modelId
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-11 12:33:28
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 555
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
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| card
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teven/cross_all_bs192_hardneg_finetuned_WebNLG2020_correctness
|
teven
| 2022-09-21T15:43:25Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-21T15:43:18Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# teven/cross_all_bs192_hardneg_finetuned_WebNLG2020_correctness
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('teven/cross_all_bs192_hardneg_finetuned_WebNLG2020_correctness')
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('teven/cross_all_bs192_hardneg_finetuned_WebNLG2020_correctness')
model = AutoModel.from_pretrained('teven/cross_all_bs192_hardneg_finetuned_WebNLG2020_correctness')
# 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=teven/cross_all_bs192_hardneg_finetuned_WebNLG2020_correctness)
## 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 -->
|
teven/cross_all_bs320_vanilla_finetuned_WebNLG2020_correctness
|
teven
| 2022-09-21T15:42:49Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-21T15:42:41Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# teven/cross_all_bs320_vanilla_finetuned_WebNLG2020_correctness
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('teven/cross_all_bs320_vanilla_finetuned_WebNLG2020_correctness')
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('teven/cross_all_bs320_vanilla_finetuned_WebNLG2020_correctness')
model = AutoModel.from_pretrained('teven/cross_all_bs320_vanilla_finetuned_WebNLG2020_correctness')
# 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=teven/cross_all_bs320_vanilla_finetuned_WebNLG2020_correctness)
## 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 -->
|
teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_correctness
|
teven
| 2022-09-21T15:41:08Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-21T15:41:00Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_correctness
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('teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_correctness')
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('teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_correctness')
model = AutoModel.from_pretrained('teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_correctness')
# 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=teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_correctness)
## 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 -->
|
teven/bi_all_bs320_vanilla_finetuned_WebNLG2020_correctness
|
teven
| 2022-09-21T15:40:30Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-21T15:40:23Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# teven/bi_all_bs320_vanilla_finetuned_WebNLG2020_correctness
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('teven/bi_all_bs320_vanilla_finetuned_WebNLG2020_correctness')
embeddings = model.encode(sentences)
print(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=teven/bi_all_bs320_vanilla_finetuned_WebNLG2020_correctness)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 321 with parameters:
```
{'batch_size': 8, '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": 50,
"evaluation_steps": 0,
"evaluator": "better_cross_encoder.PearsonCorrelationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 5e-05
},
"scheduler": "warmupcosine",
"steps_per_epoch": null,
"warmup_steps": 1605,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, '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})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
teven/bi_all-mpnet-base-v2_finetuned_WebNLG2020_correctness
|
teven
| 2022-09-21T15:37:39Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-21T15:37:31Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# teven/bi_all-mpnet-base-v2_finetuned_WebNLG2020_correctness
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('teven/bi_all-mpnet-base-v2_finetuned_WebNLG2020_correctness')
embeddings = model.encode(sentences)
print(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=teven/bi_all-mpnet-base-v2_finetuned_WebNLG2020_correctness)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 41 with parameters:
```
{'batch_size': 64, '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": 50,
"evaluation_steps": 0,
"evaluator": "better_cross_encoder.PearsonCorrelationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 0.0002
},
"scheduler": "warmupcosine",
"steps_per_epoch": null,
"warmup_steps": 205,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, '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})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
GItaf/gpt2-gpt2-TF-weight0.5-epoch5
|
GItaf
| 2022-09-21T15:24:17Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-21T12:07:29Z |
---
tags:
- generated_from_trainer
model-index:
- name: gpt2-gpt2-TF-weight0.5-epoch5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-gpt2-TF-weight0.5-epoch5
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4047
- Cls loss: 0.8943
- Lm loss: 3.9573
- Cls Accuracy: 0.8305
- Cls F1: 0.8305
- Cls Precision: 0.8305
- Cls Recall: 0.8305
- Perplexity: 52.31
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cls loss | Lm loss | Cls Accuracy | Cls F1 | Cls Precision | Cls Recall | Perplexity |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------:|:------------:|:------:|:-------------:|:----------:|:----------:|
| 4.4891 | 1.0 | 3470 | 4.2525 | 0.4695 | 4.0177 | 0.8046 | 0.8023 | 0.8093 | 0.8046 | 55.57 |
| 4.2708 | 2.0 | 6940 | 4.2621 | 0.5568 | 3.9835 | 0.8398 | 0.8383 | 0.8438 | 0.8398 | 53.71 |
| 4.1614 | 3.0 | 10410 | 4.2509 | 0.5637 | 3.9689 | 0.8444 | 0.8443 | 0.8443 | 0.8444 | 52.93 |
| 4.0683 | 4.0 | 13880 | 4.3454 | 0.7723 | 3.9591 | 0.8282 | 0.8281 | 0.8281 | 0.8282 | 52.41 |
| 4.0036 | 5.0 | 17350 | 4.4047 | 0.8943 | 3.9573 | 0.8305 | 0.8305 | 0.8305 | 0.8305 | 52.31 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/kogatan-shiny
|
sd-concepts-library
| 2022-09-21T15:11:22Z | 0 | 3 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T15:11:16Z |
---
license: mit
---
### kogatan_shiny on Stable Diffusion
This is the `kogatan` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
sd-concepts-library/homestuck-sprite
|
sd-concepts-library
| 2022-09-21T15:08:58Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T15:08:54Z |
---
license: mit
---
### homestuck sprite on Stable Diffusion
This is the `<homestuck-sprite>` 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`:
|
minminzi/t5-base-finetuned-eli5
|
minminzi
| 2022-09-21T15:02:46Z | 126 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:eli5",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-20T15:35:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
metrics:
- rouge
model-index:
- name: t5-base-finetuned-eli5
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: eli5
type: eli5
config: LFQA_reddit
split: train_eli5
args: LFQA_reddit
metrics:
- name: Rouge1
type: rouge
value: 0.0
---
<!-- 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-finetuned-eli5
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the eli5 dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 17040 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.0
- Tokenizers 0.12.1
|
rugo/xlm-roberta-base-finetuned
|
rugo
| 2022-09-21T14:07:10Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-21T13:43:38Z |
xml-roberta-base-finetuned
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an legal documents dataset.
|
RandomLegend/Cyberpunk-Lucy
|
RandomLegend
| 2022-09-21T14:03:17Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T13:51:48Z |
---
license: mit
---
Cyberpunk-Lucy on Stable Diffusion
This is the <cyberpunk-lucy> concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the Stable
Conceptualizer notebook. You can also train your own concepts and load them into the concept libraries using this notebook.
Here is the new concept you will be able to use as an object: cyberpunk-lucy
Training Images:
|
sd-concepts-library/david-martinez-cyberpunk
|
sd-concepts-library
| 2022-09-21T14:03:07Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T14:02:55Z |
---
license: mit
---
### david martinez cyberpunk on Stable Diffusion
This is the `<david-martinez-cyberpunk>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
sd-concepts-library/giygas
|
sd-concepts-library
| 2022-09-21T14:01:37Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T13:51:32Z |
---
license: mit
---
### giygas on Stable Diffusion
This is the `<giygas>` 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).
Trained using the initializer token "swirl".
It will primarily generate patterns of usually red and black swirls, patterns that sometimes tile. It may be prone to triggering the "Potential NSFW content" check, despite the training data used.
Here is the new concept you will be able to use as an `object`:
|
Wanjiru/autotrain_gro_ner
|
Wanjiru
| 2022-09-21T13:54:32Z | 106 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"sequence-tagger-model",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-18T12:54:37Z |
---
tags:
- bert
- token-classification
- sequence-tagger-model
language: en
widget:
- text: "Total exports of maize"
---
## Token Classification
Classifies Gro's items and metrics
| **tag** | **token** |
|---------------------------------|-----------|
|B-ITEM | BEGINNING ITEM|
|I-ITEM | INSIDE ITEM|
|B-METRIC |BEGINNING METRIC |
|I-METRIC | INSIDE METRIC|
|O | OUTSIDE |
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("Wanjiru/autotrain_gro_ner")
model = AutoModelForTokenClassification.from_pretrained("Wanjiru/autotrain_gro_ner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Wanjru"
ner_res = nlp(example)
```
---
|
truongpdd/vietnews-gpt2
|
truongpdd
| 2022-09-21T13:01:10Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-08T12:20:20Z |
## How to use:
```
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('truongpdd/vietnews-gpt2')
model = AutoModelForCausalLM.from_pretrained('truongpdd/vietnews-gpt2')
```
|
sd-concepts-library/child-zombie
|
sd-concepts-library
| 2022-09-21T12:17:50Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T12:17:38Z |
---
license: mit
---
### child zombie on Stable Diffusion
This is the `<child-zombie>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
GItaf/gpt2-gpt2-TF-weight2-epoch5
|
GItaf
| 2022-09-21T12:02:13Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-21T08:54:13Z |
---
tags:
- generated_from_trainer
model-index:
- name: gpt2-gpt2-TF-weight2-epoch5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-gpt2-TF-weight2-epoch5
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.8190
- Cls loss: 0.9275
- Lm loss: 3.9629
- Cls Accuracy: 0.8467
- Cls F1: 0.8462
- Cls Precision: 0.8470
- Cls Recall: 0.8467
- Perplexity: 52.61
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cls loss | Lm loss | Cls Accuracy | Cls F1 | Cls Precision | Cls Recall | Perplexity |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------:|:------------:|:------:|:-------------:|:----------:|:----------:|
| 5.612 | 1.0 | 3470 | 5.5564 | 0.7637 | 4.0282 | 0.7689 | 0.7591 | 0.7959 | 0.7689 | 56.16 |
| 5.2267 | 2.0 | 6940 | 5.2872 | 0.6471 | 3.9922 | 0.8444 | 0.8434 | 0.8463 | 0.8444 | 54.17 |
| 4.9082 | 3.0 | 10410 | 5.5032 | 0.7631 | 3.9761 | 0.8415 | 0.8405 | 0.8435 | 0.8415 | 53.31 |
| 4.5998 | 4.0 | 13880 | 5.6560 | 0.8448 | 3.9654 | 0.8484 | 0.8483 | 0.8483 | 0.8484 | 52.74 |
| 4.4024 | 5.0 | 17350 | 5.8190 | 0.9275 | 3.9629 | 0.8467 | 0.8462 | 0.8470 | 0.8467 | 52.61 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
mayorov-s/q-Taxi-v3
|
mayorov-s
| 2022-09-21T11:53:14Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-21T11:53:07Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="mayorov-s/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
GItaf/roberta-base-roberta-base-TF-weight2-epoch5
|
GItaf
| 2022-09-21T11:19:37Z | 47 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-21T08:55:59Z |
---
tags:
- generated_from_trainer
model-index:
- name: roberta-base-roberta-base-TF-weight2-epoch5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-roberta-base-TF-weight2-epoch5
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5174
- Cls loss: 0.6899
- Lm loss: 4.1376
- Cls Accuracy: 0.5401
- Cls F1: 0.3788
- Cls Precision: 0.2917
- Cls Recall: 0.5401
- Perplexity: 62.65
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cls loss | Lm loss | Cls Accuracy | Cls F1 | Cls Precision | Cls Recall | Perplexity |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------:|:------------:|:------:|:-------------:|:----------:|:----------:|
| 6.023 | 1.0 | 3470 | 5.6863 | 0.6910 | 4.3046 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 74.04 |
| 5.6871 | 2.0 | 6940 | 5.5897 | 0.6926 | 4.2045 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 66.99 |
| 5.5587 | 3.0 | 10410 | 5.5414 | 0.6905 | 4.1604 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 64.10 |
| 5.481 | 4.0 | 13880 | 5.5208 | 0.6900 | 4.1409 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 62.86 |
| 5.4338 | 5.0 | 17350 | 5.5174 | 0.6899 | 4.1376 | 0.5401 | 0.3788 | 0.2917 | 0.5401 | 62.65 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
research-backup/roberta-large-semeval2012-average-no-mask-prompt-e-loob-conceptnet-validated
|
research-backup
| 2022-09-21T11:05:28Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-09-21T10:33:17Z |
---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-average-no-mask-prompt-e-loob-conceptnet-validated
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.9192460317460317
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5588235294117647
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5548961424332344
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7876598110061145
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.864
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5833333333333334
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6087962962962963
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.923610064788308
- name: F1 (macro)
type: f1_macro
value: 0.9181612533056485
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8495305164319249
- name: F1 (macro)
type: f1_macro
value: 0.6830369838555483
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6630552546045504
- name: F1 (macro)
type: f1_macro
value: 0.6572125224644058
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9648744522501217
- name: F1 (macro)
type: f1_macro
value: 0.8873701584242761
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8959573801316203
- name: F1 (macro)
type: f1_macro
value: 0.8927635190182807
---
# relbert/roberta-large-semeval2012-average-no-mask-prompt-e-loob-conceptnet-validated
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-e-loob-conceptnet-validated/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5588235294117647
- Accuracy on SAT: 0.5548961424332344
- Accuracy on BATS: 0.7876598110061145
- Accuracy on U2: 0.5833333333333334
- Accuracy on U4: 0.6087962962962963
- Accuracy on Google: 0.864
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-e-loob-conceptnet-validated/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.923610064788308
- Micro F1 score on CogALexV: 0.8495305164319249
- Micro F1 score on EVALution: 0.6630552546045504
- Micro F1 score on K&H+N: 0.9648744522501217
- Micro F1 score on ROOT09: 0.8959573801316203
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-e-loob-conceptnet-validated/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.9192460317460317
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-semeval2012-average-no-mask-prompt-e-loob-conceptnet-validated")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: average_no_mask
- data: relbert/semeval2012_relational_similarity
- template_mode: manual
- template: I wasn’t aware of this relationship, but I just read in the encyclopedia that <obj> is <subj>’s <mask>
- loss_function: info_loob
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 21
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-e-loob-conceptnet-validated/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
hadiqa123/xls-r-ur-large
|
hadiqa123
| 2022-09-21T10:33:30Z | 78 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_8_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-20T21:18:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_8_0
model-index:
- name: xls-r-ur-large
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. -->
# xls-r-ur-large
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_8_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8056
- Wer: 0.4716
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.5282 | 3.25 | 1000 | 3.0650 | 0.9989 |
| 1.7351 | 6.49 | 2000 | 0.8798 | 0.6284 |
| 0.7662 | 9.74 | 3000 | 0.7720 | 0.5399 |
| 0.5675 | 12.99 | 4000 | 0.7661 | 0.5229 |
| 0.4591 | 16.23 | 5000 | 0.7849 | 0.5041 |
| 0.3881 | 19.48 | 6000 | 0.8065 | 0.4893 |
| 0.3522 | 22.73 | 7000 | 0.7915 | 0.4804 |
| 0.3127 | 25.97 | 8000 | 0.8119 | 0.4804 |
| 0.2932 | 29.22 | 9000 | 0.8056 | 0.4716 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/raichu
|
sd-concepts-library
| 2022-09-21T10:17:46Z | 0 | 3 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T10:17:41Z |
---
license: mit
---
### Raichu on Stable Diffusion
This is the `<raichu>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
GItaf/gpt2-gpt2-TF-weight1-epoch5
|
GItaf
| 2022-09-21T10:11:48Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-14T13:43:08Z |
---
tags:
- generated_from_trainer
model-index:
- name: gpt2-gpt2-TF-weight1-epoch5-with-eval
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-gpt2-TF-weight1-epoch5-with-eval
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.9349
- Cls loss: 0.9747
- Lm loss: 3.9596
- Cls Accuracy: 0.8340
- Cls F1: 0.8334
- Cls Precision: 0.8346
- Cls Recall: 0.8340
- Perplexity: 52.44
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cls loss | Lm loss | Cls Accuracy | Cls F1 | Cls Precision | Cls Recall | Perplexity |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------:|:------------:|:------:|:-------------:|:----------:|:----------:|
| 4.8702 | 1.0 | 3470 | 4.7157 | 0.6951 | 4.0201 | 0.7752 | 0.7670 | 0.7978 | 0.7752 | 55.71 |
| 4.5856 | 2.0 | 6940 | 4.6669 | 0.6797 | 3.9868 | 0.8352 | 0.8333 | 0.8406 | 0.8352 | 53.88 |
| 4.4147 | 3.0 | 10410 | 4.6619 | 0.6899 | 3.9716 | 0.8375 | 0.8368 | 0.8384 | 0.8375 | 53.07 |
| 4.2479 | 4.0 | 13880 | 4.8305 | 0.8678 | 3.9622 | 0.8403 | 0.8396 | 0.8413 | 0.8403 | 52.57 |
| 4.1281 | 5.0 | 17350 | 4.9349 | 0.9747 | 3.9596 | 0.8340 | 0.8334 | 0.8346 | 0.8340 | 52.44 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
research-backup/roberta-large-semeval2012-average-no-mask-prompt-c-loob-conceptnet-validated
|
research-backup
| 2022-09-21T10:02:42Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-09-21T09:32:18Z |
---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-average-no-mask-prompt-c-loob-conceptnet-validated
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8421031746031746
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6550802139037433
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6528189910979229
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.8226792662590328
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.936
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6666666666666666
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6712962962962963
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9219526894681332
- name: F1 (macro)
type: f1_macro
value: 0.9178510964329792
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8687793427230047
- name: F1 (macro)
type: f1_macro
value: 0.7117047995829158
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6982665222101841
- name: F1 (macro)
type: f1_macro
value: 0.6850278585111483
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9657786742714057
- name: F1 (macro)
type: f1_macro
value: 0.8948443517322162
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9084926355374491
- name: F1 (macro)
type: f1_macro
value: 0.9067514826619919
---
# relbert/roberta-large-semeval2012-average-no-mask-prompt-c-loob-conceptnet-validated
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-c-loob-conceptnet-validated/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.6550802139037433
- Accuracy on SAT: 0.6528189910979229
- Accuracy on BATS: 0.8226792662590328
- Accuracy on U2: 0.6666666666666666
- Accuracy on U4: 0.6712962962962963
- Accuracy on Google: 0.936
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-c-loob-conceptnet-validated/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9219526894681332
- Micro F1 score on CogALexV: 0.8687793427230047
- Micro F1 score on EVALution: 0.6982665222101841
- Micro F1 score on K&H+N: 0.9657786742714057
- Micro F1 score on ROOT09: 0.9084926355374491
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-c-loob-conceptnet-validated/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8421031746031746
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-semeval2012-average-no-mask-prompt-c-loob-conceptnet-validated")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: average_no_mask
- data: relbert/semeval2012_relational_similarity
- template_mode: manual
- template: Today, I finally discovered the relation between <subj> and <obj> : <mask>
- loss_function: info_loob
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 21
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-c-loob-conceptnet-validated/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
darkproger/pruned-transducer-stateless5-ukrainian-1
|
darkproger
| 2022-09-21T09:51:45Z | 0 | 2 | null |
[
"automatic-speech-recognition",
"audio",
"uk",
"license:cc-by-nc-sa-4.0",
"model-index",
"region:us"
] |
automatic-speech-recognition
| 2022-09-11T13:15:02Z |
---
language:
- uk
tags:
- automatic-speech-recognition
- audio
license: cc-by-nc-sa-4.0
datasets:
- https://github.com/egorsmkv/speech-recognition-uk
- mozilla-foundation/common_voice_6_1
metrics:
- wer
model-index:
- name: Ukrainian pruned_transducer_stateless5 v1.0.0
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice uk
type: mozilla-foundation/common_voice_6_1
split: test
args: uk
metrics:
- name: Validation WER
type: wer
value: 13.37
---
`pruned_transducer_stateless5` with Conformer encoder for Ukrainian: https://github.com/proger/icefall/tree/uk
[Data Filtering](https://github.com/proger/uk)
[Tensorboard run](https://tensorboard.dev/experiment/8WizOEvHR8CqmQAOsr4ALg/)
```
./pruned_transducer_stateless5/train.py \
--world-size 2 \
--num-epochs 30 \
--start-epoch 1 \
--full-libri 1 \
--exp-dir pruned_transducer_stateless5/exp-uk-shuf \
--max-duration 500 \
--use-fp16 1 \
--num-encoder-layers 18 \
--dim-feedforward 1024 \
--nhead 4 \
--encoder-dim 256 \
--decoder-dim 512 \
--joiner-dim 512 \
--bpe-model uk/data/lang_bpe_250/bpe.model
```
```
./pruned_transducer_stateless5/decode.py \
--epoch 27 \
--avg 15 \
--use-averaged-model True \
--exp-dir pruned_transducer_stateless5/exp-uk-shuf \
--decoding-method fast_beam_search \
--num-encoder-layers 18 \
--dim-feedforward 1024 \
--nhead 4 \
--encoder-dim 256 \
--decoder-dim 512 \
--joiner-dim 512 \
--bpe-model uk/data/lang_bpe_250/bpe.model \
--lang-dir uk/data/lang_bpe_250
```
|
darkproger/pruned-transducer-stateless5-ukrainian-1-causal
|
darkproger
| 2022-09-21T09:51:22Z | 0 | 1 | null |
[
"automatic-speech-recognition",
"audio",
"uk",
"license:cc-by-nc-sa-4.0",
"model-index",
"region:us"
] |
automatic-speech-recognition
| 2022-09-20T21:26:48Z |
---
language:
- uk
tags:
- automatic-speech-recognition
- audio
license: cc-by-nc-sa-4.0
datasets:
- https://github.com/egorsmkv/speech-recognition-uk
- mozilla-foundation/common_voice_6_1
metrics:
- wer
model-index:
- name: Ukrainian causal pruned_transducer_stateless5 v1.0.0
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 6.1 uk
type: mozilla-foundation/common_voice_6_1
split: test
args: uk
metrics:
- name: Validation WER
type: wer
value: 17.26
---
Online variant of `pruned_transducer_stateless5` for Ukrainian: https://github.com/proger/icefall/tree/uk
Decoding demo using [Sherpa](https://k2-fsa.github.io/sherpa/): [https://twitter.com/darkproger/status/1570733844114046976](https://twitter.com/darkproger/status/1570733844114046976)
Trained on pseudolabels generated by [darkproger/pruned-transducer-stateless5-ukrainian-1](https://huggingface.co/darkproger/pruned-transducer-stateless5-ukrainian-1) on the noisy 1200 hours [training set](https://github.com/egorsmkv/speech-recognition-uk). Common Voice data was used only for validation.
[Tensorboard run](https://tensorboard.dev/experiment/uMmMmZvwS2euyCrj7BlPOQ/)
```
./pruned_transducer_stateless5/train.py \
--world-size 2 \
--num-epochs 31 \
--start-epoch 1 \
--full-libri 1 \
--exp-dir pruned_transducer_stateless5/exp-uk-filtered2 \
--max-duration 600 \
--use-fp16 1 \
--num-encoder-layers 18 \
--dim-feedforward 1024 \
--nhead 4 \
--encoder-dim 256 \
--decoder-dim 512 \
--joiner-dim 512 \
--bpe-model uk/data/lang_bpe_250/bpe.model \
--causal-convolution True \
--dynamic-chunk-training True
```
|
gary109/ai-light-dance_singing4_ft_wav2vec2-large-xlsr-53-5gram-v4-2-1
|
gary109
| 2022-09-21T09:08:14Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-19T03:39:24Z |
---
tags:
- automatic-speech-recognition
- gary109/AI_Light_Dance
- generated_from_trainer
model-index:
- name: ai-light-dance_singing4_ft_wav2vec2-large-xlsr-53-5gram-v4-2-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-light-dance_singing4_ft_wav2vec2-large-xlsr-53-5gram-v4-2-1
This model is a fine-tuned version of [gary109/ai-light-dance_singing4_ft_wav2vec2-large-xlsr-53-5gram-v4-2](https://huggingface.co/gary109/ai-light-dance_singing4_ft_wav2vec2-large-xlsr-53-5gram-v4-2) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2219
- Wer: 0.0976
## 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: 4e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4531 | 1.0 | 72 | 0.2317 | 0.1021 |
| 0.4479 | 2.0 | 144 | 0.2335 | 0.1014 |
| 0.4475 | 3.0 | 216 | 0.2340 | 0.1000 |
| 0.4432 | 4.0 | 288 | 0.2372 | 0.0993 |
| 0.447 | 5.0 | 360 | 0.2350 | 0.1008 |
| 0.4318 | 6.0 | 432 | 0.2332 | 0.0989 |
| 0.4162 | 7.0 | 504 | 0.2338 | 0.1002 |
| 0.4365 | 8.0 | 576 | 0.2321 | 0.0990 |
| 0.4318 | 9.0 | 648 | 0.2313 | 0.0992 |
| 0.4513 | 10.0 | 720 | 0.2336 | 0.0994 |
| 0.4257 | 11.0 | 792 | 0.2310 | 0.0982 |
| 0.418 | 12.0 | 864 | 0.2316 | 0.0989 |
| 0.4122 | 13.0 | 936 | 0.2341 | 0.0971 |
| 0.4265 | 14.0 | 1008 | 0.2322 | 0.0992 |
| 0.4477 | 15.0 | 1080 | 0.2334 | 0.0987 |
| 0.4023 | 16.0 | 1152 | 0.2351 | 0.0971 |
| 0.4095 | 17.0 | 1224 | 0.2304 | 0.0977 |
| 0.42 | 18.0 | 1296 | 0.2313 | 0.0976 |
| 0.3988 | 19.0 | 1368 | 0.2299 | 0.0984 |
| 0.4078 | 20.0 | 1440 | 0.2310 | 0.0970 |
| 0.4131 | 21.0 | 1512 | 0.2293 | 0.1007 |
| 0.4209 | 22.0 | 1584 | 0.2313 | 0.0998 |
| 0.3931 | 23.0 | 1656 | 0.2351 | 0.1014 |
| 0.406 | 24.0 | 1728 | 0.2336 | 0.0992 |
| 0.3998 | 25.0 | 1800 | 0.2355 | 0.1009 |
| 0.4197 | 26.0 | 1872 | 0.2346 | 0.0996 |
| 0.4289 | 27.0 | 1944 | 0.2283 | 0.1001 |
| 0.4197 | 28.0 | 2016 | 0.2281 | 0.1000 |
| 0.4107 | 29.0 | 2088 | 0.2327 | 0.1007 |
| 0.442 | 30.0 | 2160 | 0.2279 | 0.0985 |
| 0.4315 | 31.0 | 2232 | 0.2284 | 0.0993 |
| 0.4095 | 32.0 | 2304 | 0.2275 | 0.0998 |
| 0.4277 | 33.0 | 2376 | 0.2281 | 0.0996 |
| 0.4114 | 34.0 | 2448 | 0.2267 | 0.1008 |
| 0.4311 | 35.0 | 2520 | 0.2274 | 0.0982 |
| 0.4193 | 36.0 | 2592 | 0.2259 | 0.0987 |
| 0.421 | 37.0 | 2664 | 0.2277 | 0.0989 |
| 0.4084 | 38.0 | 2736 | 0.2268 | 0.0992 |
| 0.4302 | 39.0 | 2808 | 0.2287 | 0.0996 |
| 0.4379 | 40.0 | 2880 | 0.2281 | 0.0984 |
| 0.415 | 41.0 | 2952 | 0.2270 | 0.1006 |
| 0.4035 | 42.0 | 3024 | 0.2299 | 0.0992 |
| 0.4103 | 43.0 | 3096 | 0.2257 | 0.0987 |
| 0.4187 | 44.0 | 3168 | 0.2260 | 0.0975 |
| 0.4254 | 45.0 | 3240 | 0.2273 | 0.0985 |
| 0.415 | 46.0 | 3312 | 0.2312 | 0.1000 |
| 0.4069 | 47.0 | 3384 | 0.2270 | 0.1003 |
| 0.4085 | 48.0 | 3456 | 0.2230 | 0.0978 |
| 0.4287 | 49.0 | 3528 | 0.2241 | 0.0989 |
| 0.4227 | 50.0 | 3600 | 0.2233 | 0.0994 |
| 0.3998 | 51.0 | 3672 | 0.2268 | 0.0991 |
| 0.4139 | 52.0 | 3744 | 0.2224 | 0.0987 |
| 0.409 | 53.0 | 3816 | 0.2256 | 0.1001 |
| 0.4191 | 54.0 | 3888 | 0.2264 | 0.0991 |
| 0.4156 | 55.0 | 3960 | 0.2237 | 0.0993 |
| 0.4252 | 56.0 | 4032 | 0.2250 | 0.0988 |
| 0.4207 | 57.0 | 4104 | 0.2246 | 0.0989 |
| 0.4143 | 58.0 | 4176 | 0.2248 | 0.0981 |
| 0.4261 | 59.0 | 4248 | 0.2237 | 0.0973 |
| 0.4212 | 60.0 | 4320 | 0.2243 | 0.0976 |
| 0.426 | 61.0 | 4392 | 0.2230 | 0.0983 |
| 0.4257 | 62.0 | 4464 | 0.2230 | 0.0977 |
| 0.4102 | 63.0 | 4536 | 0.2219 | 0.0976 |
| 0.4133 | 64.0 | 4608 | 0.2221 | 0.0984 |
| 0.4257 | 65.0 | 4680 | 0.2236 | 0.0982 |
| 0.4006 | 66.0 | 4752 | 0.2231 | 0.0992 |
| 0.404 | 67.0 | 4824 | 0.2227 | 0.0983 |
| 0.409 | 68.0 | 4896 | 0.2235 | 0.0991 |
| 0.4075 | 69.0 | 4968 | 0.2242 | 0.0978 |
| 0.4167 | 70.0 | 5040 | 0.2248 | 0.0989 |
| 0.4026 | 71.0 | 5112 | 0.2242 | 0.0985 |
| 0.404 | 72.0 | 5184 | 0.2236 | 0.0989 |
| 0.4162 | 73.0 | 5256 | 0.2241 | 0.0986 |
| 0.4094 | 74.0 | 5328 | 0.2244 | 0.0991 |
| 0.4147 | 75.0 | 5400 | 0.2247 | 0.0989 |
| 0.4096 | 76.0 | 5472 | 0.2244 | 0.0983 |
| 0.4112 | 77.0 | 5544 | 0.2236 | 0.0981 |
| 0.3987 | 78.0 | 5616 | 0.2242 | 0.0982 |
| 0.3953 | 79.0 | 5688 | 0.2259 | 0.0983 |
| 0.4093 | 80.0 | 5760 | 0.2239 | 0.0991 |
| 0.406 | 81.0 | 5832 | 0.2238 | 0.0980 |
| 0.4149 | 82.0 | 5904 | 0.2240 | 0.0995 |
| 0.4017 | 83.0 | 5976 | 0.2240 | 0.0987 |
| 0.4065 | 84.0 | 6048 | 0.2245 | 0.0979 |
| 0.4315 | 85.0 | 6120 | 0.2249 | 0.0978 |
| 0.421 | 86.0 | 6192 | 0.2239 | 0.0977 |
| 0.4061 | 87.0 | 6264 | 0.2243 | 0.0974 |
| 0.4096 | 88.0 | 6336 | 0.2244 | 0.0982 |
| 0.4171 | 89.0 | 6408 | 0.2246 | 0.0974 |
| 0.4189 | 90.0 | 6480 | 0.2240 | 0.0980 |
| 0.4106 | 91.0 | 6552 | 0.2236 | 0.0978 |
| 0.408 | 92.0 | 6624 | 0.2234 | 0.0983 |
| 0.4218 | 93.0 | 6696 | 0.2239 | 0.0985 |
| 0.3997 | 94.0 | 6768 | 0.2237 | 0.0983 |
| 0.4173 | 95.0 | 6840 | 0.2238 | 0.0980 |
| 0.4134 | 96.0 | 6912 | 0.2235 | 0.0982 |
| 0.3959 | 97.0 | 6984 | 0.2237 | 0.0979 |
| 0.4149 | 98.0 | 7056 | 0.2238 | 0.0982 |
| 0.4125 | 99.0 | 7128 | 0.2238 | 0.0983 |
| 0.4111 | 100.0 | 7200 | 0.2235 | 0.0982 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
buddhist-nlp/mbart-buddhist-many-to-one
|
buddhist-nlp
| 2022-09-21T09:06:13Z | 135 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-20T16:36:45Z |
This is a multilingual model that translates Buddhist Chinese, Tibetan and Pali into English.
Chinese input should be in simplified characters (簡體字).
Tibetan should be input in Wylie transliteration, with "/" as shad and no space between the last word and a shad. For example "gang zag la bdag med par khong du chud pa ni 'jig tshogs la lta ba'i gnyen po yin pas na de spangs na nyon mongs pa thams cad spong bar 'gyur ro//".
Pāli works with IAST transliteration: "Evaṁ me sutaṁ — ekaṁ samayaṁ bhagavā antarā ca rājagahaṁ antarā ca nāḷandaṁ addhānamaggappaṭipanno hoti mahatā bhikkhusaṅghena saddhiṁ pañcamattehi bhikkhusatehi."
Multiple sentences are best translated when each sentence is on a separate line.
|
Souvik123/layoutlmv3-finetuned-cord_100
|
Souvik123
| 2022-09-21T08:58:14Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:cord-layoutlmv3",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-21T08:17:48Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cord-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cord-layoutlmv3
type: cord-layoutlmv3
config: cord
split: train
args: cord
metrics:
- name: Precision
type: precision
value: 0.9415680473372781
- name: Recall
type: recall
value: 0.9528443113772455
- name: F1
type: f1
value: 0.947172619047619
- name: Accuracy
type: accuracy
value: 0.9592529711375212
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv3-finetuned-cord_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2132
- Precision: 0.9416
- Recall: 0.9528
- F1: 0.9472
- Accuracy: 0.9593
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.56 | 250 | 1.0604 | 0.7085 | 0.7732 | 0.7394 | 0.7806 |
| 1.4262 | 3.12 | 500 | 0.5754 | 0.8504 | 0.8683 | 0.8593 | 0.8705 |
| 1.4262 | 4.69 | 750 | 0.4026 | 0.8949 | 0.9109 | 0.9028 | 0.9189 |
| 0.4088 | 6.25 | 1000 | 0.3129 | 0.9232 | 0.9356 | 0.9294 | 0.9406 |
| 0.4088 | 7.81 | 1250 | 0.2691 | 0.9290 | 0.9401 | 0.9345 | 0.9452 |
| 0.2193 | 9.38 | 1500 | 0.2260 | 0.9278 | 0.9431 | 0.9354 | 0.9499 |
| 0.2193 | 10.94 | 1750 | 0.2447 | 0.9260 | 0.9371 | 0.9315 | 0.9469 |
| 0.1547 | 12.5 | 2000 | 0.2113 | 0.9394 | 0.9521 | 0.9457 | 0.9601 |
| 0.1547 | 14.06 | 2250 | 0.2138 | 0.9430 | 0.9543 | 0.9487 | 0.9605 |
| 0.1163 | 15.62 | 2500 | 0.2132 | 0.9416 | 0.9528 | 0.9472 | 0.9593 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Sphere-Fall2022/nima-test-bert-glue
|
Sphere-Fall2022
| 2022-09-21T08:12:31Z | 105 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-21T08:03:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
model-index:
- name: nima-test-bert-glue
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. -->
# nima-test-bert-glue
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 367 | 0.4436 | 0.8106 | 0.8597 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
CptBaas/Bio_ClinicalBERT-finetuned-skinwound
|
CptBaas
| 2022-09-21T08:03:52Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-18T09:59:40Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: Bio_ClinicalBERT-finetuned-skinwound
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. -->
# Bio_ClinicalBERT-finetuned-skinwound
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3435
- Accuracy: 0.8938
- F1: 0.8884
- Recall: 0.8938
- Precision: 0.8857
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.5905 | 1.0 | 154 | 0.3423 | 0.8828 | 0.8416 | 0.8828 | 0.8064 |
| 0.3472 | 2.0 | 308 | 0.2942 | 0.8901 | 0.8753 | 0.8901 | 0.8800 |
| 0.2651 | 3.0 | 462 | 0.2977 | 0.8974 | 0.8858 | 0.8974 | 0.8889 |
| 0.2203 | 4.0 | 616 | 0.3224 | 0.9011 | 0.8945 | 0.9011 | 0.8930 |
| 0.164 | 5.0 | 770 | 0.3435 | 0.8938 | 0.8884 | 0.8938 | 0.8857 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
research-backup/roberta-large-semeval2012-average-prompt-d-loob-conceptnet-validated
|
research-backup
| 2022-09-21T08:01:23Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-09-21T07:30:35Z |
---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-average-prompt-d-loob-conceptnet-validated
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8684920634920635
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7032085561497327
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7091988130563798
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.8182323513062812
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.962
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6535087719298246
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6342592592592593
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9210486665662196
- name: F1 (macro)
type: f1_macro
value: 0.9165417055118773
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8650234741784038
- name: F1 (macro)
type: f1_macro
value: 0.7151332055781292
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6939328277356447
- name: F1 (macro)
type: f1_macro
value: 0.6806208180350917
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9643875634694303
- name: F1 (macro)
type: f1_macro
value: 0.8887533067064131
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9066123472265747
- name: F1 (macro)
type: f1_macro
value: 0.904696288402551
---
# relbert/roberta-large-semeval2012-average-prompt-d-loob-conceptnet-validated
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-d-loob-conceptnet-validated/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.7032085561497327
- Accuracy on SAT: 0.7091988130563798
- Accuracy on BATS: 0.8182323513062812
- Accuracy on U2: 0.6535087719298246
- Accuracy on U4: 0.6342592592592593
- Accuracy on Google: 0.962
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-d-loob-conceptnet-validated/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9210486665662196
- Micro F1 score on CogALexV: 0.8650234741784038
- Micro F1 score on EVALution: 0.6939328277356447
- Micro F1 score on K&H+N: 0.9643875634694303
- Micro F1 score on ROOT09: 0.9066123472265747
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-d-loob-conceptnet-validated/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8684920634920635
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-semeval2012-average-prompt-d-loob-conceptnet-validated")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity
- template_mode: manual
- template: I wasn’t aware of this relationship, but I just read in the encyclopedia that <subj> is the <mask> of <obj>
- loss_function: info_loob
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 22
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-d-loob-conceptnet-validated/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
AlbedoAI/DialoGPT-medium-Albedo
|
AlbedoAI
| 2022-09-21T07:46:12Z | 112 | 2 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-08-14T04:44:07Z |
---
tags:
- conversational
---
# Albedo Medium DialoGPT Model Casual
This model does not do well with short greetings. But it can handle question and answer types of conversations most of the time.
It is trained on Albedo's dialogues from his story quests [Princeps Cretaceus Chapter](https://genshin-impact.fandom.com/wiki/Princeps_Cretaceus_Chapter) and [Shadows Amidst Snowstorms Event Story](https://genshin-impact.fandom.com/wiki/Shadows_Amidst_Snowstorms/Story)
Socials
- Twitter: [@tofuboy05](https://twitter.com/tofuboy05) (Creator)
- Tiktok: [@tofuboyart](https://www.tiktok.com/@tofuboyart)
- HoYoLAB: [TofuBoy](https://www.hoyolab.com/accountCenter/postList?id=78394798)
|
zhanglu/distilbert-base-uncased-finetuned-cola
|
zhanglu
| 2022-09-21T06:52:55Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-21T06:41:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5470036892050114
---
<!-- 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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5657
- Matthews Correlation: 0.5470
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.521 | 1.0 | 535 | 0.5159 | 0.4152 |
| 0.3445 | 2.0 | 1070 | 0.4905 | 0.5022 |
| 0.2317 | 3.0 | 1605 | 0.5657 | 0.5470 |
| 0.1774 | 4.0 | 2140 | 0.7557 | 0.5282 |
| 0.1323 | 5.0 | 2675 | 0.8073 | 0.5455 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
huggingtweets/celcom
|
huggingtweets
| 2022-09-21T06:49:14Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-21T06:49:06Z |
---
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/1473653305486176257/bzxJRVyG_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Celcom</div>
<div style="text-align: center; font-size: 14px;">@celcom</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 Celcom.
| Data | Celcom |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 0 |
| Short tweets | 20 |
| Tweets kept | 3230 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3km1q9ay/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 @celcom's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2qtqkfif) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2qtqkfif/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/celcom')
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)
|
sd-concepts-library/hanfu-anime-style
|
sd-concepts-library
| 2022-09-21T06:42:02Z | 0 | 17 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-18T08:25:18Z |
---
license: mit
---
### Hanfu anime style on Stable Diffusion
This is the `<hanfu-anime-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
sd-concepts-library/nikodim
|
sd-concepts-library
| 2022-09-21T06:41:09Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T20:02:46Z |
---
license: mit
---
### nikodim on Stable Diffusion
This is the `<nikodim>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
sd-concepts-library/bamse-og-kylling
|
sd-concepts-library
| 2022-09-21T06:23:26Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T06:23:17Z |
---
license: mit
---
### Bamse og kylling on Stable Diffusion
This is the `<bamse-kylling>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
sd-concepts-library/noggles
|
sd-concepts-library
| 2022-09-21T06:00:49Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T06:00:32Z |
---
license: mit
---
### noggles on Stable Diffusion
This is the `<noggles>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
huggingtweets/houstonhotwife-thongwife
|
huggingtweets
| 2022-09-21T05:43:45Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-21T05:38:58Z |
---
language: en
thumbnail: http://www.huggingtweets.com/houstonhotwife-thongwife/1663739021491/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1571839912202178561/tbXoqNM5_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/722128170808320000/YNGcYakC_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">Houston Hotwife & Thongwife</div>
<div style="text-align: center; font-size: 14px;">@houstonhotwife-thongwife</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 Houston Hotwife & Thongwife.
| Data | Houston Hotwife | Thongwife |
| --- | --- | --- |
| Tweets downloaded | 3173 | 3225 |
| Retweets | 1166 | 1469 |
| Short tweets | 524 | 1560 |
| Tweets kept | 1483 | 196 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2g5af0zu/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 @houstonhotwife-thongwife's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1uh4ivfz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1uh4ivfz/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/houstonhotwife-thongwife')
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)
|
research-backup/roberta-large-semeval2012-average-prompt-a-loob-conceptnet-validated
|
research-backup
| 2022-09-21T05:40:34Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-09-21T04:45:26Z |
---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-average-prompt-a-loob-conceptnet-validated
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8358531746031747
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6310160427807486
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6320474777448071
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7409672040022235
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.918
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5745614035087719
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6018518518518519
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9133644718999548
- name: F1 (macro)
type: f1_macro
value: 0.9091653089166233
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8523474178403756
- name: F1 (macro)
type: f1_macro
value: 0.6906026137184262
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6700975081256771
- name: F1 (macro)
type: f1_macro
value: 0.6599264465141299
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9501286777491827
- name: F1 (macro)
type: f1_macro
value: 0.8552943975279798
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8987778125979317
- name: F1 (macro)
type: f1_macro
value: 0.8958673797671589
---
# relbert/roberta-large-semeval2012-average-prompt-a-loob-conceptnet-validated
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-loob-conceptnet-validated/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.6310160427807486
- Accuracy on SAT: 0.6320474777448071
- Accuracy on BATS: 0.7409672040022235
- Accuracy on U2: 0.5745614035087719
- Accuracy on U4: 0.6018518518518519
- Accuracy on Google: 0.918
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-loob-conceptnet-validated/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9133644718999548
- Micro F1 score on CogALexV: 0.8523474178403756
- Micro F1 score on EVALution: 0.6700975081256771
- Micro F1 score on K&H+N: 0.9501286777491827
- Micro F1 score on ROOT09: 0.8987778125979317
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-loob-conceptnet-validated/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8358531746031747
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-semeval2012-average-prompt-a-loob-conceptnet-validated")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: average
- data: relbert/semeval2012_relational_similarity
- template_mode: manual
- template: Today, I finally discovered the relation between <subj> and <obj> : <subj> is the <mask> of <obj>
- loss_function: info_loob
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 21
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-loob-conceptnet-validated/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
nrtf/nerf_factory
|
nrtf
| 2022-09-21T04:37:38Z | 0 | 6 | null |
[
"tensorboard",
"license:mit",
"region:us"
] | null | 2022-09-02T07:20:46Z |
---
license: mit
---
# Pretrained models of NeRF-Factory
Here, we provide the checkpoints for NeRF-Factory, a PyTorch NeRF collection.
For the code to reproduce each checkpoint, please refer to the official code and manuals on the project page.
Code: https://github.com/kakaobrain/NeRF-Factory
Project Page: https://kakaobrain.github.io/NeRF-Factory/
|
research-backup/roberta-large-semeval2012-mask-prompt-d-loob-conceptnet-validated
|
research-backup
| 2022-09-21T03:49:49Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-09-21T02:54:39Z |
---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-mask-prompt-d-loob-conceptnet-validated
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8116468253968254
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7058823529411765
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7002967359050445
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.8121178432462479
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.944
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6973684210526315
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6550925925925926
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9291848726834414
- name: F1 (macro)
type: f1_macro
value: 0.9241488028701781
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8809859154929578
- name: F1 (macro)
type: f1_macro
value: 0.7410143358933853
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7210184182015169
- name: F1 (macro)
type: f1_macro
value: 0.7105268293048113
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9658482298115045
- name: F1 (macro)
type: f1_macro
value: 0.8964930098442265
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9053588216859918
- name: F1 (macro)
type: f1_macro
value: 0.9027585355457223
---
# relbert/roberta-large-semeval2012-mask-prompt-d-loob-conceptnet-validated
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-d-loob-conceptnet-validated/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.7058823529411765
- Accuracy on SAT: 0.7002967359050445
- Accuracy on BATS: 0.8121178432462479
- Accuracy on U2: 0.6973684210526315
- Accuracy on U4: 0.6550925925925926
- Accuracy on Google: 0.944
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-d-loob-conceptnet-validated/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9291848726834414
- Micro F1 score on CogALexV: 0.8809859154929578
- Micro F1 score on EVALution: 0.7210184182015169
- Micro F1 score on K&H+N: 0.9658482298115045
- Micro F1 score on ROOT09: 0.9053588216859918
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-d-loob-conceptnet-validated/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8116468253968254
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-semeval2012-mask-prompt-d-loob-conceptnet-validated")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: mask
- data: relbert/semeval2012_relational_similarity
- template_mode: manual
- template: I wasn’t aware of this relationship, but I just read in the encyclopedia that <subj> is the <mask> of <obj>
- loss_function: info_loob
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 22
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-d-loob-conceptnet-validated/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
sd-concepts-library/stretch-re1-robot
|
sd-concepts-library
| 2022-09-21T02:56:12Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T02:56:06Z |
---
license: mit
---
### Stretch RE1 Robot on Stable Diffusion
This is the `<stretch>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
Arnaudmkonan/xlm-roberta-base-finetuned-panx-de-fr
|
Arnaudmkonan
| 2022-09-21T02:53:34Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-21T02:31:34Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1654
- F1: 0.8590
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2845 | 1.0 | 715 | 0.1831 | 0.8249 |
| 0.1449 | 2.0 | 1430 | 0.1643 | 0.8479 |
| 0.0929 | 3.0 | 2145 | 0.1654 | 0.8590 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Arnaudmkonan/xlm-roberta-base-finetuned-panx-de
|
Arnaudmkonan
| 2022-09-21T02:24:12Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-21T01:52:25Z |
---
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: 0.863677639046538
---
<!-- 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: 0.1343
- F1: 0.8637
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2578 | 1.0 | 525 | 0.1562 | 0.8273 |
| 0.1297 | 2.0 | 1050 | 0.1330 | 0.8474 |
| 0.0809 | 3.0 | 1575 | 0.1343 | 0.8637 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
bouim/wav2vec2-base-timit-demo-google-colab
|
bouim
| 2022-09-21T02:04:57Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-21T01:47:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-google-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7468
- Wer: 0.5736
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.671 | 10.42 | 500 | 2.1264 | 0.9972 |
| 0.7223 | 20.83 | 1000 | 0.7468 | 0.5736 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
research-backup/roberta-large-semeval2012-mask-prompt-b-loob-conceptnet-validated
|
research-backup
| 2022-09-21T02:00:11Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-09-21T01:04:59Z |
---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-mask-prompt-b-loob-conceptnet-validated
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8465079365079365
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.56951871657754
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5727002967359051
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7459699833240689
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.912
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5087719298245614
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5601851851851852
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9311435889709206
- name: F1 (macro)
type: f1_macro
value: 0.9271973871730766
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8654929577464788
- name: F1 (macro)
type: f1_macro
value: 0.7067494314299665
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6998916576381365
- name: F1 (macro)
type: f1_macro
value: 0.6882463597195224
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.961466230785282
- name: F1 (macro)
type: f1_macro
value: 0.8903751547538185
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9109996866186149
- name: F1 (macro)
type: f1_macro
value: 0.9101384826079929
---
# relbert/roberta-large-semeval2012-mask-prompt-b-loob-conceptnet-validated
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-b-loob-conceptnet-validated/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.56951871657754
- Accuracy on SAT: 0.5727002967359051
- Accuracy on BATS: 0.7459699833240689
- Accuracy on U2: 0.5087719298245614
- Accuracy on U4: 0.5601851851851852
- Accuracy on Google: 0.912
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-b-loob-conceptnet-validated/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9311435889709206
- Micro F1 score on CogALexV: 0.8654929577464788
- Micro F1 score on EVALution: 0.6998916576381365
- Micro F1 score on K&H+N: 0.961466230785282
- Micro F1 score on ROOT09: 0.9109996866186149
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-b-loob-conceptnet-validated/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8465079365079365
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-semeval2012-mask-prompt-b-loob-conceptnet-validated")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: mask
- data: relbert/semeval2012_relational_similarity
- template_mode: manual
- template: Today, I finally discovered the relation between <subj> and <obj> : <obj> is <subj>'s <mask>
- loss_function: info_loob
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 21
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-b-loob-conceptnet-validated/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
alexperez26/lol
|
alexperez26
| 2022-09-21T00:23:37Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2022-09-21T00:22:53Z |
---
license: openrail
---
pip install diffusers==0.3.0 transformers scipy ftfy
|
research-backup/roberta-large-conceptnet-average-no-mask-prompt-e-nce
|
research-backup
| 2022-09-21T00:19:44Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-08-11T08:58:39Z |
---
datasets:
- relbert/conceptnet_high_confidence
model-index:
- name: relbert/roberta-large-conceptnet-average-no-mask-prompt-e-nce
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.9089285714285714
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4919786096256685
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4836795252225519
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.763757643135075
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.87
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5043859649122807
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5532407407407407
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.909899050775953
- name: F1 (macro)
type: f1_macro
value: 0.9035218705294538
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8448356807511737
- name: F1 (macro)
type: f1_macro
value: 0.6709243676319924
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6419284940411701
- name: F1 (macro)
type: f1_macro
value: 0.6383736607412034
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9563886763580719
- name: F1 (macro)
type: f1_macro
value: 0.8644345928364743
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8865559385772485
- name: F1 (macro)
type: f1_macro
value: 0.8837249815439944
---
# relbert/roberta-large-conceptnet-average-no-mask-prompt-e-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-e-nce/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.4919786096256685
- Accuracy on SAT: 0.4836795252225519
- Accuracy on BATS: 0.763757643135075
- Accuracy on U2: 0.5043859649122807
- Accuracy on U4: 0.5532407407407407
- Accuracy on Google: 0.87
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-e-nce/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.909899050775953
- Micro F1 score on CogALexV: 0.8448356807511737
- Micro F1 score on EVALution: 0.6419284940411701
- Micro F1 score on K&H+N: 0.9563886763580719
- Micro F1 score on ROOT09: 0.8865559385772485
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-e-nce/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.9089285714285714
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-conceptnet-average-no-mask-prompt-e-nce")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: average_no_mask
- data: relbert/conceptnet_high_confidence
- template_mode: manual
- template: I wasn’t aware of this relationship, but I just read in the encyclopedia that <obj> is <subj>’s <mask>
- loss_function: nce_logout
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 87
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-e-nce/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
research-backup/roberta-large-conceptnet-average-no-mask-prompt-c-nce
|
research-backup
| 2022-09-21T00:18:57Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-08-09T16:52:12Z |
---
datasets:
- relbert/conceptnet_high_confidence
model-index:
- name: relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8786507936507937
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4919786096256685
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.49554896142433236
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7937743190661478
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.918
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6271929824561403
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6527777777777778
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9215006780171764
- name: F1 (macro)
type: f1_macro
value: 0.9174763167950964
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8678403755868545
- name: F1 (macro)
type: f1_macro
value: 0.7086241190414728
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6825568797399784
- name: F1 (macro)
type: f1_macro
value: 0.6689609208642026
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.962092230646171
- name: F1 (macro)
type: f1_macro
value: 0.8907595805779478
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9025383892196804
- name: F1 (macro)
type: f1_macro
value: 0.900780083743733
---
# relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.4919786096256685
- Accuracy on SAT: 0.49554896142433236
- Accuracy on BATS: 0.7937743190661478
- Accuracy on U2: 0.6271929824561403
- Accuracy on U4: 0.6527777777777778
- Accuracy on Google: 0.918
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9215006780171764
- Micro F1 score on CogALexV: 0.8678403755868545
- Micro F1 score on EVALution: 0.6825568797399784
- Micro F1 score on K&H+N: 0.962092230646171
- Micro F1 score on ROOT09: 0.9025383892196804
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8786507936507937
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: average_no_mask
- data: relbert/conceptnet_high_confidence
- template_mode: manual
- template: Today, I finally discovered the relation between <subj> and <obj> : <mask>
- loss_function: nce_logout
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 196
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
research-backup/roberta-large-conceptnet-average-no-mask-prompt-b-nce
|
research-backup
| 2022-09-21T00:18:30Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-08-08T20:45:41Z |
---
datasets:
- relbert/conceptnet_high_confidence
model-index:
- name: relbert/roberta-large-conceptnet-average-no-mask-prompt-b-nce
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8198809523809524
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5294117647058824
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5252225519287834
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7821011673151751
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.894
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5263157894736842
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5717592592592593
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9020641856260359
- name: F1 (macro)
type: f1_macro
value: 0.8948753350691158
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.846244131455399
- name: F1 (macro)
type: f1_macro
value: 0.6730554272487049
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6625135427952329
- name: F1 (macro)
type: f1_macro
value: 0.6558813092612158
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9580580093204424
- name: F1 (macro)
type: f1_macro
value: 0.8732893037249027
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8884362268881228
- name: F1 (macro)
type: f1_macro
value: 0.8878260786406326
---
# relbert/roberta-large-conceptnet-average-no-mask-prompt-b-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-b-nce/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5294117647058824
- Accuracy on SAT: 0.5252225519287834
- Accuracy on BATS: 0.7821011673151751
- Accuracy on U2: 0.5263157894736842
- Accuracy on U4: 0.5717592592592593
- Accuracy on Google: 0.894
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-b-nce/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9020641856260359
- Micro F1 score on CogALexV: 0.846244131455399
- Micro F1 score on EVALution: 0.6625135427952329
- Micro F1 score on K&H+N: 0.9580580093204424
- Micro F1 score on ROOT09: 0.8884362268881228
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-b-nce/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8198809523809524
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-conceptnet-average-no-mask-prompt-b-nce")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: average_no_mask
- data: relbert/conceptnet_high_confidence
- template_mode: manual
- template: Today, I finally discovered the relation between <subj> and <obj> : <obj> is <subj>'s <mask>
- loss_function: nce_logout
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 86
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-b-nce/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
research-backup/roberta-large-conceptnet-average-no-mask-prompt-a-nce
|
research-backup
| 2022-09-21T00:18:03Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-08-08T00:41:18Z |
---
datasets:
- relbert/conceptnet_high_confidence
model-index:
- name: relbert/roberta-large-conceptnet-average-no-mask-prompt-a-nce
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8500793650793651
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4839572192513369
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4807121661721068
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7459699833240689
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.906
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4824561403508772
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5532407407407407
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9005574807895134
- name: F1 (macro)
type: f1_macro
value: 0.8954808213200831
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.853755868544601
- name: F1 (macro)
type: f1_macro
value: 0.6802055698495575
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6419284940411701
- name: F1 (macro)
type: f1_macro
value: 0.6339711674670336
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9538151213744175
- name: F1 (macro)
type: f1_macro
value: 0.8692018519841085
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8931369476653087
- name: F1 (macro)
type: f1_macro
value: 0.8893923558911556
---
# relbert/roberta-large-conceptnet-average-no-mask-prompt-a-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-a-nce/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.4839572192513369
- Accuracy on SAT: 0.4807121661721068
- Accuracy on BATS: 0.7459699833240689
- Accuracy on U2: 0.4824561403508772
- Accuracy on U4: 0.5532407407407407
- Accuracy on Google: 0.906
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-a-nce/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9005574807895134
- Micro F1 score on CogALexV: 0.853755868544601
- Micro F1 score on EVALution: 0.6419284940411701
- Micro F1 score on K&H+N: 0.9538151213744175
- Micro F1 score on ROOT09: 0.8931369476653087
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-a-nce/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8500793650793651
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-conceptnet-average-no-mask-prompt-a-nce")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: average_no_mask
- data: relbert/conceptnet_high_confidence
- template_mode: manual
- template: Today, I finally discovered the relation between <subj> and <obj> : <subj> is the <mask> of <obj>
- loss_function: nce_logout
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 85
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-a-nce/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
research-backup/roberta-large-conceptnet-mask-prompt-d-nce
|
research-backup
| 2022-09-21T00:17:10Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-08-06T08:31:35Z |
---
datasets:
- relbert/conceptnet_high_confidence
model-index:
- name: relbert/roberta-large-conceptnet-mask-prompt-d-nce
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8807936507936508
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5828877005347594
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5786350148367952
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7787659811006115
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.958
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6140350877192983
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6226851851851852
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9213500075335241
- name: F1 (macro)
type: f1_macro
value: 0.9170167858091296
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8814553990610329
- name: F1 (macro)
type: f1_macro
value: 0.7355097106184322
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7036836403033586
- name: F1 (macro)
type: f1_macro
value: 0.6966787116526776
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9632051192877513
- name: F1 (macro)
type: f1_macro
value: 0.895336152433551
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9003447195236602
- name: F1 (macro)
type: f1_macro
value: 0.8993684208521904
---
# relbert/roberta-large-conceptnet-mask-prompt-d-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-d-nce/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5828877005347594
- Accuracy on SAT: 0.5786350148367952
- Accuracy on BATS: 0.7787659811006115
- Accuracy on U2: 0.6140350877192983
- Accuracy on U4: 0.6226851851851852
- Accuracy on Google: 0.958
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-d-nce/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9213500075335241
- Micro F1 score on CogALexV: 0.8814553990610329
- Micro F1 score on EVALution: 0.7036836403033586
- Micro F1 score on K&H+N: 0.9632051192877513
- Micro F1 score on ROOT09: 0.9003447195236602
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-d-nce/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8807936507936508
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-conceptnet-mask-prompt-d-nce")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: mask
- data: relbert/conceptnet_high_confidence
- template_mode: manual
- template: I wasn’t aware of this relationship, but I just read in the encyclopedia that <subj> is the <mask> of <obj>
- loss_function: nce_logout
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 88
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-d-nce/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
research-backup/roberta-large-conceptnet-mask-prompt-b-nce
|
research-backup
| 2022-09-21T00:16:23Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-08-04T16:33:12Z |
---
datasets:
- relbert/conceptnet_high_confidence
model-index:
- name: relbert/roberta-large-conceptnet-mask-prompt-b-nce
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.844484126984127
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5026737967914439
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5074183976261127
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7837687604224569
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.914
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4868421052631579
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5717592592592593
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9169805635076088
- name: F1 (macro)
type: f1_macro
value: 0.9124828189963239
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8615023474178404
- name: F1 (macro)
type: f1_macro
value: 0.6923470637031117
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6917659804983749
- name: F1 (macro)
type: f1_macro
value: 0.6818037583371511
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9652917854907144
- name: F1 (macro)
type: f1_macro
value: 0.8914930968868111
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9025383892196804
- name: F1 (macro)
type: f1_macro
value: 0.9012451685993444
---
# relbert/roberta-large-conceptnet-mask-prompt-b-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-b-nce/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5026737967914439
- Accuracy on SAT: 0.5074183976261127
- Accuracy on BATS: 0.7837687604224569
- Accuracy on U2: 0.4868421052631579
- Accuracy on U4: 0.5717592592592593
- Accuracy on Google: 0.914
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-b-nce/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9169805635076088
- Micro F1 score on CogALexV: 0.8615023474178404
- Micro F1 score on EVALution: 0.6917659804983749
- Micro F1 score on K&H+N: 0.9652917854907144
- Micro F1 score on ROOT09: 0.9025383892196804
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-b-nce/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.844484126984127
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-conceptnet-mask-prompt-b-nce")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: mask
- data: relbert/conceptnet_high_confidence
- template_mode: manual
- template: Today, I finally discovered the relation between <subj> and <obj> : <obj> is <subj>'s <mask>
- loss_function: nce_logout
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 114
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-b-nce/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
research-backup/roberta-large-conceptnet-average-prompt-e-nce
|
research-backup
| 2022-09-21T00:15:31Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-07-30T19:48:25Z |
---
datasets:
- relbert/conceptnet_high_confidence
model-index:
- name: relbert/roberta-large-conceptnet-average-prompt-e-nce
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8862103174603174
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.49258160237388726
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7443023902167871
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.886
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5526315789473685
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5439814814814815
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9085430164230828
- name: F1 (macro)
type: f1_macro
value: 0.9007282568605484
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8380281690140845
- name: F1 (macro)
type: f1_macro
value: 0.656362704638303
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6657638136511376
- name: F1 (macro)
type: f1_macro
value: 0.6498144246049421
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9565277874382695
- name: F1 (macro)
type: f1_macro
value: 0.8746667490411619
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8896897524287057
- name: F1 (macro)
type: f1_macro
value: 0.8862724322889753
---
# relbert/roberta-large-conceptnet-average-prompt-e-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-prompt-e-nce/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5
- Accuracy on SAT: 0.49258160237388726
- Accuracy on BATS: 0.7443023902167871
- Accuracy on U2: 0.5526315789473685
- Accuracy on U4: 0.5439814814814815
- Accuracy on Google: 0.886
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-prompt-e-nce/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9085430164230828
- Micro F1 score on CogALexV: 0.8380281690140845
- Micro F1 score on EVALution: 0.6657638136511376
- Micro F1 score on K&H+N: 0.9565277874382695
- Micro F1 score on ROOT09: 0.8896897524287057
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-prompt-e-nce/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8862103174603174
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-conceptnet-average-prompt-e-nce")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: average
- data: relbert/conceptnet_high_confidence
- template_mode: manual
- template: I wasn’t aware of this relationship, but I just read in the encyclopedia that <obj> is <subj>’s <mask>
- loss_function: nce_logout
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 85
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-conceptnet-average-prompt-e-nce/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
research-backup/roberta-large-conceptnet-average-prompt-b-nce
|
research-backup
| 2022-09-21T00:14:38Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-07-29T03:34:21Z |
---
datasets:
- relbert/conceptnet_high_confidence
model-index:
- name: relbert/roberta-large-conceptnet-average-prompt-b-nce
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8097222222222222
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5106951871657754
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.49554896142433236
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7982212340188994
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.926
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5350877192982456
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6064814814814815
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9061322886846467
- name: F1 (macro)
type: f1_macro
value: 0.8998351544602654
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8483568075117371
- name: F1 (macro)
type: f1_macro
value: 0.6691324528607947
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6538461538461539
- name: F1 (macro)
type: f1_macro
value: 0.6461615360778927
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9576406760798497
- name: F1 (macro)
type: f1_macro
value: 0.8666219776970888
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8934503290504543
- name: F1 (macro)
type: f1_macro
value: 0.8921114555442471
---
# relbert/roberta-large-conceptnet-average-prompt-b-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-prompt-b-nce/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5106951871657754
- Accuracy on SAT: 0.49554896142433236
- Accuracy on BATS: 0.7982212340188994
- Accuracy on U2: 0.5350877192982456
- Accuracy on U4: 0.6064814814814815
- Accuracy on Google: 0.926
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-prompt-b-nce/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9061322886846467
- Micro F1 score on CogALexV: 0.8483568075117371
- Micro F1 score on EVALution: 0.6538461538461539
- Micro F1 score on K&H+N: 0.9576406760798497
- Micro F1 score on ROOT09: 0.8934503290504543
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-prompt-b-nce/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8097222222222222
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-conceptnet-average-prompt-b-nce")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: average
- data: relbert/conceptnet_high_confidence
- template_mode: manual
- template: Today, I finally discovered the relation between <subj> and <obj> : <obj> is <subj>'s <mask>
- loss_function: nce_logout
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 87
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-conceptnet-average-prompt-b-nce/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
research-backup/roberta-large-conceptnet-average-prompt-a-nce
|
research-backup
| 2022-09-21T00:14:10Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-07-28T07:15:54Z |
---
datasets:
- relbert/conceptnet_high_confidence
model-index:
- name: relbert/roberta-large-conceptnet-average-prompt-a-nce
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8665079365079364
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5320855614973262
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5222551928783383
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7443023902167871
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.878
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4605263157894737
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5347222222222222
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8993521169202953
- name: F1 (macro)
type: f1_macro
value: 0.8963727826344479
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8438967136150235
- name: F1 (macro)
type: f1_macro
value: 0.66545380757752
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.66738894907909
- name: F1 (macro)
type: f1_macro
value: 0.6565002007521079
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9625095638867636
- name: F1 (macro)
type: f1_macro
value: 0.8900641561378133
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8959573801316203
- name: F1 (macro)
type: f1_macro
value: 0.8953395093791771
---
# relbert/roberta-large-conceptnet-average-prompt-a-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-prompt-a-nce/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5320855614973262
- Accuracy on SAT: 0.5222551928783383
- Accuracy on BATS: 0.7443023902167871
- Accuracy on U2: 0.4605263157894737
- Accuracy on U4: 0.5347222222222222
- Accuracy on Google: 0.878
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-prompt-a-nce/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.8993521169202953
- Micro F1 score on CogALexV: 0.8438967136150235
- Micro F1 score on EVALution: 0.66738894907909
- Micro F1 score on K&H+N: 0.9625095638867636
- Micro F1 score on ROOT09: 0.8959573801316203
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-prompt-a-nce/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8665079365079364
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-conceptnet-average-prompt-a-nce")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: average
- data: relbert/conceptnet_high_confidence
- template_mode: manual
- template: Today, I finally discovered the relation between <subj> and <obj> : <subj> is the <mask> of <obj>
- loss_function: nce_logout
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 81
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-conceptnet-average-prompt-a-nce/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
research-backup/roberta-large-conceptnet-average-prompt-d-nce
|
research-backup
| 2022-09-21T00:13:42Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-07-26T01:21:24Z |
---
datasets:
- relbert/conceptnet_high_confidence
model-index:
- name: relbert/roberta-large-conceptnet-average-prompt-d-nce
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8258730158730159
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5828877005347594
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5875370919881305
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7732073374096721
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.938
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6535087719298246
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6898148148148148
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9177339159258702
- name: F1 (macro)
type: f1_macro
value: 0.9126636774713573
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8631455399061033
- name: F1 (macro)
type: f1_macro
value: 0.7055114627284782
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6706392199349945
- name: F1 (macro)
type: f1_macro
value: 0.6653188542990761
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.961188008624887
- name: F1 (macro)
type: f1_macro
value: 0.8756147854478619
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8975242870573488
- name: F1 (macro)
type: f1_macro
value: 0.8941497729254518
---
# relbert/roberta-large-conceptnet-average-prompt-d-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-prompt-d-nce/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5828877005347594
- Accuracy on SAT: 0.5875370919881305
- Accuracy on BATS: 0.7732073374096721
- Accuracy on U2: 0.6535087719298246
- Accuracy on U4: 0.6898148148148148
- Accuracy on Google: 0.938
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-prompt-d-nce/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9177339159258702
- Micro F1 score on CogALexV: 0.8631455399061033
- Micro F1 score on EVALution: 0.6706392199349945
- Micro F1 score on K&H+N: 0.961188008624887
- Micro F1 score on ROOT09: 0.8975242870573488
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-prompt-d-nce/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8258730158730159
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-conceptnet-average-prompt-d-nce")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: average
- data: relbert/conceptnet_high_confidence
- template_mode: manual
- template: I wasn’t aware of this relationship, but I just read in the encyclopedia that <subj> is the <mask> of <obj>
- loss_function: nce_logout
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 147
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-conceptnet-average-prompt-d-nce/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
sd-concepts-library/wojaks-now-now-now
|
sd-concepts-library
| 2022-09-20T23:26:04Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T23:25:59Z |
---
license: mit
---
### wojaks-now-now-now on Stable Diffusion
This is the `<red-wojak>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
PDatt/outcome
|
PDatt
| 2022-09-20T21:07:10Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-20T20:47:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: outcome
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. -->
# outcome
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/lavko
|
sd-concepts-library
| 2022-09-20T20:44:57Z | 1 | 0 |
transformers
|
[
"transformers",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-09-20T20:44:50Z |
---
license: mit
---
### lavko on Stable Diffusion
This is the `<lavko>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
sd-concepts-library/anya-forger
|
sd-concepts-library
| 2022-09-20T20:14:15Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T20:14:02Z |
---
license: mit
---
### anya forger on Stable Diffusion
This is the `<anya-forger>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
mdround/dqn-SpaceInvadersNoFrameskip-v4-1e6ts
|
mdround
| 2022-09-20T19:43:43Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-20T19:43:11Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 525.00 +/- 135.70
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **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 utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mdround -f logs/
python enjoy.py --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 utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mdround
```
## 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)])
```
|
sd-concepts-library/nouns-glasses
|
sd-concepts-library
| 2022-09-20T19:27:23Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T19:27:13Z |
---
license: mit
---
### nouns glasses on Stable Diffusion
This is the `<nouns glasses>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
AlexLach/deepcard
|
AlexLach
| 2022-09-20T18:51:46Z | 0 | 0 |
fastai
|
[
"fastai",
"image-classification",
"region:us"
] |
image-classification
| 2022-09-15T20:57:00Z |
---
tags:
- fastai
- image-classification
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
sd-concepts-library/tili-concept
|
sd-concepts-library
| 2022-09-20T18:30:34Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T18:30:21Z |
---
license: mit
---
### tili_concept on Stable Diffusion
This is the `<tili>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
mdround/dqn-SpaceInvadersNoFrameskip-v4-1e5ts
|
mdround
| 2022-09-20T18:21:06Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-20T18:20:41Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 6.50 +/- 16.29
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **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 utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mdround -f logs/
python enjoy.py --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 utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mdround
```
## 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', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
sd-concepts-library/dreamcore
|
sd-concepts-library
| 2022-09-20T17:03:47Z | 0 | 18 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T17:03:42Z |
---
license: mit
---
### Dreamcore on Stable Diffusion
This is the `<dreamcore>` 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`:
|
sd-concepts-library/quiesel
|
sd-concepts-library
| 2022-09-20T16:34:00Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T16:33:47Z |
---
license: mit
---
### Quiesel on Stable Diffusion
This is the `<quiesel>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
jayanta/distilbert-base-uncased-sentiment-finetuned-memes-30epochs
|
jayanta
| 2022-09-20T16:16:19Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-20T14:02:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilbert-base-uncased-sentiment-finetuned-memes-30epochs
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-sentiment-finetuned-memes-30epochs
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8839
- Accuracy: 0.8365
- Precision: 0.8373
- Recall: 0.8365
- F1: 0.8368
## 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4774 | 1.0 | 2147 | 0.4463 | 0.7453 | 0.7921 | 0.7453 | 0.7468 |
| 0.4036 | 2.0 | 4294 | 0.5419 | 0.7835 | 0.8072 | 0.7835 | 0.7858 |
| 0.3163 | 3.0 | 6441 | 0.6776 | 0.7982 | 0.7970 | 0.7982 | 0.7954 |
| 0.2613 | 4.0 | 8588 | 0.6988 | 0.7966 | 0.7953 | 0.7966 | 0.7956 |
| 0.229 | 5.0 | 10735 | 0.8523 | 0.8003 | 0.8033 | 0.8003 | 0.8013 |
| 0.1893 | 6.0 | 12882 | 1.0472 | 0.8056 | 0.8166 | 0.8056 | 0.8074 |
| 0.1769 | 7.0 | 15029 | 1.0321 | 0.8150 | 0.8193 | 0.8150 | 0.8161 |
| 0.1648 | 8.0 | 17176 | 1.1623 | 0.8129 | 0.8159 | 0.8129 | 0.8138 |
| 0.1366 | 9.0 | 19323 | 1.1932 | 0.8255 | 0.8257 | 0.8255 | 0.8256 |
| 0.1191 | 10.0 | 21470 | 1.2308 | 0.8349 | 0.8401 | 0.8349 | 0.8361 |
| 0.1042 | 11.0 | 23617 | 1.3166 | 0.8297 | 0.8288 | 0.8297 | 0.8281 |
| 0.0847 | 12.0 | 25764 | 1.3542 | 0.8286 | 0.8278 | 0.8286 | 0.8280 |
| 0.0785 | 13.0 | 27911 | 1.3925 | 0.8291 | 0.8293 | 0.8291 | 0.8292 |
| 0.0674 | 14.0 | 30058 | 1.4191 | 0.8255 | 0.8307 | 0.8255 | 0.8267 |
| 0.0694 | 15.0 | 32205 | 1.5601 | 0.8255 | 0.8281 | 0.8255 | 0.8263 |
| 0.0558 | 16.0 | 34352 | 1.6110 | 0.8265 | 0.8302 | 0.8265 | 0.8275 |
| 0.045 | 17.0 | 36499 | 1.5730 | 0.8270 | 0.8303 | 0.8270 | 0.8280 |
| 0.0436 | 18.0 | 38646 | 1.6081 | 0.8365 | 0.8361 | 0.8365 | 0.8363 |
| 0.028 | 19.0 | 40793 | 1.5569 | 0.8375 | 0.8371 | 0.8375 | 0.8373 |
| 0.0262 | 20.0 | 42940 | 1.6976 | 0.8286 | 0.8324 | 0.8286 | 0.8296 |
| 0.0183 | 21.0 | 45087 | 1.6368 | 0.8333 | 0.8354 | 0.8333 | 0.8340 |
| 0.0225 | 22.0 | 47234 | 1.7570 | 0.8318 | 0.8357 | 0.8318 | 0.8328 |
| 0.0118 | 23.0 | 49381 | 1.7233 | 0.8360 | 0.8369 | 0.8360 | 0.8363 |
| 0.0152 | 24.0 | 51528 | 1.8027 | 0.8360 | 0.8371 | 0.8360 | 0.8364 |
| 0.0079 | 25.0 | 53675 | 1.7908 | 0.8412 | 0.8423 | 0.8412 | 0.8416 |
| 0.0102 | 26.0 | 55822 | 1.8247 | 0.8344 | 0.8339 | 0.8344 | 0.8341 |
| 0.0111 | 27.0 | 57969 | 1.8123 | 0.8391 | 0.8394 | 0.8391 | 0.8392 |
| 0.0078 | 28.0 | 60116 | 1.8630 | 0.8354 | 0.8352 | 0.8354 | 0.8353 |
| 0.0058 | 29.0 | 62263 | 1.8751 | 0.8339 | 0.8343 | 0.8339 | 0.8341 |
| 0.0028 | 30.0 | 64410 | 1.8839 | 0.8365 | 0.8373 | 0.8365 | 0.8368 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 1.15.2.dev0
- Tokenizers 0.10.1
|
jgiral95/ppo-LunarLander-v2
|
jgiral95
| 2022-09-20T16:07:21Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-20T16:06:56Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 250.29 +/- 21.30
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
hadiqa123/XLS-R_53_english
|
hadiqa123
| 2022-09-20T16:05:32Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-08-25T14:28:20Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: XLS-R_53_english
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. -->
# XLS-R_53_english
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: 0.3430
- Wer: 0.3033
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.6589 | 1.65 | 500 | 3.1548 | 1.0 |
| 2.5363 | 3.3 | 1000 | 1.0250 | 0.8707 |
| 0.849 | 4.95 | 1500 | 0.3964 | 0.4636 |
| 0.4812 | 6.6 | 2000 | 0.3341 | 0.3907 |
| 0.3471 | 8.25 | 2500 | 0.3351 | 0.3659 |
| 0.2797 | 9.9 | 3000 | 0.3104 | 0.3475 |
| 0.2336 | 11.55 | 3500 | 0.3545 | 0.3419 |
| 0.2116 | 13.2 | 4000 | 0.3577 | 0.3353 |
| 0.1688 | 14.85 | 4500 | 0.3383 | 0.3302 |
| 0.1587 | 16.5 | 5000 | 0.3431 | 0.3235 |
| 0.1358 | 18.15 | 5500 | 0.3504 | 0.3209 |
| 0.1323 | 19.8 | 6000 | 0.3468 | 0.3191 |
| 0.115 | 21.45 | 6500 | 0.3331 | 0.3127 |
| 0.108 | 23.1 | 7000 | 0.3497 | 0.3099 |
| 0.0938 | 24.75 | 7500 | 0.3532 | 0.3091 |
| 0.0974 | 26.4 | 8000 | 0.3461 | 0.3086 |
| 0.0867 | 28.05 | 8500 | 0.3422 | 0.3054 |
| 0.0852 | 29.7 | 9000 | 0.3430 | 0.3033 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
ericntay/stbl_clinical_bert_ft_rs2bs
|
ericntay
| 2022-09-20T16:00:55Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-20T15:36:54Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: stbl_clinical_bert_ft_rs2bs
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. -->
# stbl_clinical_bert_ft_rs2bs
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1189
- F1: 0.8982
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2719 | 1.0 | 101 | 0.0878 | 0.8458 |
| 0.0682 | 2.0 | 202 | 0.0678 | 0.8838 |
| 0.0321 | 3.0 | 303 | 0.0617 | 0.9041 |
| 0.0149 | 4.0 | 404 | 0.0709 | 0.9061 |
| 0.0097 | 5.0 | 505 | 0.0766 | 0.9114 |
| 0.0059 | 6.0 | 606 | 0.0803 | 0.9174 |
| 0.0035 | 7.0 | 707 | 0.0845 | 0.9160 |
| 0.0023 | 8.0 | 808 | 0.0874 | 0.9158 |
| 0.0016 | 9.0 | 909 | 0.0928 | 0.9188 |
| 0.0016 | 10.0 | 1010 | 0.0951 | 0.9108 |
| 0.0011 | 11.0 | 1111 | 0.0938 | 0.9178 |
| 0.0009 | 12.0 | 1212 | 0.0945 | 0.9185 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
jayanta/distilbert-base-uncased-sentiment-finetuned-memes-20epoch
|
jayanta
| 2022-09-20T15:42:04Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-20T13:36:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilbert-base-uncased-sentiment-finetuned-memes-20epoch
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-sentiment-finetuned-memes-20epoch
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: 2.2252
- Accuracy: 0.8160
- Precision: 0.8165
- Recall: 0.8160
- F1: 0.8162
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5319 | 1.0 | 4293 | 0.5560 | 0.7699 | 0.7777 | 0.7699 | 0.7704 |
| 0.4627 | 2.0 | 8586 | 0.6588 | 0.7856 | 0.7868 | 0.7856 | 0.7860 |
| 0.3735 | 3.0 | 12879 | 0.8583 | 0.7835 | 0.7854 | 0.7835 | 0.7813 |
| 0.3549 | 4.0 | 17172 | 1.0078 | 0.7872 | 0.7869 | 0.7872 | 0.7866 |
| 0.2995 | 5.0 | 21465 | 1.0007 | 0.8024 | 0.8030 | 0.8024 | 0.8012 |
| 0.2579 | 6.0 | 25758 | 1.1501 | 0.8150 | 0.8152 | 0.8150 | 0.8151 |
| 0.2078 | 7.0 | 30051 | 1.2604 | 0.8097 | 0.8137 | 0.8097 | 0.8102 |
| 0.1635 | 8.0 | 34344 | 1.3755 | 0.8092 | 0.8092 | 0.8092 | 0.8085 |
| 0.1453 | 9.0 | 38637 | 1.4639 | 0.8097 | 0.8137 | 0.8097 | 0.8102 |
| 0.1431 | 10.0 | 42930 | 1.5612 | 0.8050 | 0.8048 | 0.8050 | 0.8044 |
| 0.085 | 11.0 | 47223 | 1.8216 | 0.8097 | 0.8121 | 0.8097 | 0.8101 |
| 0.0693 | 12.0 | 51516 | 1.7761 | 0.8087 | 0.8100 | 0.8087 | 0.8090 |
| 0.041 | 13.0 | 55809 | 1.8538 | 0.8082 | 0.8083 | 0.8082 | 0.8082 |
| 0.0391 | 14.0 | 60102 | 2.0022 | 0.8160 | 0.8158 | 0.8160 | 0.8158 |
| 0.0299 | 15.0 | 64395 | 2.0101 | 0.8124 | 0.8121 | 0.8124 | 0.8121 |
| 0.0226 | 16.0 | 68688 | 2.0396 | 0.8150 | 0.8152 | 0.8150 | 0.8151 |
| 0.0229 | 17.0 | 72981 | 2.1071 | 0.8171 | 0.8170 | 0.8171 | 0.8171 |
| 0.0133 | 18.0 | 77274 | 2.1047 | 0.8181 | 0.8182 | 0.8181 | 0.8182 |
| 0.0268 | 19.0 | 81567 | 2.2037 | 0.8208 | 0.8208 | 0.8208 | 0.8208 |
| 0.0068 | 20.0 | 85860 | 2.2252 | 0.8160 | 0.8165 | 0.8160 | 0.8162 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
duyduong9htv/electra-qa-finetuned-viet-qa
|
duyduong9htv
| 2022-09-20T14:14:48Z | 120 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-19T23:10:13Z |
---
tags:
- generated_from_trainer
model-index:
- name: electra-qa-finetuned-viet-qa
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. -->
# electra-qa-finetuned-viet-qa
This model is a fine-tuned version of [NlpHUST/electra-base-vn](https://huggingface.co/NlpHUST/electra-base-vn) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4144
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6859 | 1.0 | 1015 | 1.4759 |
| 1.1843 | 2.0 | 2030 | 1.3652 |
| 0.9369 | 3.0 | 3045 | 1.4144 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/heather
|
sd-concepts-library
| 2022-09-20T13:58:23Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T13:58:19Z |
---
license: mit
---
### Heather on Stable Diffusion
This is the `Heather*` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
mozilla-foundation/youtube_video_similarity_model_nt
|
mozilla-foundation
| 2022-09-20T13:54:31Z | 48 | 7 |
transformers
|
[
"transformers",
"pytorch",
"youtube",
"video",
"multilingual",
"license:apache-2.0",
"region:us"
] | null | 2022-09-19T06:36:11Z |
---
language:
- multilingual
license: apache-2.0
inference: false
tags:
- youtube
- video
- pytorch
---
# YouTube video semantic similarity model (NT = no transcripts)
This YouTube video semantic similarity model was developed as part of the RegretsReporter research project at Mozilla Foundation. You can read more about the project [here](https://foundation.mozilla.org/en/youtube/user-controls/) and about the semantic similarity model [here](https://foundation.mozilla.org/en/blog/the-regretsreporter-user-controls-study-machine-learning-to-measure-semantic-similarity-of-youtube-videos/).
You can also easily try this model with this [Spaces demo app](https://huggingface.co/spaces/mozilla-foundation/youtube_video_similarity). Just provide two YouTube video links and you can see how similar those two videos are according to the model. For your convenience, the demo also includes a few predefined video pair examples.
## Model description
This model is custom PyTorch model for predicting whether a pair of YouTube videos are similar or not. The model does not take video data itself as an input but instead it relies on video metadata to save computing resources. The input for the model consists of video titles, descriptions, transcripts and YouTube channel-equality signal of video pairs. As illustrated below, the model includes three [cross-encoders](https://www.sbert.net/examples/applications/cross-encoder/README.html) for determining the similarity of each of the text components of the videos, which are then connected directly, along with a channel-equality signal into a single linear layer with a sigmoid output. The output is a similarity probability as follows:
- If the output is close to 1, the model is very confident that the videos are similar
- If the output is close to 0, the model is very confident that the videos are not similar
- If the output is close to 0.5, the model is uncertain
For pretrained cross-encoders, [mmarco-mMiniLMv2-L12-H384-v1](https://huggingface.co/cross-encoder/mmarco-mMiniLMv2-L12-H384-v1) was used to be further trained as part of this model.
**Note**: sometimes YouTube videos lack transcripts so actually there are two different versions of this model trained: a model with trascripts (WT = with transcripts) and a model without transcripts (NT = no transcripts). This model is without transcripts and the model with transcripts is available [here](https://huggingface.co/mozilla-foundation/youtube_video_similarity_model_wt).
**Note**: Possible model architecture enhancements are discussed a bit on [this blog post](https://foundation.mozilla.org/en/blog/the-regretsreporter-user-controls-study-machine-learning-to-measure-semantic-similarity-of-youtube-videos/) and some of the ideas were implemented and tried on experimental v2 version of the model which code is available on the RegretsReporter [GitHub repository](https://github.com/mozilla-extensions/regrets-reporter/tree/main/analysis/semsim). Based on the test set evaluation, the experimental v2 model didn't significantly improve the results. Thus, it was decided that more complex v2 model weights are not released at this time.
## Intended uses & limitations
This model is intended to be used for analyzing whether a pair of YouTube videos are similar or not. We hope that this model will prove valuable to other researchers investigating YouTube.
### How to use
As this model is a custom PyTorch model, not normal transformers model, you need to clone this model repository first. The repository contains model code in `RRUM` class (RRUM stands for RegretsReporter Unified Model) in `unifiedmodel.py` file. For loading the model from Hugging Face model hub, there also is a Hugging Face model wrapper named `YoutubeVideoSimilarityModel` in `huggingface_model_wrapper.py` file. Needed Python requirements are specified in `requirements.txt` file. To load the model, follow these steps:
1. `git clone https://huggingface.co/mozilla-foundation/youtube_video_similarity_model_nt`
2. `pip install -r requirements.txt`
And finally load the model with the following example code:
```python
from huggingface_model_wrapper import YoutubeVideoSimilarityModel
model = YoutubeVideoSimilarityModel.from_pretrained('mozilla-foundation/youtube_video_similarity_model_nt')
```
For loading and preprocessing input data into correct format, the `unifiedmodel.py` file also contains a `RRUMDataset` class. To use the loaded model for predicting video pair similarity, you can use the following example code:
```python
import torch
import pandas as pd
from torch.utils.data import DataLoader
from unifiedmodel import RRUMDataset
video1_channel = "Mozilla"
video1_title = "YouTube Regrets"
video1_description = "Are your YouTube recommendations sometimes lies? Conspiracy theories? Or just weird as hell?\n\n\nYou’re not alone. That’s why Mozilla and 37,380 YouTube users conducted a study to better understand harmful YouTube recommendations. This is what we learned about YouTube regrets: https://foundation.mozilla.org/regrets/"
video2_channel = "Mozilla"
video2_title = "YouTube Regrets Reporter"
video2_description = "Are you choosing what to watch, or is YouTube choosing for you?\n\nTheir algorithm is responsible for over 70% of viewing time, which can include recommending harmful videos.\n\nHelp us hold them responsible. Install RegretsReporter: https://mzl.la/37BT2vA"
df = pd.DataFrame([[video1_title, video1_description, None] + [video2_title, video2_description, None] + [int(video1_channel == video2_channel)]], columns=['regret_title', 'regret_description', 'regret_transcript', 'recommendation_title', 'recommendation_description', 'recommendation_transcript', 'channel_sim'])
dataset = RRUMDataset(df, with_transcript=False, label_col=None, cross_encoder_model_name_or_path=model.cross_encoder_model_name_or_path)
data_loader = DataLoader(dataset.test_dataset)
with torch.inference_mode():
prediction = model(next(iter(data_loader)))
prediction = torch.special.expit(prediction).squeeze().tolist()
```
Some more code and examples are also available at RegretsReporter [GitHub repository](https://github.com/mozilla-extensions/regrets-reporter/tree/main/analysis/semsim).
### Limitations and bias
The cross-encoders that we use to determine similarity of texts are also trained on texts that inevitably reflect social bias. To understand the implications of this, we need to consider the application of the model: to determine if videos are semantically similar or not. So the concern is that our model may, in some systematic way, think certain kinds of videos are more or less similar to each other.
For example, it's possible that the models have encoded a social bias that certain ethnicities are more often involved in violent situations. If this were the case, it is possible that videos about people of one ethnicity may be more likely to be rated similar to videos about violent situations. This could be evaluated by applying the model to synthetic video pairs crafted to test these situations. There is also [active research](https://www.aaai.org/AAAI22Papers/AISI-7742.KanekoM.pdf) in measuring bias in language models, as part of the broader field of [AI fairness](https://facctconference.org/2022/index.html).
We have not analyzed the biases in our model as, for our original application, potential for harm was extremely low. Care should be taken in future applications.
A more difficult issue is the multilingual nature of our data. For the pretrained cross-encoders in our model, we used the [mmarco-mMiniLMv2-L12-H384-v1](https://huggingface.co/cross-encoder/mmarco-mMiniLMv2-L12-H384-v1) model which supports a set of 100 languages (the original mMiniLMv2 base model) including English, German, Spanish and Chinese. However, it is reasonable to expect that the model's performance varies among the languages that it supports. The impact can vary — the model may fail either with false positives, in which it thinks a dissimilar pair is similar, or false negatives, in which it thinks a similar pair is dissimilar. We performed a basic analysis to evaluate the performance of our model in different languages and it suggested that our model performs well across languages, but the potential differences in the quality of our labels between languages reduced our confidence.
## Training data
Since the RegretsReporter project operates without YouTube's support, we were limited to the publicly available data we could fetch from YouTube. The RegretsReporter project developed a browser extension that our volunteer project participants used to send us data about their YouTube usage and what videos YouTube recommended for them. We also used automated methods to acquire additional needed model training data (title, channel, description, transcript) for videos from the YouTube site directly.
To get labeled training data, we contracted 24 research assistants, all graduate students at Exeter University, to perform 20 hours each, classifying gathered video pairs using a [classification tool](https://github.com/mozilla-extensions/regrets-reporter/tree/main/analysis/classification) that we developed. There are many subtleties in defining similarity of two videos, so we are not able to precisely describe what we mean by "similar", but we developed a [policy](https://docs.google.com/document/d/1VB7YAENmuMDMW_kPPUbuDPbHfQBDhF5ylzHA3cAZywg/) to guide our research assistants in classifying video pairs. Research assistants all read the classification policy and worked with Dr. Chico Camargo, who ensured they had all the support they needed to contribute to this work. These research assistants were partners in our research and are named for their contributions in our [final report](https://foundation.mozilla.org/en/research/library/user-controls/report/).
Thanks to our research assistants, we had 44,434 labeled video pairs to train our model (although about 3% of these were labeled "unsure" and so unused). For each of these pairs, the research assistant determined whether the videos are similar or not, and our model is able to learn from these examples.
## Training procedure
### Preprocessing
Our training data of YouTube video titles, descriptions and transcripts tend to include a lot of noisy text having, for example, URLs, emojis and other potential noise. Thus, we used text cleaning functions to clean some of the noise. Text cleaning seemed to improve the model accuracy on test set but the text cleaning was disabled in the end because it added extra latency to the data preprocessing which would have made the project's model prediction run slower when predictions were ran for hundreds of millions of video pairs. The data loading and preprocessing class `RRUMDataset` in `unifiedmodel.py` file still includes text cleaning option by setting the parameter `clean_text=True` on the class initialization.
The text data was tokenized with [mmarco-mMiniLMv2-L12-H384-v1](https://huggingface.co/cross-encoder/mmarco-mMiniLMv2-L12-H384-v1) cross-encoder's SentencePiece tokenizer having a vocabulary size of 250,002. Tokenization was done with maximum length of 128 tokens.
### Training
The model was trained using [PyTorch Lightning](https://pytorch-lightning.readthedocs.io/en/stable/) on NVIDIA A100 GPU. The model can also be trained on lower resources, for example with the free T4 GPU on Google Colab. The optimizer used was a Adam with learning rate 5e-3, learning rate warmup for 5% steps of total training steps and linear decay of the learning rate after. The model was trained with batch size of 128 for 15 epochs. Based on per epoch evaluation, the final model uses the checkpoint from epoch 13.
## Evaluation results
With the final test set, our models were achieving following scores presented on the table below:
| Metric | Model with transcripts | Model without transcripts |
|--------------------------------|------------------------|---------------------------|
| Accuracy | 0.93 | 0.92 |
| Precision | 0.81 | 0.81 |
| Recall | 0.91 | 0.87 |
| AUROC | 0.97 | 0.96 |
## Acknowledgements
We're grateful to Chico Camargo and Ranadheer Malla from the University of Exeter for leading the analysis of RegretsReporter data. Thank you to the research assistants at the University of Exeter for analyzing the video data: Josh Adebayo, Sharon Choi, Henry Cook, Alex Craig, Bee Dally, Seb Dixon, Aditi Dutta, Ana Lucia Estrada Jaramillo, Jamie Falla, Alice Gallagher Boyden, Adriano Giunta, Lisa Greghi, Keanu Hambali, Clare Keeton Graddol, Kien Khuong, Mitran Malarvannan, Zachary Marre, Inês Mendes de Sousa, Dario Notarangelo, Izzy Sebire, Tawhid Shahrior, Shambhavi Shivam, Marti Toneva, Anthime Valin, and Ned Westwood.
Finally, we're so grateful for the 22,722 RegretsReporter participants who contributed their data.
## Contact
If these models are useful to you, we'd love to hear from you. Please write to publicdata@mozillafoundation.org
|
misterneil/xlm-roberta-base-finetuned-panx-de-fr
|
misterneil
| 2022-09-20T13:45:06Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-20T13:14:52Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1608
- F1: 0.8593
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2888 | 1.0 | 715 | 0.1779 | 0.8233 |
| 0.1437 | 2.0 | 1430 | 0.1570 | 0.8497 |
| 0.0931 | 3.0 | 2145 | 0.1608 | 0.8593 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
jayanta/distilbert-base-uncased-sentiment-finetuned-memes
|
jayanta
| 2022-09-20T13:38:12Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-20T10:51:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilbert-base-uncased-sentiment-finetuned-memes
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-sentiment-finetuned-memes
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1824
- Accuracy: 0.8270
- Precision: 0.8270
- Recall: 0.8270
- F1: 0.8270
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5224 | 1.0 | 4293 | 0.5321 | 0.7720 | 0.8084 | 0.7720 | 0.7721 |
| 0.4386 | 2.0 | 8586 | 0.4930 | 0.7961 | 0.7980 | 0.7961 | 0.7967 |
| 0.3722 | 3.0 | 12879 | 0.7652 | 0.7925 | 0.7955 | 0.7925 | 0.7932 |
| 0.3248 | 4.0 | 17172 | 0.9827 | 0.8045 | 0.8047 | 0.8045 | 0.8023 |
| 0.308 | 5.0 | 21465 | 0.9518 | 0.8244 | 0.8260 | 0.8244 | 0.8249 |
| 0.2906 | 6.0 | 25758 | 1.0971 | 0.8155 | 0.8166 | 0.8155 | 0.8159 |
| 0.2036 | 7.0 | 30051 | 1.1457 | 0.8260 | 0.8271 | 0.8260 | 0.8264 |
| 0.1747 | 8.0 | 34344 | 1.1824 | 0.8270 | 0.8270 | 0.8270 | 0.8270 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
adil-o/A2C-Pong-v1
|
adil-o
| 2022-09-20T13:31:59Z | 0 | 0 | null |
[
"Pong-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-20T13:31:49Z |
---
tags:
- Pong-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: A2C-Pong-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-PLE-v0
type: Pong-PLE-v0
metrics:
- type: mean_reward
value: -16.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
farleyknight/patent-summarization-t5-base-2022-09-20
|
farleyknight
| 2022-09-20T13:31:24Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:farleyknight/big_patent_5_percent",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-20T00:31:18Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- farleyknight/big_patent_5_percent
metrics:
- rouge
model-index:
- name: patent-summarization-t5-base-2022-09-20
results:
- task:
name: Summarization
type: summarization
dataset:
name: farleyknight/big_patent_5_percent
type: farleyknight/big_patent_5_percent
config: all
split: train
args: all
metrics:
- name: Rouge1
type: rouge
value: 36.0843
---
<!-- 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. -->
# patent-summarization-t5-base-2022-09-20
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the farleyknight/big_patent_5_percent dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9975
- Rouge1: 36.0843
- Rouge2: 12.1856
- Rougel: 25.8099
- Rougelsum: 30.1664
- Gen Len: 118.3137
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.2811 | 0.08 | 5000 | 2.1767 | 18.5624 | 6.8795 | 15.5361 | 16.6836 | 19.0 |
| 2.2551 | 0.17 | 10000 | 2.1327 | 19.077 | 6.8512 | 15.79 | 17.086 | 19.0 |
| 2.2818 | 0.25 | 15000 | 2.1029 | 18.8637 | 6.9233 | 15.7341 | 16.9717 | 19.0 |
| 2.1952 | 0.33 | 20000 | 2.0805 | 18.962 | 7.1157 | 15.8297 | 17.0333 | 19.0 |
| 2.157 | 0.41 | 25000 | 2.0641 | 19.1418 | 7.315 | 16.05 | 17.2551 | 19.0 |
| 2.1775 | 0.5 | 30000 | 2.0452 | 19.2387 | 7.3193 | 16.0852 | 17.3563 | 19.0 |
| 2.1376 | 0.58 | 35000 | 2.0308 | 19.291 | 7.363 | 16.1243 | 17.4151 | 19.0 |
| 2.1853 | 0.66 | 40000 | 2.0207 | 19.2808 | 7.4671 | 16.1593 | 17.3836 | 19.0 |
| 2.1416 | 0.75 | 45000 | 2.0113 | 19.0414 | 7.3335 | 15.9747 | 17.1899 | 19.0 |
| 2.1245 | 0.83 | 50000 | 2.0055 | 19.1445 | 7.3715 | 16.0166 | 17.2621 | 19.0 |
| 2.133 | 0.91 | 55000 | 1.9997 | 19.3033 | 7.4821 | 16.1413 | 17.3949 | 19.0 |
| 2.1191 | 0.99 | 60000 | 1.9973 | 19.4044 | 7.5483 | 16.2429 | 17.488 | 19.0 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1
|
adil-o/A2C-Pixelcopter-v1
|
adil-o
| 2022-09-20T13:03:10Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-20T13:03:03Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: A2C-Pixelcopter-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 11.00 +/- 8.37
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
teven/bi_all-mpnet-base-v2_finetuned_WebNLG2017
|
teven
| 2022-09-20T12:52:26Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-20T12:52:20Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# teven/bi_all-mpnet-base-v2_finetuned_WebNLG2017
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('teven/bi_all-mpnet-base-v2_finetuned_WebNLG2017')
embeddings = model.encode(sentences)
print(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=teven/bi_all-mpnet-base-v2_finetuned_WebNLG2017)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 666 with parameters:
```
{'batch_size': 8, '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": 50,
"evaluation_steps": 0,
"evaluator": "better_cross_encoder.PearsonCorrelationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 5e-06
},
"scheduler": "warmupcosine",
"steps_per_epoch": null,
"warmup_steps": 3330,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, '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})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
teven/bi_all_bs320_vanilla_finetuned_WebNLG2017
|
teven
| 2022-09-20T12:51:14Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-20T12:51:06Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# teven/bi_all_bs320_vanilla_finetuned_WebNLG2017
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('teven/bi_all_bs320_vanilla_finetuned_WebNLG2017')
embeddings = model.encode(sentences)
print(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=teven/bi_all_bs320_vanilla_finetuned_WebNLG2017)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 666 with parameters:
```
{'batch_size': 8, '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": 50,
"evaluation_steps": 0,
"evaluator": "better_cross_encoder.PearsonCorrelationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 5e-06
},
"scheduler": "warmupcosine",
"steps_per_epoch": null,
"warmup_steps": 3330,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, '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})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
teven/bi_all_bs160_allneg_finetuned_WebNLG2017
|
teven
| 2022-09-20T12:50:38Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-20T12:50:31Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# teven/bi_all_bs160_allneg_finetuned_WebNLG2017
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('teven/bi_all_bs160_allneg_finetuned_WebNLG2017')
embeddings = model.encode(sentences)
print(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=teven/bi_all_bs160_allneg_finetuned_WebNLG2017)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 167 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": 50,
"evaluation_steps": 0,
"evaluator": "better_cross_encoder.PearsonCorrelationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 5e-05
},
"scheduler": "warmupcosine",
"steps_per_epoch": null,
"warmup_steps": 835,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, '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})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
teven/bi_all_bs192_hardneg_finetuned_WebNLG2017
|
teven
| 2022-09-20T12:49:30Z | 163 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-20T12:49:23Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# teven/bi_all_bs192_hardneg_finetuned_WebNLG2017
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('teven/bi_all_bs192_hardneg_finetuned_WebNLG2017')
embeddings = model.encode(sentences)
print(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=teven/bi_all_bs192_hardneg_finetuned_WebNLG2017)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 666 with parameters:
```
{'batch_size': 8, '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": 50,
"evaluation_steps": 0,
"evaluator": "better_cross_encoder.PearsonCorrelationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 5e-06
},
"scheduler": "warmupcosine",
"steps_per_epoch": null,
"warmup_steps": 3330,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, '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})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
sd-concepts-library/stardew-valley-pixel-art
|
sd-concepts-library
| 2022-09-20T12:37:25Z | 0 | 27 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T12:37:20Z |
---
license: mit
---
### Stardew Valley Pixel Art on Stable Diffusion
This is the `<pixelart-stardew>` 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`:
|
sd-concepts-library/001glitch-core
|
sd-concepts-library
| 2022-09-20T12:27:19Z | 0 | 21 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T12:27:14Z |
---
license: mit
---
### 001glitch_core on Stable Diffusion
This is the `001glitch_core` 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`:
|
sd-concepts-library/m-geoo
|
sd-concepts-library
| 2022-09-20T12:00:43Z | 0 | 3 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T12:00:32Z |
---
license: mit
---
### m-geoo on Stable Diffusion
This is the `<m-geo>` 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`:
|
jamescalam/mpnet-qa
|
jamescalam
| 2022-09-20T11:36:57Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-20T11:31:00Z |
---
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 500001 with parameters:
```
{'batch_size': 24, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 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": null,
"warmup_steps": 50000,
"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 -->
|
bkaemper/testpyramidsrnd
|
bkaemper
| 2022-09-20T11:22:11Z | 6 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2022-09-20T11:22:03Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** 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-Pyramids
2. Step 1: Write your model_id: bkaemper/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dinesh123/wav2vec2-large-xlsr-53-english
|
dinesh123
| 2022-09-20T11:05:12Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-20T06:19:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-53-english
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-english
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3950
- Wer: 0.3496
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4499 | 1.0 | 500 | 1.4091 | 0.9755 |
| 0.7934 | 2.01 | 1000 | 0.5071 | 0.5282 |
| 0.43 | 3.01 | 1500 | 0.4686 | 0.4661 |
| 0.2848 | 4.02 | 2000 | 0.4051 | 0.4136 |
| 0.2035 | 5.02 | 2500 | 0.4133 | 0.3982 |
| 0.1612 | 6.02 | 3000 | 0.3933 | 0.3889 |
| 0.1212 | 7.03 | 3500 | 0.3841 | 0.3536 |
| 0.1029 | 8.03 | 4000 | 0.3999 | 0.3607 |
| 0.0822 | 9.04 | 4500 | 0.3950 | 0.3496 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
sd-concepts-library/morino-hon-style
|
sd-concepts-library
| 2022-09-20T10:59:30Z | 0 | 15 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T10:59:11Z |
---
license: mit
---
### Morino hon Style on Stable Diffusion
This is the `<morino-hon>` 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`:
|
sd-concepts-library/hiyuki-chan
|
sd-concepts-library
| 2022-09-20T10:28:31Z | 0 | 3 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T10:28:27Z |
---
license: mit
---
### Hiyuki chan on Stable Diffusion
This is the `<hiyuki-chan>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
sd-concepts-library/ralph-mcquarrie
|
sd-concepts-library
| 2022-09-20T10:12:30Z | 0 | 4 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T10:12:25Z |
---
license: mit
---
### Ralph McQuarrie on Stable Diffusion
This is the `<ralph-mcquarrie>` 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`:
|
sd-concepts-library/blue-zombiee
|
sd-concepts-library
| 2022-09-20T09:29:28Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T09:29:23Z |
---
license: mit
---
### blue-zombiee on Stable Diffusion
This is the `<blue-zombie>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
gcmsrc/xlm-roberta-base-finetuned-panx-it
|
gcmsrc
| 2022-09-20T09:19:59Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-20T09:17:52Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8207236842105264
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2571
- F1: 0.8207
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8262 | 1.0 | 70 | 0.3182 | 0.7502 |
| 0.2785 | 2.0 | 140 | 0.2685 | 0.7966 |
| 0.1816 | 3.0 | 210 | 0.2571 | 0.8207 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
|
gcmsrc/xlm-roberta-base-finetuned-panx-de-fr
|
gcmsrc
| 2022-09-20T09:11:56Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-19T16:28:48Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1642
- F1: 0.8589
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2886 | 1.0 | 715 | 0.1804 | 0.8293 |
| 0.1458 | 2.0 | 1430 | 0.1574 | 0.8494 |
| 0.0931 | 3.0 | 2145 | 0.1642 | 0.8589 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
|
denden/new_iloko
|
denden
| 2022-09-20T08:47:29Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"en",
"dataset:timit_asr",
"license:afl-3.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- en
license: afl-3.0
tags:
- audio # Example: audio
- automatic-speech-recognition # Example: automatic-speech-recognition
- speech # Example: speech
pipeline_tag: automatic-speech-recognition
datasets:
- timit_asr # Example: common_voice. Use dataset id from https://hf.co/datasets
metrics:
- wer
# Optional. Add this if you want to encode your eval results in a structured way.
model-index:
- name: iloko-model
results:
- task:
type: automatic-speech-recognition # Required. Example: automatic-speech-recognition
name: Iloko Speech Recognition # Optional. Example: Speech Recognition
metrics:
- type: wer # Required. Example: wer
value: 0.009 # Required. Example: 20.90
name: TEST WETR # Optional. Example: Test WER
# args: {arg_0} # Optional. Example for BLEU: max_order
---
FINETUNED ILOKANO SPEECH RECOGNITION FROM WAV2VEC-XLSR-S3
|
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