396 |
| 0.0395 | 4.0 | 1136 | 0.0374 |
| 0.0379 | 5.0 | 1420 | 0.0379 |
| 0.037 | 6.0 | 1704 | 0.0399 |
| 0.0368 | 7.0 | 1988 | 0.0382 |
| 0.0378 | 8.0 | 2272 | 0.0378 |
| 0.0365 | 9.0 | 2556 | 0.0362 |
| 0.0374 | 10.0 | 2840 | 0.0359 |
| 0.0372 | 11.0 | 3124 | 0.0373 |
| 0.0358 | 12.0 | 3408 | 0.0378 |
| 0.0361 | 13.0 | 3692 | 0.0385 |
| 0.0364 | 14.0 | 3976 | 0.0383 |
| 0.035 | 15.0 | 4260 | 0.0376 |
| 0.035 | 16.0 | 4544 | 0.0376 |
| 0.036 | 17.0 | 4828 | 0.0388 |
| 0.0365 | 18.0 | 5112 | 0.0372 |
| 0.0355 | 19.0 | 5396 | 0.0363 |
| 0.0349 | 20.0 | 5680 | 0.0378 |
| 0.0345 | 21.0 | 5964 | 0.0377 |
| 0.0349 | 22.0 | 6248 | 0.0372 |
| 0.035 | 23.0 | 6532 | 0.0374 |
| 0.0351 | 24.0 | 6816 | 0.0379 |
| 0.0351 | 25.0 | 7100 | 0.0374 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Nobody138/xlm-roberta-base-finetuned-panx-all | Nobody138 | 2022-10-22T08:30:29Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-10-22T07:58:56Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1745
- F1: 0.8505
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3055 | 1.0 | 835 | 0.1842 | 0.8099 |
| 0.1561 | 2.0 | 1670 | 0.1711 | 0.8452 |
| 0.1016 | 3.0 | 2505 | 0.1745 | 0.8505 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
somemusicnerdwoops/DialoGPT-distilgpt2-sonicfandub | somemusicnerdwoops | 2022-10-22T08:06:05Z | 3 | 1 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-10-22T07:30:03Z | ---
tags:
- conversational
- text-generation
--- |
Nobody138/xlm-roberta-base-finetuned-panx-it | Nobody138 | 2022-10-22T07:39:49Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-10-22T07:21:11Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.it
split: train
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8124233755619126
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2630
- F1: 0.8124
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8193 | 1.0 | 70 | 0.3200 | 0.7356 |
| 0.2773 | 2.0 | 140 | 0.2841 | 0.7882 |
| 0.1807 | 3.0 | 210 | 0.2630 | 0.8124 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
yongjian/wav2vec2-large-a | yongjian | 2022-10-22T07:21:15Z | 10 | 5 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"en",
"dataset:LIUM/tedlium",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-15T15:03:12Z | ---
language: en
datasets:
- LIUM/tedlium
tags:
- speech
- audio
- automatic-speech-recognition
---
Finetuned from [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self).
# Installation
1. PyTorch installation: https://pytorch.org/
2. Install transformers: https://huggingface.co/docs/transformers/installation
e.g., installation by conda
```
>> conda create -n wav2vec2 python=3.8
>> conda install pytorch cudatoolkit=11.3 -c pytorch
>> conda install -c conda-forge transformers
```
# Usage
```python
# Load the model and processor
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import numpy as np
import torch
model = Wav2Vec2ForCTC.from_pretrained(r'yongjian/wav2vec2-large-a') # Note: PyTorch Model
processor = Wav2Vec2Processor.from_pretrained(r'yongjian/wav2vec2-large-a')
# Load input
np_wav = np.random.normal(size=(16000)).clip(-1, 1) # change it to your sample
# Inference
sample_rate = processor.feature_extractor.sampling_rate
with torch.no_grad():
model_inputs = processor(np_wav, sampling_rate=sample_rate, return_tensors="pt", padding=True)
logits = model(model_inputs.input_values, attention_mask=model_inputs.attention_mask).logits # use .cuda() for GPU acceleration
pred_ids = torch.argmax(logits, dim=-1).cpu()
pred_text = processor.batch_decode(pred_ids)
print('Transcription:', pred_text)
```
# Code
GitHub Repo:
https://github.com/CassiniHuy/wav2vec2_finetune |
Nobody138/xlm-roberta-base-finetuned-panx-fr | Nobody138 | 2022-10-22T07:12:34Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-10-22T06:51:51Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.fr
split: train
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8346456692913387
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2763
- F1: 0.8346
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5779 | 1.0 | 191 | 0.3701 | 0.7701 |
| 0.2735 | 2.0 | 382 | 0.2908 | 0.8254 |
| 0.1769 | 3.0 | 573 | 0.2763 | 0.8346 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
dbocanegrac/ddpm-butterflies-128 | dbocanegrac | 2022-10-22T06:51:59Z | 1 | 0 | diffusers | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
]
| null | 2022-10-22T05:36:43Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/dbocanegrac/ddpm-butterflies-128/tensorboard?#scalars)
|
Nobody138/xlm-roberta-base-finetuned-panx-de-fr | Nobody138 | 2022-10-22T06:51:00Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-10-22T02:24:24Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1608
- F1: 0.8593
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2888 | 1.0 | 715 | 0.1779 | 0.8233 |
| 0.1437 | 2.0 | 1430 | 0.1570 | 0.8497 |
| 0.0931 | 3.0 | 2145 | 0.1608 | 0.8593 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
sd-concepts-library/munch-leaks-style | sd-concepts-library | 2022-10-22T06:31:39Z | 0 | 1 | null | [
"license:mit",
"region:us"
]
| null | 2022-10-22T06:14:27Z | ---
license: mit
---
### Munch Leaks Style on Stable Diffusion
This is the `<munch-leaks-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
Here are images generated in this style:
 |
stanford-crfm/levanter-gpt2-7B | stanford-crfm | 2022-10-22T05:00:03Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-10-21T18:36:04Z | ---
pipeline_tag: text-generation
widget:
text: You could not prevent a thunderstorm, but you could use
---
Levanter GPT2 6.7B is trained on OpenWebText2. This model is still being trained and the intermediate checkpoints are probably not good.
More complete model card will be made in the future. |
rahul77/t5-small-finetuned-thehindu1 | rahul77 | 2022-10-22T02:54:27Z | 9 | 0 | transformers | [
"transformers",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-10-22T02:37:33Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: rahul77/t5-small-finetuned-thehindu1
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. -->
# rahul77/t5-small-finetuned-thehindu1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4672
- Validation Loss: 0.7612
- Train Rouge1: 29.6559
- Train Rouge2: 24.0992
- Train Rougel: 27.7417
- Train Rougelsum: 28.4408
- Train Gen Len: 19.0
- Epoch: 49
## 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 | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 1.2252 | 0.9927 | 25.8031 | 17.7261 | 23.4483 | 25.0648 | 19.0 | 0 |
| 1.0509 | 0.9137 | 28.0482 | 20.6823 | 25.5396 | 27.0125 | 19.0 | 1 |
| 0.9961 | 0.8638 | 28.2964 | 22.1783 | 26.4157 | 27.4368 | 19.0 | 2 |
| 0.9266 | 0.8321 | 27.7054 | 21.8853 | 26.0306 | 26.9068 | 19.0 | 3 |
| 0.8851 | 0.8117 | 28.3740 | 22.8198 | 26.8479 | 27.5047 | 19.0 | 4 |
| 0.8505 | 0.7975 | 28.7979 | 23.1437 | 27.0745 | 27.7887 | 19.0 | 5 |
| 0.8247 | 0.7890 | 28.9634 | 23.3567 | 27.3117 | 28.0320 | 19.0 | 6 |
| 0.8154 | 0.7827 | 28.8667 | 23.4468 | 27.1404 | 27.8453 | 19.0 | 7 |
| 0.7889 | 0.7813 | 29.0498 | 23.6403 | 27.5662 | 28.1518 | 19.0 | 8 |
| 0.7676 | 0.7774 | 29.1829 | 23.5778 | 27.7014 | 28.3268 | 19.0 | 9 |
| 0.7832 | 0.7714 | 29.1040 | 23.3700 | 27.6605 | 28.2650 | 19.0 | 10 |
| 0.7398 | 0.7676 | 29.1040 | 23.3700 | 27.6605 | 28.2650 | 19.0 | 11 |
| 0.7473 | 0.7644 | 29.4387 | 24.1983 | 27.9842 | 28.5700 | 19.0 | 12 |
| 0.7270 | 0.7628 | 29.3128 | 24.1484 | 27.8565 | 28.4215 | 19.0 | 13 |
| 0.7174 | 0.7615 | 29.3128 | 24.1484 | 27.8565 | 28.4215 | 19.0 | 14 |
| 0.7231 | 0.7577 | 29.3838 | 23.9483 | 27.6550 | 28.3416 | 19.0 | 15 |
| 0.7099 | 0.7558 | 29.4866 | 24.1703 | 27.8649 | 28.4404 | 19.0 | 16 |
| 0.7060 | 0.7548 | 29.4866 | 24.1703 | 27.8649 | 28.4404 | 19.0 | 17 |
| 0.6884 | 0.7539 | 29.4866 | 24.1703 | 27.8649 | 28.4404 | 19.0 | 18 |
| 0.6778 | 0.7546 | 29.4866 | 24.1703 | 27.8649 | 28.4404 | 19.0 | 19 |
| 0.6586 | 0.7519 | 29.4866 | 24.1703 | 27.8649 | 28.4404 | 19.0 | 20 |
| 0.6474 | 0.7521 | 29.4866 | 24.1703 | 27.8649 | 28.4404 | 19.0 | 21 |
| 0.6392 | 0.7527 | 29.4866 | 24.1703 | 27.8649 | 28.4404 | 19.0 | 22 |
| 0.6424 | 0.7537 | 29.4866 | 24.1703 | 27.8649 | 28.4404 | 19.0 | 23 |
| 0.6184 | 0.7536 | 29.4866 | 24.1703 | 27.8649 | 28.4404 | 19.0 | 24 |
| 0.6164 | 0.7520 | 29.4866 | 24.0547 | 27.7388 | 28.3416 | 19.0 | 25 |
| 0.6115 | 0.7502 | 29.4866 | 23.9746 | 27.8232 | 28.4227 | 19.0 | 26 |
| 0.6056 | 0.7498 | 29.4866 | 23.9746 | 27.8232 | 28.4227 | 19.0 | 27 |
| 0.6004 | 0.7488 | 29.4451 | 23.7671 | 27.5435 | 28.2982 | 19.0 | 28 |
| 0.5851 | 0.7478 | 29.4451 | 23.7671 | 27.5435 | 28.2982 | 19.0 | 29 |
| 0.5777 | 0.7496 | 29.4866 | 23.9746 | 27.8232 | 28.4227 | 19.0 | 30 |
| 0.5751 | 0.7486 | 29.4866 | 23.9746 | 27.8232 | 28.4227 | 19.0 | 31 |
| 0.5730 | 0.7485 | 29.4866 | 23.9746 | 27.8232 | 28.4227 | 19.0 | 32 |
| 0.5487 | 0.7499 | 29.4962 | 24.0563 | 27.8422 | 28.4356 | 19.0 | 33 |
| 0.5585 | 0.7517 | 29.4962 | 24.0563 | 27.8422 | 28.4356 | 19.0 | 34 |
| 0.5450 | 0.7538 | 29.4962 | 24.0563 | 27.8422 | 28.4356 | 19.0 | 35 |
| 0.5427 | 0.7509 | 29.4962 | 24.0563 | 27.8422 | 28.4356 | 19.0 | 36 |
| 0.5287 | 0.7500 | 29.4962 | 24.0563 | 27.8422 | 28.4356 | 19.0 | 37 |
| 0.5231 | 0.7486 | 29.4962 | 24.0563 | 27.8422 | 28.4356 | 19.0 | 38 |
| 0.5155 | 0.7523 | 29.4962 | 24.0563 | 27.8422 | 28.4356 | 19.0 | 39 |
| 0.5105 | 0.7550 | 29.4962 | 24.0563 | 27.8422 | 28.4356 | 19.0 | 40 |
| 0.5175 | 0.7557 | 29.6736 | 24.3120 | 28.0332 | 28.5828 | 19.0 | 41 |
| 0.5053 | 0.7560 | 29.6736 | 24.3120 | 28.0332 | 28.5828 | 19.0 | 42 |
| 0.4928 | 0.7548 | 29.6736 | 24.3120 | 28.0332 | 28.5828 | 19.0 | 43 |
| 0.4913 | 0.7568 | 29.6559 | 24.0992 | 27.7417 | 28.4408 | 19.0 | 44 |
| 0.4841 | 0.7574 | 29.6559 | 24.0992 | 27.7417 | 28.4408 | 19.0 | 45 |
| 0.4770 | 0.7583 | 29.6736 | 24.3120 | 28.0332 | 28.5828 | 19.0 | 46 |
| 0.4727 | 0.7581 | 29.6736 | 24.3120 | 28.0332 | 28.5828 | 19.0 | 47 |
| 0.4612 | 0.7623 | 29.6736 | 24.3120 | 28.0332 | 28.5828 | 19.0 | 48 |
| 0.4672 | 0.7612 | 29.6559 | 24.0992 | 27.7417 | 28.4408 | 19.0 | 49 |
### Framework versions
- Transformers 4.23.1
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.1
|
sd-concepts-library/dreamy-painting | sd-concepts-library | 2022-10-22T02:48:34Z | 0 | 3 | null | [
"license:mit",
"region:us"
]
| null | 2022-10-22T02:35:47Z | ---
license: mit
---
### Dreamy Painting on Stable Diffusion
This is the `<dreamy-painting>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
Here are images generated in this style:
 |
santoshvutukuri/xlm-roberta-base-esg-ner | santoshvutukuri | 2022-10-22T00:05:18Z | 18 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-10-21T23:21:22Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-esg-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.5073101990487934
- name: Recall
type: recall
value: 0.4846852911477617
- name: F1
type: f1
value: 0.4957397366382649
- name: Accuracy
type: accuracy
value: 0.8926532053923588
---
<!-- 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-esg-ner
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3380
- Precision: 0.5073
- Recall: 0.4847
- F1: 0.4957
- Accuracy: 0.8927
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4859 | 1.0 | 1756 | 0.3975 | 0.4732 | 0.4137 | 0.4415 | 0.8766 |
| 0.331 | 2.0 | 3512 | 0.3380 | 0.5073 | 0.4847 | 0.4957 | 0.8927 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Shaier/longformer_openbook | Shaier | 2022-10-21T23:14:01Z | 1 | 0 | transformers | [
"transformers",
"pytorch",
"longformer",
"multiple-choice",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
]
| multiple-choice | 2022-10-21T22:31:44Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: longformer_openbook
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. -->
# longformer_openbook
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7773
- Accuracy: 0.71
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 25
- total_train_batch_size: 100
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.99 | 49 | 0.8618 | 0.662 |
| No log | 1.99 | 98 | 0.7773 | 0.71 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0
|
dslack/t5-flan-small | dslack | 2022-10-21T22:46:33Z | 12 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-10-21T22:33:36Z | T5 FLAN small model from Google t5x release, compatible with hugging face for ease of use. |
Dickwold/Fv | Dickwold | 2022-10-21T22:01:11Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
]
| null | 2022-10-21T22:01:11Z | ---
license: bigscience-openrail-m
---
|
jayanta/resnet-50-FV2-finetuned-memes | jayanta | 2022-10-21T21:40:08Z | 97 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"resnet",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-10-21T20:49:32Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: resnet-50-FV2-finetuned-memes
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6452859350850078
- name: Precision
type: precision
value: 0.5727919568038408
- name: Recall
type: recall
value: 0.6452859350850078
- name: F1
type: f1
value: 0.5963647629954705
---
<!-- 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. -->
# resnet-50-FV2-finetuned-memes
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9263
- Accuracy: 0.6453
- Precision: 0.5728
- Recall: 0.6453
- F1: 0.5964
## 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: 0.00012
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.5763 | 0.99 | 20 | 1.5575 | 0.4281 | 0.2966 | 0.4281 | 0.2669 |
| 1.4761 | 1.99 | 40 | 1.4424 | 0.4343 | 0.1886 | 0.4343 | 0.2630 |
| 1.3563 | 2.99 | 60 | 1.3240 | 0.4343 | 0.1886 | 0.4343 | 0.2630 |
| 1.2824 | 3.99 | 80 | 1.2636 | 0.4389 | 0.3097 | 0.4389 | 0.2734 |
| 1.2315 | 4.99 | 100 | 1.2119 | 0.4529 | 0.3236 | 0.4529 | 0.3042 |
| 1.1956 | 5.99 | 120 | 1.1764 | 0.4900 | 0.3731 | 0.4900 | 0.3692 |
| 1.1452 | 6.99 | 140 | 1.1424 | 0.5147 | 0.3963 | 0.5147 | 0.4090 |
| 1.1076 | 7.99 | 160 | 1.1190 | 0.5371 | 0.4121 | 0.5371 | 0.4392 |
| 1.0679 | 8.99 | 180 | 1.0825 | 0.5719 | 0.4465 | 0.5719 | 0.4831 |
| 1.0432 | 9.99 | 200 | 1.0482 | 0.5750 | 0.5404 | 0.5750 | 0.4930 |
| 0.9903 | 10.99 | 220 | 1.0275 | 0.5958 | 0.5459 | 0.5958 | 0.5241 |
| 0.9675 | 11.99 | 240 | 1.0145 | 0.6051 | 0.5350 | 0.6051 | 0.5379 |
| 0.9335 | 12.99 | 260 | 0.9860 | 0.6175 | 0.5537 | 0.6175 | 0.5527 |
| 0.9157 | 13.99 | 280 | 0.9683 | 0.6105 | 0.5386 | 0.6105 | 0.5504 |
| 0.8901 | 14.99 | 300 | 0.9558 | 0.6352 | 0.5686 | 0.6352 | 0.5833 |
| 0.8722 | 15.99 | 320 | 0.9382 | 0.6345 | 0.5657 | 0.6345 | 0.5807 |
| 0.854 | 16.99 | 340 | 0.9322 | 0.6376 | 0.5623 | 0.6376 | 0.5856 |
| 0.8494 | 17.99 | 360 | 0.9287 | 0.6422 | 0.6675 | 0.6422 | 0.5918 |
| 0.8652 | 18.99 | 380 | 0.9212 | 0.6399 | 0.5640 | 0.6399 | 0.5863 |
| 0.846 | 19.99 | 400 | 0.9263 | 0.6453 | 0.5728 | 0.6453 | 0.5964 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1.dev0
- Tokenizers 0.13.1
|
jayanta/mit-b2-VF2-finetuned-memes | jayanta | 2022-10-21T20:42:31Z | 44 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:other",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-10-21T18:35:47Z | ---
license: other
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: mit-b2-VF2-finetuned-memes
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8307573415765069
- name: Precision
type: precision
value: 0.8272186656187493
- name: Recall
type: recall
value: 0.8307573415765069
- name: F1
type: f1
value: 0.8286939083150942
---
<!-- 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. -->
# mit-b2-VF2-finetuned-memes
This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6547
- Accuracy: 0.8308
- Precision: 0.8272
- Recall: 0.8308
- F1: 0.8287
## 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: 0.00012
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.3077 | 0.99 | 20 | 1.1683 | 0.5549 | 0.5621 | 0.5549 | 0.5286 |
| 0.9359 | 1.99 | 40 | 0.8573 | 0.6731 | 0.6807 | 0.6731 | 0.6535 |
| 0.7219 | 2.99 | 60 | 0.7106 | 0.7272 | 0.7359 | 0.7272 | 0.7246 |
| 0.6013 | 3.99 | 80 | 0.6445 | 0.7550 | 0.7686 | 0.7550 | 0.7558 |
| 0.5243 | 4.99 | 100 | 0.6717 | 0.7573 | 0.8077 | 0.7573 | 0.7584 |
| 0.4409 | 5.99 | 120 | 0.5315 | 0.8068 | 0.8027 | 0.8068 | 0.7989 |
| 0.3325 | 6.99 | 140 | 0.5159 | 0.8230 | 0.8236 | 0.8230 | 0.8158 |
| 0.2719 | 7.99 | 160 | 0.5250 | 0.8215 | 0.8227 | 0.8215 | 0.8202 |
| 0.242 | 8.99 | 180 | 0.5087 | 0.8277 | 0.8260 | 0.8277 | 0.8268 |
| 0.2247 | 9.99 | 200 | 0.5313 | 0.8215 | 0.8275 | 0.8215 | 0.8218 |
| 0.1955 | 10.99 | 220 | 0.6167 | 0.8130 | 0.8062 | 0.8130 | 0.8073 |
| 0.1567 | 11.99 | 240 | 0.5859 | 0.8168 | 0.8185 | 0.8168 | 0.8173 |
| 0.1479 | 12.99 | 260 | 0.5938 | 0.8215 | 0.8169 | 0.8215 | 0.8178 |
| 0.1241 | 13.99 | 280 | 0.6187 | 0.8261 | 0.8234 | 0.8261 | 0.8239 |
| 0.1114 | 14.99 | 300 | 0.6419 | 0.8261 | 0.8351 | 0.8261 | 0.8293 |
| 0.1022 | 15.99 | 320 | 0.6322 | 0.8323 | 0.8284 | 0.8323 | 0.8294 |
| 0.0941 | 16.99 | 340 | 0.6595 | 0.8269 | 0.8266 | 0.8269 | 0.8263 |
| 0.0935 | 17.99 | 360 | 0.6674 | 0.8269 | 0.8218 | 0.8269 | 0.8237 |
| 0.089 | 18.99 | 380 | 0.6533 | 0.8253 | 0.8222 | 0.8253 | 0.8235 |
| 0.0794 | 19.99 | 400 | 0.6547 | 0.8308 | 0.8272 | 0.8308 | 0.8287 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1.dev0
- Tokenizers 0.13.1
|
sd-dreambooth-library/MoonKnightCkpt | sd-dreambooth-library | 2022-10-21T20:00:11Z | 0 | 2 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2022-10-21T19:29:02Z | ---
license: creativeml-openrail-m
---
Model trained with the SD 1.5: runwayml/stable-diffusion-v1-5
Dreambooth google colab: https://colab.research.google.com/github/ShivamShrirao/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb
youtube video of the training: https://www.youtube.com/watch?v=uzmJXDSxoRk&ab_channel=InversiaImages
|
alefarasin/halfcheetah-expert-v2 | alefarasin | 2022-10-21T19:42:48Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"dataset:decision_transformer_gym_replay",
"endpoints_compatible",
"region:us"
]
| null | 2022-10-21T19:36:40Z | ---
tags:
- generated_from_trainer
datasets:
- decision_transformer_gym_replay
model-index:
- name: output
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output
This model is a fine-tuned version of [](https://huggingface.co/) on the decision_transformer_gym_replay dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 120
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
chinhon/pegasus-multi_news_wires_hdwriter42k | chinhon | 2022-10-21T18:28:09Z | 11 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-10-21T10:06:21Z | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pegasus-multi_news_wires_hdwriter42k
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. -->
# pegasus-multi_news_wires_hdwriter42k
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6427
- Rouge1: 37.3045
- Rouge2: 17.2478
- Rougel: 30.7768
- Rougelsum: 31.3514
- Gen Len: 34.6955
## 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: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.7914 | 1.0 | 16875 | 1.6849 | 36.6608 | 17.005 | 30.4166 | 30.9289 | 35.4077 |
| 1.6658 | 2.0 | 33750 | 1.6452 | 37.2837 | 17.3162 | 30.8358 | 31.3382 | 34.7757 |
| 1.5478 | 3.0 | 50625 | 1.6427 | 37.3045 | 17.2478 | 30.7768 | 31.3514 | 34.6955 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
g30rv17ys/tjkicksmodel3 | g30rv17ys | 2022-10-21T18:17:46Z | 2 | 0 | diffusers | [
"diffusers",
"tensorboard",
"en",
"dataset:geevegeorge/tjkicksdb3",
"license:apache-2.0",
"diffusers:AudioDiffusionPipeline",
"region:us"
]
| null | 2022-10-21T18:17:14Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: geevegeorge/tjkicksdb3
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# tjkicksmodel3
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `geevegeorge/tjkicksdb3` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- gradient_accumulation_steps: 8
- optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08
- lr_scheduler: cosine
- lr_warmup_steps: 500
- ema_inv_gamma: 1.0
- ema_inv_gamma: 0.75
- ema_inv_gamma: 0.9999
- mixed_precision: no
### Training results
📈 [TensorBoard logs](https://huggingface.co/geevegeorge/tjkicksmodel3/tensorboard?#scalars)
|
jteng/bert-finetuned-syllabus | jteng | 2022-10-21T17:59:58Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2022-09-24T02:57:43Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-syllabus
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-syllabus
This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on an unknown dataset.
## 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
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu102
- Datasets 2.5.1
- Tokenizers 0.12.1
|
ViktorDo/DistilBERT-WIKI_Climber_Finetuned | ViktorDo | 2022-10-21T17:42:57Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-10-21T16:56:38Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: DistilBERT-WIKI_Climber_Finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DistilBERT-WIKI_Climber_Finetuned
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: 0.0613
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0741 | 1.0 | 2364 | 0.0757 |
| 0.0504 | 2.0 | 4728 | 0.0586 |
| 0.0361 | 3.0 | 7092 | 0.0613 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
mayankb96/bert-base-uncased-finetuned-lexglue | mayankb96 | 2022-10-21T17:24:15Z | 4 | 2 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:lex_glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-10-21T17:01:39Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- lex_glue
model-index:
- name: bert-base-uncased-finetuned-lexglue
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-lexglue
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the lex_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0125
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7154 | 1.0 | 1250 | 1.1155 |
| 0.9658 | 2.0 | 2500 | 1.0348 |
| 1.0321 | 3.0 | 3750 | 1.0125 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.6.1
- Tokenizers 0.12.1
|
jayanta/convnext-large-224-22k-1k-FV2-finetuned-memes | jayanta | 2022-10-21T16:48:07Z | 40 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"convnext",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-10-21T16:09:30Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: convnext-large-224-22k-1k-FV2-finetuned-memes
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.866306027820711
- name: Precision
type: precision
value: 0.8617341777601428
- name: Recall
type: recall
value: 0.866306027820711
- name: F1
type: f1
value: 0.8629450778711495
---
<!-- 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. -->
# convnext-large-224-22k-1k-FV2-finetuned-memes
This model is a fine-tuned version of [facebook/convnext-large-224-22k-1k](https://huggingface.co/facebook/convnext-large-224-22k-1k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4290
- Accuracy: 0.8663
- Precision: 0.8617
- Recall: 0.8663
- F1: 0.8629
## 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: 0.00012
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8992 | 0.99 | 20 | 0.6455 | 0.7658 | 0.7512 | 0.7658 | 0.7534 |
| 0.4245 | 1.99 | 40 | 0.4008 | 0.8539 | 0.8680 | 0.8539 | 0.8541 |
| 0.2054 | 2.99 | 60 | 0.3245 | 0.8694 | 0.8631 | 0.8694 | 0.8650 |
| 0.1102 | 3.99 | 80 | 0.3231 | 0.8671 | 0.8624 | 0.8671 | 0.8645 |
| 0.0765 | 4.99 | 100 | 0.3882 | 0.8563 | 0.8603 | 0.8563 | 0.8556 |
| 0.0642 | 5.99 | 120 | 0.4133 | 0.8601 | 0.8604 | 0.8601 | 0.8598 |
| 0.0574 | 6.99 | 140 | 0.3889 | 0.8694 | 0.8657 | 0.8694 | 0.8667 |
| 0.0526 | 7.99 | 160 | 0.4145 | 0.8655 | 0.8705 | 0.8655 | 0.8670 |
| 0.0468 | 8.99 | 180 | 0.4256 | 0.8679 | 0.8642 | 0.8679 | 0.8650 |
| 0.0472 | 9.99 | 200 | 0.4290 | 0.8663 | 0.8617 | 0.8663 | 0.8629 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1.dev0
- Tokenizers 0.13.1
|
sd-concepts-library/azura-from-vibrant-venture | sd-concepts-library | 2022-10-21T14:50:19Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2022-10-21T14:50:13Z | ---
license: mit
---
### azura-from-vibrant-venture on Stable Diffusion
This is the `<azura>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
nlp-waseda/roberta-large-japanese-seq512 | nlp-waseda | 2022-10-21T14:49:40Z | 145 | 4 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-06-13T09:46:45Z | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
- cc100
mask_token: "[MASK]"
widget:
- text: "早稲田 大学 で 自然 言語 処理 を [MASK] する 。"
---
# nlp-waseda/roberta-large-japanese-seq512
## Model description
This is a Japanese RoBERTa large model pretrained on Japanese Wikipedia and the Japanese portion of CC-100 with the maximum sequence length of 512.
## How to use
You can use this model for masked language modeling as follows:
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("nlp-waseda/roberta-large-japanese-seq512")
model = AutoModelForMaskedLM.from_pretrained("nlp-waseda/roberta-large-japanese-seq512")
sentence = '早稲田 大学 で 自然 言語 処理 を [MASK] する 。' # input should be segmented into words by Juman++ in advance
encoding = tokenizer(sentence, return_tensors='pt')
...
```
You can fine-tune this model on downstream tasks.
## Tokenization
The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in advance. Juman++ 2.0.0-rc3 was used for pretraining. Each word is tokenized into tokens by [sentencepiece](https://github.com/google/sentencepiece).
`BertJapaneseTokenizer` now supports automatic `JumanppTokenizer` and `SentencepieceTokenizer`. You can use [this model](https://huggingface.co/nlp-waseda/roberta-large-japanese-seq512-with-auto-jumanpp) without any data preprocessing.
## Vocabulary
The vocabulary consists of 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece).
## Training procedure
This model was trained on Japanese Wikipedia (as of 20210920) and the Japanese portion of CC-100 from the checkpoint of [nlp-waseda/roberta-large-japanese](https://huggingface.co/nlp-waseda/roberta-large-japanese). It took a week using eight NVIDIA A100 GPUs.
The following hyperparameters were used during pretraining:
- learning_rate: 6e-5
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 4120 (max_seq_length=128), 4032 (max_seq_length=512)
- max_seq_length: 512
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-6
- lr_scheduler_type: linear
- training_steps: 670000 (max_seq_length=128) + 70000 (max_seq_length=512)
- warmup_steps: 10000
- mixed_precision_training: Native AMP
|
nlp-waseda/roberta-large-japanese | nlp-waseda | 2022-10-21T14:48:46Z | 118 | 23 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-05-10T08:37:48Z | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
- cc100
mask_token: "[MASK]"
widget:
- text: "早稲田 大学 で 自然 言語 処理 を [MASK] する 。"
---
# nlp-waseda/roberta-large-japanese
## Model description
This is a Japanese RoBERTa large model pretrained on Japanese Wikipedia and the Japanese portion of CC-100.
## How to use
You can use this model for masked language modeling as follows:
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("nlp-waseda/roberta-large-japanese")
model = AutoModelForMaskedLM.from_pretrained("nlp-waseda/roberta-large-japanese")
sentence = '早稲田 大学 で 自然 言語 処理 を [MASK] する 。' # input should be segmented into words by Juman++ in advance
encoding = tokenizer(sentence, return_tensors='pt')
...
```
You can fine-tune this model on downstream tasks.
## Tokenization
The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in advance. Juman++ 2.0.0-rc3 was used for pretraining. Each word is tokenized into tokens by [sentencepiece](https://github.com/google/sentencepiece).
`BertJapaneseTokenizer` now supports automatic `JumanppTokenizer` and `SentencepieceTokenizer`. You can use [this model](https://huggingface.co/nlp-waseda/roberta-large-japanese-with-auto-jumanpp) without any data preprocessing.
## Vocabulary
The vocabulary consists of 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece).
## Training procedure
This model was trained on Japanese Wikipedia (as of 20210920) and the Japanese portion of CC-100. It took two weeks using eight NVIDIA A100 GPUs.
The following hyperparameters were used during pretraining:
- learning_rate: 6e-5
- per_device_train_batch_size: 103
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 5
- total_train_batch_size: 4120
- max_seq_length: 128
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-6
- lr_scheduler_type: linear
- training_steps: 670000
- warmup_steps: 10000
- mixed_precision_training: Native AMP
## Performance on JGLUE
See the [Baseline Scores](https://github.com/yahoojapan/JGLUE#baseline-scores) of JGLUE.
|
huggingtweets/iangabchri-nisipisa-tyler02020202 | huggingtweets | 2022-10-21T14:48:20Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-10-21T14:46:52Z | ---
language: en
thumbnail: http://www.huggingtweets.com/iangabchri-nisipisa-tyler02020202/1666363695853/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/1563876002329231363/RPhmnhOa_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/1474994961896644608/um4unzmz_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/1548021440191926272/FaXKxAO__400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">gab & tyler & nisa, from online</div>
<div style="text-align: center; font-size: 14px;">@iangabchri-nisipisa-tyler02020202</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 gab & tyler & nisa, from online.
| Data | gab | tyler | nisa, from online |
| --- | --- | --- | --- |
| Tweets downloaded | 253 | 2595 | 3221 |
| Retweets | 66 | 102 | 237 |
| Short tweets | 5 | 632 | 342 |
| Tweets kept | 182 | 1861 | 2642 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3rlxqnm8/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 @iangabchri-nisipisa-tyler02020202's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/gg2ms4z1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/gg2ms4z1/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/iangabchri-nisipisa-tyler02020202')
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)
|
nlp-waseda/roberta-base-japanese | nlp-waseda | 2022-10-21T14:46:36Z | 1,339 | 32 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-03-02T23:29:05Z | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
- cc100
mask_token: "[MASK]"
widget:
- text: "早稲田 大学 で 自然 言語 処理 を [MASK] する 。"
---
# nlp-waseda/roberta-base-japanese
## Model description
This is a Japanese RoBERTa base model pretrained on Japanese Wikipedia and the Japanese portion of CC-100.
## How to use
You can use this model for masked language modeling as follows:
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("nlp-waseda/roberta-base-japanese")
model = AutoModelForMaskedLM.from_pretrained("nlp-waseda/roberta-base-japanese")
sentence = '早稲田 大学 で 自然 言語 処理 を [MASK] する 。' # input should be segmented into words by Juman++ in advance
encoding = tokenizer(sentence, return_tensors='pt')
...
```
You can fine-tune this model on downstream tasks.
## Tokenization
The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in advance. Juman++ 2.0.0-rc3 was used for pretraining. Each word is tokenized into tokens by [sentencepiece](https://github.com/google/sentencepiece).
`BertJapaneseTokenizer` now supports automatic `JumanppTokenizer` and `SentencepieceTokenizer`. You can use [this model](https://huggingface.co/nlp-waseda/roberta-base-japanese-with-auto-jumanpp) without any data preprocessing.
## Vocabulary
The vocabulary consists of 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece).
## Training procedure
This model was trained on Japanese Wikipedia (as of 20210920) and the Japanese portion of CC-100. It took a week using eight NVIDIA A100 GPUs.
The following hyperparameters were used during pretraining:
- learning_rate: 1e-4
- per_device_train_batch_size: 256
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 4096
- max_seq_length: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 700000
- warmup_steps: 10000
- mixed_precision_training: Native AMP
## Performance on JGLUE
See the [Baseline Scores](https://github.com/yahoojapan/JGLUE#baseline-scores) of JGLUE.
|
criadorabr/fffff | criadorabr | 2022-10-21T14:20:10Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2022-10-21T14:20:10Z | ---
license: creativeml-openrail-m
---
|
ctu-aic/xlm-roberta-large-xnli-csfever_nli | ctu-aic | 2022-10-21T14:10:34Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"arxiv:2201.11115",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-10-21T14:04:49Z | ('---\ndatasets:\n- ctu-aic/csfever_nli\nlanguages:\n- cs\nlicense: cc-by-sa-4.0\ntags:\n- natural-language-inference\n\n---',)
# 🦾 xlm-roberta-large-xnli-csfever_nli
Transformer model for **Natural Language Inference** in ['cs'] languages finetuned on ['ctu-aic/csfever_nli'] datasets.
## 🧰 Usage
### 👾 Using UKPLab `sentence_transformers` `CrossEncoder`
The model was trained using the `CrossEncoder` API and we recommend it for its usage.
```python
from sentence_transformers.cross_encoder import CrossEncoder
model = CrossEncoder('ctu-aic/xlm-roberta-large-xnli-csfever_nli')
scores = model.predict([["My first context.", "My first hypothesis."],
["Second context.", "Hypothesis."]])
```
### 🤗 Using Huggingface `transformers`
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ctu-aic/xlm-roberta-large-xnli-csfever_nli")
tokenizer = AutoTokenizer.from_pretrained("ctu-aic/xlm-roberta-large-xnli-csfever_nli")
```
## 🌳 Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
## 👬 Authors
The model was trained and uploaded by **[ullriher](https://udb.fel.cvut.cz/?uid=ullriher&sn=&givenname=&_cmd=Hledat&_reqn=1&_type=user&setlang=en)** (e-mail: [[email protected]](mailto:[email protected]))
The code was codeveloped by the NLP team at Artificial Intelligence Center of CTU in Prague ([AIC](https://www.aic.fel.cvut.cz/)).
## 🔐 License
[cc-by-sa-4.0](https://choosealicense.com/licenses/cc-by-sa-4.0)
## 💬 Citation
If you find this repository helpful, feel free to cite our publication:
```
@article{DBLP:journals/corr/abs-2201-11115,
author = {Herbert Ullrich and
Jan Drchal and
Martin R{'{y}}par and
Hana Vincourov{'{a}} and
V{'{a}}clav Moravec},
title = {CsFEVER and CTKFacts: Acquiring Czech Data for Fact Verification},
journal = {CoRR},
volume = {abs/2201.11115},
year = {2022},
url = {https://arxiv.org/abs/2201.11115},
eprinttype = {arXiv},
eprint = {2201.11115},
timestamp = {Tue, 01 Feb 2022 14:59:01 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-11115.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
ctu-aic/xlm-roberta-large-squad2-csfever_nearestp | ctu-aic | 2022-10-21T14:04:35Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"arxiv:2201.11115",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-10-21T13:56:25Z | ('---\ndatasets:\n- ctu-aic/csfever_nearestp\nlanguages:\n- cs\nlicense: cc-by-sa-4.0\ntags:\n- natural-language-inference\n\n---',)
# 🦾 xlm-roberta-large-squad2-csfever_nearestp
Transformer model for **Natural Language Inference** in ['cs'] languages finetuned on ['ctu-aic/csfever_nearestp'] datasets.
## 🧰 Usage
### 👾 Using UKPLab `sentence_transformers` `CrossEncoder`
The model was trained using the `CrossEncoder` API and we recommend it for its usage.
```python
from sentence_transformers.cross_encoder import CrossEncoder
model = CrossEncoder('ctu-aic/xlm-roberta-large-squad2-csfever_nearestp')
scores = model.predict([["My first context.", "My first hypothesis."],
["Second context.", "Hypothesis."]])
```
### 🤗 Using Huggingface `transformers`
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ctu-aic/xlm-roberta-large-squad2-csfever_nearestp")
tokenizer = AutoTokenizer.from_pretrained("ctu-aic/xlm-roberta-large-squad2-csfever_nearestp")
```
## 🌳 Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
## 👬 Authors
The model was trained and uploaded by **[ullriher](https://udb.fel.cvut.cz/?uid=ullriher&sn=&givenname=&_cmd=Hledat&_reqn=1&_type=user&setlang=en)** (e-mail: [[email protected]](mailto:[email protected]))
The code was codeveloped by the NLP team at Artificial Intelligence Center of CTU in Prague ([AIC](https://www.aic.fel.cvut.cz/)).
## 🔐 License
[cc-by-sa-4.0](https://choosealicense.com/licenses/cc-by-sa-4.0)
## 💬 Citation
If you find this repository helpful, feel free to cite our publication:
```
@article{DBLP:journals/corr/abs-2201-11115,
author = {Herbert Ullrich and
Jan Drchal and
Martin R{'{y}}par and
Hana Vincourov{'{a}} and
V{'{a}}clav Moravec},
title = {CsFEVER and CTKFacts: Acquiring Czech Data for Fact Verification},
journal = {CoRR},
volume = {abs/2201.11115},
year = {2022},
url = {https://arxiv.org/abs/2201.11115},
eprinttype = {arXiv},
eprint = {2201.11115},
timestamp = {Tue, 01 Feb 2022 14:59:01 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-11115.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
ctu-aic/bert-base-multilingual-cased-csfever_nearestp | ctu-aic | 2022-10-21T13:56:12Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"arxiv:2201.11115",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-10-21T13:53:09Z | ('---\ndatasets:\n- ctu-aic/csfever_nearestp\nlanguages:\n- cs\nlicense: cc-by-sa-4.0\ntags:\n- natural-language-inference\n\n---',)
# 🦾 bert-base-multilingual-cased-csfever_nearestp
Transformer model for **Natural Language Inference** in ['cs'] languages finetuned on ['ctu-aic/csfever_nearestp'] datasets.
## 🧰 Usage
### 👾 Using UKPLab `sentence_transformers` `CrossEncoder`
The model was trained using the `CrossEncoder` API and we recommend it for its usage.
```python
from sentence_transformers.cross_encoder import CrossEncoder
model = CrossEncoder('ctu-aic/bert-base-multilingual-cased-csfever_nearestp')
scores = model.predict([["My first context.", "My first hypothesis."],
["Second context.", "Hypothesis."]])
```
### 🤗 Using Huggingface `transformers`
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ctu-aic/bert-base-multilingual-cased-csfever_nearestp")
tokenizer = AutoTokenizer.from_pretrained("ctu-aic/bert-base-multilingual-cased-csfever_nearestp")
```
## 🌳 Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
## 👬 Authors
The model was trained and uploaded by **[ullriher](https://udb.fel.cvut.cz/?uid=ullriher&sn=&givenname=&_cmd=Hledat&_reqn=1&_type=user&setlang=en)** (e-mail: [[email protected]](mailto:[email protected]))
The code was codeveloped by the NLP team at Artificial Intelligence Center of CTU in Prague ([AIC](https://www.aic.fel.cvut.cz/)).
## 🔐 License
[cc-by-sa-4.0](https://choosealicense.com/licenses/cc-by-sa-4.0)
## 💬 Citation
If you find this repository helpful, feel free to cite our publication:
```
@article{DBLP:journals/corr/abs-2201-11115,
author = {Herbert Ullrich and
Jan Drchal and
Martin R{'{y}}par and
Hana Vincourov{'{a}} and
V{'{a}}clav Moravec},
title = {CsFEVER and CTKFacts: Acquiring Czech Data for Fact Verification},
journal = {CoRR},
volume = {abs/2201.11115},
year = {2022},
url = {https://arxiv.org/abs/2201.11115},
eprinttype = {arXiv},
eprint = {2201.11115},
timestamp = {Tue, 01 Feb 2022 14:59:01 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-11115.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
DemangeJeremy/4-sentiments-with-flaubert | DemangeJeremy | 2022-10-21T13:46:12Z | 13 | 0 | transformers | [
"transformers",
"pytorch",
"flaubert",
"text-classification",
"sentiments",
"french",
"flaubert-large",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-03-02T23:29:04Z | ---
language: fr
tags:
- sentiments
- text-classification
- flaubert
- french
- flaubert-large
---
# Modèle de détection de 4 sentiments avec FlauBERT (mixed, negative, objective, positive)
### Comment l'utiliser ?
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
loaded_tokenizer = AutoTokenizer.from_pretrained('flaubert/flaubert_large_cased')
loaded_model = AutoModelForSequenceClassification.from_pretrained("DemangeJeremy/4-sentiments-with-flaubert")
nlp = pipeline('sentiment-analysis', model=loaded_model, tokenizer=loaded_tokenizer)
print(nlp("Je suis plutôt confiant."))
```
```
[{'label': 'OBJECTIVE', 'score': 0.3320835530757904}]
```
## Résultats de l'évaluation du modèle
| Epoch | Validation Loss | Samples Per Second |
|:------:|:--------------:|:------------------:|
| 1 | 2.219246 | 49.476000 |
| 2 | 1.883753 | 47.259000 |
| 3 | 1.747969 | 44.957000 |
| 4 | 1.695606 | 43.872000 |
| 5 | 1.641470 | 45.726000 |
## Citation
Pour toute utilisation de ce modèle, merci d'utiliser cette citation :
> Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, <https://huggingface.co/DemangeJeremy/4-sentiments-with-flaubert>
|
orkg/orkgnlp-predicates-clustering | orkg | 2022-10-21T13:40:57Z | 0 | 0 | null | [
"onnx",
"license:mit",
"region:us"
]
| null | 2022-05-09T08:02:12Z | ---
license: mit
---
This Repository includes the files required to run the `Predicates Clustering` ORKG-NLP service.
Please check [this article](https://orkg-nlp-pypi.readthedocs.io/en/latest/services/services.html) for more details about the service.
The [Scikit-Learn](https://scikit-learn.org/stable/) models are converted using [skl2onnx](https://github.com/onnx/sklearn-onnx) and may not include all original scikit-learn functionalities. |
ctu-aic/xlm-roberta-large-squad2-ctkfacts_nli | ctu-aic | 2022-10-21T13:32:10Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"arxiv:2201.11115",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-10-21T13:24:29Z | ('---\ndatasets:\n- ctu-aic/ctkfacts_nli\nlanguages:\n- cs\nlicense: cc-by-sa-4.0\ntags:\n- natural-language-inference\n\n---',)
# 🦾 xlm-roberta-large-squad2-ctkfacts_nli
Transformer model for **Natural Language Inference** in ['cs'] languages finetuned on ['ctu-aic/ctkfacts_nli'] datasets.
## 🧰 Usage
### 👾 Using UKPLab `sentence_transformers` `CrossEncoder`
The model was trained using the `CrossEncoder` API and we recommend it for its usage.
```python
from sentence_transformers.cross_encoder import CrossEncoder
model = CrossEncoder('ctu-aic/xlm-roberta-large-squad2-ctkfacts_nli')
scores = model.predict([["My first context.", "My first hypothesis."],
["Second context.", "Hypothesis."]])
```
### 🤗 Using Huggingface `transformers`
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ctu-aic/xlm-roberta-large-squad2-ctkfacts_nli")
tokenizer = AutoTokenizer.from_pretrained("ctu-aic/xlm-roberta-large-squad2-ctkfacts_nli")
```
## 🌳 Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
## 👬 Authors
The model was trained and uploaded by **[ullriher](https://udb.fel.cvut.cz/?uid=ullriher&sn=&givenname=&_cmd=Hledat&_reqn=1&_type=user&setlang=en)** (e-mail: [[email protected]](mailto:[email protected]))
The code was codeveloped by the NLP team at Artificial Intelligence Center of CTU in Prague ([AIC](https://www.aic.fel.cvut.cz/)).
## 🔐 License
[cc-by-sa-4.0](https://choosealicense.com/licenses/cc-by-sa-4.0)
## 💬 Citation
If you find this repository helpful, feel free to cite our publication:
```
@article{DBLP:journals/corr/abs-2201-11115,
author = {Herbert Ullrich and
Jan Drchal and
Martin R{'{y}}par and
Hana Vincourov{'{a}} and
V{'{a}}clav Moravec},
title = {CsFEVER and CTKFacts: Acquiring Czech Data for Fact Verification},
journal = {CoRR},
volume = {abs/2201.11115},
year = {2022},
url = {https://arxiv.org/abs/2201.11115},
eprinttype = {arXiv},
eprint = {2201.11115},
timestamp = {Tue, 01 Feb 2022 14:59:01 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-11115.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
ViktorDo/DistilBERT-WIKI_Growth_Form_Finetuned | ViktorDo | 2022-10-21T13:25:16Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-10-21T12:41:06Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: DistilBERT-WIKI_Growth_Form_Finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DistilBERT-WIKI_Growth_Form_Finetuned
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: 0.2666
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2454 | 1.0 | 2320 | 0.2530 |
| 0.1875 | 2.0 | 4640 | 0.2578 |
| 0.1386 | 3.0 | 6960 | 0.2666 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
GeniusVoice/bertje-visio-retriever | GeniusVoice | 2022-10-21T12:35:40Z | 3 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2022-10-21T12:22:56Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 217 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 21,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
sd-concepts-library/cortana | sd-concepts-library | 2022-10-21T12:32:55Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2022-10-21T12:32:43Z | ---
license: mit
---
### cortana on Stable Diffusion
This is the `<cortana>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
huggingtweets/tszzl | huggingtweets | 2022-10-21T12:32:06Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: http://www.huggingtweets.com/tszzl/1666355521581/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/1572784789291401216/1WrwslUF_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">roon</div>
<div style="text-align: center; font-size: 14px;">@tszzl</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 roon.
| Data | roon |
| --- | --- |
| Tweets downloaded | 3207 |
| Retweets | 779 |
| Short tweets | 375 |
| Tweets kept | 2053 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/nr9oggv1/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 @tszzl's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/12g6sck7) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/12g6sck7/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/tszzl')
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)
|
reza-aditya/bert-finetuned-squad | reza-aditya | 2022-10-21T12:22:10Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2022-10-21T09:57:54Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
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 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Yinxing/ddpm-butterflies-128 | Yinxing | 2022-10-21T12:05:23Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
]
| null | 2022-10-21T10:51:28Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/Yinxing/ddpm-butterflies-128/tensorboard?#scalars)
|
philschmid/setfit-ag-news-endpoint | philschmid | 2022-10-21T11:04:26Z | 11 | 8 | setfit | [
"setfit",
"pytorch",
"mpnet",
"endpoints-template",
"text-classification",
"arxiv:2209.11055",
"license:mit",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-10-21T09:53:46Z | ---
license: mit
tags:
- setfit
- endpoints-template
- text-classification
inference: false
---
# SetFit AG News
This is a [SetFit](https://github.com/huggingface/setfit/tree/main) classifier fine-tuned on the [AG News](https://huggingface.co/datasets/ag_news) dataset.
The model was created following the [Outperform OpenAI GPT-3 with SetFit for text-classifiation](https://www.philschmid.de/getting-started-setfit) blog post of [Philipp Schmid](https://www.linkedin.com/in/philipp-schmid-a6a2bb196/).
The model achieves an accuracy of 0.87 on the test set and was only trained with `32` total examples (8 per class).
```bash
***** Running evaluation *****
model used: sentence-transformers/all-mpnet-base-v2
train dataset: 32 samples
accuracy: 0.8731578947368421
```
#### What is SetFit?
"SetFit" (https://arxiv.org/abs/2209.11055) is a new approach that can be used to create high accuracte text-classification models with limited labeled data. SetFit is outperforming GPT-3 in 7 out of 11 tasks, while being 1600x smaller.
Check out the blog to learn more: [Outperform OpenAI GPT-3 with SetFit for text-classifiation](https://www.philschmid.de/getting-started-setfit)
# Inference Endpoints
The model repository also implements a generic custom `handler.py` as an example for how to use `SetFit` models with [inference-endpoints](https://hf.co/inference-endpoints).
Code: https://huggingface.co/philschmid/setfit-ag-news-endpoint/blob/main/handler.py
## Send requests with Pyton
We are going to use requests to send our requests. (make your you have it installed `pip install requests`)
```python
import json
import requests as r
ENDPOINT_URL=""# url of your endpoint
HF_TOKEN=""
# payload samples
regular_payload = { "inputs": "Coming to The Rescue Got a unique problem? Not to worry: you can find a financial planner for every specialized need"}
# HTTP headers for authorization
headers= {
"Authorization": f"Bearer {HF_TOKEN}",
"Content-Type": "application/json"
}
# send request
response = r.post(ENDPOINT_URL, headers=headers, json=paramter_payload)
classified = response.json()
print(classified)
# [ { "label": "World", "score": 0.12341519122860946 }, { "label": "Sports", "score": 0.11741269832494523 }, { "label": "Business", "score": 0.6124446065942992 }, { "label": "Sci/Tech", "score": 0.14672750385214603 } ]
```
**curl example**
```bash
curl https://YOURDOMAIN.us-east-1.aws.endpoints.huggingface.cloud \
-X POST \
-d '{"inputs": "Coming to The Rescue Got a unique problem? Not to worry: you can find a financial planner for every specialized need"}' \
-H "Authorization: Bearer XXX" \
-H "Content-Type: application/json"
``` |
ashish23993/t5-small-finetuned-xsum-a | ashish23993 | 2022-10-21T10:48:19Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-10-21T10:43:22Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-small-finetuned-xsum-a
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum-a
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 8 | 2.2554 | 21.1449 | 9.0713 | 17.7765 | 20.1134 | 19.0 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Rohit129/rohit-setfit-model | Rohit129 | 2022-10-21T10:45:05Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2022-10-21T10:44:51Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 40,
"warmup_steps": 4,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
gulermuslim/distilbert-base-uncased-finetuned-emotion | gulermuslim | 2022-10-21T09:48:14Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-10-21T09:38:18Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9245
- name: F1
type: f1
value: 0.9246934497325665
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2157
- Accuracy: 0.9245
- F1: 0.9247
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8781 | 1.0 | 250 | 0.3374 | 0.8995 | 0.8951 |
| 0.2546 | 2.0 | 500 | 0.2157 | 0.9245 | 0.9247 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
kwdev2000/finetuning-sentiment-model-3000-samples | kwdev2000 | 2022-10-21T08:23:12Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-10-20T21:24:15Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8533333333333334
- name: F1
type: f1
value: 0.8543046357615894
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3398
- Accuracy: 0.8533
- F1: 0.8543
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
nicolarici/LawBERT-IT | nicolarici | 2022-10-21T07:59:20Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"pretraining",
"endpoints_compatible",
"region:us"
]
| null | 2022-10-21T07:40:09Z | **LawBERT-IT**
An Italian BERT model for the legal domain.
The code used for developing and training the model and the dataset used to extract the new words and continue the training of the BERT model are available on [GitHub](https://github.com/nicolarici/LawBERT-IT). |
teacookies/autotrain-21102022-cert-1827562840 | teacookies | 2022-10-21T07:41:52Z | 12 | 0 | transformers | [
"transformers",
"pytorch",
"autotrain",
"token-classification",
"unk",
"dataset:teacookies/autotrain-data-21102022-cert",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-10-21T07:29:56Z | ---
tags:
- autotrain
- token-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- teacookies/autotrain-data-21102022-cert
co2_eq_emissions:
emissions: 19.94429730071814
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1827562840
- CO2 Emissions (in grams): 19.9443
## Validation Metrics
- Loss: 0.028
- Accuracy: 0.992
- Precision: 0.820
- Recall: 0.885
- F1: 0.851
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/teacookies/autotrain-21102022-cert-1827562840
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("teacookies/autotrain-21102022-cert-1827562840", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autotrain-21102022-cert-1827562840", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
sujit27/q-FrozenLake-v1-4x4-noSlippery | sujit27 | 2022-10-21T07:20:31Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-10-21T07:20:23Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="sujit27/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
nlp-waseda/roberta-large-japanese-seq512-with-auto-jumanpp | nlp-waseda | 2022-10-21T06:56:38Z | 1,666 | 5 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-10-15T06:04:06Z | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
- cc100
mask_token: "[MASK]"
widget:
- text: "早稲田大学で自然言語処理を[MASK]する。"
---
# nlp-waseda/roberta-large-japanese-seq512-with-auto-jumanpp
## Model description
This is a Japanese RoBERTa large model pretrained on Japanese Wikipedia and the Japanese portion of CC-100 with the maximum sequence length of 512.
## How to use
You can use this model for masked language modeling as follows:
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("nlp-waseda/roberta-large-japanese-seq512-with-auto-jumanpp")
model = AutoModelForMaskedLM.from_pretrained("nlp-waseda/roberta-large-japanese-seq512-with-auto-jumanpp")
sentence = '早稲田大学で自然言語処理を[MASK]する。'
encoding = tokenizer(sentence, return_tensors='pt')
...
```
You can fine-tune this model on downstream tasks.
## Tokenization
`BertJapaneseTokenizer` now supports automatic tokenization for [Juman++](https://github.com/ku-nlp/jumanpp). However, if your dataset is large, you may take a long time since `BertJapaneseTokenizer` still does not supoort fast tokenization. You can still do the Juman++ tokenization by your self and use the old model [nlp-waseda/roberta-large-japanese-seq512](https://huggingface.co/nlp-waseda/roberta-large-japanese-seq512).
Juman++ 2.0.0-rc3 was used for pretraining. Each word is tokenized into tokens by [sentencepiece](https://github.com/google/sentencepiece).
## Vocabulary
The vocabulary consists of 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece).
## Training procedure
This model was trained on Japanese Wikipedia (as of 20210920) and the Japanese portion of CC-100 from the checkpoint of [nlp-waseda/roberta-large-japanese](https://huggingface.co/nlp-waseda/roberta-large-japanese). It took a week using eight NVIDIA A100 GPUs.
The following hyperparameters were used during pretraining:
- learning_rate: 6e-5
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 4120 (max_seq_length=128), 4032 (max_seq_length=512)
- max_seq_length: 512
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-6
- lr_scheduler_type: linear
- training_steps: 670000 (max_seq_length=128) + 70000 (max_seq_length=512)
- warmup_steps: 10000
- mixed_precision_training: Native AMP
|
salascorp/distilroberta-base-mrpc-glue-oscar-salas2 | salascorp | 2022-10-21T06:40:47Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-10-21T06:36:59Z | ---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
model-index:
- name: distilroberta-base-mrpc-glue-oscar-salas2
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-mrpc-glue-oscar-salas2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the datasetX dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5094
## 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 4.23.1
- Pytorch 1.12.1+cpu
- Datasets 2.6.1
- Tokenizers 0.13.1
|
amanneo/distilgpt2-finetuned-custom-mail | amanneo | 2022-10-21T04:59:12Z | 4 | 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 | 2022-10-21T04:21:46Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-custom-mail
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-custom-mail
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: 3.1905
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 7 | 3.5915 |
| No log | 2.0 | 14 | 3.4986 |
| No log | 3.0 | 21 | 3.4418 |
| No log | 4.0 | 28 | 3.3970 |
| No log | 5.0 | 35 | 3.3569 |
| No log | 6.0 | 42 | 3.3207 |
| No log | 7.0 | 49 | 3.2972 |
| No log | 8.0 | 56 | 3.2806 |
| No log | 9.0 | 63 | 3.2620 |
| No log | 10.0 | 70 | 3.2451 |
| No log | 11.0 | 77 | 3.2302 |
| No log | 12.0 | 84 | 3.2177 |
| No log | 13.0 | 91 | 3.2083 |
| No log | 14.0 | 98 | 3.2024 |
| No log | 15.0 | 105 | 3.1984 |
| No log | 16.0 | 112 | 3.1962 |
| No log | 17.0 | 119 | 3.1938 |
| No log | 18.0 | 126 | 3.1920 |
| No log | 19.0 | 133 | 3.1913 |
| No log | 20.0 | 140 | 3.1905 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
jinhybr/layoutlm-funsd-pytorch | jinhybr | 2022-10-21T04:48:34Z | 12 | 0 | transformers | [
"transformers",
"pytorch",
"layoutlm",
"token-classification",
"generated_from_trainer",
"dataset:funsd",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-10-21T03:08:16Z | ---
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-funsd-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. -->
# layoutlm-funsd-pytorch
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7042
- Answer: {'precision': 0.712403951701427, 'recall': 0.8022249690976514, 'f1': 0.7546511627906977, 'number': 809}
- Header: {'precision': 0.3203125, 'recall': 0.3445378151260504, 'f1': 0.33198380566801616, 'number': 119}
- Question: {'precision': 0.7747589833479404, 'recall': 0.8300469483568075, 'f1': 0.8014505893019038, 'number': 1065}
- Overall Precision: 0.7220
- Overall Recall: 0.7898
- Overall F1: 0.7544
- Overall Accuracy: 0.8078
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7641 | 1.0 | 10 | 1.5569 | {'precision': 0.01979045401629802, 'recall': 0.021013597033374538, 'f1': 0.02038369304556355, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.20930232558139536, 'recall': 0.15211267605633802, 'f1': 0.1761827079934747, 'number': 1065} | 0.1096 | 0.0898 | 0.0987 | 0.3917 |
| 1.4096 | 2.0 | 20 | 1.1718 | {'precision': 0.18729096989966554, 'recall': 0.138442521631644, 'f1': 0.15920398009950248, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4800601956358164, 'recall': 0.5990610328638498, 'f1': 0.5329991645781119, 'number': 1065} | 0.3892 | 0.3763 | 0.3827 | 0.6045 |
| 1.0362 | 3.0 | 30 | 0.9322 | {'precision': 0.5212620027434842, 'recall': 0.46971569839307786, 'f1': 0.494148244473342, 'number': 809} | {'precision': 0.10344827586206896, 'recall': 0.025210084033613446, 'f1': 0.040540540540540536, 'number': 119} | {'precision': 0.6362847222222222, 'recall': 0.6882629107981221, 'f1': 0.661253946774921, 'number': 1065} | 0.5843 | 0.5600 | 0.5719 | 0.7091 |
| 0.8024 | 4.0 | 40 | 0.7725 | {'precision': 0.6457858769931663, 'recall': 0.7008652657601978, 'f1': 0.6721991701244814, 'number': 809} | {'precision': 0.1791044776119403, 'recall': 0.10084033613445378, 'f1': 0.12903225806451613, 'number': 119} | {'precision': 0.6911130284728214, 'recall': 0.752112676056338, 'f1': 0.7203237410071942, 'number': 1065} | 0.6559 | 0.6924 | 0.6737 | 0.7700 |
| 0.6483 | 5.0 | 50 | 0.7035 | {'precision': 0.6575790621592148, 'recall': 0.7453646477132262, 'f1': 0.6987253765932794, 'number': 809} | {'precision': 0.26881720430107525, 'recall': 0.21008403361344538, 'f1': 0.2358490566037736, 'number': 119} | {'precision': 0.7120067170445005, 'recall': 0.7962441314553991, 'f1': 0.75177304964539, 'number': 1065} | 0.6706 | 0.7406 | 0.7039 | 0.7857 |
| 0.5298 | 6.0 | 60 | 0.6747 | {'precision': 0.6925601750547046, 'recall': 0.7824474660074165, 'f1': 0.73476494486361, 'number': 809} | {'precision': 0.3472222222222222, 'recall': 0.21008403361344538, 'f1': 0.2617801047120419, 'number': 119} | {'precision': 0.7333333333333333, 'recall': 0.8366197183098592, 'f1': 0.7815789473684212, 'number': 1065} | 0.7038 | 0.7772 | 0.7387 | 0.7984 |
| 0.4644 | 7.0 | 70 | 0.6752 | {'precision': 0.6750261233019854, 'recall': 0.7985166872682324, 'f1': 0.7315968289920726, 'number': 809} | {'precision': 0.29357798165137616, 'recall': 0.2689075630252101, 'f1': 0.28070175438596495, 'number': 119} | {'precision': 0.7529812606473595, 'recall': 0.8300469483568075, 'f1': 0.7896382313532827, 'number': 1065} | 0.6973 | 0.7837 | 0.7380 | 0.8010 |
| 0.4253 | 8.0 | 80 | 0.6664 | {'precision': 0.699666295884316, 'recall': 0.7775030902348579, 'f1': 0.7365339578454333, 'number': 809} | {'precision': 0.3106796116504854, 'recall': 0.2689075630252101, 'f1': 0.28828828828828823, 'number': 119} | {'precision': 0.7704485488126649, 'recall': 0.8225352112676056, 'f1': 0.7956403269754768, 'number': 1065} | 0.7186 | 0.7712 | 0.7439 | 0.8017 |
| 0.3815 | 9.0 | 90 | 0.6658 | {'precision': 0.6973684210526315, 'recall': 0.7861557478368356, 'f1': 0.7391051714119697, 'number': 809} | {'precision': 0.3228346456692913, 'recall': 0.3445378151260504, 'f1': 0.3333333333333333, 'number': 119} | {'precision': 0.7474916387959866, 'recall': 0.8394366197183099, 'f1': 0.7908005307386111, 'number': 1065} | 0.7029 | 0.7883 | 0.7431 | 0.8053 |
| 0.3391 | 10.0 | 100 | 0.6736 | {'precision': 0.7022900763358778, 'recall': 0.796044499381953, 'f1': 0.7462340672074159, 'number': 809} | {'precision': 0.3252032520325203, 'recall': 0.33613445378151263, 'f1': 0.3305785123966942, 'number': 119} | {'precision': 0.7681034482758621, 'recall': 0.8366197183098592, 'f1': 0.8008988764044945, 'number': 1065} | 0.7159 | 0.7903 | 0.7513 | 0.8073 |
| 0.3117 | 11.0 | 110 | 0.6947 | {'precision': 0.7086956521739131, 'recall': 0.8059332509270705, 'f1': 0.7541931752458069, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.3445378151260504, 'f1': 0.33884297520661155, 'number': 119} | {'precision': 0.7992667277726856, 'recall': 0.8187793427230047, 'f1': 0.8089053803339518, 'number': 1065} | 0.7334 | 0.7852 | 0.7584 | 0.8083 |
| 0.2991 | 12.0 | 120 | 0.6963 | {'precision': 0.7058823529411765, 'recall': 0.8009888751545118, 'f1': 0.7504342790966995, 'number': 809} | {'precision': 0.33064516129032256, 'recall': 0.3445378151260504, 'f1': 0.33744855967078186, 'number': 119} | {'precision': 0.7716262975778547, 'recall': 0.8375586854460094, 'f1': 0.8032417829806394, 'number': 1065} | 0.7193 | 0.7933 | 0.7545 | 0.8076 |
| 0.282 | 13.0 | 130 | 0.6991 | {'precision': 0.7153846153846154, 'recall': 0.8046971569839307, 'f1': 0.7574171029668412, 'number': 809} | {'precision': 0.336, 'recall': 0.35294117647058826, 'f1': 0.3442622950819672, 'number': 119} | {'precision': 0.7898032200357782, 'recall': 0.8291079812206573, 'f1': 0.8089784699954191, 'number': 1065} | 0.7320 | 0.7908 | 0.7603 | 0.8102 |
| 0.2722 | 14.0 | 140 | 0.7044 | {'precision': 0.712253829321663, 'recall': 0.8046971569839307, 'f1': 0.7556587347649449, 'number': 809} | {'precision': 0.3228346456692913, 'recall': 0.3445378151260504, 'f1': 0.3333333333333333, 'number': 119} | {'precision': 0.7811120917917035, 'recall': 0.8309859154929577, 'f1': 0.8052775250227479, 'number': 1065} | 0.7254 | 0.7913 | 0.7569 | 0.8081 |
| 0.2634 | 15.0 | 150 | 0.7042 | {'precision': 0.712403951701427, 'recall': 0.8022249690976514, 'f1': 0.7546511627906977, 'number': 809} | {'precision': 0.3203125, 'recall': 0.3445378151260504, 'f1': 0.33198380566801616, 'number': 119} | {'precision': 0.7747589833479404, 'recall': 0.8300469483568075, 'f1': 0.8014505893019038, 'number': 1065} | 0.7220 | 0.7898 | 0.7544 | 0.8078 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|
huggingtweets/elonmusk-mar15sa-sergiorocks | huggingtweets | 2022-10-21T04:07:50Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-10-21T04:06:32Z | ---
language: en
thumbnail: http://www.huggingtweets.com/elonmusk-mar15sa-sergiorocks/1666325239514/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/1580062742693699584/RJ5EI7PS_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/1142324885550751744/wVNatx7J_400x400.png')">
</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/566329118489194496/f_ALTi7v_400x400.jpeg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Sergio Pereira 🚀 & Marissa Goldberg</div>
<div style="text-align: center; font-size: 14px;">@elonmusk-mar15sa-sergiorocks</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 Elon Musk & Sergio Pereira 🚀 & Marissa Goldberg.
| Data | Elon Musk | Sergio Pereira 🚀 | Marissa Goldberg |
| --- | --- | --- | --- |
| Tweets downloaded | 3200 | 3250 | 3248 |
| Retweets | 133 | 18 | 301 |
| Short tweets | 949 | 54 | 110 |
| Tweets kept | 2118 | 3178 | 2837 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ahul38aq/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 @elonmusk-mar15sa-sergiorocks's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1r3916r2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1r3916r2/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/elonmusk-mar15sa-sergiorocks')
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)
|
edbeeching/atari_2B_atari_upndown_2222 | edbeeching | 2022-10-21T04:06:56Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-10-21T04:05:32Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_upndown
type: atari_upndown
metrics:
- type: mean_reward
value: 427506.50 +/- 5992.08
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_upndown** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Adipta/setfit-model-test-sensitve-v1 | Adipta | 2022-10-21T03:40:49Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2022-10-21T03:40:37Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 125 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 125,
"warmup_steps": 13,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Shaier/longformer_race | Shaier | 2022-10-21T02:22:30Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"longformer",
"multiple-choice",
"generated_from_trainer",
"dataset:race",
"endpoints_compatible",
"region:us"
]
| multiple-choice | 2022-10-20T20:03:38Z | ---
tags:
- generated_from_trainer
datasets:
- race
model-index:
- name: longformer_race
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. -->
# longformer_race
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the race dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.8572
- eval_accuracy: 0.6647
- eval_runtime: 327.7157
- eval_samples_per_second: 10.674
- eval_steps_per_second: 10.674
- epoch: 1.0
- step: 2497
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 25
- total_train_batch_size: 25
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0
|
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