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| first_commit
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Chungu424/qazwsx | []
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} | 0 | null | ---
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 1320 with parameters:
```
{'batch_size': 32, '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": 1320,
"warmup_steps": 132,
"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 --> |
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} | 0 | null | ---
language:
- pl
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small PL
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: pl
split: test
args: pl
metrics:
- name: Wer
type: wer
value: 14.49425614099627
---
<!-- 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. -->
# Whisper Small PL
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4444
- Wer: 14.4943
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1639 | 0.1 | 500 | 0.3290 | 16.6413 |
| 0.0674 | 1.1 | 1000 | 0.3224 | 15.1782 |
| 0.0335 | 2.09 | 1500 | 0.3186 | 14.5394 |
| 0.0161 | 3.09 | 2000 | 0.3445 | 15.0026 |
| 0.0101 | 4.08 | 2500 | 0.3777 | 14.5260 |
| 0.0064 | 5.08 | 3000 | 0.3977 | 14.6264 |
| 0.0036 | 6.08 | 3500 | 0.4621 | 14.6180 |
| 0.0025 | 7.07 | 4000 | 0.4639 | 14.5193 |
| 0.0017 | 8.07 | 4500 | 0.4971 | 14.4725 |
| 0.0017 | 9.07 | 5000 | 0.4444 | 14.4943 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Ci/Pai | []
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} | 0 | null | Access to model darkVOYAGE/mj-v4-look is restricted and you are not in the authorized list. Visit https://huggingface.co/darkVOYAGE/mj-v4-look to ask for access. |
Cinnamon/electra-small-japanese-discriminator | [
"pytorch",
"electra",
"pretraining",
"ja",
"transformers",
"license:apache-2.0"
]
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} | 419 | null | ---
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: 284.47 +/- 14.71
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
...
```
|
Clarianliz30/Caitlyn | []
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} | 0 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mtop
model-index:
- name: t5-small-pointer-mtop
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-pointer-mtop
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the mtop dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1202
- Exact Match: 0.7445
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|
| 2.1451 | 6.65 | 200 | 0.5966 | 0.0134 |
| 0.4695 | 13.33 | 400 | 0.2264 | 0.2998 |
| 0.2229 | 19.98 | 600 | 0.1446 | 0.4649 |
| 0.1389 | 26.65 | 800 | 0.1227 | 0.5154 |
| 0.097 | 33.33 | 1000 | 0.1213 | 0.5221 |
| 0.0724 | 39.98 | 1200 | 0.1202 | 0.5365 |
| 0.0562 | 46.65 | 1400 | 0.1207 | 0.5436 |
| 0.0457 | 53.33 | 1600 | 0.1240 | 0.5441 |
| 0.0399 | 59.98 | 1800 | 0.1349 | 0.5441 |
| 0.0317 | 66.65 | 2000 | 0.1369 | 0.5477 |
| 0.0271 | 73.33 | 2200 | 0.1409 | 0.5490 |
| 0.0237 | 79.98 | 2400 | 0.1462 | 0.5539 |
| 0.0207 | 86.65 | 2600 | 0.1470 | 0.5517 |
| 0.0188 | 93.33 | 2800 | 0.1505 | 0.5508 |
| 0.0174 | 99.98 | 3000 | 0.1505 | 0.5512 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
ClaudeCOULOMBE/RickBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 9 | null | ---
language:
- pl
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Tiny Pl
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: pl
split: test
args: pl
metrics:
- name: Wer
type: wer
value: 39.469591826496995
---
<!-- 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. -->
# Whisper Tiny Pl
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6919
- Wer: 39.4696
## 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: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.3647 | 1.05 | 500 | 0.6999 | 43.0430 |
| 0.2752 | 3.04 | 1000 | 0.6002 | 39.3275 |
| 0.2513 | 5.04 | 1500 | 0.5911 | 37.8643 |
| 0.1661 | 7.04 | 2000 | 0.6381 | 37.6887 |
| 0.154 | 9.03 | 2500 | 0.6157 | 37.9362 |
| 0.1126 | 11.03 | 3000 | 0.6319 | 38.6402 |
| 0.0763 | 13.02 | 3500 | 0.6539 | 38.9730 |
| 0.0729 | 15.02 | 4000 | 0.6583 | 38.9362 |
| 0.0778 | 17.02 | 4500 | 0.6653 | 39.6769 |
| 0.0698 | 19.01 | 5000 | 0.6919 | 39.4696 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
ClaudeYang/awesome_fb_model | [
"pytorch",
"bart",
"text-classification",
"dataset:multi_nli",
"transformers",
"zero-shot-classification"
]
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} | 26 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: all-roberta-large-v1-auto_and_commute-2-16-5-oos
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. -->
# all-roberta-large-v1-auto_and_commute-2-16-5-oos
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2614
- Accuracy: 0.4289
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7929 | 1.0 | 1 | 2.5690 | 0.2667 |
| 2.267 | 2.0 | 2 | 2.4558 | 0.3533 |
| 1.8495 | 3.0 | 3 | 2.3630 | 0.3911 |
| 1.4397 | 4.0 | 4 | 2.2956 | 0.4133 |
| 1.2985 | 5.0 | 5 | 2.2614 | 0.4289 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
CleveGreen/FieldClassifier | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 34 | null | ---
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 1320 with parameters:
```
{'batch_size': 32, '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": 1320,
"warmup_steps": 132,
"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 --> |
CleveGreen/FieldClassifier_v2 | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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} | 46 | null | ---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Small English
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs en_us
type: google/fleurs
config: en_us
split: test
args: en_us
metrics:
- type: wer
value: 7.990755655157924
name: Wer
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: en
split: test
metrics:
- type: wer
value: 18.21
name: WER
---
<!-- 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. -->
# Whisper Small English
This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the google/fleurs en_us dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6007
- Wer: 7.9908
## Model description
This model was created as part of the Whisper Fine-Tune Event. This is my first attempt at fine-tuning the Whisper neural network.
Honestly, it's my second time ever trying anything related to training a neural network, and my first time was pretty bad (but I did
get a lot of rather funny images out of it, so perhaps it wasn't entirely fruitless?), and it seems like the WER only went up after step 2000,
so... I'm not sure if I did a good job or if I just wasted GPU cycles, but maybe I can try again and get a better score?
I'm learning.
## 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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0005 | 24.0 | 1000 | 0.5092 | 7.5566 |
| 0.0002 | 48.01 | 2000 | 0.5528 | 7.7526 |
| 0.0001 | 73.0 | 3000 | 0.5785 | 7.8507 |
| 0.0001 | 97.0 | 4000 | 0.5936 | 7.9908 |
| 0.0001 | 121.01 | 5000 | 0.6007 | 7.9908 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
CleveGreen/JobClassifier | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
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} | 31 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: 05_model_sales_external_imbalanced
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. -->
# 05_model_sales_external_imbalanced
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: 0.2421
- Accuracy: 0.9294
- F1: 0.3654
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
CleveGreen/JobClassifier_v2 | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
} | 37 | 2022-12-05T21:34:12Z | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: true
--- |
CleveGreen/JobClassifier_v2_gpt | [
"pytorch",
"gpt2",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"GPT2ForSequenceClassification"
],
"model_type": "gpt2",
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}
} | 27 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mtop
model-index:
- name: t5-base-pointer-mtop
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-base-pointer-mtop
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the mtop dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1131
- Exact Match: 0.7199
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|
| 1.7749 | 6.65 | 200 | 0.5892 | 0.0031 |
| 0.6021 | 13.33 | 400 | 0.5160 | 0.0139 |
| 0.6044 | 19.98 | 600 | 0.4080 | 0.0532 |
| 0.3302 | 26.65 | 800 | 0.1865 | 0.3620 |
| 0.1483 | 33.33 | 1000 | 0.1267 | 0.5105 |
| 0.0768 | 39.98 | 1200 | 0.1131 | 0.5298 |
| 0.0525 | 46.65 | 1400 | 0.1219 | 0.5414 |
| 0.0801 | 53.33 | 1600 | 0.1186 | 0.5275 |
| 0.0331 | 59.98 | 1800 | 0.1306 | 0.5423 |
| 0.0254 | 66.65 | 2000 | 0.1396 | 0.5396 |
| 0.0168 | 73.33 | 2200 | 0.1560 | 0.5436 |
| 0.0129 | 79.98 | 2400 | 0.1659 | 0.5494 |
| 0.0105 | 86.65 | 2600 | 0.1699 | 0.5423 |
| 0.0088 | 93.33 | 2800 | 0.1742 | 0.5472 |
| 0.0077 | 99.98 | 3000 | 0.1775 | 0.5468 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Clint/clinton | []
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} | 0 | null | ---
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: 260.80 +/- 24.69
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
...
```
|
Cloudy/DialoGPT-CJ-large | [
"pytorch",
"conversational"
]
| conversational | {
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} | 1 | null | ---
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 1320 with parameters:
```
{'batch_size': 32, '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": 1320,
"warmup_steps": 132,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(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 --> |
ClydeWasTaken/DialoGPT-small-joshua | [
"conversational"
]
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: all-roberta-large-v1-auto_and_commute-4-16-5-oos
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. -->
# all-roberta-large-v1-auto_and_commute-4-16-5-oos
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2614
- Accuracy: 0.4289
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7929 | 1.0 | 1 | 2.5690 | 0.2667 |
| 2.267 | 2.0 | 2 | 2.4558 | 0.3533 |
| 1.8495 | 3.0 | 3 | 2.3630 | 0.3911 |
| 1.4397 | 4.0 | 4 | 2.2956 | 0.4133 |
| 1.2985 | 5.0 | 5 | 2.2614 | 0.4289 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
CoachCarter/distilbert-base-uncased | []
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} | 0 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1552729971956727808/zVaFH3ex_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/1583521884590772232/DGBIkzGk_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">🌞 & 9</div>
<div style="text-align: center; font-size: 14px;">@jellynatelli-raspberryl0ver</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 🌞 & 9.
| Data | 🌞 | 9 |
| --- | --- | --- |
| Tweets downloaded | 1797 | 3205 |
| Retweets | 413 | 202 |
| Short tweets | 206 | 633 |
| Tweets kept | 1178 | 2370 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2nlgvuz7/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 @jellynatelli-raspberryl0ver's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1pu0nfyz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1pu0nfyz/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/jellynatelli-raspberryl0ver')
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)
|
CodeDanCode/SP-KyleBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 15 | null | ---
license: creativeml-openrail-m
---
Squishmallow Diffusion
 |
CodeMonkey98/distilroberta-base-finetuned-wikitext2 | []
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} | 0 | null | ---
language:
- fi
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Finnish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 fi
type: mozilla-foundation/common_voice_11_0
config: fi
split: test
args: fi
metrics:
- name: Wer
type: wer
value: 28.928033157498117
---
<!-- 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. -->
# Whisper Small Finnish
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 fi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4146
- Wer: 28.9280
## 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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
CodeNinja1126/bert-p-encoder | [
"pytorch"
]
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: all-roberta-large-v1-auto_and_commute-8-16-5-oos
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. -->
# all-roberta-large-v1-auto_and_commute-8-16-5-oos
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2614
- Accuracy: 0.4289
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7929 | 1.0 | 1 | 2.5690 | 0.2667 |
| 2.267 | 2.0 | 2 | 2.4558 | 0.3533 |
| 1.8495 | 3.0 | 3 | 2.3630 | 0.3911 |
| 1.4397 | 4.0 | 4 | 2.2956 | 0.4133 |
| 1.2985 | 5.0 | 5 | 2.2614 | 0.4289 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
CodeNinja1126/bert-q-encoder | [
"pytorch"
]
| null | {
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-burak-new-300-v2-7
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-burak-new-300-v2-7
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3394
- Wer: 0.2306
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 151
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 5.3306 | 9.43 | 500 | 2.8304 | 1.0 |
| 0.9789 | 18.87 | 1000 | 0.3443 | 0.4136 |
| 0.3622 | 28.3 | 1500 | 0.3110 | 0.3235 |
| 0.2535 | 37.74 | 2000 | 0.3166 | 0.3014 |
| 0.1985 | 47.17 | 2500 | 0.3247 | 0.2980 |
| 0.1673 | 56.6 | 3000 | 0.3082 | 0.2608 |
| 0.1435 | 66.04 | 3500 | 0.3155 | 0.2615 |
| 0.1281 | 75.47 | 4000 | 0.3480 | 0.2636 |
| 0.1164 | 84.91 | 4500 | 0.3459 | 0.2560 |
| 0.1055 | 94.34 | 5000 | 0.3330 | 0.2505 |
| 0.0949 | 103.77 | 5500 | 0.3522 | 0.2436 |
| 0.0891 | 113.21 | 6000 | 0.3511 | 0.2381 |
| 0.0814 | 122.64 | 6500 | 0.3365 | 0.2347 |
| 0.0785 | 132.08 | 7000 | 0.3426 | 0.2374 |
| 0.0722 | 141.51 | 7500 | 0.3483 | 0.2312 |
| 0.0704 | 150.94 | 8000 | 0.3394 | 0.2306 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
CoderBoy432/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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"GPT2LMHeadModel"
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} | 11 | null | ---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- alanila/autotrain-data-acc_keys
co2_eq_emissions:
emissions: 1.3599341780747405
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 2347073860
- CO2 Emissions (in grams): 1.3599
## Validation Metrics
- Loss: 1.255
- Accuracy: 0.500
- Macro F1: 0.445
- Micro F1: 0.500
- Weighted F1: 0.421
- Macro Precision: 0.498
- Micro Precision: 0.500
- Weighted Precision: 0.508
- Macro Recall: 0.481
- Micro Recall: 0.500
- Weighted Recall: 0.500
## 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/alanila/autotrain-acc_keys-2347073860
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("alanila/autotrain-acc_keys-2347073860", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("alanila/autotrain-acc_keys-2347073860", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Venkatakrishnan-Ramesh/Text_gen | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: all-roberta-large-v1-auto_and_commute-16-16-5-oos
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. -->
# all-roberta-large-v1-auto_and_commute-16-16-5-oos
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2614
- Accuracy: 0.4289
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7929 | 1.0 | 1 | 2.5690 | 0.2667 |
| 2.267 | 2.0 | 2 | 2.4558 | 0.3533 |
| 1.8495 | 3.0 | 3 | 2.3630 | 0.3911 |
| 1.4397 | 4.0 | 4 | 2.2956 | 0.4133 |
| 1.2985 | 5.0 | 5 | 2.2614 | 0.4289 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
CoffeeAddict93/gpt1-call-of-the-wild | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 8 | null | ---
license: mit
---
### vie-proceres on Stable Diffusion
This is the `<vie-proceres>` 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`:
|
CoffeeAddict93/gpt2-call-of-the-wild | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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} | 6 | null | ---
language:
- sv
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Small - Swedish
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. -->
# Whisper Small - Swedish
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4915
- Wer: 25.5384
## 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: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 6000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2107 | 1.3 | 1000 | 0.4673 | 34.0432 |
| 0.0821 | 2.59 | 2000 | 0.4284 | 27.4152 |
| 0.0378 | 3.89 | 3000 | 0.4210 | 25.3637 |
| 0.0042 | 5.18 | 4000 | 0.4247 | 23.5541 |
| 0.001 | 6.48 | 5000 | 0.4286 | 22.7770 |
| 0.0106 | 7.77 | 6000 | 0.4915 | 25.5384 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.13.2
|
CoffeeAddict93/gpt2-medium-call-of-the-wild | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 14 | null | ---
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: 254.36 +/- 15.06
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
...
```
|
CoffeeAddict93/gpt2-medium-modest-proposal | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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} | 7 | null | ---
tags:
- autotrain
- text-classification
language:
- pt
widget:
- text: "febre"
- text: "dor de cabeça"
- text: "corpo inteiro doendo"
datasets:
- famube/autotrain-data-ciap2
co2_eq_emissions:
emissions: 4.825567476024859
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 2347173866
- CO2 Emissions (in grams): 4.8256
## Validation Metrics
- Loss: 1.932
- Accuracy: 0.681
- Macro F1: 0.609
- Micro F1: 0.681
- Weighted F1: 0.622
- Macro Precision: 0.592
- Micro Precision: 0.681
- Weighted Precision: 0.610
- Macro Recall: 0.669
- Micro Recall: 0.681
- Weighted Recall: 0.681
## 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/famube/autotrain-ciap2-2347173866
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("famube/autotrain-ciap2-2347173866", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("famube/autotrain-ciap2-2347173866", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
CoffeeAddict93/gpt2-modest-proposal | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 12 | null | ---
library_name: stable-baselines3
tags:
- FrozenLake-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1
type: FrozenLake-v1
metrics:
- type: mean_reward
value: 0.00 +/- 0.00
name: mean_reward
verified: false
---
# **PPO** Agent playing **FrozenLake-v1**
This is a trained model of a **PPO** agent playing **FrozenLake-v1**
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
...
```
|
CogComp/bart-faithful-summary-detector | [
"pytorch",
"jax",
"bart",
"text-classification",
"en",
"dataset:xsum",
"transformers",
"xsum",
"license:cc-by-sa-4.0"
]
| text-classification | {
"architectures": [
"BartForSequenceClassification"
],
"model_type": "bart",
"task_specific_params": {
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},
"summarization": {
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}
}
} | 234 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: bert-base-historic-multilingual-cased-squad-en
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-historic-multilingual-cased-squad-en
This model is a fine-tuned version of [dbmdz/bert-base-historic-multilingual-cased](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5307
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.881 | 1.0 | 4820 | 1.5507 |
| 1.5883 | 2.0 | 9640 | 1.5307 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Contrastive-Tension/RoBerta-Large-CT-STSb | [
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
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}
} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: train[:1500]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6932
- Accuracy: 0.5
## 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: 1
- 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
- lr_scheduler_warmup_ratio: 0.5
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 653.8917 | 1.0 | 600 | 110.7460 | 0.48 |
| 0.6772 | 2.0 | 1200 | 0.6932 | 0.5 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Cooker/cicero-similis | []
| null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: all-roberta-large-v1-travel-8-16-5-oos
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. -->
# all-roberta-large-v1-travel-8-16-5-oos
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1384
- Accuracy: 0.4289
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7625 | 1.0 | 1 | 2.5258 | 0.2933 |
| 2.0955 | 2.0 | 2 | 2.3775 | 0.3333 |
| 1.7076 | 3.0 | 3 | 2.2590 | 0.38 |
| 1.3257 | 4.0 | 4 | 2.1788 | 0.4089 |
| 1.1109 | 5.0 | 5 | 2.1384 | 0.4289 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Cool/Demo | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 607.50 +/- 240.11
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga TUMxudashuai -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga TUMxudashuai -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga TUMxudashuai
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Coolhand/Sentiment | []
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}
} | 0 | null | ---
license: bigscience-bloom-rail-1.0
tags:
- generated_from_trainer
datasets:
- pszemraj/riddlesense_plusplus
model-index:
- name: tst-modeling
results: []
parameters:
min_length: 16
max_length: 96
no_repeat_ngram_size: 1
do_sample: True
pipeline_tag:
text-generation
---
<!-- 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. -->
# tst-modeling
This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the pszemraj/riddlesense_plusplus dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.0
- Tokenizers 0.13.2
|
CouchCat/ma_sa_v7_distil | [
"pytorch",
"distilbert",
"text-classification",
"en",
"transformers",
"sentiment-analysis",
"license:mit"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
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}
} | 38 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: all-roberta-large-v1-travel-1000-16-5-oos
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. -->
# all-roberta-large-v1-travel-1000-16-5-oos
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1418
- Accuracy: 0.4289
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 5.0007 | 1.0 | 1 | 4.7613 | 0.1844 |
| 4.2689 | 2.0 | 2 | 4.5231 | 0.3133 |
| 3.7175 | 3.0 | 3 | 4.3336 | 0.38 |
| 3.2765 | 4.0 | 4 | 4.2055 | 0.4156 |
| 2.9513 | 5.0 | 5 | 4.1418 | 0.4289 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
CracklesCreeper/Piglin-Talks-Harry-Potter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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} | 10 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-demo-F03-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-demo-F03-2
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4472
- Wer: 0.8797
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 24.7815 | 0.97 | 500 | 3.3881 | 1.0 |
| 3.3791 | 1.94 | 1000 | 3.2550 | 1.0 |
| 2.9748 | 2.91 | 1500 | 2.8719 | 1.0 |
| 2.8305 | 3.88 | 2000 | 2.7878 | 1.0 |
| 2.6289 | 4.85 | 2500 | 2.5009 | 1.2082 |
| 2.1553 | 5.83 | 3000 | 1.8680 | 1.3270 |
| 1.4669 | 6.8 | 3500 | 1.5138 | 1.3266 |
| 1.0475 | 7.77 | 4000 | 1.3531 | 1.2078 |
| 0.8132 | 8.74 | 4500 | 1.2666 | 1.1926 |
| 0.665 | 9.71 | 5000 | 1.2461 | 1.0980 |
| 0.5538 | 10.68 | 5500 | 1.3152 | 1.0719 |
| 0.4759 | 11.65 | 6000 | 1.3190 | 1.0902 |
| 0.4221 | 12.62 | 6500 | 1.3077 | 1.0184 |
| 0.3836 | 13.59 | 7000 | 1.3410 | 1.0645 |
| 0.3409 | 14.56 | 7500 | 1.2378 | 1.0246 |
| 0.3068 | 15.53 | 8000 | 1.3002 | 1.0152 |
| 0.2911 | 16.5 | 8500 | 1.3603 | 1.0074 |
| 0.2647 | 17.48 | 9000 | 1.3479 | 0.9375 |
| 0.2446 | 18.45 | 9500 | 1.3462 | 0.9187 |
| 0.2208 | 19.42 | 10000 | 1.4029 | 0.9109 |
| 0.2203 | 20.39 | 10500 | 1.4036 | 0.8977 |
| 0.204 | 21.36 | 11000 | 1.3755 | 0.8668 |
| 0.191 | 22.33 | 11500 | 1.3042 | 0.8691 |
| 0.1784 | 23.3 | 12000 | 1.4343 | 0.8758 |
| 0.1692 | 24.27 | 12500 | 1.4351 | 0.8492 |
| 0.1527 | 25.24 | 13000 | 1.4415 | 0.8645 |
| 0.1444 | 26.21 | 13500 | 1.4684 | 0.8812 |
| 0.1432 | 27.18 | 14000 | 1.4406 | 0.8906 |
| 0.1385 | 28.16 | 14500 | 1.4900 | 0.8840 |
| 0.1392 | 29.13 | 15000 | 1.4472 | 0.8797 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.13.2
|
Craftified/Bob | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test_trainer_XLNET_3ep_5e-5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_trainer_XLNET_3ep_5e-5
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5405
- Accuracy: 0.8773
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7984 | 1.0 | 1125 | 0.6647 | 0.7923 |
| 0.5126 | 2.0 | 2250 | 0.4625 | 0.862 |
| 0.409 | 3.0 | 3375 | 0.5405 | 0.8773 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Craig/mGqFiPhu | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
]
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: starbot-transformers
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. -->
# starbot-transformers
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: 3.4079
## 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: 0.005
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.3942 | 1.0 | 2992 | 3.3385 |
| 3.2566 | 2.0 | 5984 | 3.2760 |
| 3.4112 | 3.0 | 8976 | 3.4710 |
| 3.4887 | 4.0 | 11968 | 3.5264 |
| 3.4856 | 5.0 | 14960 | 3.5181 |
| 3.4359 | 6.0 | 17952 | 3.5079 |
| 3.4115 | 7.0 | 20944 | 3.4954 |
| 3.3657 | 8.0 | 23936 | 3.4482 |
| 3.3018 | 9.0 | 26928 | 3.4207 |
| 3.2435 | 10.0 | 29920 | 3.4079 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Craig/paraphrase-MiniLM-L6-v2 | [
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
]
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} | 1,026 | 2022-12-06T01:31:27Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: all-roberta-large-v1-utility-4-16-5-oos
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. -->
# all-roberta-large-v1-utility-4-16-5-oos
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3728
- Accuracy: 0.3956
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.8194 | 1.0 | 1 | 2.6027 | 0.3156 |
| 2.2337 | 2.0 | 2 | 2.5079 | 0.3778 |
| 1.7996 | 3.0 | 3 | 2.4293 | 0.3822 |
| 1.4591 | 4.0 | 4 | 2.3728 | 0.3956 |
| 1.3205 | 5.0 | 5 | 2.3439 | 0.3956 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Crasher222/kaggle-comp-test | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:Crasher222/autonlp-data-kaggle-test",
"transformers",
"autonlp",
"co2_eq_emissions"
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} | 29 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- top_v2
model-index:
- name: t5-small-pointer-top_v2
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-pointer-top_v2
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the top_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0306
- Exact Match: 0.8264
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|
| 1.9316 | 0.82 | 200 | 0.4566 | 0.0084 |
| 0.3713 | 1.65 | 400 | 0.1473 | 0.1230 |
| 0.1747 | 2.47 | 600 | 0.0788 | 0.1984 |
| 0.1104 | 3.29 | 800 | 0.0568 | 0.2149 |
| 0.0842 | 4.12 | 1000 | 0.0473 | 0.2217 |
| 0.0694 | 4.94 | 1200 | 0.0426 | 0.2260 |
| 0.0603 | 5.76 | 1400 | 0.0383 | 0.2279 |
| 0.0534 | 6.58 | 1600 | 0.0367 | 0.2281 |
| 0.0477 | 7.41 | 1800 | 0.0347 | 0.2301 |
| 0.0441 | 8.23 | 2000 | 0.0334 | 0.2314 |
| 0.0413 | 9.05 | 2200 | 0.0323 | 0.2315 |
| 0.0387 | 9.88 | 2400 | 0.0316 | 0.2316 |
| 0.0366 | 10.7 | 2600 | 0.0311 | 0.2324 |
| 0.0358 | 11.52 | 2800 | 0.0307 | 0.2324 |
| 0.0343 | 12.35 | 3000 | 0.0306 | 0.2327 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
CrayonShinchan/fine_tune_try_1 | []
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} | 0 | null | ---
language: multilingual
datasets:
- librispeech_asr
- Murple/ksponspeech
- Murple/csj
- Murple/mmcrsc
tags:
- speech
license: apache-2.0
---
# Wav2Vec2-Base
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model.
[Paper](https://arxiv.org/abs/2006.11477)
Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli
**Abstract**
We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.
The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
# Usage
See [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how to fine-tune the model.
The results can be seen [here](https://wandb.ai/toraruka623/wav2vec2-pretraining/reports/Wav2Vec2-base-4k--VmlldzozMDkxMDk3?accessToken=lfn2kwe9pzmvdonhx7hihd9nf13wzby7odu0iakdubwep3le4ywirxc3gx9w66fi) |
CrisLeaf/generador-de-historias-de-tolkien | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 8 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cstop_artificial
model-index:
- name: byt5-base-cstop_artificial
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. -->
# byt5-base-cstop_artificial
This model is a fine-tuned version of [google/byt5-base](https://huggingface.co/google/byt5-base) on the cstop_artificial dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0461
- Exact Match: 0.7996
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match |
|:-------------:|:------:|:----:|:---------------:|:-----------:|
| 0.2563 | 28.5 | 200 | 0.0461 | 0.0376 |
| 0.0065 | 57.13 | 400 | 0.0563 | 0.0376 |
| 0.0021 | 85.63 | 600 | 0.0592 | 0.0358 |
| 0.0013 | 114.25 | 800 | 0.0569 | 0.0376 |
| 0.0008 | 142.75 | 1000 | 0.0675 | 0.0358 |
| 0.0007 | 171.38 | 1200 | 0.0627 | 0.0394 |
| 0.0004 | 199.88 | 1400 | 0.0677 | 0.0358 |
| 0.0003 | 228.5 | 1600 | 0.0650 | 0.0376 |
| 0.0002 | 257.13 | 1800 | 0.0693 | 0.0394 |
| 0.0002 | 285.63 | 2000 | 0.0721 | 0.0394 |
| 0.0002 | 314.25 | 2200 | 0.0714 | 0.0376 |
| 0.0002 | 342.75 | 2400 | 0.0701 | 0.0394 |
| 0.0002 | 371.38 | 2600 | 0.0750 | 0.0394 |
| 0.0001 | 399.88 | 2800 | 0.0739 | 0.0394 |
| 0.0001 | 428.5 | 3000 | 0.0745 | 0.0394 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.7.0
- Tokenizers 0.13.2
|
Crisblair/Wkwk | []
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} | 0 | null | ---
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: 267.30 +/- 18.25
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
...
```
|
Crispy/dialopt-small-kratos | []
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}
} | 0 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cstop_artificial
model-index:
- name: t5-small-pointer-cstop_artificial
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-pointer-cstop_artificial
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the cstop_artificial dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0816
- Exact Match: 0.8050
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match |
|:-------------:|:------:|:----:|:---------------:|:-----------:|
| 2.08 | 28.5 | 200 | 0.3320 | 0.0376 |
| 0.272 | 57.13 | 400 | 0.1084 | 0.2630 |
| 0.0789 | 85.63 | 600 | 0.0830 | 0.3184 |
| 0.0355 | 114.25 | 800 | 0.0816 | 0.3363 |
| 0.0207 | 142.75 | 1000 | 0.0868 | 0.3292 |
| 0.014 | 171.38 | 1200 | 0.0952 | 0.3399 |
| 0.0099 | 199.88 | 1400 | 0.1089 | 0.3381 |
| 0.0076 | 228.5 | 1600 | 0.1104 | 0.3381 |
| 0.0057 | 257.13 | 1800 | 0.1153 | 0.3292 |
| 0.0048 | 285.63 | 2000 | 0.1153 | 0.3327 |
| 0.004 | 314.25 | 2200 | 0.1206 | 0.3363 |
| 0.0032 | 342.75 | 2400 | 0.1229 | 0.3363 |
| 0.0028 | 371.38 | 2600 | 0.1268 | 0.3381 |
| 0.0023 | 399.88 | 2800 | 0.1288 | 0.3399 |
| 0.002 | 428.5 | 3000 | 0.1292 | 0.3399 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
CuongLD/wav2vec2-large-xlsr-vietnamese | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"vi",
"dataset:common_voice, infore_25h",
"arxiv:2006.11477",
"arxiv:2006.13979",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
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}
} | 8 | 2022-12-06T02:44:21Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: all-roberta-large-v1-utility-1000-16-5-oos
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. -->
# all-roberta-large-v1-utility-1000-16-5-oos
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2920
- Accuracy: 0.3733
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 5.0353 | 1.0 | 1 | 4.7572 | 0.2044 |
| 4.377 | 2.0 | 2 | 4.5884 | 0.3111 |
| 3.8842 | 3.0 | 3 | 4.4469 | 0.3467 |
| 3.3633 | 4.0 | 4 | 4.3454 | 0.3644 |
| 3.0949 | 5.0 | 5 | 4.2920 | 0.3733 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
CurtisASmith/GPT-JRT | []
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} | 0 | 2022-12-06T02:47:44Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-finetuned-billsum
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-billsum
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:
- Loss: 1.6488
- Rouge1: 17.5109
- Rouge2: 13.5664
- Rougel: 16.6319
- Rougelsum: 16.9423
- Gen Len: 18.9997
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.8982 | 1.0 | 2369 | 1.6488 | 17.5109 | 13.5664 | 16.6319 | 16.9423 | 18.9997 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
CurtisBowser/DialoGPT-medium-sora-three | []
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} | 0 | 2022-12-06T02:54:23Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: danie94-lml/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CurtisBowser/DialoGPT-medium-sora-two | [
"pytorch",
"conversational"
]
| conversational | {
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}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: all-roberta-large-v1-work-2-16-5-oos
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. -->
# all-roberta-large-v1-work-2-16-5-oos
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3586
- Accuracy: 0.3689
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.8058 | 1.0 | 1 | 2.6169 | 0.2356 |
| 2.3524 | 2.0 | 2 | 2.5215 | 0.2978 |
| 1.9543 | 3.0 | 3 | 2.4427 | 0.3422 |
| 1.5539 | 4.0 | 4 | 2.3874 | 0.36 |
| 1.4133 | 5.0 | 5 | 2.3586 | 0.3689 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
CurtisBowser/DialoGPT-medium-sora | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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} | 7 | null | ---
language: en
tags:
- question-answering
---
# ReAtt
ReAtt is a retrieval-augmented model for knowledge-intensive tasks proposed in [Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer](https://arxiv.org/pdf/2212.02027.pdf). The original Github repository is [https://github.com/jzbjyb/ReAtt](https://github.com/jzbjyb/ReAtt).
## Description
`neulab/reatt-large-nq` (based on T5 architecture) is initialized with `google/t5-large-lm-adapt` and fine-tuned on Natural Questions with end-to-end retrieval-augmented training.
## Usage
Please refer to [https://github.com/jzbjyb/ReAtt](https://github.com/jzbjyb/ReAtt) for instructions to use this model.
## Reference
```bibtex
@inproceedings{jiang-etal-2022-reatt,
title = {Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer},
author = {Zhengbao Jiang and Luyu Gao and Jun Araki and Haibo Ding and Zhiruo Wang and Jamie Callan and Graham Neubig},
booktitle = {Conference on Empirical Methods in Natural Language Processing (EMNLP)},
address = {Abu Dhabi, UAE},
month = {December},
year = {2022}
}
```
|
CurtisBowser/DialoGPT-small-sora | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: all-roberta-large-v1-work-4-16-5-oos
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. -->
# all-roberta-large-v1-work-4-16-5-oos
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3586
- Accuracy: 0.3689
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.8058 | 1.0 | 1 | 2.6169 | 0.2356 |
| 2.3524 | 2.0 | 2 | 2.5215 | 0.2978 |
| 1.9543 | 3.0 | 3 | 2.4427 | 0.3422 |
| 1.5539 | 4.0 | 4 | 2.3874 | 0.36 |
| 1.4133 | 5.0 | 5 | 2.3586 | 0.3689 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
CyberMuffin/DialoGPT-small-ChandlerBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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} | 9 | null | ---
language: en
tags:
- question-answering
---
# ReAtt
ReAtt is a retrieval-augmented model for knowledge-intensive tasks proposed in [Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer](https://arxiv.org/pdf/2212.02027.pdf). The original Github repository is [https://github.com/jzbjyb/ReAtt](https://github.com/jzbjyb/ReAtt).
## Description
`neulab/reatt-large-nq-bioasq` (based on T5 architecture) is initialized with `neulab/reatt-large-nq` and adapted on BioASQ dataset with end-to-end retrieval-augmented training.
## Usage
Please refer to [https://github.com/jzbjyb/ReAtt](https://github.com/jzbjyb/ReAtt) for instructions to use this model.
## Reference
```bibtex
@inproceedings{jiang-etal-2022-reatt,
title = {Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer},
author = {Zhengbao Jiang and Luyu Gao and Jun Araki and Haibo Ding and Zhiruo Wang and Jamie Callan and Graham Neubig},
booktitle = {Conference on Empirical Methods in Natural Language Processing (EMNLP)},
address = {Abu Dhabi, UAE},
month = {December},
year = {2022}
}
```
|
Cyrell/Cyrell | []
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} | 0 | null | ---
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: 257.80 +/- 15.76
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
...
```
|
Czapla/Rick | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: donut-kyc-id-model-525-with-aug
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. -->
# donut-kyc-id-model-525-with-aug
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
D3vil/DialoGPT-smaall-harrypottery | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: all-roberta-large-v1-work-8-16-5-oos
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. -->
# all-roberta-large-v1-work-8-16-5-oos
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3586
- Accuracy: 0.3689
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.8058 | 1.0 | 1 | 2.6169 | 0.2356 |
| 2.3524 | 2.0 | 2 | 2.5215 | 0.2978 |
| 1.9543 | 3.0 | 3 | 2.4427 | 0.3422 |
| 1.5539 | 4.0 | 4 | 2.3874 | 0.36 |
| 1.4133 | 5.0 | 5 | 2.3586 | 0.3689 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
D3xter1922/electra-base-discriminator-finetuned-cola | [
"pytorch",
"tensorboard",
"electra",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
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} | 68 | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- prompt-generator
- distilgpt2
datasets:
- FredZhang7/krea-ai-prompts
- Gustavosta/Stable-Diffusion-Prompts
- bartman081523/stable-diffusion-discord-prompts
widget:
- text: "amazing"
- text: "a photo of"
- text: "a sci-fi"
- text: "a portrait of"
- text: "a person standing"
- text: "a boy watching"
---
# DistilGPT2 Stable Diffusion Model Card
<a href="https://huggingface.co/FredZhang7/distilgpt2-stable-diffusion-v2"> <font size="4"> <bold> Version 2 is here! </bold> </font> </a>
DistilGPT2 Stable Diffusion is a text generation model used to generate creative and coherent prompts for text-to-image models, given any text.
This model was finetuned on 2.03 million descriptive stable diffusion prompts from [Stable Diffusion discord](https://huggingface.co/datasets/bartman081523/stable-diffusion-discord-prompts), [Lexica.art](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts), and (my hand-picked) [Krea.ai](https://huggingface.co/datasets/FredZhang7/krea-ai-prompts). I filtered the hand-picked prompts based on the output results from Stable Diffusion v1.4.
Compared to other prompt generation models using GPT2, this one runs with 50% faster forwardpropagation and 40% less disk space & RAM.
### PyTorch
```bash
pip install --upgrade transformers
```
```python
from transformers import GPT2Tokenizer, GPT2LMHeadModel
# load the pretrained tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
tokenizer.add_special_tokens({'pad_token': '[PAD]'})
tokenizer.max_len = 512
# load the fine-tuned model
model = GPT2LMHeadModel.from_pretrained('FredZhang7/distilgpt2-stable-diffusion')
# generate text using fine-tuned model
from transformers import pipeline
nlp = pipeline('text-generation', model=model, tokenizer=tokenizer)
ins = "a beautiful city"
# generate 10 samples
outs = nlp(ins, max_length=80, num_return_sequences=10)
# print the 10 samples
for i in range(len(outs)):
outs[i] = str(outs[i]['generated_text']).replace(' ', '')
print('\033[96m' + ins + '\033[0m')
print('\033[93m' + '\n\n'.join(outs) + '\033[0m')
```
Example Output:
 |
D3xter1922/electra-base-discriminator-finetuned-mnli | []
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} | 0 | 2022-12-06T04:07:32Z | ---
license: creativeml-openrail-m
---
Science Fiction space station textual embedding for Stable Diffusion 2.0.
This embedding is trained on 42 images from Marcel Deneuve's Artstation (https://www.artstation.com/marceldeneuve), then further tuned with an expanded dataset that includes 96 additional images generated with the initial embedding alongside specific prompting tailored to improving the quality.
Example generations:
_Prompt: "Deneuve Station" Steps: 10, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 461940410, Size: 768x768, Model hash: 2c02b20a_
_Prompt: "Deneuve Station" Steps: 10, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 2907310488, Size: 768x768, Model hash: 2c02b20a_
_Prompt: "Deneuve Station" Steps: 10, Sampler: DPM++ SDE Karras, CFG scale: 5, Seed: 2937662716, Size: 768x768, Model hash: 2c02b20a_
|
D4RL1NG/yes | []
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} | 0 | 2022-12-06T04:09:12Z | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
inference: true
---
## Informations
Fine-tuned SD v1-5 model, 25040 steps, 10 epochs
Aspect Ratio Bucketing centered at 768 resolution
Made with 250 pictures of the artist NIXEU;
if you like the artist support their work on https://www.artstation.com/nixeu - https://www.deviantart.com/nixeu
## Tags
Tokens are in the tags.txt along with their occurrences in [#] format
<img alt="Showcase" src="https://huggingface.co/FloydianSound/Nixeu_Diffusion/resolve/main/00000-nurse%20single%20realistic%20lips%20highres%20fringe%20tall%20image%20absurdres%20long%20hair%20black%20hair%20upper%20body%20dress%20nixeu%20-%201522939414%20-%20Nixeu_Artstyle_nixeu_artstyle_768_e10.png"/>
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) |
DCU-NLP/electra-base-irish-cased-discriminator-v1 | [
"pytorch",
"electra",
"pretraining",
"ga",
"transformers",
"irish",
"license:apache-2.0"
]
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} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: all-roberta-large-v1-work-16-16-5-oos
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. -->
# all-roberta-large-v1-work-16-16-5-oos
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3586
- Accuracy: 0.3689
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.8058 | 1.0 | 1 | 2.6169 | 0.2356 |
| 2.3524 | 2.0 | 2 | 2.5215 | 0.2978 |
| 1.9543 | 3.0 | 3 | 2.4427 | 0.3422 |
| 1.5539 | 4.0 | 4 | 2.3874 | 0.36 |
| 1.4133 | 5.0 | 5 | 2.3586 | 0.3689 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
DCU-NLP/electra-base-irish-cased-generator-v1 | [
"pytorch",
"electra",
"fill-mask",
"ga",
"transformers",
"irish",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 7 | 2022-12-06T04:22:38Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8629349125903991
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1351
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2594 | 1.0 | 525 | 0.1613 | 0.8225 |
| 0.126 | 2.0 | 1050 | 0.1345 | 0.8516 |
| 0.0796 | 3.0 | 1575 | 0.1351 | 0.8629 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
DHBaek/gpt2-stackoverflow-question-contents-generator | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 14 | 2022-12-06T04:32:44Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: recipe-nlg-gpt2-ingredient-fixer
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. -->
# recipe-nlg-gpt2-ingredient-fixer
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.13.2
|
DHBaek/xlm-roberta-large-korquad-mask | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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"XLMRobertaForQuestionAnswering"
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} | 9 | null | Access to model Grendar/Dialo-GPT-medium-shiro is restricted and you are not in the authorized list. Visit https://huggingface.co/Grendar/Dialo-GPT-medium-shiro to ask for access. |
DJSammy/bert-base-danish-uncased_BotXO-ai | [
"pytorch",
"jax",
"da",
"dataset:common_crawl",
"dataset:wikipedia",
"transformers",
"bert",
"masked-lm",
"license:cc-by-4.0",
"fill-mask"
]
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} | 14 | 2022-12-06T04:42:42Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: all-roberta-large-v1-work-1000-16-5-oos
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. -->
# all-roberta-large-v1-work-1000-16-5-oos
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4222
- Accuracy: 0.3467
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 5.0697 | 1.0 | 1 | 4.7805 | 0.2533 |
| 4.3663 | 2.0 | 2 | 4.5884 | 0.3422 |
| 3.8736 | 3.0 | 3 | 4.4222 | 0.3467 |
| 3.3926 | 4.0 | 4 | 4.3029 | 0.3422 |
| 3.0423 | 5.0 | 5 | 4.2419 | 0.3467 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
DKpro000/DialoGPT-medium-harrypotter | []
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}
} | 0 | null | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- alanila/autotrain-data-tc_ac
co2_eq_emissions:
emissions: 1.196433244085964
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 2349273884
- CO2 Emissions (in grams): 1.1964
## Validation Metrics
- Loss: 1.271
- Accuracy: 0.517
- Macro F1: 0.465
- Micro F1: 0.517
- Weighted F1: 0.437
- Macro Precision: 0.495
- Micro Precision: 0.517
- Weighted Precision: 0.488
- Macro Recall: 0.501
- Micro Recall: 0.517
- Weighted Recall: 0.517
## 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/alanila/autotrain-tc_ac-2349273884
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("alanila/autotrain-tc_ac-2349273884", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("alanila/autotrain-tc_ac-2349273884", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
DKpro000/DialoGPT-small-harrypotter | []
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} | 0 | 2022-12-06T04:53:32Z | ---
license: apache-2.0
tags:
- generated_from_trainer
- summarization
- stacked summaries
- prompt engineering
metrics:
- rouge
datasets:
- stacked-summaries/stacked-samsum-1024
model-index:
- name: flan-t5-large-stacked-samsum1024-WIP3
results:
- task:
type: summarization
name: Summarization
dataset:
name: samsum
type: samsum
config: samsum
split: test
metrics:
- name: ROUGE-1
type: rouge
value: 47.6682
verified: true
- name: ROUGE-2
type: rouge
value: 23.3053
verified: true
- name: ROUGE-L
type: rouge
value: 39.7678
verified: true
- name: ROUGE-LSUM
type: rouge
value: 43.259
verified: true
- name: loss
type: loss
value: 2.372586965560913
verified: true
- name: gen_len
type: gen_len
value: 17.4237
verified: true
language:
- en
library_name: transformers
pipeline_tag: summarization
---
# flan-t5-large-stacked-samsum-1024
<a href="https://colab.research.google.com/gist/pszemraj/a4bf61f593ebda9a8db6dc58839d9de4/brief-demo-flan-t5-stacked-samsum.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the `stacked-summaries/stacked-samsum-1024` dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1846
- Rouge1: 57.9637
- Rouge2: 28.7446
- Rougel: 44.3826
- Rougelsum: 54.0399
- Gen Len: 122.77
## Model description
This model card presents a model trained on a stacked dataset that aims to improve summarization by testing the benefits of "task-oriented pretraining". The model is designed to learn how to effectively condense and distill information from text by stacking summaries and separating them into independent concepts. In this way, the model can learn to identify essential information without simply mimicking the style of the dataset summaries.
The token used to identify a new concept in the summary is `[NEXT_CONCEPT]`. You can split an output summary based on this token to see how it split the input text information: `summary_text.split("[NEXT_CONCEPT]")` etc.
## Intended uses & limitations
- max input/output is 1024 tokens
- this is mostly a test because `samsum` is not exactly the best dataset for general-purpose summarization
## Training and evaluation data
See the dataset card linked on this page for info
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 24915
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.1195 | 0.17 | 20 | 2.0635 | 57.8829 | 28.7887 | 44.4256 | 54.1299 | 121.8 |
| 0.1084 | 0.35 | 40 | 2.1178 | 58.0416 | 28.6487 | 44.3905 | 54.1557 | 122.893 |
| 0.1019 | 0.52 | 60 | 2.1576 | 57.816 | 28.7069 | 44.4242 | 53.9598 | 120.524 |
| 0.0975 | 0.7 | 80 | 2.1821 | 57.9597 | 28.8178 | 44.4854 | 54.068 | 121.793 |
| 0.0947 | 0.87 | 100 | 2.1846 | 57.9637 | 28.7446 | 44.3826 | 54.0399 | 122.77 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1 |
DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support | [
"pytorch",
"jax",
"bert",
"text-classification",
"multilingual",
"nl",
"fr",
"en",
"arxiv:2104.09947",
"transformers",
"Tweets",
"Sentiment analysis"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
"summarization": {
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},
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}
}
} | 29 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: all-roberta-large-v1-small_talk-2-16-5-oos
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. -->
# all-roberta-large-v1-small_talk-2-16-5-oos
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3566
- Accuracy: 0.3855
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7259 | 1.0 | 1 | 2.5917 | 0.2551 |
| 2.217 | 2.0 | 2 | 2.5059 | 0.3275 |
| 1.7237 | 3.0 | 3 | 2.4355 | 0.3768 |
| 1.4001 | 4.0 | 4 | 2.3837 | 0.3739 |
| 1.1937 | 5.0 | 5 | 2.3566 | 0.3855 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
DTAI-KULeuven/mbert-corona-tweets-belgium-topics | [
"pytorch",
"jax",
"bert",
"text-classification",
"multilingual",
"nl",
"fr",
"en",
"arxiv:2104.09947",
"transformers",
"Dutch",
"French",
"English",
"Tweets",
"Topic classification"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
}
} | 167 | null | ---
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: 253.91 +/- 68.63
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)
```python
import gym
from huggingface_sb3 import load_from_hub, package_to_hub, push_to_hub
from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub.
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.env_util import make_vec_env
# Create the environment
env = make_vec_env('LunarLander-v2', n_envs=16)
model = PPO(
policy = 'MlpPolicy',
env = env,
n_steps = 1024,
batch_size = 64,
n_epochs = 8,
gamma = 0.995,
gae_lambda = 1,
ent_coef = 0.001,
verbose=1
)
model.learn(total_timesteps=2_000_000, log_interval=25, progress_bar=True)
model_name = "ppo-LunarLander-v2"
# Evaluate the agent
# Create a new environment for evaluation
eval_env = gym.make("LunarLander-v2")
# Evaluate the model with 10 evaluation episodes and deterministic=True
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
# Print the results
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
# Upload to Hugging Face Hub
...
```
|
DTAI-KULeuven/robbertje-1-gb-non-shuffled | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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}
}
} | 53 | 2022-12-06T05:22:47Z | ---
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: 262.43 +/- 17.53
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
...
```
|
alexandrainst/da-binary-emotion-classification-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
} | 1,066 | 2022-12-06T05:30:36Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: all-roberta-large-v1-small_talk-4-16-5-oos
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. -->
# all-roberta-large-v1-small_talk-4-16-5-oos
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3566
- Accuracy: 0.3855
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7259 | 1.0 | 1 | 2.5917 | 0.2551 |
| 2.217 | 2.0 | 2 | 2.5059 | 0.3275 |
| 1.7237 | 3.0 | 3 | 2.4355 | 0.3768 |
| 1.4001 | 4.0 | 4 | 2.3837 | 0.3739 |
| 1.1937 | 5.0 | 5 | 2.3566 | 0.3855 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
alexandrainst/da-hatespeech-classification-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
}
} | 866 | 2022-12-06T05:41:19Z | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# kornwtp/ConGen-simcse-model-roberta-base-thai
This is a [ConGen](https://github.com/KornWtp/ConGen) model: It maps sentences to a 768 dimensional dense vector space and can be used for tasks like semantic search.
## Usage
Using this model becomes easy when you have [ConGen](https://github.com/KornWtp/ConGen) installed:
```
pip install -U git+https://github.com/KornWtp/ConGen.git
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["กลุ่มผู้ชายเล่นฟุตบอลบนชายหาด", "กลุ่มเด็กชายกำลังเล่นฟุตบอลบนชายหาด"]
model = SentenceTransformer('kornwtp/ConGen-simcse-model-roberta-base-thai')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Thai Sentence Embeddings Benchmark*: [Semantic Textual Similarity](https://github.com/KornWtp/ConGen#thai-semantic-textual-similarity-benchmark)
## Citing & Authors
```bibtex
@inproceedings{limkonchotiwat-etal-2022-congen,
title = "{ConGen}: Unsupervised Control and Generalization Distillation For Sentence Representation",
author = "Limkonchotiwat, Peerat and
Ponwitayarat, Wuttikorn and
Lowphansirikul, Lalita and
Udomcharoenchaikit, Can and
Chuangsuwanich, Ekapol and
Nutanong, Sarana",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
year = "2022",
publisher = "Association for Computational Linguistics",
}
``` |
alexandrainst/da-hatespeech-detection-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
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},
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},
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}
}
} | 1,719 | null | ---
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 48 with parameters:
```
{'batch_size': 10, '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": 48,
"warmup_steps": 5,
"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 --> |
alexandrainst/da-subjectivivity-classification-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"dataset:DDSC/twitter-sent",
"dataset:DDSC/europarl",
"transformers",
"license:cc-by-sa-4.0"
]
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} | 846 | null | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# kornwtp/ConGen-paraphrase-multilingual-mpnet-base-v2
This is a [ConGen](https://github.com/KornWtp/ConGen) model: It maps sentences to a 768 dimensional dense vector space and can be used for tasks like semantic search.
## Usage
Using this model becomes easy when you have [ConGen](https://github.com/KornWtp/ConGen) installed:
```
pip install -U git+https://github.com/KornWtp/ConGen.git
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["กลุ่มผู้ชายเล่นฟุตบอลบนชายหาด", "กลุ่มเด็กชายกำลังเล่นฟุตบอลบนชายหาด"]
model = SentenceTransformer('kornwtp/ConGen-paraphrase-multilingual-mpnet-base-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Thai Sentence Embeddings Benchmark*: [Semantic Textual Similarity](https://github.com/KornWtp/ConGen#thai-semantic-textual-similarity-benchmark)
## Citing & Authors
```bibtex
@inproceedings{limkonchotiwat-etal-2022-congen,
title = "{ConGen}: Unsupervised Control and Generalization Distillation For Sentence Representation",
author = "Limkonchotiwat, Peerat and
Ponwitayarat, Wuttikorn and
Lowphansirikul, Lalita and
Udomcharoenchaikit, Can and
Chuangsuwanich, Ekapol and
Nutanong, Sarana",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
year = "2022",
publisher = "Association for Computational Linguistics",
}
``` |
Dablio/Dablio | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: idrak_wav2vec_tr
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. -->
# idrak_wav2vec_tr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
DarkKibble/DialoGPT-medium-Tankman | []
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} | 0 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1596198683179159557/-l7jFkeQ_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/1472319181097824256/hY5RmhQs_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/1571251248342650882/6YDG9PGc_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">floguo & em herrera is in NY 🌃 & Shravani🍓 & Sara Du & Lucy Guo (Hiring Engineers & Designers)</div>
<div style="text-align: center; font-size: 14px;">@emilyhxrrera-floguo-lucy_guo-saraduit-shrawberryy</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 floguo & em herrera is in NY 🌃 & Shravani🍓 & Sara Du & Lucy Guo (Hiring Engineers & Designers).
| Data | floguo | em herrera is in NY 🌃 | Shravani🍓 | Sara Du | Lucy Guo (Hiring Engineers & Designers) |
| --- | --- | --- | --- | --- | --- |
| Tweets downloaded | 3193 | 3234 | 1049 | 1635 | 3239 |
| Retweets | 662 | 488 | 92 | 17 | 68 |
| Short tweets | 423 | 829 | 328 | 287 | 275 |
| Tweets kept | 2108 | 1917 | 629 | 1331 | 2896 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3kqf9fmj/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 @emilyhxrrera-floguo-lucy_guo-saraduit-shrawberryy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1h2quh2b) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1h2quh2b/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/emilyhxrrera-floguo-lucy_guo-saraduit-shrawberryy')
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)
|
Darkecho789/email-gen | []
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} | 0 | 2022-12-06T06:56:01Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-base-beans-demo-v5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9849624060150376
---
<!-- 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. -->
# vit-base-beans-demo-v5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0466
- Accuracy: 0.9850
## 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: 0.0002
- 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1519 | 1.54 | 100 | 0.1535 | 0.9474 |
| 0.0447 | 3.08 | 200 | 0.0466 | 0.9850 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Darkrider/covidbert_medmarco | [
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:2010.05987",
"transformers"
]
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} | 35 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: all-roberta-large-v1-meta-2-16-5-oos
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. -->
# all-roberta-large-v1-meta-2-16-5-oos
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4797
- Accuracy: 0.28
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7721 | 1.0 | 1 | 2.6529 | 0.1889 |
| 2.2569 | 2.0 | 2 | 2.5866 | 0.2333 |
| 1.9837 | 3.0 | 3 | 2.5340 | 0.2644 |
| 1.6425 | 4.0 | 4 | 2.4980 | 0.2756 |
| 1.4612 | 5.0 | 5 | 2.4797 | 0.28 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Darkrider/covidbert_mednli | [
"transformers"
]
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} | 3 | null | ---
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: wav2vec2-base-ft-keyword-spotting
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-ft-keyword-spotting
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0795
- Accuracy: 0.9829
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5546 | 1.0 | 399 | 0.4250 | 0.9618 |
| 0.2128 | 2.0 | 798 | 0.1331 | 0.9781 |
| 0.1763 | 3.0 | 1197 | 0.0935 | 0.9807 |
| 0.1485 | 4.0 | 1596 | 0.0852 | 0.9828 |
| 0.1335 | 5.0 | 1995 | 0.0795 | 0.9829 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.10.0+cu111
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Darren/darren | [
"pytorch"
]
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} | 0 | null | Access to model kksukk/nia_hubert is restricted and you are not in the authorized list. Visit https://huggingface.co/kksukk/nia_hubert to ask for access. |
DataikuNLP/camembert-base | [
"pytorch",
"tf",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible"
]
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} | 8 | null | ---
language:
- th
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Thai Combined Concat
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 th
type: mozilla-foundation/common_voice_11_0
config: th
split: test
args: th
metrics:
- name: Wer
type: wer
value: 27.279438445464898
---
<!-- 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. -->
# Whisper Small Thai Combined Concat
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 th dataset and additional scraped data.
It achieves the following results on the evaluation set:
- Loss: 0.5034
- Wer: 27.2794 (without tokenizer)
## 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: 64
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0002 | 83.33 | 5000 | 0.5034 | 27.2794 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
|
Davlan/xlm-roberta-base-finetuned-naija | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 1 | null | ---
language: en
license: mit
tags:
- vision
- image-to-text
- image-captioning
model_name: microsoft/git-base
pipeline_tag: image-to-text
---
# GIT (GenerativeImage2Text), base-sized
GIT (short for GenerativeImage2Text) model, base-sized version. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first released in [this repository](https://github.com/microsoft/GenerativeImage2Text).
Disclaimer: The team releasing GIT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
GIT is a Transformer decoder conditioned on both CLIP image tokens and text tokens. The model is trained using "teacher forcing" on a lot of (image, text) pairs.
The goal for the model is simply to predict the next text token, giving the image tokens and previous text tokens.
The model has full access to (i.e. a bidirectional attention mask is used for) the image patch tokens, but only has access to the previous text tokens (i.e. a causal attention mask is used for the text tokens) when predicting the next text token.
This allows the model to be used for tasks like:
- image and video captioning
- visual question answering (VQA) on images and videos
- even image classification (by simply conditioning the model on the image and asking it to generate a class for it in text).
## Intended uses & limitations
You can use the raw model for image captioning. See the [model hub](https://huggingface.co/models?search=microsoft/git) to look for
fine-tuned versions on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/model_doc/git#transformers.GitForCausalLM.forward.example).
## Training data
From the paper:
> We collect 0.8B image-text pairs for pre-training, which include COCO (Lin et al., 2014), Conceptual Captions
(CC3M) (Sharma et al., 2018), SBU (Ordonez et al., 2011), Visual Genome (VG) (Krishna et al., 2016),
Conceptual Captions (CC12M) (Changpinyo et al., 2021), ALT200M (Hu et al., 2021a), and an extra 0.6B
data following a similar collection procedure in Hu et al. (2021a).
=> however this is for the model referred to as "GIT" in the paper, which is not open-sourced.
This checkpoint is "GIT-base", which is a smaller variant of GIT trained on 10 million image-text pairs.
See table 11 in the [paper](https://arxiv.org/abs/2205.14100) for more details.
### Preprocessing
We refer to the original repo regarding details for preprocessing during training.
During validation, one resizes the shorter edge of each image, after which center cropping is performed to a fixed-size resolution. Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.
## Evaluation results
For evaluation results, we refer readers to the [paper](https://arxiv.org/abs/2205.14100). |
Davlan/xlm-roberta-base-finetuned-shona | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 5 | null | ---
language: en
license: mit
tags:
- vision
- image-captioning
model_name: microsoft/git-base-coco
pipeline_tag: image-to-text
---
# GIT (GenerativeImage2Text), base-sized, fine-tuned on COCO
GIT (short for GenerativeImage2Text) model, base-sized version, fine-tuned on COCO. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first released in [this repository](https://github.com/microsoft/GenerativeImage2Text).
Disclaimer: The team releasing GIT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
GIT is a Transformer decoder conditioned on both CLIP image tokens and text tokens. The model is trained using "teacher forcing" on a lot of (image, text) pairs.
The goal for the model is simply to predict the next text token, giving the image tokens and previous text tokens.
The model has full access to (i.e. a bidirectional attention mask is used for) the image patch tokens, but only has access to the previous text tokens (i.e. a causal attention mask is used for the text tokens) when predicting the next text token.
This allows the model to be used for tasks like:
- image and video captioning
- visual question answering (VQA) on images and videos
- even image classification (by simply conditioning the model on the image and asking it to generate a class for it in text).
## Intended uses & limitations
You can use the raw model for image captioning. See the [model hub](https://huggingface.co/models?search=microsoft/git) to look for
fine-tuned versions on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/model_doc/git#transformers.GitForCausalLM.forward.example).
## Training data
From the paper:
> We collect 0.8B image-text pairs for pre-training, which include COCO (Lin et al., 2014), Conceptual Captions
(CC3M) (Sharma et al., 2018), SBU (Ordonez et al., 2011), Visual Genome (VG) (Krishna et al., 2016),
Conceptual Captions (CC12M) (Changpinyo et al., 2021), ALT200M (Hu et al., 2021a), and an extra 0.6B
data following a similar collection procedure in Hu et al. (2021a).
=> however this is for the model referred to as "GIT" in the paper, which is not open-sourced.
This checkpoint is "GIT-base", which is a smaller variant of GIT trained on 10 million image-text pairs.
Next, the model was fine-tuned on COCO.
See table 11 in the [paper](https://arxiv.org/abs/2205.14100) for more details.
### Preprocessing
We refer to the original repo regarding details for preprocessing during training.
During validation, one resizes the shorter edge of each image, after which center cropping is performed to a fixed-size resolution. Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.
## Evaluation results
For evaluation results, we refer readers to the [paper](https://arxiv.org/abs/2205.14100). |
Davlan/xlm-roberta-base-finetuned-somali | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4898 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.12.1+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Davlan/xlm-roberta-base-finetuned-swahili | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 40 | null | ---
language: en
datasets:
- patrickvonplaten/librispeech_asr_dummy
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- en
- speech
---
# Fine-tuned facebook/wav2vec2-base large model for speech recognition in English
Fine-tuned [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on English using the train and validation splits of [zodata](https://www.kaggle.com/datasets/mohamedk0emad/zodata).
the dataset has 307912 transcibed voice samples, we used 6158 samples for training and 6036 samples for testing
and the result for testing with WER accuracy metric is:
Test WER: 0.340
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned thanks to the GPU credits given by the [kaggle](https://www.kaggle.com/)
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
# load model and tokenizer
processor = Wav2Vec2Processor.from_pretrained("souzan/zomodel")
model = Wav2Vec2ForCTC.from_pretrained("souzan/zomodel")
# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# tokenize
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
## Evaluation
This code snippet shows how to evaluate **facebook/wav2vec2-base-960h** on LibriSpeech's "clean" and "other" test data.
```python
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import torch
from jiwer import wer
librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
model = Wav2Vec2ForCTC.from_pretrained("souzan/zomodel").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("souzan/zomodel")
def map_to_pred(batch):
input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values
with torch.no_grad():
logits = model(input_values.to("cuda")).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
batch["transcription"] = transcription
return batch
result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["audio"])
print("WER:", wer(result["text"], result["transcription"]))
```
|
Davlan/xlm-roberta-base-finetuned-wolof | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 3 | 2022-12-06T09:34:05Z | ---
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: 263.35 +/- 26.01
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
...
```
|
Davlan/xlm-roberta-base-finetuned-xhosa | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 12 | null | ---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion model
Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
Davlan/xlm-roberta-base-finetuned-yoruba | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 29 | null | ---
language: en
license: mit
tags:
- vision
- image-captioning
model_name: microsoft/git-base-textcaps
pipeline_tag: image-to-text
---
# GIT (GenerativeImage2Text), base-sized, fine-tuned on TextCaps
GIT (short for GenerativeImage2Text) model, base-sized version, fine-tuned on TextCaps. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first released in [this repository](https://github.com/microsoft/GenerativeImage2Text).
Disclaimer: The team releasing GIT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
GIT is a Transformer decoder conditioned on both CLIP image tokens and text tokens. The model is trained using "teacher forcing" on a lot of (image, text) pairs.
The goal for the model is simply to predict the next text token, giving the image tokens and previous text tokens.
The model has full access to (i.e. a bidirectional attention mask is used for) the image patch tokens, but only has access to the previous text tokens (i.e. a causal attention mask is used for the text tokens) when predicting the next text token.
This allows the model to be used for tasks like:
- image and video captioning
- visual question answering (VQA) on images and videos
- even image classification (by simply conditioning the model on the image and asking it to generate a class for it in text).
## Intended uses & limitations
You can use the raw model for image captioning. See the [model hub](https://huggingface.co/models?search=microsoft/git) to look for
fine-tuned versions on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/git.html).
## Training data
From the paper:
> We collect 0.8B image-text pairs for pre-training, which include COCO (Lin et al., 2014), Conceptual Captions
(CC3M) (Sharma et al., 2018), SBU (Ordonez et al., 2011), Visual Genome (VG) (Krishna et al., 2016),
Conceptual Captions (CC12M) (Changpinyo et al., 2021), ALT200M (Hu et al., 2021a), and an extra 0.6B
data following a similar collection procedure in Hu et al. (2021a).
=> however this is for the model referred to as "GIT" in the paper, which is not open-sourced.
This checkpoint is "GIT-base", which is a smaller variant of GIT trained on 10 million image-text pairs.
Next, the model was fine-tuned on TextCaps.
See table 11 in the [paper](https://arxiv.org/abs/2205.14100) for more details.
### Preprocessing
We refer to the original repo regarding details for preprocessing during training.
During validation, one resizes the shorter edge of each image, after which center cropping is performed to a fixed-size resolution. Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.
## Evaluation results
For evaluation results, we refer readers to the [paper](https://arxiv.org/abs/2205.14100). |
Davlan/xlm-roberta-base-finetuned-zulu | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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},
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}
}
} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-ks
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0823
- Accuracy: 0.9819
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3582 | 1.0 | 1596 | 0.1846 | 0.9681 |
| 0.2013 | 2.0 | 3192 | 0.1051 | 0.9776 |
| 0.1656 | 3.0 | 4788 | 0.0823 | 0.9819 |
| 0.1363 | 4.0 | 6384 | 0.0801 | 0.9819 |
| 0.0918 | 5.0 | 7980 | 0.0838 | 0.9809 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Davlan/xlm-roberta-base-masakhaner | [
"pytorch",
"xlm-roberta",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
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}
}
} | 3 | null | ---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion model
Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
Davlan/xlm-roberta-base-ner-hrl | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
} | 760 | null | ---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion model
Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
Davlan/xlm-roberta-base-sadilar-ner | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
} | 12 | null | ---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion model
Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
Davlan/xlm-roberta-base-wikiann-ner | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 235 | null | ---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion model
Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
Davlan/xlm-roberta-large-masakhaner | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
} | 1,449 | 2022-12-06T09:41:41Z | ---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion model
Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
Dawit/DialogGPT-small-ironman | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
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}
} | 7 | 2022-12-06T09:42:07Z | ---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion model
Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
Dawn576/Dawn | []
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}
}
} | 0 | 2022-12-06T09:42:19Z | ---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion model
Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
Daymarebait/Discord_BOT_RICK | [
"conversational"
]
| conversational | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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},
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}
}
} | 3 | null | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: t5-base-disfluent-fluent
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-base-disfluent-fluent
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1221
- Bleu: 13.8066
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Dayout/test | []
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}
}
} | 0 | 2022-12-06T09:42:31Z | ---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion model
Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
Dazai/Ko | []
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"prefix": null
}
}
} | 0 | 2022-12-06T09:42:42Z | ---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion model
Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
Dbluciferm3737/Idk | []
| null | {
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}
} | 0 | null | ---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion model
Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
Dbluciferm3737/U | []
| null | {
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}
} | 0 | null | ---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion model
Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
DeadBeast/roberta-base-pretrained-mr | [
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"length_penalty": null,
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"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 6 | null | ---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion model
Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
Dean/summarsiation | []
| null | {
"architectures": null,
"model_type": null,
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"conversational": {
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},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion model
Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
## Model Recycling
[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=2.50&mnli_lp=nan&20_newsgroup=1.08&ag_news=-0.47&amazon_reviews_multi=0.14&anli=2.75&boolq=3.32&cb=21.52&cola=0.07&copa=24.30&dbpedia=0.17&esnli=0.05&financial_phrasebank=2.19&imdb=-0.03&isear=0.67&mnli=0.41&mrpc=-0.12&multirc=2.46&poem_sentiment=4.52&qnli=0.27&qqp=0.37&rotten_tomatoes=3.04&rte=10.99&sst2=1.18&sst_5bins=1.47&stsb=1.72&trec_coarse=-0.11&trec_fine=3.24&tweet_ev_emoji=-1.35&tweet_ev_emotion=1.22&tweet_ev_hate=-0.34&tweet_ev_irony=5.48&tweet_ev_offensive=1.49&tweet_ev_sentiment=-1.25&wic=4.58&wnli=-5.49&wsc=0.19&yahoo_answers=0.16&model_name=ibm%2FColD-Fusion-itr13-seed2&base_name=roberta-base) using ibm/ColD-Fusion-itr13-seed2 as a base model yields average score of 78.72 in comparison to 76.22 by roberta-base.
The model is ranked 1st among all tested models for the roberta-base architecture as of 13/12/2022
Results:
| 20_newsgroup | ag_news | amazon_reviews_multi | anli | boolq | cb | cola | copa | dbpedia | esnli | financial_phrasebank | imdb | isear | mnli | mrpc | multirc | poem_sentiment | qnli | qqp | rotten_tomatoes | rte | sst2 | sst_5bins | stsb | trec_coarse | trec_fine | tweet_ev_emoji | tweet_ev_emotion | tweet_ev_hate | tweet_ev_irony | tweet_ev_offensive | tweet_ev_sentiment | wic | wnli | wsc | yahoo_answers |
|---------------:|----------:|-----------------------:|--------:|--------:|--------:|-------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|--------:|----------:|-----------------:|-------:|--------:|------------------:|--------:|--------:|------------:|--------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|--------:|--------:|--------:|----------------:|
| 86.3648 | 89.3 | 66.72 | 53.0937 | 82.0183 | 89.2857 | 83.605 | 73 | 77.4667 | 91.0423 | 87.3 | 93.868 | 73.1421 | 87.3881 | 87.7451 | 63.6757 | 88.4615 | 92.678 | 91.0809 | 91.4634 | 83.3935 | 95.2982 | 58.1448 | 91.6334 | 97 | 91 | 44.95 | 83.0401 | 52.5589 | 77.0408 | 86.0465 | 69.7818 | 70.0627 | 49.2958 | 63.4615 | 72.5667 |
For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
Declan/Breitbart_modelv7 | []
| null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion model
Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
Declan/CNN_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 7 | null | ---
language: en
tags:
- exbert
license: mit
---
# ColD Fusion model
Finetuned model that aims to be a great base model. It improves over RoBERTa base, trained on 35 datasets.
Full details at [this paper](https://arxiv.org/abs/2212.01378).
## Paper Abstract:
Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a
mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now,
massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources
that are only available to well-resourced teams.
In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed
computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic
loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that
ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on
all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find
ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets,
ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
### How to use
Best way to use is to finetune on your own task, but you can also extract features directly.
To get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = RobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion')
model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Evaluation results
See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html)
When fine-tuned on downstream tasks, this model achieves the following results:
### BibTeX entry and citation info
```bibtex
@article{ColDFusion,
author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and},
title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning},
journal = {CoRR},
volume = {abs/2212.01378},
year = {2022},
url = {https://arxiv.org/abs/2212.01378},
archivePrefix = {arXiv},
eprint = {2212.01378},
}
```
<a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
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