modelId
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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-05 06:27:37
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
stringclasses 539
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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teven/cross_all_bs192_hardneg_finetuned_WebNLG2020_data_coverage
|
teven
| 2022-09-21T15:52:36Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-21T15:52:29Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# teven/cross_all_bs192_hardneg_finetuned_WebNLG2020_data_coverage
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_data_coverage')
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_data_coverage')
model = AutoModel.from_pretrained('teven/cross_all_bs192_hardneg_finetuned_WebNLG2020_data_coverage')
# 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_data_coverage)
## 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_bs160_allneg_finetuned_WebNLG2020_data_coverage
|
teven
| 2022-09-21T15:52:01Z | 6 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-21T15:51:53Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# teven/cross_all_bs160_allneg_finetuned_WebNLG2020_data_coverage
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_bs160_allneg_finetuned_WebNLG2020_data_coverage')
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_bs160_allneg_finetuned_WebNLG2020_data_coverage')
model = AutoModel.from_pretrained('teven/cross_all_bs160_allneg_finetuned_WebNLG2020_data_coverage')
# 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_bs160_allneg_finetuned_WebNLG2020_data_coverage)
## 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 -->
|
julius-br/gottbert-base-finetuned-fbi-german
|
julius-br
| 2022-09-21T15:51:49Z | 106 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"gottbert",
"de",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-07T11:43:30Z |
---
language: de
license: mit
tags:
- roberta
- gottbert
---
# Fine-tuned gottbert-base to detect Feature Requests & Bug Reports in German App Store Reviews
## Overview
**Language model:** uklfr/gottbert-base
**Language:** German
**Training & Eval data:** [GARFAB2022Weighted](https://huggingface.co/datasets/julius-br/GARFAB) <br>
**Published**: September 21th, 2022 <br>
**Author**: Julius Breiholz
## Performance
| Label | Precision | Recall | F1-Score |
| --- | --- | --- | --- |
| Irrelevant | 0,95 | 0,91 | 0,93 |
| Bug Report | 0,82 | 0,91 | 0,86 |
| Feature Request | 0,87 | 0,82 | 0,85 |
| all classes (avg.) | 0,88 | 0,88 | 0,88 |
|
teven/bi_all_bs192_hardneg_finetuned_WebNLG2020_data_coverage
|
teven
| 2022-09-21T15:50:15Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-21T15:50:08Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# teven/bi_all_bs192_hardneg_finetuned_WebNLG2020_data_coverage
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_WebNLG2020_data_coverage')
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_WebNLG2020_data_coverage)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 161 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 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": 805,
"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_WebNLG2020_data_coverage
|
teven
| 2022-09-21T15:49:40Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-21T15:49:33Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# teven/bi_all_bs320_vanilla_finetuned_WebNLG2020_data_coverage
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_data_coverage')
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_data_coverage)
## 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": 0.0001
},
"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 -->
|
teven/cross_all_bs192_hardneg_finetuned_WebNLG2020_relevance
|
teven
| 2022-09-21T15:47:52Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-21T15:47:45Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# teven/cross_all_bs192_hardneg_finetuned_WebNLG2020_relevance
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_relevance')
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_relevance')
model = AutoModel.from_pretrained('teven/cross_all_bs192_hardneg_finetuned_WebNLG2020_relevance')
# 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_relevance)
## 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_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_bs160_allneg_finetuned_WebNLG2020_correctness
|
teven
| 2022-09-21T15:41:45Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-21T15:41:37Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# teven/cross_all_bs160_allneg_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_bs160_allneg_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_bs160_allneg_finetuned_WebNLG2020_correctness')
model = AutoModel.from_pretrained('teven/cross_all_bs160_allneg_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_bs160_allneg_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_bs192_hardneg_finetuned_WebNLG2020_correctness
|
teven
| 2022-09-21T15:38:16Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-21T15:38:09Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# teven/bi_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/bi_all_bs192_hardneg_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_bs192_hardneg_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": 0.0001
},
"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 -->
|
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 -->
|
teven/bi_all_bs160_allneg_finetuned_WebNLG2020_correctness
|
teven
| 2022-09-21T15:37:00Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-21T15:36:53Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# teven/bi_all_bs160_allneg_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_bs160_allneg_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_bs160_allneg_finetuned_WebNLG2020_correctness)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 81 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": 0.0002
},
"scheduler": "warmupcosine",
"steps_per_epoch": null,
"warmup_steps": 405,
"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 -->
|
matemato/testpyramidsrnd
|
matemato
| 2022-09-21T15:25:39Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2022-09-21T15:25:31Z |
---
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: matemato/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
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`:
|
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
|
sd-concepts-library/phan-s-collage
|
sd-concepts-library
| 2022-09-21T14:44:10Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T14:44:04Z |
---
license: mit
---
### Phan's Collage on Stable Diffusion
This is the `<pcollage>` 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`:
|
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.
|
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`:
|
Xinrui/t5-small-finetuned-eli5
|
Xinrui
| 2022-09-21T13:39:23Z | 109 | 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
| 2022-09-20T16:12:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
metrics:
- rouge
model-index:
- name: t5-small-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: 11.8922
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-eli5
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eli5 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7555
- Rouge1: 11.8922
- Rouge2: 1.88
- Rougel: 9.6595
- Rougelsum: 10.8308
- Gen Len: 18.9911
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:|
| 3.9546 | 1.0 | 34080 | 3.7555 | 11.8922 | 1.88 | 9.6595 | 10.8308 | 18.9911 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
juancavallotti/roberta-base-culinary
|
juancavallotti
| 2022-09-21T13:32:02Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-20T23:48:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: roberta-base-culinary
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-culinary
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1032
## 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: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 1.5135 | 1.0 | 39823 | 1.4635 |
| 1.454 | 2.0 | 79646 | 1.3753 |
| 1.3924 | 3.0 | 119469 | 1.3375 |
| 1.3379 | 4.0 | 159292 | 1.2886 |
| 1.2969 | 5.0 | 199115 | 1.2595 |
| 1.2495 | 6.0 | 238938 | nan |
| 1.1768 | 7.0 | 278761 | 1.2283 |
| 1.1687 | 8.0 | 318584 | 1.2109 |
| 1.2148 | 9.0 | 358407 | 1.1671 |
| 1.133 | 10.0 | 398230 | 1.1721 |
| 1.0882 | 11.0 | 438053 | 1.1624 |
| 1.0749 | 12.0 | 477876 | 1.1321 |
| 1.092 | 13.0 | 517699 | nan |
| 1.0594 | 14.0 | 557522 | 1.1186 |
| 1.0292 | 15.0 | 597345 | 1.1074 |
| 0.9973 | 16.0 | 637168 | 1.1032 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/titan-robot
|
sd-concepts-library
| 2022-09-21T13:20:01Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T13:19:47Z |
---
license: mit
---
### Titan Robot on Stable Diffusion
This is the `<titan>` 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`:
|
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/yf21
|
sd-concepts-library
| 2022-09-21T12:32:51Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T12:32:47Z |
---
license: mit
---
### YF21 on Stable Diffusion
This is the `<YF21>` 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
|
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
|
jayanta/ProsusAI-finbert-sentiment-finetuned-memes
|
jayanta
| 2022-09-21T11:04:31Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-21T09:58:31Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: ProsusAI-finbert-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. -->
# ProsusAI-finbert-sentiment-finetuned-memes
This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3093
- Accuracy: 0.8407
- Precision: 0.8412
- Recall: 0.8407
- F1: 0.8409
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.467 | 1.0 | 2147 | 0.4178 | 0.7898 | 0.7882 | 0.7898 | 0.7883 |
| 0.3669 | 2.0 | 4294 | 0.4876 | 0.7940 | 0.8073 | 0.7940 | 0.7961 |
| 0.2801 | 3.0 | 6441 | 0.6222 | 0.8040 | 0.8034 | 0.8040 | 0.8037 |
| 0.2402 | 4.0 | 8588 | 0.8062 | 0.8229 | 0.8219 | 0.8229 | 0.8211 |
| 0.2099 | 5.0 | 10735 | 0.9219 | 0.8197 | 0.8263 | 0.8197 | 0.8211 |
| 0.16 | 6.0 | 12882 | 1.0393 | 0.8312 | 0.8342 | 0.8312 | 0.8321 |
| 0.1192 | 7.0 | 15029 | 1.1263 | 0.8333 | 0.8337 | 0.8333 | 0.8335 |
| 0.0979 | 8.0 | 17176 | 1.2048 | 0.8328 | 0.8324 | 0.8328 | 0.8326 |
| 0.0691 | 9.0 | 19323 | 1.2891 | 0.8323 | 0.8327 | 0.8323 | 0.8325 |
| 0.0458 | 10.0 | 21470 | 1.3093 | 0.8407 | 0.8412 | 0.8407 | 0.8409 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 1.15.2.dev0
- Tokenizers 0.10.1
|
research-backup/roberta-large-semeval2012-average-no-mask-prompt-d-loob-conceptnet-validated
|
research-backup
| 2022-09-21T10:33:11Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-09-21T10:02:47Z |
---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-average-no-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: 0.8325396825396826
- 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: 0.6925133689839572
- 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: 0.81100611450806
- 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: 0.964
- 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: 0.6574074074074074
- 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: 0.9156245291547386
- name: F1 (macro)
type: f1_macro
value: 0.9111335097935093
- 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: 0.8556338028169014
- name: F1 (macro)
type: f1_macro
value: 0.6954232134946761
- 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: 0.6863488624052004
- name: F1 (macro)
type: f1_macro
value: 0.6687072468924556
- 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: 0.9609793420045907
- name: F1 (macro)
type: f1_macro
value: 0.8894889212672226
- 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: 0.911939830774052
- name: F1 (macro)
type: f1_macro
value: 0.9099470654822349
---
# relbert/roberta-large-semeval2012-average-no-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-average-no-mask-prompt-d-loob-conceptnet-validated/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.6925133689839572
- Accuracy on SAT: 0.7002967359050445
- Accuracy on BATS: 0.81100611450806
- Accuracy on U2: 0.6535087719298246
- Accuracy on U4: 0.6574074074074074
- Accuracy on Google: 0.964
- 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-d-loob-conceptnet-validated/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9156245291547386
- Micro F1 score on CogALexV: 0.8556338028169014
- Micro F1 score on EVALution: 0.6863488624052004
- Micro F1 score on K&H+N: 0.9609793420045907
- Micro F1 score on ROOT09: 0.911939830774052
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-d-loob-conceptnet-validated/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8325396825396826
### 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-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_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 <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-no-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",
}
```
|
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
|
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
```
|
sd-concepts-library/naoki-saito
|
sd-concepts-library
| 2022-09-21T09:45:44Z | 0 | 3 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T09:45:40Z |
---
license: mit
---
### Naoki Saito on Stable Diffusion
This is the `<naoki_saito>` 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`:
|
research-backup/roberta-large-semeval2012-average-no-mask-prompt-b-loob-conceptnet-validated
|
research-backup
| 2022-09-21T09:32:13Z | 82 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-09-21T09:02:16Z |
---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-average-no-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: 0.8555952380952381
- 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: 0.5989304812834224
- 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: 0.5964391691394659
- 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: 0.7904391328515842
- 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: 0.922
- 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: 0.5570175438596491
- 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: 0.5949074074074074
- 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: 0.9121591080307367
- name: F1 (macro)
type: f1_macro
value: 0.9078060229626019
- 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: 0.8514084507042253
- name: F1 (macro)
type: f1_macro
value: 0.6896967047437524
- 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: 0.6603466955579632
- name: F1 (macro)
type: f1_macro
value: 0.6556621881083923
- 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: 0.9592404535021214
- name: F1 (macro)
type: f1_macro
value: 0.8779080502283186
- 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: 0.8912566593544343
- name: F1 (macro)
type: f1_macro
value: 0.8904384231466876
---
# relbert/roberta-large-semeval2012-average-no-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-average-no-mask-prompt-b-loob-conceptnet-validated/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5989304812834224
- Accuracy on SAT: 0.5964391691394659
- Accuracy on BATS: 0.7904391328515842
- Accuracy on U2: 0.5570175438596491
- Accuracy on U4: 0.5949074074074074
- Accuracy on Google: 0.922
- 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-b-loob-conceptnet-validated/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9121591080307367
- Micro F1 score on CogALexV: 0.8514084507042253
- Micro F1 score on EVALution: 0.6603466955579632
- Micro F1 score on K&H+N: 0.9592404535021214
- Micro F1 score on ROOT09: 0.8912566593544343
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-b-loob-conceptnet-validated/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8555952380952381
### 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-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: average_no_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-average-no-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",
}
```
|
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.
|
research-backup/roberta-large-semeval2012-average-no-mask-prompt-a-loob-conceptnet-validated
|
research-backup
| 2022-09-21T09:02:10Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-09-21T08:31:53Z |
---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-average-no-mask-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: 0.8261309523809524
- 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: 0.6417112299465241
- 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: 0.6409495548961425
- 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: 0.7871039466370205
- 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: 0.946
- 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: 0.5921052631578947
- 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: 0.9100497212596053
- name: F1 (macro)
type: f1_macro
value: 0.9039162913439194
- 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: 0.8556338028169014
- name: F1 (macro)
type: f1_macro
value: 0.6945383312136448
- 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: 0.6852654387865655
- name: F1 (macro)
type: f1_macro
value: 0.6774872040266507
- 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: 0.9572233428392571
- name: F1 (macro)
type: f1_macro
value: 0.879744388826254
- 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: 0.9037919147602632
- name: F1 (macro)
type: f1_macro
value: 0.9024843094207563
---
# relbert/roberta-large-semeval2012-average-no-mask-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-no-mask-prompt-a-loob-conceptnet-validated/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.6417112299465241
- Accuracy on SAT: 0.6409495548961425
- Accuracy on BATS: 0.7871039466370205
- Accuracy on U2: 0.5921052631578947
- Accuracy on U4: 0.6527777777777778
- Accuracy on Google: 0.946
- 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-a-loob-conceptnet-validated/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9100497212596053
- Micro F1 score on CogALexV: 0.8556338028169014
- Micro F1 score on EVALution: 0.6852654387865655
- Micro F1 score on K&H+N: 0.9572233428392571
- Micro F1 score on ROOT09: 0.9037919147602632
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-a-loob-conceptnet-validated/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8261309523809524
### 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-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_no_mask
- 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-no-mask-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",
}
```
|
yarongef/DistilProtBert
|
yarongef
| 2022-09-21T08:38:51Z | 1,665 | 8 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"protein language model",
"dataset:Uniref50",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-30T10:07:23Z |
---
license: mit
tags:
- protein language model
datasets:
- Uniref50
---
# DistilProtBert
A distilled version of [ProtBert-UniRef100](https://huggingface.co/Rostlab/prot_bert) model.
In addition to cross entropy and cosine teacher-student losses, DistilProtBert was pretrained on a masked language modeling (MLM) objective and it only works with capital letter amino acids.
Check out our paper [DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts](https://doi.org/10.1093/bioinformatics/btac474) for more details.
[Git](https://github.com/yarongef/DistilProtBert) repository.
# Model details
| **Model** | **# of parameters** | **# of hidden layers** | **Pretraining dataset** | **# of proteins** | **Pretraining hardware** |
|:--------------:|:-------------------:|:----------------------:|:-----------------------:|:------------------------------:|:------------------------:|
| ProtBert | 420M | 30 | UniRef100 | 216M | 512 16GB TPUs |
| DistilProtBert | 230M | 15 | UniRef50 | 43M | 5 v100 32GB GPUs |
## Intended uses & limitations
The model could be used for protein feature extraction or to be fine-tuned on downstream tasks.
### How to use
The model can be used the same as ProtBert and with ProtBert's tokenizer.
## Training data
DistilProtBert model was pretrained on [Uniref50](https://www.uniprot.org/downloads), a dataset consisting of ~43 million protein sequences (only sequences of length between 20 to 512 amino acids were used).
# Pretraining procedure
Preprocessing was done using ProtBert's tokenizer.
The details of the masking procedure for each sequence followed the original Bert (as mentioned in [ProtBert](https://huggingface.co/Rostlab/prot_bert)).
The model was pretrained on a single DGX cluster for 3 epochs in total. local batch size was 16, the optimizer used was AdamW with a learning rate of 5e-5 and mixed precision settings.
## Evaluation results
When fine-tuned on downstream tasks, this model achieves the following results:
| Task/Dataset | secondary structure (3-states) | Membrane |
|:-----:|:-----:|:-----:|
| CASP12 | 72 | |
| TS115 | 81 | |
| CB513 | 79 | |
| DeepLoc | | 86 |
Distinguish between proteins and their k-let shuffled versions:
_Singlet_ ([dataset](https://huggingface.co/datasets/yarongef/human_proteome_singlets))
| Model | AUC |
|:--------------:|:-------:|
| LSTM | 0.71 |
| ProtBert | 0.93 |
| DistilProtBert | 0.92 |
_Doublet_ ([dataset](https://huggingface.co/datasets/yarongef/human_proteome_doublets))
| Model | AUC |
|:--------------:|:-------:|
| LSTM | 0.68 |
| ProtBert | 0.92 |
| DistilProtBert | 0.91 |
_Triplet_ ([dataset](https://huggingface.co/datasets/yarongef/human_proteome_triplets))
| Model | AUC |
|:--------------:|:-------:|
| LSTM | 0.61 |
| ProtBert | 0.92 |
| DistilProtBert | 0.87 |
## **Citation**
If you use this model, please cite our paper:
```
@article {
author = {Geffen, Yaron and Ofran, Yanay and Unger, Ron},
title = {DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts},
year = {2022},
doi = {10.1093/bioinformatics/btac474},
URL = {https://doi.org/10.1093/bioinformatics/btac474},
journal = {Bioinformatics}
}
```
|
sd-concepts-library/dq10-anrushia
|
sd-concepts-library
| 2022-09-21T08:36:25Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T08:36:16Z |
---
license: mit
---
### dq10-anrushia on Stable Diffusion
This is the `<anrushia>` 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`:
|
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
|
sd-concepts-library/bamse
|
sd-concepts-library
| 2022-09-21T07:58:30Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T07:58:23Z |
---
license: mit
---
### Bamse on Stable Diffusion
This is the `<bamse>` 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`:
|
Psunrise/finetuning-customer-sentiment-model-300-samples
|
Psunrise
| 2022-09-21T07:34:59Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-01T13:14:44Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuning-customer-sentiment-model-300-samples
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-customer-sentiment-model-300-samples
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5949
- Accuracy: 0.7558
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
research-backup/roberta-large-semeval2012-average-prompt-c-loob-conceptnet-validated
|
research-backup
| 2022-09-21T07:30:29Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-09-21T06:35:28Z |
---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-average-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: 0.8907142857142857
- 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: 0.6283422459893048
- 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: 0.6201780415430267
- 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: 0.8143413007226237
- 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: 0.924
- 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: 0.631578947368421
- 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: 0.6574074074074074
- 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: 0.9159258701220431
- name: F1 (macro)
type: f1_macro
value: 0.9095586987612104
- 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: 0.8671361502347418
- name: F1 (macro)
type: f1_macro
value: 0.715166083502338
- 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: 0.6847237269772481
- name: F1 (macro)
type: f1_macro
value: 0.6678741454455487
- 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: 0.9613966752451832
- name: F1 (macro)
type: f1_macro
value: 0.8893979772488178
- 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: 0.9088060169225948
- name: F1 (macro)
type: f1_macro
value: 0.9059327892815358
---
# relbert/roberta-large-semeval2012-average-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-prompt-c-loob-conceptnet-validated/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.6283422459893048
- Accuracy on SAT: 0.6201780415430267
- Accuracy on BATS: 0.8143413007226237
- Accuracy on U2: 0.631578947368421
- Accuracy on U4: 0.6574074074074074
- Accuracy on Google: 0.924
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-c-loob-conceptnet-validated/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9159258701220431
- Micro F1 score on CogALexV: 0.8671361502347418
- Micro F1 score on EVALution: 0.6847237269772481
- Micro F1 score on K&H+N: 0.9613966752451832
- Micro F1 score on ROOT09: 0.9088060169225948
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-c-loob-conceptnet-validated/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8907142857142857
### 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-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
- 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-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",
}
```
|
sd-concepts-library/joemad
|
sd-concepts-library
| 2022-09-21T07:30:18Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T07:30:15Z |
---
license: mit
---
### JoeMad on Stable Diffusion
This is the `<joemad>` 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/marbling-art
|
sd-concepts-library
| 2022-09-21T07:00:10Z | 0 | 4 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T07:00:04Z |
---
license: mit
---
### Marbling art on Stable Diffusion
This is the `<marbling-art>` 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`:
|
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)
|
Lemming/distilbert-base-uncased-finetuned-emotion
|
Lemming
| 2022-09-21T06:36:30Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-21T05:13:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9215
- name: F1
type: f1
value: 0.9216499948953181
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2104
- Accuracy: 0.9215
- F1: 0.9216
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8206 | 1.0 | 250 | 0.2908 | 0.92 | 0.9183 |
| 0.2399 | 2.0 | 500 | 0.2104 | 0.9215 | 0.9216 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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)
|
2en/distilbert-base-uncased-finetuned-emotion
|
2en
| 2022-09-21T05:40:08Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-21T05:29:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9285
- name: F1
type: f1
value: 0.9289358360077076
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2061
- Accuracy: 0.9285
- F1: 0.9289
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7986 | 1.0 | 250 | 0.2955 | 0.9065 | 0.9042 |
| 0.2351 | 2.0 | 500 | 0.2061 | 0.9285 | 0.9289 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.11.0a0+17540c5
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/insidewhale
|
sd-concepts-library
| 2022-09-21T05:16:17Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T05:16:11Z |
---
license: mit
---
### InsideWhale on Stable Diffusion
This is the `<InsideWhale>` 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`:
|
research-backup/roberta-large-semeval2012-mask-prompt-e-loob-conceptnet-validated
|
research-backup
| 2022-09-21T04:45:21Z | 92 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-09-21T03:49:54Z |
---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-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: 0.8323412698412699
- 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: 0.6176470588235294
- 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: 0.6231454005934718
- 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: 0.7570872707059477
- 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: 0.874
- 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: 0.6008771929824561
- 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: 0.9242127467229169
- name: F1 (macro)
type: f1_macro
value: 0.9198550816036225
- 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: 0.8744131455399061
- name: F1 (macro)
type: f1_macro
value: 0.7269598631142125
- 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: 0.699349945828819
- name: F1 (macro)
type: f1_macro
value: 0.6904954951631552
- 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: 0.9664046741322946
- name: F1 (macro)
type: f1_macro
value: 0.8975350605960287
- 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: 0.90414989526156
---
# relbert/roberta-large-semeval2012-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-mask-prompt-e-loob-conceptnet-validated/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.6176470588235294
- Accuracy on SAT: 0.6231454005934718
- Accuracy on BATS: 0.7570872707059477
- Accuracy on U2: 0.6008771929824561
- Accuracy on U4: 0.6226851851851852
- Accuracy on Google: 0.874
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-e-loob-conceptnet-validated/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9242127467229169
- Micro F1 score on CogALexV: 0.8744131455399061
- Micro F1 score on EVALution: 0.699349945828819
- Micro F1 score on K&H+N: 0.9664046741322946
- 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-e-loob-conceptnet-validated/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8323412698412699
### 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-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: 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: 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-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",
}
```
|
Arnaudmkonan/xlm-roberta-base-finetuned-panx-fr
|
Arnaudmkonan
| 2022-09-21T03:10:06Z | 103 | 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
| 2022-09-21T02:54:31Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8299296953465015
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2848
- F1: 0.8299
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5989 | 1.0 | 191 | 0.3383 | 0.7928 |
| 0.2617 | 2.0 | 382 | 0.2966 | 0.8318 |
| 0.1672 | 3.0 | 573 | 0.2848 | 0.8299 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
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
|
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",
}
```
|
sd-concepts-library/red-glasses
|
sd-concepts-library
| 2022-09-21T01:58:23Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-21T01:58:18Z |
---
license: mit
---
### red-glasses on Stable Diffusion
This is the `<red-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`:
|
sd-concepts-library/jinjoon-lee-they
|
sd-concepts-library
| 2022-09-21T01:56:30Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T09:37:35Z |
---
license: mit
---
### Jinjoon Lee, They on Stable Diffusion
This is the `<jinjoon_lee_they>` 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`:
|
Assadullah/donut-base-sroie
|
Assadullah
| 2022-09-21T01:30:52Z | 55 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2022-09-07T07:08:30Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-sroie
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# donut-base-sroie
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.8.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
research-backup/roberta-large-semeval2012-mask-prompt-a-loob-conceptnet-validated
|
research-backup
| 2022-09-21T01:04:53Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/semeval2012_relational_similarity",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-09-21T00:09:37Z |
---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-mask-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: 0.824484126984127
- 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: 0.655786350148368
- 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: 0.8043357420789328
- 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: 0.95
- 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: 0.631578947368421
- 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: 0.6412037037037037
- 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: 0.9237607352719602
- name: F1 (macro)
type: f1_macro
value: 0.919419928141617
- 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: 0.8805164319248826
- name: F1 (macro)
type: f1_macro
value: 0.7325530539422306
- 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: 0.7145178764897074
- name: F1 (macro)
type: f1_macro
value: 0.6979294478299565
- 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: 0.9625791194268624
- name: F1 (macro)
type: f1_macro
value: 0.8922496181655498
- 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: 0.9113130680037606
- name: F1 (macro)
type: f1_macro
value: 0.9090503526285939
---
# relbert/roberta-large-semeval2012-mask-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-mask-prompt-a-loob-conceptnet-validated/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.6550802139037433
- Accuracy on SAT: 0.655786350148368
- Accuracy on BATS: 0.8043357420789328
- Accuracy on U2: 0.631578947368421
- Accuracy on U4: 0.6412037037037037
- Accuracy on Google: 0.95
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-a-loob-conceptnet-validated/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9237607352719602
- Micro F1 score on CogALexV: 0.8805164319248826
- Micro F1 score on EVALution: 0.7145178764897074
- Micro F1 score on K&H+N: 0.9625791194268624
- Micro F1 score on ROOT09: 0.9113130680037606
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-a-loob-conceptnet-validated/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.824484126984127
### 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-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: mask
- 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-mask-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",
}
```
|
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-e-nce
|
research-backup
| 2022-09-21T00:17:37Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-08-07T04:35:51Z |
---
datasets:
- relbert/conceptnet_high_confidence
model-index:
- name: relbert/roberta-large-conceptnet-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: 0.9325396825396826
- 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: 0.5561497326203209
- 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: 0.5578635014836796
- 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: 0.7593107281823235
- 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: 0.898
- 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: 0.5657894736842105
- 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: 0.5902777777777778
- 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: 0.9303902365526593
- name: F1 (macro)
type: f1_macro
value: 0.9253608458682704
- 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: 0.8781690140845071
- name: F1 (macro)
type: f1_macro
value: 0.7319159638510688
- 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: 0.6992515104207172
- 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: 0.9631355637476525
- name: F1 (macro)
type: f1_macro
value: 0.8833254511680932
- 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: 0.9088060169225948
- name: F1 (macro)
type: f1_macro
value: 0.9064745584707974
---
# relbert/roberta-large-conceptnet-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-mask-prompt-e-nce/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5561497326203209
- Accuracy on SAT: 0.5578635014836796
- Accuracy on BATS: 0.7593107281823235
- Accuracy on U2: 0.5657894736842105
- Accuracy on U4: 0.5902777777777778
- Accuracy on Google: 0.898
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-e-nce/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9303902365526593
- Micro F1 score on CogALexV: 0.8781690140845071
- Micro F1 score on EVALution: 0.6939328277356447
- Micro F1 score on K&H+N: 0.9631355637476525
- Micro F1 score on ROOT09: 0.9088060169225948
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-e-nce/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.9325396825396826
### 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-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: 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: 146
- 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-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-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-mask-prompt-a-nce
|
research-backup
| 2022-09-21T00:15:56Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-08-03T20:34:22Z |
---
datasets:
- relbert/conceptnet_high_confidence
model-index:
- name: relbert/roberta-large-conceptnet-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: 0.806984126984127
- 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: 0.5748663101604278
- 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: 0.7620900500277932
- 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: 0.93
- 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: 0.6403508771929824
- 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: 0.9251167696248305
- name: F1 (macro)
type: f1_macro
value: 0.919619692834177
- 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: 0.8767605633802817
- name: F1 (macro)
type: f1_macro
value: 0.7257293877329338
- 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: 0.6912242686890574
- name: F1 (macro)
type: f1_macro
value: 0.6877048241118354
- 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: 0.9590317868818251
- name: F1 (macro)
type: f1_macro
value: 0.8690566710191301
- 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: 0.8947038545910373
- name: F1 (macro)
type: f1_macro
value: 0.889766384814178
---
# relbert/roberta-large-conceptnet-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-mask-prompt-a-nce/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5748663101604278
- Accuracy on SAT: 0.5727002967359051
- Accuracy on BATS: 0.7620900500277932
- Accuracy on U2: 0.6403508771929824
- Accuracy on U4: 0.6342592592592593
- Accuracy on Google: 0.93
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-a-nce/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9251167696248305
- Micro F1 score on CogALexV: 0.8767605633802817
- Micro F1 score on EVALution: 0.6912242686890574
- Micro F1 score on K&H+N: 0.9590317868818251
- Micro F1 score on ROOT09: 0.8947038545910373
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-a-nce/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.806984126984127
### 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-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: 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: 90
- 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-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-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-c-nce
|
research-backup
| 2022-09-21T00:15:05Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-07-29T23:51:05Z |
---
datasets:
- relbert/conceptnet_high_confidence
model-index:
- name: relbert/roberta-large-conceptnet-average-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: 0.7826388888888889
- 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: 0.5454545454545454
- 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: 0.5489614243323442
- 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: 0.792106725958866
- 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: 0.93
- 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: 0.6096491228070176
- 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: 0.6134259259259259
- 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: 0.9091456983576918
- name: F1 (macro)
type: f1_macro
value: 0.9025708311029935
- 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: 0.8744131455399061
- name: F1 (macro)
type: f1_macro
value: 0.7154495605637783
- 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: 0.6738894907908992
- name: F1 (macro)
type: f1_macro
value: 0.6505462224375916
- 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: 0.9601446755234054
- name: F1 (macro)
type: f1_macro
value: 0.8892142921251124
- 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: 0.9031651519899718
- name: F1 (macro)
type: f1_macro
value: 0.9011299997530173
---
# relbert/roberta-large-conceptnet-average-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-prompt-c-nce/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5454545454545454
- Accuracy on SAT: 0.5489614243323442
- Accuracy on BATS: 0.792106725958866
- Accuracy on U2: 0.6096491228070176
- Accuracy on U4: 0.6134259259259259
- Accuracy on Google: 0.93
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-prompt-c-nce/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9091456983576918
- Micro F1 score on CogALexV: 0.8744131455399061
- Micro F1 score on EVALution: 0.6738894907908992
- Micro F1 score on K&H+N: 0.9601446755234054
- Micro F1 score on ROOT09: 0.9031651519899718
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-prompt-c-nce/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7826388888888889
### 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-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
- 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: 112
- 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-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-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",
}
```
|
hadiqa123/xlsr_ur_training
|
hadiqa123
| 2022-09-20T22:28:40Z | 82 | 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
| 2022-09-03T04:23:40Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_8_0
model-index:
- name: xlsr_ur_training
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. -->
# xlsr_ur_training
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: 0.8325
- Wer: 0.4863
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.9537 | 3.25 | 1000 | 3.0940 | 0.9989 |
| 2.1696 | 6.49 | 2000 | 0.9705 | 0.6830 |
| 0.8637 | 9.74 | 3000 | 0.8098 | 0.5919 |
| 0.6297 | 12.99 | 4000 | 0.8002 | 0.5469 |
| 0.5034 | 16.23 | 5000 | 0.8019 | 0.5214 |
| 0.4267 | 19.48 | 6000 | 0.8223 | 0.5085 |
| 0.3847 | 22.73 | 7000 | 0.8081 | 0.4948 |
| 0.342 | 25.97 | 8000 | 0.8300 | 0.4930 |
| 0.3201 | 29.22 | 9000 | 0.8325 | 0.4863 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
sd-concepts-library/fasina
|
sd-concepts-library
| 2022-09-20T21:52:54Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T21:52:48Z |
---
license: mit
---
### Fasina" on Stable Diffusion
This is the `<Fasina>` 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`:
|
fusing/stable-diffusion-flax-new
|
fusing
| 2022-09-20T21:28:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-09-20T21:04:34Z |
```python
#!/usr/bin/env python3
from diffusers import FlaxStableDiffusionPipeline
from jax import pmap
import numpy as np
import jax
from flax.jax_utils import replicate
from flax.training.common_utils import shard
prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 50
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained("fusing/stable-diffusion-flax-new", use_auth_token=True)
del params["safety_checker"]
# pmap
p_sample = pmap(pipeline.__call__, static_broadcasted_argnums=(3,))
# prep prompts
prompt = "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of field, close up, split lighting, cinematic"
num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = pipeline.prepare_inputs(prompt)
# replicate
params = replicate(params)
prng_seed = jax.random.split(prng_seed, 8)
prompt_ids = shard(prompt_ids)
# run
images = p_sample(prompt_ids, params, prng_seed, num_inference_steps).images
# get pil images
images_pil = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))
import ipdb; ipdb.set_trace()
print("Images should be good")
# images_pil[0].save(...)
```
|
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`:
|
farleyknight/patent-summarization-fb-bart-base-2022-09-20
|
farleyknight
| 2022-09-20T20:30:00Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:farleyknight/big_patent_5_percent",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-20T13:51:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- farleyknight/big_patent_5_percent
metrics:
- rouge
model-index:
- name: patent-summarization-fb-bart-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: 39.4401
---
<!-- 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-fb-bart-base-2022-09-20
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the farleyknight/big_patent_5_percent dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4088
- Rouge1: 39.4401
- Rouge2: 14.2445
- Rougel: 26.2701
- Rougelsum: 33.7535
- Gen Len: 78.9702
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 3.0567 | 0.08 | 5000 | 2.8864 | 18.9387 | 7.1014 | 15.4506 | 16.8377 | 19.9979 |
| 2.9285 | 0.17 | 10000 | 2.7800 | 19.8983 | 7.3258 | 16.0823 | 17.7019 | 20.0 |
| 2.9252 | 0.25 | 15000 | 2.7080 | 19.6623 | 7.4627 | 16.0153 | 17.4485 | 20.0 |
| 2.8123 | 0.33 | 20000 | 2.6585 | 19.7414 | 7.5251 | 15.8166 | 17.4668 | 20.0 |
| 2.7117 | 0.41 | 25000 | 2.6070 | 19.7661 | 7.7193 | 16.2795 | 17.7884 | 20.0 |
| 2.7131 | 0.5 | 30000 | 2.5616 | 19.6706 | 7.4229 | 15.7998 | 17.4324 | 20.0 |
| 2.6373 | 0.58 | 35000 | 2.5250 | 20.0155 | 7.6811 | 16.1231 | 17.7578 | 20.0 |
| 2.6785 | 0.66 | 40000 | 2.4977 | 20.0974 | 7.9578 | 16.543 | 18.0242 | 20.0 |
| 2.6265 | 0.75 | 45000 | 2.4701 | 19.994 | 7.9114 | 16.3501 | 17.8786 | 20.0 |
| 2.5833 | 0.83 | 50000 | 2.4441 | 19.9981 | 7.934 | 16.3033 | 17.8674 | 20.0 |
| 2.5579 | 0.91 | 55000 | 2.4251 | 20.0544 | 7.8966 | 16.3889 | 17.9491 | 20.0 |
| 2.5242 | 0.99 | 60000 | 2.4097 | 20.1093 | 8.0572 | 16.4935 | 17.9823 | 20.0 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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`:
|
sd-concepts-library/ori
|
sd-concepts-library
| 2022-09-20T20:05:06Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T20:04:55Z |
---
license: mit
---
### Ori on Stable Diffusion
This is the `<Ori>` 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`:
|
BigSalmon/InformalToFormalLincoln79Paraphrase
|
BigSalmon
| 2022-09-20T18:26:42Z | 167 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-20T18:13:04Z |
data: https://github.com/BigSalmon2/InformalToFormalDataset
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln79Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln79Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
```
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
- penny has practically no value
- should be taken out of circulation
- just as other coins have been in us history
- lost use
- value not enough
- to make environmental consequences worthy
text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
```
first: ( was complicit in / was involved in ).
antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ).
***
first: ( have no qualms about / see no issue with ).
antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ).
***
first: ( do not see eye to eye / disagree often ).
antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ).
***
first:
```
```
stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground.
***
languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo.
***
dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia.
***
embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons.
```
Infill / Infilling / Masking / Phrase Masking (Works pretty decently actually, especially when you use logprobs code from above):
```
his contention [blank] by the evidence [sep] was refuted [answer]
***
few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
the joy of sport is that no two games are alike. for every exhilarating experience, however, there is an interminable one. the national pastime, unfortunately, has a penchant for the latter. what begins as a summer evening at the ballpark can quickly devolve into a game of tedium. the primary culprit is the [blank] of play. from batters readjusting their gloves to fielders spitting on their mitts, the action is [blank] unnecessary interruptions. the sport's future is [blank] if these tendencies are not addressed [sep] plodding pace [answer] riddled with [answer] bleak [answer]
***
microsoft word's [blank] pricing [blank] competition [sep] unconscionable [answer] invites [answer]
***
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
Backwards
```
Essay Intro (National Parks):
text: tourists are at ease in the national parks, ( swept up in the beauty of their natural splendor ).
***
Essay Intro (D.C. Statehood):
washington, d.c. is a city of outsize significance, ( ground zero for the nation's political life / center stage for the nation's political machinations ).
```
```
topic: the Golden State Warriors.
characterization 1: the reigning kings of the NBA.
characterization 2: possessed of a remarkable cohesion.
characterization 3: helmed by superstar Stephen Curry.
characterization 4: perched atop the league’s hierarchy.
characterization 5: boasting a litany of hall-of-famers.
***
topic: emojis.
characterization 1: shorthand for a digital generation.
characterization 2: more versatile than words.
characterization 3: the latest frontier in language.
characterization 4: a form of self-expression.
characterization 5: quintessentially millennial.
characterization 6: reflective of a tech-centric world.
***
topic:
```
```
regular: illinois went against the census' population-loss prediction by getting more residents.
VBG: defying the census' prediction of population loss, illinois experienced growth.
***
regular: microsoft word’s high pricing increases the likelihood of competition.
VBG: extortionately priced, microsoft word is inviting competition.
***
regular:
```
```
source: badminton should be more popular in the US.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) games played with racquets are popular, (2) just look at tennis and ping pong, (3) but badminton underappreciated, (4) fun, fast-paced, competitive, (5) needs to be marketed more
text: the sporting arena is dominated by games that are played with racquets. tennis and ping pong, in particular, are immensely popular. somewhat curiously, however, badminton is absent from this pantheon. exciting, fast-paced, and competitive, it is an underappreciated pastime. all that it lacks is more effective marketing.
***
source: movies in theaters should be free.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) movies provide vital life lessons, (2) many venues charge admission, (3) those without much money
text: the lessons that movies impart are far from trivial. the vast catalogue of cinematic classics is replete with inspiring sagas of friendship, bravery, and tenacity. it is regrettable, then, that admission to theaters is not free. in their current form, the doors of this most vital of institutions are closed to those who lack the means to pay.
***
source:
```
```
in the private sector, { transparency } is vital to the business’s credibility. the { disclosure of information } can be the difference between success and failure.
***
the labor market is changing, with { remote work } now the norm. this { flexible employment } allows the individual to design their own schedule.
***
the { cubicle } is the locus of countless grievances. many complain that the { enclosed workspace } restricts their freedom of movement.
***
```
```
it would be natural to assume that americans, as a people whose ancestors { immigrated to this country }, would be sympathetic to those seeking to do likewise.
question: what does “do likewise” mean in the above context?
(a) make the same journey
(b) share in the promise of the american dream
(c) start anew in the land of opportunity
(d) make landfall on the united states
***
in the private sector, { transparency } is vital to the business’s credibility. this orientation can be the difference between success and failure.
question: what does “this orientation” mean in the above context?
(a) visible business practices
(b) candor with the public
(c) open, honest communication
(d) culture of accountability
```
```
example: suppose you are a teacher. further suppose you want to tell an accurate telling of history. then suppose a parent takes offense. they do so in the name of name of their kid. this happens a lot.
text: educators' responsibility to remain true to the historical record often clashes with the parent's desire to shelter their child from uncomfortable realities.
***
example: suppose you are a student at college. now suppose you have to buy textbooks. that is going to be worth hundreds of dollars. given how much you already spend on tuition, that is going to hard cost to bear.
text: the exorbitant cost of textbooks, which often reaches hundreds of dollars, imposes a sizable financial burden on the already-strapped college student.
```
```
<Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle>
***
<Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle>
```
|
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
|
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
|
adil-o/ppo-CartPole-v1
|
adil-o
| 2022-09-20T15:11:45Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-20T15:11:39Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -15.80 +/- 20.49
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit8
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'adil-o/ppo-CartPole-v1'
'batch_size': 512
'minibatch_size': 128}
```
|
adil-o/a2c-AntBulletEnv-v0
|
adil-o
| 2022-09-20T14:44:36Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-20T14:43:33Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 1328.81 +/- 262.74
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
...
```
|
sd-concepts-library/rail-scene-style
|
sd-concepts-library
| 2022-09-20T14:40:44Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T14:40:39Z |
---
license: mit
---
### Rail Scene Style on Stable Diffusion
This is the `<rail-pov>` 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`:
|
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
|
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
|
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_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 -->
|
adil-o/A2C-Cartpole-v1
|
adil-o
| 2022-09-20T12:40:01Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-20T12:25:02Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: A2C-Cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
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
|
jamescalam/deberta-v3-base-qa
|
jamescalam
| 2022-09-20T11:37:20Z | 3,000 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"deberta-v2",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-20T11:04:06Z |
---
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 528353 with parameters:
```
{'batch_size': 16, '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": 52835,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
sd-concepts-library/artist-yukiko-kanagai
|
sd-concepts-library
| 2022-09-20T11:13:52Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T11:13:48Z |
---
license: mit
---
### Artist_Yukiko Kanagai on Stable Diffusion
This is the `<Yukiko Kanagai >` 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/liliana
|
sd-concepts-library
| 2022-09-20T10:53:27Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-20T10:53:24Z |
---
license: mit
---
### liliana on Stable Diffusion
This is the `<liliana>` 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-all
|
gcmsrc
| 2022-09-20T09:35:18Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-20T09:22:38Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1454
- F1: 0.8732
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.297 | 1.0 | 739 | 0.1785 | 0.8273 |
| 0.1536 | 2.0 | 1478 | 0.1524 | 0.8574 |
| 0.0998 | 3.0 | 2217 | 0.1454 | 0.8732 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
|
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-en
|
gcmsrc
| 2022-09-20T09:21:59Z | 107 | 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
| 2022-09-20T09:20:09Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.6931246506428173
---
<!-- 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-en
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: 0.3965
- F1: 0.6931
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1324 | 1.0 | 50 | 0.5803 | 0.4726 |
| 0.5091 | 2.0 | 100 | 0.4381 | 0.6559 |
| 0.3699 | 3.0 | 150 | 0.3965 | 0.6931 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
|
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