modelId
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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-13 06:30:42
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 556
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listlengths 1
4.05k
| pipeline_tag
stringclasses 55
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KoboldAI/GPT-NeoX-20B-Erebus
|
KoboldAI
| 2022-09-26T19:05:19Z | 3,741 | 84 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"en",
"arxiv:2204.06745",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2022-09-02T18:07:19Z |
---
language: en
license: apache-2.0
inference: false
---
# GPT-NeoX-20B-Erebus
## Model description
This is the second generation of the original Shinen made by Mr. Seeker. The full dataset consists of 6 different sources, all surrounding the "Adult" theme. The name "Erebus" comes from the greek mythology, also named "darkness". This is in line with Shin'en, or "deep abyss". For inquiries, please contact the KoboldAI community. **Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.**
## Training procedure
GPT-NeoX-20B-Erebus was trained on a TPUv3-256 TPU pod using a heavily modified version of Ben Wang's Mesh Transformer JAX library, the original version of which was used by EleutherAI to train their GPT-J-6B model.
## Training data
The data can be divided in 6 different datasets:
- Literotica (everything with 4.5/5 or higher)
- Sexstories (everything with 90 or higher)
- Dataset-G (private dataset of X-rated stories)
- Doc's Lab (all stories)
- Pike Dataset (novels with "adult" rating)
- SoFurry (collection of various animals)
The dataset uses `[Genre: <comma-separated list of genres>]` for tagging.
## Limitations and biases
Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion). **Warning: This model has a very strong NSFW bias!**
## Citation details
The GPT-NeoX-20B model weights:
```bibtex
@inproceedings{gpt-neox-20b,
title={{GPT-NeoX-20B}: An Open-Source Autoregressive Language Model},
author={Black, Sid and Biderman, Stella and Hallahan, Eric and Anthony, Quentin and Gao, Leo and Golding, Laurence and He, Horace and Leahy, Connor and McDonell, Kyle and Phang, Jason and Pieler, Michael and Prashanth, USVSN Sai and Purohit, Shivanshu and Reynolds, Laria and Tow, Jonathan and Wang, Ben and Weinbach, Samuel},
booktitle={Proceedings of the ACL Workshop on Challenges \& Perspectives in Creating Large Language Models},
url={https://arxiv.org/abs/2204.06745},
year={2022}
}
```
The Mesh Transformer JAX library:
```bibtex
@misc{mesh-transformer-jax,
author = {Wang, Ben},
title = {{Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX}},
howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
year = 2021,
month = May
}
```
|
mrm8488/setfit-mpnet-base-v2-finetuned-sentEval-CR
|
mrm8488
| 2022-09-26T18:50:11Z | 7 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-26T18:49:59Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 20,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 40,
"warmup_steps": 4,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
LucasBorth/jurisbert-base-classify
|
LucasBorth
| 2022-09-26T18:16:34Z | 101 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-26T18:12:23Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: jurisbert-base-classify
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. -->
# jurisbert-base-classify
This model is a fine-tuned version of [juridics/jurisbert-base-portuguese-uncased](https://huggingface.co/juridics/jurisbert-base-portuguese-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4893
- Accuracy: 0.8991
## 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: 24
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
AbhijeetA/PIE
|
AbhijeetA
| 2022-09-26T17:30:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:04Z |
Model details available [here](https://github.com/awasthiabhijeet/PIE)
|
sd-concepts-library/fairytale
|
sd-concepts-library
| 2022-09-26T17:23:45Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-26T17:23:44Z |
---
license: mit
---
### fAIrytale on Stable Diffusion
This is the `<fAIrytale>` 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`:
|
microsoft/graphcodebert-base
|
microsoft
| 2022-09-26T17:06:54Z | 104,959 | 56 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"arxiv:2009.08366",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
## GraphCodeBERT model
GraphCodeBERT is a graph-based pre-trained model based on the Transformer architecture for programming language, which also considers data-flow information along with code sequences. GraphCodeBERT consists of 12 layers, 768 dimensional hidden states, and 12 attention heads. The maximum sequence length for the model is 512. The model is trained on the CodeSearchNet dataset, which includes 2.3M functions with document pairs for six programming languages.
More details can be found in the [paper](https://arxiv.org/abs/2009.08366) by Guo et. al.
**Disclaimer:** The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face community members.
|
cuongnt/wav2vec2-base-timit-demo-google-colab
|
cuongnt
| 2022-09-26T16:39:26Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-26T16:07:16Z |
---
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.
## 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: 16
- 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: 4
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.13.0
|
ammarpl/t5-small-finetuned-xsum
|
ammarpl
| 2022-09-26T16:38:17Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-25T16:48:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: t5-small-finetuned-xsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum 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: 0.01
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 0.01 | 128 | 3.0141 | 18.0313 | 2.7105 | 14.1325 | 14.3393 | 18.8882 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
pjcordero04/distilbert-base-uncased-finetuned-cola
|
pjcordero04
| 2022-09-26T16:32:49Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-26T14:35:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5442538936990396
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8348
- Matthews Correlation: 0.5443
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5236 | 1.0 | 535 | 0.5495 | 0.4205 |
| 0.3505 | 2.0 | 1070 | 0.5176 | 0.4977 |
| 0.2401 | 3.0 | 1605 | 0.5498 | 0.5354 |
| 0.1751 | 4.0 | 2140 | 0.7975 | 0.5270 |
| 0.1229 | 5.0 | 2675 | 0.8348 | 0.5443 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
shoang/wav2vec2-base-timit-demo-google-colab
|
shoang
| 2022-09-26T16:25:55Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-26T14:27:33Z |
---
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: 0.5218
- Wer: 0.3434
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5634 | 1.0 | 500 | 2.0727 | 1.0096 |
| 0.9357 | 2.01 | 1000 | 0.6623 | 0.5634 |
| 0.4536 | 3.01 | 1500 | 1.4421 | 0.4829 |
| 0.3044 | 4.02 | 2000 | 0.4361 | 0.4363 |
| 0.2369 | 5.02 | 2500 | 0.5098 | 0.4495 |
| 0.1994 | 6.02 | 3000 | 0.4741 | 0.3711 |
| 0.1699 | 7.03 | 3500 | 0.4652 | 0.3898 |
| 0.1499 | 8.03 | 4000 | 0.4151 | 0.3949 |
| 0.1308 | 9.04 | 4500 | 0.4685 | 0.3838 |
| 0.1234 | 10.04 | 5000 | 0.5076 | 0.3794 |
| 0.1055 | 11.04 | 5500 | 0.4492 | 0.3790 |
| 0.0953 | 12.05 | 6000 | 0.4726 | 0.3679 |
| 0.0863 | 13.05 | 6500 | 0.4797 | 0.3717 |
| 0.0816 | 14.06 | 7000 | 0.4725 | 0.3655 |
| 0.0842 | 15.06 | 7500 | 0.5181 | 0.3405 |
| 0.0661 | 16.06 | 8000 | 0.5315 | 0.3510 |
| 0.0593 | 17.07 | 8500 | 0.5024 | 0.3668 |
| 0.0624 | 18.07 | 9000 | 0.5374 | 0.3663 |
| 0.0535 | 19.08 | 9500 | 0.4861 | 0.3517 |
| 0.0524 | 20.08 | 10000 | 0.4812 | 0.3574 |
| 0.0461 | 21.08 | 10500 | 0.4976 | 0.3431 |
| 0.0363 | 22.09 | 11000 | 0.5062 | 0.3476 |
| 0.0351 | 23.09 | 11500 | 0.5094 | 0.3479 |
| 0.0327 | 24.1 | 12000 | 0.5291 | 0.3455 |
| 0.0319 | 25.1 | 12500 | 0.5209 | 0.3460 |
| 0.0268 | 26.1 | 13000 | 0.5173 | 0.3481 |
| 0.0263 | 27.11 | 13500 | 0.5362 | 0.3486 |
| 0.0234 | 28.11 | 14000 | 0.5333 | 0.3444 |
| 0.0237 | 29.12 | 14500 | 0.5218 | 0.3434 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.13.0
|
jamieai/t5-small-finetuned-xsum
|
jamieai
| 2022-09-26T16:04:00Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:eli5",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-26T15:56:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
model-index:
- name: t5-small-finetuned-xsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eli5 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
tner/deberta-v3-large-bc5cdr
|
tner
| 2022-09-26T15:27:41Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/bc5cdr",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-09T23:31:56Z |
---
datasets:
- tner/bc5cdr
metrics:
- f1
- precision
- recall
model-index:
- name: tner/deberta-v3-large-bc5cdr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/bc5cdr
type: tner/bc5cdr
args: tner/bc5cdr
metrics:
- name: F1
type: f1
value: 0.8902493653874869
- name: Precision
type: precision
value: 0.8697724178175452
- name: Recall
type: recall
value: 0.9117137322866755
- name: F1 (macro)
type: f1_macro
value: 0.8863403908610603
- name: Precision (macro)
type: precision_macro
value: 0.8657302393432342
- name: Recall (macro)
type: recall_macro
value: 0.9080747413030301
- name: F1 (entity span)
type: f1_entity_span
value: 0.8929371360310587
- name: Precision (entity span)
type: precision_entity_span
value: 0.8723983660766388
- name: Recall (entity span)
type: recall_entity_span
value: 0.9144663064532572
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/deberta-v3-large-bc5cdr
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/bc5cdr](https://huggingface.co/datasets/tner/bc5cdr) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.8902493653874869
- Precision (micro): 0.8697724178175452
- Recall (micro): 0.9117137322866755
- F1 (macro): 0.8863403908610603
- Precision (macro): 0.8657302393432342
- Recall (macro): 0.9080747413030301
The per-entity breakdown of the F1 score on the test set are below:
- chemical: 0.9298502009499452
- disease: 0.8428305807721753
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.885162383660078, 0.8951239957151518]
- 95%: [0.8838793313408008, 0.8959517574197015]
- F1 (macro):
- 90%: [0.885162383660078, 0.8951239957151518]
- 95%: [0.8838793313408008, 0.8959517574197015]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-bc5cdr/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-bc5cdr/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip
```shell
pip install tner
```
and activate model as below.
```python
from tner import TransformersNER
model = TransformersNER("tner/deberta-v3-large-bc5cdr")
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/bc5cdr']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: microsoft/deberta-v3-large
- crf: True
- max_length: 128
- epoch: 15
- batch_size: 16
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 4
- weight_decay: 1e-07
- lr_warmup_step_ratio: 0.1
- max_grad_norm: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/deberta-v3-large-bc5cdr/raw/main/trainer_config.json).
### Reference
If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
|
tner/deberta-v3-large-bionlp2004
|
tner
| 2022-09-26T15:11:33Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/bionlp2004",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-12T23:58:35Z |
---
datasets:
- tner/bionlp2004
metrics:
- f1
- precision
- recall
model-index:
- name: tner/deberta-v3-large-bionlp2004
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/bionlp2004
type: tner/bionlp2004
args: tner/bionlp2004
metrics:
- name: F1
type: f1
value: 0.758624442267929
- name: Precision
type: precision
value: 0.7174763277068753
- name: Recall
type: recall
value: 0.8047794966520434
- name: F1 (macro)
type: f1_macro
value: 0.7195387988303987
- name: Precision (macro)
type: precision_macro
value: 0.681309505763584
- name: Recall (macro)
type: recall_macro
value: 0.7691804743892025
- name: F1 (entity span)
type: f1_entity_span
value: 0.796539152201121
- name: Precision (entity span)
type: precision_entity_span
value: 0.7533710756562018
- name: Recall (entity span)
type: recall_entity_span
value: 0.8449549757561764
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/deberta-v3-large-bionlp2004
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/bionlp2004](https://huggingface.co/datasets/tner/bionlp2004) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.758624442267929
- Precision (micro): 0.7174763277068753
- Recall (micro): 0.8047794966520434
- F1 (macro): 0.7195387988303987
- Precision (macro): 0.681309505763584
- Recall (macro): 0.7691804743892025
The per-entity breakdown of the F1 score on the test set are below:
- cell_line: 0.6465517241379309
- cell_type: 0.7562483203439935
- dna: 0.7449506810709253
- protein: 0.7757859652283577
- rna: 0.6741573033707865
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.7508836679942893, 0.7667327003308145]
- 95%: [0.7498144548458301, 0.7680807868080707]
- F1 (macro):
- 90%: [0.7508836679942893, 0.7667327003308145]
- 95%: [0.7498144548458301, 0.7680807868080707]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-bionlp2004/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-bionlp2004/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip
```shell
pip install tner
```
and activate model as below.
```python
from tner import TransformersNER
model = TransformersNER("tner/deberta-v3-large-bionlp2004")
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/bionlp2004']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: microsoft/deberta-v3-large
- crf: True
- max_length: 128
- epoch: 15
- batch_size: 16
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 8
- weight_decay: 1e-07
- lr_warmup_step_ratio: 0.1
- max_grad_norm: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/deberta-v3-large-bionlp2004/raw/main/trainer_config.json).
### Reference
If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
|
tner/deberta-v3-large-wnut2017
|
tner
| 2022-09-26T15:10:46Z | 30 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/wnut2017",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-09T23:14:32Z |
---
datasets:
- tner/wnut2017
metrics:
- f1
- precision
- recall
model-index:
- name: tner/deberta-v3-large-wnut2017
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/wnut2017
type: tner/wnut2017
args: tner/wnut2017
metrics:
- name: F1
type: f1
value: 0.5047353760445682
- name: Precision
type: precision
value: 0.63268156424581
- name: Recall
type: recall
value: 0.4198331788693234
- name: F1 (macro)
type: f1_macro
value: 0.4165125500830091
- name: Precision (macro)
type: precision_macro
value: 0.5356144444686111
- name: Recall (macro)
type: recall_macro
value: 0.3573954549633822
- name: F1 (entity span)
type: f1_entity_span
value: 0.6249999999999999
- name: Precision (entity span)
type: precision_entity_span
value: 0.7962697274031564
- name: Recall (entity span)
type: recall_entity_span
value: 0.5143651529193698
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/deberta-v3-large-wnut2017
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/wnut2017](https://huggingface.co/datasets/tner/wnut2017) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.5047353760445682
- Precision (micro): 0.63268156424581
- Recall (micro): 0.4198331788693234
- F1 (macro): 0.4165125500830091
- Precision (macro): 0.5356144444686111
- Recall (macro): 0.3573954549633822
The per-entity breakdown of the F1 score on the test set are below:
- corporation: 0.25477707006369427
- group: 0.34309623430962344
- location: 0.6187050359712232
- person: 0.6721763085399448
- product: 0.18579234972677597
- work_of_art: 0.42452830188679247
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.4752384997212858, 0.5329114690850492]
- 95%: [0.46929053844001617, 0.537282841423422]
- F1 (macro):
- 90%: [0.4752384997212858, 0.5329114690850492]
- 95%: [0.46929053844001617, 0.537282841423422]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-wnut2017/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-wnut2017/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip
```shell
pip install tner
```
and activate model as below.
```python
from tner import TransformersNER
model = TransformersNER("tner/deberta-v3-large-wnut2017")
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/wnut2017']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: microsoft/deberta-v3-large
- crf: False
- max_length: 128
- epoch: 15
- batch_size: 16
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 4
- weight_decay: 1e-07
- lr_warmup_step_ratio: 0.1
- max_grad_norm: 10.0
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/deberta-v3-large-wnut2017/raw/main/trainer_config.json).
### Reference
If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
|
tner/deberta-v3-large-mit-restaurant
|
tner
| 2022-09-26T15:04:38Z | 15 | 2 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/mit_restaurant",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-12T10:41:07Z |
---
datasets:
- tner/mit_restaurant
metrics:
- f1
- precision
- recall
model-index:
- name: tner/deberta-v3-large-mit-restaurant
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/mit_restaurant
type: tner/mit_restaurant
args: tner/mit_restaurant
metrics:
- name: F1
type: f1
value: 0.8158890290037831
- name: Precision
type: precision
value: 0.8105230191042906
- name: Recall
type: recall
value: 0.8213265629958744
- name: F1 (macro)
type: f1_macro
value: 0.8072607717138172
- name: Precision (macro)
type: precision_macro
value: 0.7973293573334044
- name: Recall (macro)
type: recall_macro
value: 0.8183493118743246
- name: F1 (entity span)
type: f1_entity_span
value: 0.8515132408575031
- name: Precision (entity span)
type: precision_entity_span
value: 0.8459129345443157
- name: Recall (entity span)
type: recall_entity_span
value: 0.8571881942240559
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/deberta-v3-large-mit-restaurant
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/mit_restaurant](https://huggingface.co/datasets/tner/mit_restaurant) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.8158890290037831
- Precision (micro): 0.8105230191042906
- Recall (micro): 0.8213265629958744
- F1 (macro): 0.8072607717138172
- Precision (macro): 0.7973293573334044
- Recall (macro): 0.8183493118743246
The per-entity breakdown of the F1 score on the test set are below:
- amenity: 0.7226415094339623
- cuisine: 0.8288119738072967
- dish: 0.8283828382838284
- location: 0.8662969808995686
- money: 0.84
- rating: 0.7990430622009569
- restaurant: 0.8724489795918368
- time: 0.7004608294930875
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.8036180555961564, 0.8281173227233776]
- 95%: [0.8011397826491581, 0.8307029010155984]
- F1 (macro):
- 90%: [0.8036180555961564, 0.8281173227233776]
- 95%: [0.8011397826491581, 0.8307029010155984]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-mit-restaurant/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-mit-restaurant/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip
```shell
pip install tner
```
and activate model as below.
```python
from tner import TransformersNER
model = TransformersNER("tner/deberta-v3-large-mit-restaurant")
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/mit_restaurant']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: microsoft/deberta-v3-large
- crf: True
- max_length: 128
- epoch: 15
- batch_size: 16
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 4
- weight_decay: 1e-07
- lr_warmup_step_ratio: 0.1
- max_grad_norm: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/deberta-v3-large-mit-restaurant/raw/main/trainer_config.json).
### Reference
If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
|
tner/deberta-v3-large-tweebank-ner
|
tner
| 2022-09-26T14:39:17Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/tweebank_ner",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-10T10:07:10Z |
---
datasets:
- tner/tweebank_ner
metrics:
- f1
- precision
- recall
model-index:
- name: tner/deberta-v3-large-tweebank-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/tweebank_ner
type: tner/tweebank_ner
args: tner/tweebank_ner
metrics:
- name: F1
type: f1
value: 0.7253474520185308
- name: Precision
type: precision
value: 0.7201051248357424
- name: Recall
type: recall
value: 0.7306666666666667
- name: F1 (macro)
type: f1_macro
value: 0.701874697798745
- name: Precision (macro)
type: precision_macro
value: 0.7043005470796733
- name: Recall (macro)
type: recall_macro
value: 0.706915721861374
- name: F1 (entity span)
type: f1_entity_span
value: 0.8178343949044585
- name: Precision (entity span)
type: precision_entity_span
value: 0.7829268292682927
- name: Recall (entity span)
type: recall_entity_span
value: 0.856
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/deberta-v3-large-tweebank-ner
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/tweebank_ner](https://huggingface.co/datasets/tner/tweebank_ner) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.7253474520185308
- Precision (micro): 0.7201051248357424
- Recall (micro): 0.7306666666666667
- F1 (macro): 0.701874697798745
- Precision (macro): 0.7043005470796733
- Recall (macro): 0.706915721861374
The per-entity breakdown of the F1 score on the test set are below:
- location: 0.7289719626168224
- organization: 0.7040816326530612
- other: 0.5182926829268293
- person: 0.856152512998267
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.6978100031831928, 0.7529703029130037]
- 95%: [0.691700704571692, 0.7582901338971108]
- F1 (macro):
- 90%: [0.6978100031831928, 0.7529703029130037]
- 95%: [0.691700704571692, 0.7582901338971108]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-tweebank-ner/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-tweebank-ner/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip
```shell
pip install tner
```
and activate model as below.
```python
from tner import TransformersNER
model = TransformersNER("tner/deberta-v3-large-tweebank-ner")
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/tweebank_ner']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: microsoft/deberta-v3-large
- crf: True
- max_length: 128
- epoch: 15
- batch_size: 16
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 4
- weight_decay: 1e-07
- lr_warmup_step_ratio: 0.1
- max_grad_norm: 10.0
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/deberta-v3-large-tweebank-ner/raw/main/trainer_config.json).
### Reference
If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
|
tner/deberta-v3-large-fin
|
tner
| 2022-09-26T14:28:32Z | 8 | 2 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/fin",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-12T22:13:20Z |
---
datasets:
- tner/fin
metrics:
- f1
- precision
- recall
model-index:
- name: tner/deberta-v3-large-fin
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/fin
type: tner/fin
args: tner/fin
metrics:
- name: F1
type: f1
value: 0.7060755336617406
- name: Precision
type: precision
value: 0.738831615120275
- name: Recall
type: recall
value: 0.6761006289308176
- name: F1 (macro)
type: f1_macro
value: 0.45092058848834204
- name: Precision (macro)
type: precision_macro
value: 0.45426465258085835
- name: Recall (macro)
type: recall_macro
value: 0.45582773707773705
- name: F1 (entity span)
type: f1_entity_span
value: 0.7293729372937293
- name: Precision (entity span)
type: precision_entity_span
value: 0.7594501718213058
- name: Recall (entity span)
type: recall_entity_span
value: 0.7015873015873015
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/deberta-v3-large-fin
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/fin](https://huggingface.co/datasets/tner/fin) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.7060755336617406
- Precision (micro): 0.738831615120275
- Recall (micro): 0.6761006289308176
- F1 (macro): 0.45092058848834204
- Precision (macro): 0.45426465258085835
- Recall (macro): 0.45582773707773705
The per-entity breakdown of the F1 score on the test set are below:
- location: 0.4000000000000001
- organization: 0.5762711864406779
- other: 0.0
- person: 0.8274111675126904
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.6370316240330781, 0.7718233002182738]
- 95%: [0.6236274300363168, 0.7857205513784461]
- F1 (macro):
- 90%: [0.6370316240330781, 0.7718233002182738]
- 95%: [0.6236274300363168, 0.7857205513784461]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-fin/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-fin/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip
```shell
pip install tner
```
and activate model as below.
```python
from tner import TransformersNER
model = TransformersNER("tner/deberta-v3-large-fin")
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/fin']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: microsoft/deberta-v3-large
- crf: True
- max_length: 128
- epoch: 15
- batch_size: 16
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 4
- weight_decay: None
- lr_warmup_step_ratio: 0.1
- max_grad_norm: 10.0
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/deberta-v3-large-fin/raw/main/trainer_config.json).
### Reference
If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
|
tner/roberta-large-ttc
|
tner
| 2022-09-26T14:25:57Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/ttc",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-12T10:49:56Z |
---
datasets:
- tner/ttc
metrics:
- f1
- precision
- recall
model-index:
- name: tner/roberta-large-ttc
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/ttc
type: tner/ttc
args: tner/ttc
metrics:
- name: F1
type: f1
value: 0.8314534321624235
- name: Precision
type: precision
value: 0.8269230769230769
- name: Recall
type: recall
value: 0.8360337005832793
- name: F1 (macro)
type: f1_macro
value: 0.8317396497007042
- name: Precision (macro)
type: precision_macro
value: 0.8296690551538254
- name: Recall (macro)
type: recall_macro
value: 0.8340850231639706
- name: F1 (entity span)
type: f1_entity_span
value: 0.8739929100870126
- name: Precision (entity span)
type: precision_entity_span
value: 0.8692307692307693
- name: Recall (entity span)
type: recall_entity_span
value: 0.8788075178224238
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/roberta-large-ttc
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/ttc](https://huggingface.co/datasets/tner/ttc) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.8314534321624235
- Precision (micro): 0.8269230769230769
- Recall (micro): 0.8360337005832793
- F1 (macro): 0.8317396497007042
- Precision (macro): 0.8296690551538254
- Recall (macro): 0.8340850231639706
The per-entity breakdown of the F1 score on the test set are below:
- location: 0.7817403708987161
- organization: 0.7737656595431097
- person: 0.939712918660287
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.8153670265512099, 0.8476331336073506]
- 95%: [0.8126974643551524, 0.8505459585794019]
- F1 (macro):
- 90%: [0.8153670265512099, 0.8476331336073506]
- 95%: [0.8126974643551524, 0.8505459585794019]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-ttc/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/roberta-large-ttc/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip
```shell
pip install tner
```
and activate model as below.
```python
from tner import TransformersNER
model = TransformersNER("tner/roberta-large-ttc")
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/ttc']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: roberta-large
- crf: True
- max_length: 128
- epoch: 16
- batch_size: 64
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 2
- weight_decay: None
- lr_warmup_step_ratio: 0.1
- max_grad_norm: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-large-ttc/raw/main/trainer_config.json).
### Reference
If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
|
tner/roberta-large-mit-restaurant
|
tner
| 2022-09-26T14:24:20Z | 181 | 3 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/mit_restaurant",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-12T00:20:40Z |
---
datasets:
- tner/mit_restaurant
metrics:
- f1
- precision
- recall
model-index:
- name: tner/roberta-large-mit-restaurant
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/mit_restaurant
type: tner/mit_restaurant
args: tner/mit_restaurant
metrics:
- name: F1
type: f1
value: 0.8164676304211189
- name: Precision
type: precision
value: 0.8085901027077498
- name: Recall
type: recall
value: 0.8245001586797842
- name: F1 (macro)
type: f1_macro
value: 0.8081522050756316
- name: Precision (macro)
type: precision_macro
value: 0.7974927131040113
- name: Recall (macro)
type: recall_macro
value: 0.8199029986502094
- name: F1 (entity span)
type: f1_entity_span
value: 0.8557510999371464
- name: Precision (entity span)
type: precision_entity_span
value: 0.8474945533769063
- name: Recall (entity span)
type: recall_entity_span
value: 0.8641701047286575
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/roberta-large-mit-restaurant
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/mit_restaurant](https://huggingface.co/datasets/tner/mit_restaurant) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.8164676304211189
- Precision (micro): 0.8085901027077498
- Recall (micro): 0.8245001586797842
- F1 (macro): 0.8081522050756316
- Precision (macro): 0.7974927131040113
- Recall (macro): 0.8199029986502094
The per-entity breakdown of the F1 score on the test set are below:
- amenity: 0.7140221402214022
- cuisine: 0.8558052434456929
- dish: 0.829103214890017
- location: 0.8611793611793611
- money: 0.8579710144927537
- rating: 0.8
- restaurant: 0.8713375796178344
- time: 0.6757990867579908
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.8050039870241192, 0.8289531287254172]
- 95%: [0.8030897272187587, 0.8312785732455824]
- F1 (macro):
- 90%: [0.8050039870241192, 0.8289531287254172]
- 95%: [0.8030897272187587, 0.8312785732455824]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-mit-restaurant/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/roberta-large-mit-restaurant/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip
```shell
pip install tner
```
and activate model as below.
```python
from tner import TransformersNER
model = TransformersNER("tner/roberta-large-mit-restaurant")
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/mit_restaurant']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: roberta-large
- crf: True
- max_length: 128
- epoch: 15
- batch_size: 64
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 1
- weight_decay: None
- lr_warmup_step_ratio: 0.1
- max_grad_norm: 10.0
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-large-mit-restaurant/raw/main/trainer_config.json).
### Reference
If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
|
tner/roberta-large-btc
|
tner
| 2022-09-26T14:22:51Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/btc",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-10T00:10:29Z |
---
datasets:
- tner/btc
metrics:
- f1
- precision
- recall
model-index:
- name: tner/roberta-large-btc
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/btc
type: tner/btc
args: tner/btc
metrics:
- name: F1
type: f1
value: 0.8367557645979121
- name: Precision
type: precision
value: 0.8401290025339784
- name: Recall
type: recall
value: 0.8334095063985375
- name: F1 (macro)
type: f1_macro
value: 0.7830389304099722
- name: Precision (macro)
type: precision_macro
value: 0.7911560677795398
- name: Recall (macro)
type: recall_macro
value: 0.7756024849498971
- name: F1 (entity span)
type: f1_entity_span
value: 0.9113227027647126
- name: Precision (entity span)
type: precision_entity_span
value: 0.9149965445749827
- name: Recall (entity span)
type: recall_entity_span
value: 0.9076782449725777
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/roberta-large-btc
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/btc](https://huggingface.co/datasets/tner/btc) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.8367557645979121
- Precision (micro): 0.8401290025339784
- Recall (micro): 0.8334095063985375
- F1 (macro): 0.7830389304099722
- Precision (macro): 0.7911560677795398
- Recall (macro): 0.7756024849498971
The per-entity breakdown of the F1 score on the test set are below:
- location: 0.736756316218419
- organization: 0.6927985414767548
- person: 0.9195619335347431
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.8263755823738717, 0.8472678708881698]
- 95%: [0.8238362631404713, 0.8498613485265176]
- F1 (macro):
- 90%: [0.8263755823738717, 0.8472678708881698]
- 95%: [0.8238362631404713, 0.8498613485265176]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-btc/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/roberta-large-btc/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip
```shell
pip install tner
```
and activate model as below.
```python
from tner import TransformersNER
model = TransformersNER("tner/roberta-large-btc")
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/btc']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: roberta-large
- crf: True
- max_length: 128
- epoch: 15
- batch_size: 64
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 2
- weight_decay: 1e-07
- lr_warmup_step_ratio: 0.1
- max_grad_norm: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-large-btc/raw/main/trainer_config.json).
### Reference
If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
|
tner/roberta-large-tweebank-ner
|
tner
| 2022-09-26T14:21:19Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweebank_ner",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-10T10:03:35Z |
---
datasets:
- tner/tweebank_ner
metrics:
- f1
- precision
- recall
model-index:
- name: tner/roberta-large-tweebank-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/tweebank_ner
type: tner/tweebank_ner
args: tner/tweebank_ner
metrics:
- name: F1
type: f1
value: 0.7439490445859872
- name: Precision
type: precision
value: 0.7121951219512195
- name: Recall
type: recall
value: 0.7786666666666666
- name: F1 (macro)
type: f1_macro
value: 0.7354319457314183
- name: Precision (macro)
type: precision_macro
value: 0.712928566565599
- name: Recall (macro)
type: recall_macro
value: 0.7620465365030582
- name: F1 (entity span)
type: f1_entity_span
value: 0.8178343949044585
- name: Precision (entity span)
type: precision_entity_span
value: 0.7829268292682927
- name: Recall (entity span)
type: recall_entity_span
value: 0.856
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/roberta-large-tweebank-ner
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/tweebank_ner](https://huggingface.co/datasets/tner/tweebank_ner) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.7439490445859872
- Precision (micro): 0.7121951219512195
- Recall (micro): 0.7786666666666666
- F1 (macro): 0.7354319457314183
- Precision (macro): 0.712928566565599
- Recall (macro): 0.7620465365030582
The per-entity breakdown of the F1 score on the test set are below:
- location: 0.7782805429864253
- organization: 0.7377049180327869
- other: 0.5520581113801453
- person: 0.8736842105263157
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.7156413818791614, 0.771698046498159]
- 95%: [0.7063867669973017, 0.7763088810979543]
- F1 (macro):
- 90%: [0.7156413818791614, 0.771698046498159]
- 95%: [0.7063867669973017, 0.7763088810979543]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-tweebank-ner/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/roberta-large-tweebank-ner/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip
```shell
pip install tner
```
and activate model as below.
```python
from tner import TransformersNER
model = TransformersNER("tner/roberta-large-tweebank-ner")
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/tweebank_ner']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: roberta-large
- crf: True
- max_length: 128
- epoch: 15
- batch_size: 64
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 1
- weight_decay: None
- lr_warmup_step_ratio: 0.1
- max_grad_norm: 10.0
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-large-tweebank-ner/raw/main/trainer_config.json).
### Reference
If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
|
tner/bertweet-large-wnut2017
|
tner
| 2022-09-26T14:18:26Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/wnut2017",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-09T23:25:24Z |
---
datasets:
- tner/wnut2017
metrics:
- f1
- precision
- recall
model-index:
- name: tner/bertweet-large-wnut2017
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/wnut2017
type: tner/wnut2017
args: tner/wnut2017
metrics:
- name: F1
type: f1
value: 0.5302273987798114
- name: Precision
type: precision
value: 0.6602209944751382
- name: Recall
type: recall
value: 0.44300278035217794
- name: F1 (macro)
type: f1_macro
value: 0.4643459997680019
- name: Precision (macro)
type: precision_macro
value: 0.5792841925426832
- name: Recall (macro)
type: recall_macro
value: 0.3973128655628379
- name: F1 (entity span)
type: f1_entity_span
value: 0.6142697881828317
- name: Precision (entity span)
type: precision_entity_span
value: 0.7706293706293706
- name: Recall (entity span)
type: recall_entity_span
value: 0.5106580166821131
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/bertweet-large-wnut2017
This model is a fine-tuned version of [vinai/bertweet-large](https://huggingface.co/vinai/bertweet-large) on the
[tner/wnut2017](https://huggingface.co/datasets/tner/wnut2017) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.5302273987798114
- Precision (micro): 0.6602209944751382
- Recall (micro): 0.44300278035217794
- F1 (macro): 0.4643459997680019
- Precision (macro): 0.5792841925426832
- Recall (macro): 0.3973128655628379
The per-entity breakdown of the F1 score on the test set are below:
- corporation: 0.3902439024390244
- group: 0.37130801687763715
- location: 0.6595744680851063
- person: 0.65474552957359
- product: 0.2857142857142857
- work_of_art: 0.4244897959183674
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.5002577319587629, 0.5587481638299118]
- 95%: [0.4947163587619384, 0.5629013150503995]
- F1 (macro):
- 90%: [0.5002577319587629, 0.5587481638299118]
- 95%: [0.4947163587619384, 0.5629013150503995]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/bertweet-large-wnut2017/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/bertweet-large-wnut2017/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip
```shell
pip install tner
```
and activate model as below.
```python
from tner import TransformersNER
model = TransformersNER("tner/bertweet-large-wnut2017")
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/wnut2017']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: vinai/bertweet-large
- crf: False
- max_length: 128
- epoch: 15
- batch_size: 16
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 4
- weight_decay: 1e-07
- lr_warmup_step_ratio: 0.1
- max_grad_norm: 10.0
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/bertweet-large-wnut2017/raw/main/trainer_config.json).
### Reference
If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
|
tner/roberta-large-mit-movie-trivia
|
tner
| 2022-09-26T14:15:35Z | 17 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/mit_movie_trivia",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-12T10:37:29Z |
---
datasets:
- tner/mit_movie_trivia
metrics:
- f1
- precision
- recall
model-index:
- name: tner/roberta-large-mit-movie-trivia
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/mit_movie_trivia
type: tner/mit_movie_trivia
args: tner/mit_movie_trivia
metrics:
- name: F1
type: f1
value: 0.7284025200655909
- name: Precision
type: precision
value: 0.7151330283002881
- name: Recall
type: recall
value: 0.7421737601125572
- name: F1 (macro)
type: f1_macro
value: 0.6502255723148889
- name: Precision (macro)
type: precision_macro
value: 0.6457158565124362
- name: Recall (macro)
type: recall_macro
value: 0.6578012664661943
- name: F1 (entity span)
type: f1_entity_span
value: 0.749525289142068
- name: Precision (entity span)
type: precision_entity_span
value: 0.7359322033898306
- name: Recall (entity span)
type: recall_entity_span
value: 0.7636299683432993
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/roberta-large-mit-movie-trivia
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/mit_movie_trivia](https://huggingface.co/datasets/tner/mit_movie_trivia) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.7284025200655909
- Precision (micro): 0.7151330283002881
- Recall (micro): 0.7421737601125572
- F1 (macro): 0.6502255723148889
- Precision (macro): 0.6457158565124362
- Recall (macro): 0.6578012664661943
The per-entity breakdown of the F1 score on the test set are below:
- actor: 0.9557453416149068
- award: 0.41726618705035967
- character_name: 0.7467105263157895
- date: 0.9668674698795181
- director: 0.9148936170212766
- genre: 0.7277079593058049
- opinion: 0.43478260869565216
- origin: 0.28846153846153844
- plot: 0.5132575757575758
- quote: 0.8387096774193549
- relationship: 0.5697329376854599
- soundtrack: 0.42857142857142855
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.718570586211627, 0.7387631655667131]
- 95%: [0.7170135350354089, 0.7412372838115527]
- F1 (macro):
- 90%: [0.718570586211627, 0.7387631655667131]
- 95%: [0.7170135350354089, 0.7412372838115527]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-mit-movie-trivia/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/roberta-large-mit-movie-trivia/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip
```shell
pip install tner
```
and activate model as below.
```python
from tner import TransformersNER
model = TransformersNER("tner/roberta-large-mit-movie-trivia")
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/mit_movie_trivia']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: roberta-large
- crf: True
- max_length: 128
- epoch: 15
- batch_size: 64
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 1
- weight_decay: 1e-07
- lr_warmup_step_ratio: 0.1
- max_grad_norm: 10.0
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-large-mit-movie-trivia/raw/main/trainer_config.json).
### Reference
If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
|
tner/roberta-large-bc5cdr
|
tner
| 2022-09-26T14:13:58Z | 12 | 2 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/bc5cdr",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-09T23:32:35Z |
---
datasets:
- tner/bc5cdr
metrics:
- f1
- precision
- recall
model-index:
- name: tner/roberta-large-bc5cdr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/bc5cdr
type: tner/bc5cdr
args: tner/bc5cdr
metrics:
- name: F1
type: f1
value: 0.8840696387239609
- name: Precision
type: precision
value: 0.8728266269249876
- name: Recall
type: recall
value: 0.8956060760526048
- name: F1 (macro)
type: f1_macro
value: 0.8797360472482783
- name: Precision (macro)
type: precision_macro
value: 0.8684274142690976
- name: Recall (macro)
type: recall_macro
value: 0.8913672531528037
- name: F1 (entity span)
type: f1_entity_span
value: 0.886283586595552
- name: Precision (entity span)
type: precision_entity_span
value: 0.8750124192747144
- name: Recall (entity span)
type: recall_entity_span
value: 0.8978489142624121
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/roberta-large-bc5cdr
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/bc5cdr](https://huggingface.co/datasets/tner/bc5cdr) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.8840696387239609
- Precision (micro): 0.8728266269249876
- Recall (micro): 0.8956060760526048
- F1 (macro): 0.8797360472482783
- Precision (macro): 0.8684274142690976
- Recall (macro): 0.8913672531528037
The per-entity breakdown of the F1 score on the test set are below:
- chemical: 0.9256943167187788
- disease: 0.8337777777777777
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.878869501707946, 0.8890795634554179]
- 95%: [0.8776790106527211, 0.8897422640465147]
- F1 (macro):
- 90%: [0.878869501707946, 0.8890795634554179]
- 95%: [0.8776790106527211, 0.8897422640465147]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-bc5cdr/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/roberta-large-bc5cdr/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip
```shell
pip install tner
```
and activate model as below.
```python
from tner import TransformersNER
model = TransformersNER("tner/roberta-large-bc5cdr")
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/bc5cdr']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: roberta-large
- crf: True
- max_length: 128
- epoch: 15
- batch_size: 64
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 1
- weight_decay: None
- lr_warmup_step_ratio: 0.1
- max_grad_norm: 10.0
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-large-bc5cdr/raw/main/trainer_config.json).
### Reference
If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
|
tner/roberta-large-ontonotes5
|
tner
| 2022-09-26T14:12:05Z | 30,400 | 16 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/ontonotes5",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-12T10:33:41Z |
---
datasets:
- tner/ontonotes5
metrics:
- f1
- precision
- recall
model-index:
- name: tner/roberta-large-ontonotes5
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/ontonotes5
type: tner/ontonotes5
args: tner/ontonotes5
metrics:
- name: F1
type: f1
value: 0.908632361399938
- name: Precision
type: precision
value: 0.905148095909732
- name: Recall
type: recall
value: 0.9121435551212579
- name: F1 (macro)
type: f1_macro
value: 0.8265477704565624
- name: Precision (macro)
type: precision_macro
value: 0.8170668848546687
- name: Recall (macro)
type: recall_macro
value: 0.8387672780349001
- name: F1 (entity span)
type: f1_entity_span
value: 0.9284544931640193
- name: Precision (entity span)
type: precision_entity_span
value: 0.9248942172073342
- name: Recall (entity span)
type: recall_entity_span
value: 0.9320422848005685
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/roberta-large-ontonotes5
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/ontonotes5](https://huggingface.co/datasets/tner/ontonotes5) dataset.
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:
- F1 (micro): 0.908632361399938
- Precision (micro): 0.905148095909732
- Recall (micro): 0.9121435551212579
- F1 (macro): 0.8265477704565624
- Precision (macro): 0.8170668848546687
- Recall (macro): 0.8387672780349001
The per-entity breakdown of the F1 score on the test set are below:
- cardinal_number: 0.8605277329025309
- date: 0.872996300863132
- event: 0.7424242424242424
- facility: 0.7732342007434945
- geopolitical_area: 0.9687148323205043
- group: 0.9470588235294117
- language: 0.7499999999999999
- law: 0.6666666666666666
- location: 0.7593582887700535
- money: 0.901098901098901
- ordinal_number: 0.85785536159601
- organization: 0.9227360841872057
- percent: 0.9171428571428571
- person: 0.9556004036326943
- product: 0.7857142857142858
- quantity: 0.7945205479452055
- time: 0.6870588235294116
- work_of_art: 0.7151515151515151
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.9039454247544766, 0.9128956119702822]
- 95%: [0.9030263216115454, 0.9138350859566045]
- F1 (macro):
- 90%: [0.9039454247544766, 0.9128956119702822]
- 95%: [0.9030263216115454, 0.9138350859566045]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-ontonotes5/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/roberta-large-ontonotes5/raw/main/eval/metric_span.json).
### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip
```shell
pip install tner
```
and activate model as below.
```python
from tner import TransformersNER
model = TransformersNER("tner/roberta-large-ontonotes5")
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/ontonotes5']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: roberta-large
- crf: True
- max_length: 128
- epoch: 15
- batch_size: 64
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 1
- weight_decay: None
- lr_warmup_step_ratio: 0.1
- max_grad_norm: 10.0
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-large-ontonotes5/raw/main/trainer_config.json).
### Reference
If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-camacho-collados-2021-ner,
title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
author = "Ushio, Asahi and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.7",
doi = "10.18653/v1/2021.eacl-demos.7",
pages = "53--62",
abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}
```
|
nvidia/groupvit-gcc-yfcc
|
nvidia
| 2022-09-26T13:54:38Z | 2,514 | 6 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"groupvit",
"feature-extraction",
"vision",
"arxiv:2202.11094",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-06-21T08:48:32Z |
---
tags:
- vision
---
# Model Card: GroupViT
This checkpoint is uploaded by Jiarui Xu.
## Model Details
The GroupViT model was proposed in [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
Inspired by [CLIP](clip), GroupViT is a vision-language model that can perform zero-shot semantic segmentation on any given vocabulary categories.
### Model Date
June 2022
### Abstract
Grouping and recognition are important components of visual scene understanding, e.g., for object detection and semantic segmentation. With end-to-end deep learning systems, grouping of image regions usually happens implicitly via top-down supervision from pixel-level recognition labels. Instead, in this paper, we propose to bring back the grouping mechanism into deep networks, which allows semantic segments to emerge automatically with only text supervision. We propose a hierarchical Grouping Vision Transformer (GroupViT), which goes beyond the regular grid structure representation and learns to group image regions into progressively larger arbitrary-shaped segments. We train GroupViT jointly with a text encoder on a large-scale image-text dataset via contrastive losses. With only text supervision and without any pixel-level annotations, GroupViT learns to group together semantic regions and successfully transfers to the task of semantic segmentation in a zero-shot manner, i.e., without any further fine-tuning. It achieves a zero-shot accuracy of 52.3% mIoU on the PASCAL VOC 2012 and 22.4% mIoU on PASCAL Context datasets, and performs competitively to state-of-the-art transfer-learning methods requiring greater levels of supervision.
### Documents
- [GroupViT Paper](https://arxiv.org/abs/2202.11094)
### Use with Transformers
```python
from PIL import Image
import requests
from transformers import AutoProcessor, GroupViTModel
model = GroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
```
## Data
The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users.
For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/groupvit.html#).
### BibTeX entry and citation info
```bibtex
@article{xu2022groupvit,
author = {Xu, Jiarui and De Mello, Shalini and Liu, Sifei and Byeon, Wonmin and Breuel, Thomas and Kautz, Jan and Wang, Xiaolong},
title = {GroupViT: Semantic Segmentation Emerges from Text Supervision},
journal = {arXiv preprint arXiv:2202.11094},
year = {2022},
}
```
|
DravenTay/finetuning-sentiment-model-3000-samples
|
DravenTay
| 2022-09-26T13:14:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"opt",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:other",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-25T15:42:11Z |
---
license: other
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.92
- name: F1
type: f1
value: 0.9205298013245033
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [Tianyi98/opt-350m-finetuned-cola](https://huggingface.co/Tianyi98/opt-350m-finetuned-cola) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4133
- Accuracy: 0.92
- F1: 0.9205
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.22.1
- Pytorch 1.10.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
wangwangw/123
|
wangwangw
| 2022-09-26T12:26:00Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-09-26T12:21:49Z |
---
title: Anime Remove Background
emoji: 🪄🖼️
colorFrom: indigo
colorTo: pink
sdk: gradio
sdk_version: 3.1.4
app_file: app.py
pinned: false
license: apache-2.0
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
rolandwu/ddpm-butterflies-128
|
rolandwu
| 2022-09-26T11:48:16Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-09-26T08:45:10Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/rolandwu/ddpm-butterflies-128/tensorboard?#scalars)
|
anaasanin/layoutlmv3-finetuned-wildreceipt
|
anaasanin
| 2022-09-26T11:06:35Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:wildreceipt",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-26T09:13:35Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- wildreceipt
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-wildreceipt
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wildreceipt
type: wildreceipt
config: WildReceipt
split: train
args: WildReceipt
metrics:
- name: Precision
type: precision
value: 0.874880087707277
- name: Recall
type: recall
value: 0.878491812302188
- name: F1
type: f1
value: 0.8766822301565504
- name: Accuracy
type: accuracy
value: 0.9253043764396183
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv3-finetuned-wildreceipt
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the wildreceipt dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3111
- Precision: 0.8749
- Recall: 0.8785
- F1: 0.8767
- Accuracy: 0.9253
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.32 | 100 | 1.3060 | 0.6792 | 0.3615 | 0.4718 | 0.6966 |
| No log | 0.63 | 200 | 0.8842 | 0.6524 | 0.5193 | 0.5783 | 0.7737 |
| No log | 0.95 | 300 | 0.6795 | 0.7338 | 0.6772 | 0.7044 | 0.8336 |
| No log | 1.26 | 400 | 0.5604 | 0.7719 | 0.7390 | 0.7551 | 0.8629 |
| 1.0319 | 1.58 | 500 | 0.4862 | 0.7819 | 0.7618 | 0.7717 | 0.8730 |
| 1.0319 | 1.89 | 600 | 0.4365 | 0.7852 | 0.7807 | 0.7829 | 0.8795 |
| 1.0319 | 2.21 | 700 | 0.4182 | 0.8162 | 0.8016 | 0.8088 | 0.8897 |
| 1.0319 | 2.52 | 800 | 0.3886 | 0.8126 | 0.8196 | 0.8161 | 0.8936 |
| 1.0319 | 2.84 | 900 | 0.3637 | 0.8260 | 0.8347 | 0.8303 | 0.9004 |
| 0.4162 | 3.15 | 1000 | 0.3482 | 0.8532 | 0.8243 | 0.8385 | 0.9062 |
| 0.4162 | 3.47 | 1100 | 0.3474 | 0.8573 | 0.8248 | 0.8407 | 0.9042 |
| 0.4162 | 3.79 | 1200 | 0.3325 | 0.8408 | 0.8435 | 0.8421 | 0.9086 |
| 0.4162 | 4.1 | 1300 | 0.3262 | 0.8468 | 0.8467 | 0.8468 | 0.9095 |
| 0.4162 | 4.42 | 1400 | 0.3237 | 0.8511 | 0.8442 | 0.8477 | 0.9100 |
| 0.2764 | 4.73 | 1500 | 0.3156 | 0.8563 | 0.8456 | 0.8509 | 0.9122 |
| 0.2764 | 5.05 | 1600 | 0.3032 | 0.8558 | 0.8566 | 0.8562 | 0.9153 |
| 0.2764 | 5.36 | 1700 | 0.3120 | 0.8604 | 0.8457 | 0.8530 | 0.9142 |
| 0.2764 | 5.68 | 1800 | 0.2976 | 0.8608 | 0.8592 | 0.8600 | 0.9178 |
| 0.2764 | 5.99 | 1900 | 0.3056 | 0.8551 | 0.8676 | 0.8613 | 0.9171 |
| 0.212 | 6.31 | 2000 | 0.3191 | 0.8528 | 0.8599 | 0.8563 | 0.9147 |
| 0.212 | 6.62 | 2100 | 0.3051 | 0.8653 | 0.8635 | 0.8644 | 0.9186 |
| 0.212 | 6.94 | 2200 | 0.3022 | 0.8681 | 0.8632 | 0.8657 | 0.9208 |
| 0.212 | 7.26 | 2300 | 0.3101 | 0.8605 | 0.8643 | 0.8624 | 0.9178 |
| 0.212 | 7.57 | 2400 | 0.3100 | 0.8553 | 0.8693 | 0.8622 | 0.9163 |
| 0.1725 | 7.89 | 2500 | 0.3012 | 0.8685 | 0.8723 | 0.8704 | 0.9221 |
| 0.1725 | 8.2 | 2600 | 0.3135 | 0.8627 | 0.8756 | 0.8691 | 0.9187 |
| 0.1725 | 8.52 | 2700 | 0.3115 | 0.8768 | 0.8671 | 0.8719 | 0.9229 |
| 0.1725 | 8.83 | 2800 | 0.3044 | 0.8757 | 0.8708 | 0.8732 | 0.9231 |
| 0.1725 | 9.15 | 2900 | 0.3042 | 0.8698 | 0.8658 | 0.8678 | 0.9212 |
| 0.142 | 9.46 | 3000 | 0.3095 | 0.8677 | 0.8702 | 0.8690 | 0.9207 |
| 0.142 | 9.78 | 3100 | 0.3119 | 0.8686 | 0.8762 | 0.8724 | 0.9229 |
| 0.142 | 10.09 | 3200 | 0.3078 | 0.8713 | 0.8774 | 0.8743 | 0.9238 |
| 0.142 | 10.41 | 3300 | 0.3123 | 0.8711 | 0.8753 | 0.8732 | 0.9238 |
| 0.142 | 10.73 | 3400 | 0.3098 | 0.8688 | 0.8774 | 0.8731 | 0.9232 |
| 0.1238 | 11.04 | 3500 | 0.3120 | 0.8737 | 0.8770 | 0.8754 | 0.9247 |
| 0.1238 | 11.36 | 3600 | 0.3124 | 0.8760 | 0.8768 | 0.8764 | 0.9251 |
| 0.1238 | 11.67 | 3700 | 0.3101 | 0.8770 | 0.8759 | 0.8764 | 0.9254 |
| 0.1238 | 11.99 | 3800 | 0.3103 | 0.8767 | 0.8774 | 0.8770 | 0.9255 |
| 0.1238 | 12.3 | 3900 | 0.3122 | 0.8740 | 0.8788 | 0.8764 | 0.9251 |
| 0.1096 | 12.62 | 4000 | 0.3111 | 0.8749 | 0.8785 | 0.8767 | 0.9253 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.13.0
|
fxmarty/distilbert-base-uncased-finetuned-sst-2-english-int8-static-dedicated-qdq-everywhere
|
fxmarty
| 2022-09-26T10:52:18Z | 3 | 0 |
transformers
|
[
"transformers",
"onnx",
"distilbert",
"text-classification",
"dataset:sst2",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-26T10:27:48Z |
---
license: apache-2.0
datasets:
- sst2
- glue
---
This model is a fork of https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english , quantized using static Post-Training Quantization (PTQ) with ONNX Runtime and 🤗 Optimum library.
It achieves 0.896 accuracy on the validation set.
This model uses the ONNX Runtime static quantization configurations `qdq_add_pair_to_weight=True` and `qdq_dedicated_pair=True`, so that **weights are stored in fp32**, and full Quantize + Dequantize nodes are inserted for the weights, compared to the default where weights are stored in int8 and only a Dequantize node is inserted for weights. Moreover, here QDQ pairs have a single output. For more reference, see the documentation: https://github.com/microsoft/onnxruntime/blob/ade0d291749144e1962884a9cfa736d4e1e80ff8/onnxruntime/python/tools/quantization/quantize.py#L432-L441
This is useful to later load a static quantized model in TensorRT.
To load this model:
```python
from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained("fxmarty/distilbert-base-uncased-finetuned-sst-2-english-int8-static-dedicated-qdq-everywhere")
```
### Weights stored as int8, only DequantizeLinear nodes (model here: https://huggingface.co/fxmarty/distilbert-base-uncased-finetuned-sst-2-english-int8-static)
### Weights stored as fp32, only QuantizeLinear + DequantizeLinear nodes (this model)
|
fxmarty/distilbert-base-uncased-finetuned-sst-2-english-int8-static
|
fxmarty
| 2022-09-26T09:00:58Z | 5 | 0 |
transformers
|
[
"transformers",
"onnx",
"distilbert",
"text-classification",
"dataset:sst2",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-26T08:51:58Z |
---
license: apache-2.0
datasets:
- sst2
- glue
---
This model is a fork of https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english , quantized using static Post-Training Quantization (PTQ) with ONNX Runtime and 🤗 Optimum library.
It achieves 0.894 accuracy on the validation set.
To load this model:
```python
from optimum.onnxruntime import ORTModelForSequenceClassification
model = ORTModelForSequenceClassification.from_pretrained("fxmarty/distilbert-base-uncased-finetuned-sst-2-english-int8-static")
```
|
microsoft/deberta-large
|
microsoft
| 2022-09-26T08:50:58Z | 64,445 | 14 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"deberta",
"deberta-v1",
"fill-mask",
"en",
"arxiv:2006.03654",
"license:mit",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- deberta-v1
- fill-mask
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.
#### Fine-tuning on NLU tasks
We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.
| Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m/mm | SST-2 | QNLI | CoLA | RTE | MRPC | QQP |STS-B |
|---------------------------|-----------|-----------|-------------|-------|------|------|--------|-------|-------|------|
| | F1/EM | F1/EM | Acc | Acc | Acc | MCC | Acc |Acc/F1 |Acc/F1 |P/S |
| BERT-Large | 90.9/84.1 | 81.8/79.0 | 86.6/- | 93.2 | 92.3 | 60.6 | 70.4 | 88.0/- | 91.3/- |90.0/- |
| RoBERTa-Large | 94.6/88.9 | 89.4/86.5 | 90.2/- | 96.4 | 93.9 | 68.0 | 86.6 | 90.9/- | 92.2/- |92.4/- |
| XLNet-Large | 95.1/89.7 | 90.6/87.9 | 90.8/- | 97.0 | 94.9 | 69.0 | 85.9 | 90.8/- | 92.3/- |92.5/- |
| [DeBERTa-Large](https://huggingface.co/microsoft/deberta-large)<sup>1</sup> | 95.5/90.1 | 90.7/88.0 | 91.3/91.1| 96.5|95.3| 69.5| 91.0| 92.6/94.6| 92.3/- |92.8/92.5 |
| [DeBERTa-XLarge](https://huggingface.co/microsoft/deberta-xlarge)<sup>1</sup> | -/- | -/- | 91.5/91.2| 97.0 | - | - | 93.1 | 92.1/94.3 | - |92.9/92.7|
| [DeBERTa-V2-XLarge](https://huggingface.co/microsoft/deberta-v2-xlarge)<sup>1</sup>|95.8/90.8| 91.4/88.9|91.7/91.6| **97.5**| 95.8|71.1|**93.9**|92.0/94.2|92.3/89.8|92.9/92.9|
|**[DeBERTa-V2-XXLarge](https://huggingface.co/microsoft/deberta-v2-xxlarge)<sup>1,2</sup>**|**96.1/91.4**|**92.2/89.7**|**91.7/91.9**|97.2|**96.0**|**72.0**| 93.5| **93.1/94.9**|**92.7/90.3** |**93.2/93.1** |
--------
#### Notes.
- <sup>1</sup> Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on [DeBERTa-Large-MNLI](https://huggingface.co/microsoft/deberta-large-mnli), [DeBERTa-XLarge-MNLI](https://huggingface.co/microsoft/deberta-xlarge-mnli), [DeBERTa-V2-XLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xlarge-mnli), [DeBERTa-V2-XXLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli). The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks.
- <sup>2</sup> To try the **XXLarge** model with **[HF transformers](https://huggingface.co/transformers/main_classes/trainer.html)**, you need to specify **--sharded_ddp**
```bash
cd transformers/examples/text-classification/
export TASK_NAME=mrpc
python -m torch.distributed.launch --nproc_per_node=8 run_glue.py --model_name_or_path microsoft/deberta-v2-xxlarge \\
--task_name $TASK_NAME --do_train --do_eval --max_seq_length 128 --per_device_train_batch_size 4 \\
--learning_rate 3e-6 --num_train_epochs 3 --output_dir /tmp/$TASK_NAME/ --overwrite_output_dir --sharded_ddp --fp16
```
### Citation
If you find DeBERTa useful for your work, please cite the following paper:
``` latex
@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}
```
|
microsoft/deberta-base
|
microsoft
| 2022-09-26T08:50:43Z | 6,398,087 | 76 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"rust",
"deberta",
"deberta-v1",
"fill-mask",
"en",
"arxiv:2006.03654",
"license:mit",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- deberta-v1
- fill-mask
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.
#### Fine-tuning on NLU tasks
We present the dev results on SQuAD 1.1/2.0 and MNLI tasks.
| Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m |
|-------------------|-----------|-----------|--------|
| RoBERTa-base | 91.5/84.6 | 83.7/80.5 | 87.6 |
| XLNet-Large | -/- | -/80.2 | 86.8 |
| **DeBERTa-base** | 93.1/87.2 | 86.2/83.1 | 88.8 |
### Citation
If you find DeBERTa useful for your work, please cite the following paper:
``` latex
@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}
```
|
duchung17/wav2vec2-base-timit-demo-google-colab
|
duchung17
| 2022-09-26T08:41:07Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-02T09:42:21Z |
---
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: 0.4049
- Wer: 0.3556
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.7319 | 1.0 | 500 | 1.3558 | 0.8890 |
| 0.7826 | 2.01 | 1000 | 0.5655 | 0.5398 |
| 0.4157 | 3.01 | 1500 | 0.4692 | 0.4682 |
| 0.2722 | 4.02 | 2000 | 0.4285 | 0.4193 |
| 0.2094 | 5.02 | 2500 | 0.4170 | 0.3949 |
| 0.1682 | 6.02 | 3000 | 0.3895 | 0.3751 |
| 0.1295 | 7.03 | 3500 | 0.3943 | 0.3628 |
| 0.1064 | 8.03 | 4000 | 0.4198 | 0.3648 |
| 0.0869 | 9.04 | 4500 | 0.4049 | 0.3556 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
prikarsartam/Olga
|
prikarsartam
| 2022-09-26T08:17:24Z | 67 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-26T04:59:17Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: prikarsartam/Olga
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# prikarsartam/Olga
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.8904
- Validation Loss: 2.6281
- Train Rouge1: 25.0368
- Train Rouge2: 5.6914
- Train Rougel: 19.4806
- Train Rougelsum: 19.4874
- Train Gen Len: 18.7987
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-06, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 3.0715 | 2.6854 | 23.4337 | 4.8994 | 18.1348 | 18.1316 | 18.7024 | 0 |
| 2.8904 | 2.6281 | 25.0368 | 5.6914 | 19.4806 | 19.4874 | 18.7987 | 1 |
### Framework versions
- Transformers 4.22.1
- TensorFlow 2.8.2
- Datasets 2.5.1
- Tokenizers 0.12.1
|
sahita/lang-VoxLingua107-ecapa
|
sahita
| 2022-09-26T08:13:03Z | 16 | 0 |
speechbrain
|
[
"speechbrain",
"audio-classification",
"embeddings",
"Language",
"Identification",
"pytorch",
"ECAPA-TDNN",
"TDNN",
"VoxLingua107",
"multilingual",
"en",
"mr",
"dataset:VoxLingua107",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] |
audio-classification
| 2022-09-23T08:53:34Z |
---
language:
- multilingual
- en
- mr
thumbnail:
tags:
- audio-classification
- speechbrain
- embeddings
- Language
- Identification
- pytorch
- ECAPA-TDNN
- TDNN
- VoxLingua107
license: "apache-2.0"
datasets:
- VoxLingua107
metrics:
- Accuracy
widget:
- example_title: English Sample
src: https://cdn-media.huggingface.co/speech_samples/LibriSpeech_61-70968-0000.flac
---
# VoxLingua107 ECAPA-TDNN Spoken Language Identification Model
## Model description
This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain.
The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. However, it uses
more fully connected hidden layers after the embedding layer, and cross-entropy loss was used for training.
We observed that this improved the performance of extracted utterance embeddings for downstream tasks.
The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify_file* if needed.
The model can classify a speech utterance according to the language spoken.
It covers 2 different languages (
English,
Hindi).
## Intended uses & limitations
The model has two uses:
- use 'as is' for spoken language recognition
- use as an utterance-level feature (embedding) extractor, for creating a dedicated language ID model on your own data
The model is trained on automatically collected YouTube data. For more
information about the dataset, see [here](http://bark.phon.ioc.ee/voxlingua107/).
#### How to use
```python
import torchaudio
from speechbrain.pretrained import EncoderClassifier
language_id = EncoderClassifier.from_hparams(source="sahita/lang-VoxLingua-ecapa", savedir="tmp")
# Download Thai language sample from Omniglot and cvert to suitable form
signal = language_id.load_audio("https://omniglot.com/soundfiles/udhr/udhr_th.mp3")
prediction = language_id.classify_batch(signal)
print(prediction)
# (tensor([[-2.8646e+01, -3.0346e+01, -2.0748e+01, -2.9562e+01, -2.2187e+01,
# -3.2668e+01, -3.6677e+01, -3.3573e+01, -3.2545e+01, -2.4365e+01,
# -2.4688e+01, -3.1171e+01, -2.7743e+01, -2.9918e+01, -2.4770e+01,
# -3.2250e+01, -2.4727e+01, -2.6087e+01, -2.1870e+01, -3.2821e+01,
# -2.2128e+01, -2.2822e+01, -3.0888e+01, -3.3564e+01, -2.9906e+01,
# -2.2392e+01, -2.5573e+01, -2.6443e+01, -3.2429e+01, -3.2652e+01,
# -3.0030e+01, -2.4607e+01, -2.2967e+01, -2.4396e+01, -2.8578e+01,
# -2.5153e+01, -2.8475e+01, -2.6409e+01, -2.5230e+01, -2.7957e+01,
# -2.6298e+01, -2.3609e+01, -2.5863e+01, -2.8225e+01, -2.7225e+01,
# -3.0486e+01, -2.1185e+01, -2.7938e+01, -3.3155e+01, -1.9076e+01,
# -2.9181e+01, -2.2160e+01, -1.8352e+01, -2.5866e+01, -3.3636e+01,
# -4.2016e+00, -3.1581e+01, -3.1894e+01, -2.7834e+01, -2.5429e+01,
# -3.2235e+01, -3.2280e+01, -2.8786e+01, -2.3366e+01, -2.6047e+01,
# -2.2075e+01, -2.3770e+01, -2.2518e+01, -2.8101e+01, -2.5745e+01,
# -2.6441e+01, -2.9822e+01, -2.7109e+01, -3.0225e+01, -2.4566e+01,
# -2.9268e+01, -2.7651e+01, -3.4221e+01, -2.9026e+01, -2.6009e+01,
# -3.1968e+01, -3.1747e+01, -2.8156e+01, -2.9025e+01, -2.7756e+01,
# -2.8052e+01, -2.9341e+01, -2.8806e+01, -2.1636e+01, -2.3992e+01,
# -2.3794e+01, -3.3743e+01, -2.8332e+01, -2.7465e+01, -1.5085e-02,
# -2.9094e+01, -2.1444e+01, -2.9780e+01, -3.6046e+01, -3.7401e+01,
# -3.0888e+01, -3.3172e+01, -1.8931e+01, -2.2679e+01, -3.0225e+01,
# -2.4995e+01, -2.1028e+01]]), tensor([-0.0151]), tensor([94]), ['th'])
# The scores in the prediction[0] tensor can be interpreted as log-likelihoods that
# the given utterance belongs to the given language (i.e., the larger the better)
# The linear-scale likelihood can be retrieved using the following:
print(prediction[1].exp())
# tensor([0.9850])
# The identified language ISO code is given in prediction[3]
print(prediction[3])
# ['th: Thai']
# Alternatively, use the utterance embedding extractor:
emb = language_id.encode_batch(signal)
print(emb.shape)
# torch.Size([1, 1, 256])
```
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *encode_batch* and *classify_batch*.
#### Limitations and bias
Since the model is trained on VoxLingua107, it has many limitations and biases, some of which are:
- Probably it's accuracy on smaller languages is quite limited
- Probably it works worse on female speech than male speech (because YouTube data includes much more male speech)
- Based on subjective experiments, it doesn't work well on speech with a foreign accent
- Probably it doesn't work well on children's speech and on persons with speech disorders
## Training data
The model is trained on [VoxLingua107](http://bark.phon.ioc.ee/voxlingua107/).
VoxLingua107 is a speech dataset for training spoken language identification models.
The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives.
VoxLingua107 contains data for 107 languages. The total amount of speech in the training set is 6628 hours.
The average amount of data per language is 62 hours. However, the real amount per language varies a lot. There is also a seperate development set containing 1609 speech segments from 33 languages, validated by at least two volunteers to really contain the given language.
## Training procedure
See the [SpeechBrain recipe](https://github.com/speechbrain/speechbrain/tree/voxlingua107/recipes/VoxLingua107/lang_id).
## Evaluation results
Error rate: 6.7% on the VoxLingua107 development dataset
#### Referencing SpeechBrain
```bibtex
@misc{speechbrain,
title={{SpeechBrain}: A General-Purpose Speech Toolkit},
author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
year={2021},
eprint={2106.04624},
archivePrefix={arXiv},
primaryClass={eess.AS},
note={arXiv:2106.04624}
}
```
### Referencing VoxLingua107
```bibtex
@inproceedings{valk2021slt,
title={{VoxLingua107}: a Dataset for Spoken Language Recognition},
author={J{\"o}rgen Valk and Tanel Alum{\"a}e},
booktitle={Proc. IEEE SLT Workshop},
year={2021},
}
```
#### About SpeechBrain
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.
Website: https://speechbrain.github.io/
GitHub: https://github.com/speechbrain/speechbrain
|
sd-concepts-library/eru-chitanda-casual
|
sd-concepts-library
| 2022-09-26T07:39:50Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-26T07:39:45Z |
---
license: mit
---
### Eru Chitanda Casual on Stable Diffusion
This is the `<c-eru-chitanda>` 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`:
|
neeva/query2query
|
neeva
| 2022-09-26T07:11:21Z | 32 | 8 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-22T18:23:35Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
license: cc-by-nc-sa-4.0
---
# query2query
This is a [sentence-transformers](https://www.SBERT.net) model: It maps queries to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search over queries.
Checkout this announcing blogpost for more information: https://neeva.com/blog/state-of-the-art-query2query-similarity(https://neeva.com/blog/state-of-the-art-query2query-similarity)
**Note: we are releasing this under a license which prevents commercial use. If you want to use it for commercial purposes, please reach out to contact@neeva.co or rajhans@neeva.co with a brief description of what you want to use it for and we will try our best to respond very quickly.**
<!--- 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
queries = ["flight cost from nyc to la", "ticket prices from nyc to la"]
model = SentenceTransformer('neeva/query2query')
embeddings = model.encode(queries)
print(embeddings)
```
## Training
The model was trained for 1M steps with a batch size of 1024 at a learning rate of 2e-5 using a cosine learning rate scheduler with 10000 warmup steps.
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: DataParallel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
|
MGanesh29/parrot_paraphraser_on_T5-finetuned-xsum-v7
|
MGanesh29
| 2022-09-26T06:47:57Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-22T09:34:29Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: parrot_paraphraser_on_T5-finetuned-xsum-v7
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. -->
# parrot_paraphraser_on_T5-finetuned-xsum-v7
This model is a fine-tuned version of [prithivida/parrot_paraphraser_on_T5](https://huggingface.co/prithivida/parrot_paraphraser_on_T5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0316
- Rouge1: 86.4178
- Rouge2: 84.901
- Rougel: 86.458
- Rougelsum: 86.4281
- Gen Len: 17.887
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.0752 | 1.0 | 2000 | 0.0439 | 86.0044 | 84.1284 | 86.0265 | 86.0167 | 17.895 |
| 0.0454 | 2.0 | 4000 | 0.0352 | 86.2948 | 84.6092 | 86.3256 | 86.293 | 17.88 |
| 0.0308 | 3.0 | 6000 | 0.0324 | 86.3316 | 84.7883 | 86.374 | 86.3355 | 17.887 |
| 0.0242 | 4.0 | 8000 | 0.0316 | 86.4178 | 84.901 | 86.458 | 86.4281 | 17.887 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
SmilestheSad/hf_distilbert_uncased_somm
|
SmilestheSad
| 2022-09-26T03:13:00Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-26T03:03:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: hf_distilbert_uncased_somm
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. -->
# hf_distilbert_uncased_somm
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0481
- F1: 0.9077
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1032 | 1.0 | 565 | 0.0521 | 0.8929 |
| 0.0432 | 2.0 | 1130 | 0.0481 | 0.9077 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
ssharm87/t5-small-finetuned-eli5
|
ssharm87
| 2022-09-26T02:38:02Z | 108 | 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
| 2022-09-25T21:12:13Z |
---
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: 9.5483
---
<!-- 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: 3.7596
- Rouge1: 9.5483
- Rouge2: 1.8202
- Rougel: 7.7317
- Rougelsum: 8.8491
- Gen Len: 18.9895
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 3.9551 | 1.0 | 68159 | 3.7596 | 9.5483 | 1.8202 | 7.7317 | 8.8491 | 18.9895 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Ahmed007/BERT
|
Ahmed007
| 2022-09-26T02:32:44Z | 195 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-26T02:25:43Z |
---
tags:
- generated_from_trainer
model-index:
- name: BERT
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- 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
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
sahajrajmalla/patrakar
|
sahajrajmalla
| 2022-09-26T02:06:00Z | 107 | 1 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"nepali-nlp",
"nepali-news-classificiation",
"nlp",
"deep-learning",
"transfer-learning",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-15T07:05:22Z |
---
license: mit
tags:
- nepali-nlp
- nepali-news-classificiation
- nlp
- transformers
- deep-learning
- pytorch
- transfer-learning
model-index:
- name: patrakar
results: []
widget:
- text: "नेकपा (एमाले)का नेता गोकर्णराज विष्टले सहमति र सहकार्यबाटै संविधान बनाउने तथा जनताको जीवनस्तर उकास्ने काम गर्नु नै अबको मुख्य काम रहेको बताएका छन् ।"
example_title: "Example 1"
- text: "राजनीतिक स्थिरता नहुँदा विकास निर्माणले गति लिन सकेन ।"
example_title: "Example 2"
- text: "ठूलो उद्योग खोल्न महिलालाई ऋण दिइन्न"
example_title: "Example 3"
---
# patrakar/ पत्रकार (Nepali News Classifier)
Last updated: September 2022
## Model Details
**patrakar** is a DistilBERT pre-trained sequence classification transformer model which classifies Nepali language news into 9 newsgroup category, such as:
- politics
- opinion
- bank
- entertainment
- economy
- health
- literature
- sports
- tourism
It is developed by Sahaj Raj Malla to be generally usefuly for general public and so that others could explore them for commercial and scientific purposes. This model was trained on [Sakonii/distilgpt2-nepali](https://huggingface.co/Sakonii/distilgpt2-nepali) model.
It achieves the following results on the test dataset:
| Total Number of samples | Accuracy(%)
|:-------------:|:---------------:
| 5670 | 95.475
### Model date
September 2022
### Model type
Sequence classification model
### Model version
1.0.0
## Model Usage
This model can be used directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:
```python
from transformers import pipeline, set_seed
set_seed(42)
model_name = "sahajrajmalla/patrakar"
classifier = pipeline('text-classification', model=model_name)
text = "नेकपा (एमाले)का नेता गोकर्णराज विष्टले सहमति र सहकार्यबाटै संविधान बनाउने तथा जनताको जीवनस्तर उकास्ने काम गर्नु नै अबको मुख्य काम रहेको बताएका छन् ।"
classifier(text)
```
Here is how we can use the model to get the features of a given text in PyTorch:
```python
!pip install transformers torch
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification
import torch
import torch.nn.functional as F
# initializing model and tokenizer
model_name = "sahajrajmalla/patrakar"
# downloading tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# downloading model
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def tokenize_function(examples):
return tokenizer(examples["data"], padding="max_length", truncation=True)
# predicting with the model
sequence_i_want_to_predict = "राजनीतिक स्थिरता नहुँदा विकास निर्माणले गति लिन सकेन"
# initializing our labels
label_list = [
"bank",
"economy",
"entertainment",
"health",
"literature",
"opinion",
"politics",
"sports",
"tourism"
]
batch = tokenizer(sequence_i_want_to_predict, padding=True, truncation=True, max_length=512, return_tensors='pt')
with torch.no_grad():
outputs = model(**batch)
predictions = F.softmax(outputs.logits, dim=1)
labels = torch.argmax(predictions, dim=1)
print(f"The sequence: \n\n {word_i_want_to_predict} \n\n is predicted to be of newsgroup {label_list[labels.item()]}")
```
## Training data
This model is trained on 50,945 rows of Nepali language news grouped [dataset](https://www.kaggle.com/competitions/text-it-meet-22/data?select=train.csv) found on Kaggle which was also used in IT Meet 2022 Text challenge.
## Framework versions
- Transformers 4.20.1
- Pytorch 1.9.1
- Datasets 2.0.0
- Tokenizers 0.11.6
|
jamiehuang/t5-small-finetuned-xsum
|
jamiehuang
| 2022-09-26T01:29:12Z | 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
| 2022-09-24T21:08:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
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: 13.2962
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
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: 3.6746
- Rouge1: 13.2962
- Rouge2: 2.0081
- Rougel: 10.6529
- Rougelsum: 12.049
- Gen Len: 18.9985
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 3.8901 | 1.0 | 17040 | 3.6746 | 13.2962 | 2.0081 | 10.6529 | 12.049 | 18.9985 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
ammarpl/t5-base-finetuned-elif-attempt1
|
ammarpl
| 2022-09-26T01:14:32Z | 111 | 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
| 2022-09-25T21:01:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
metrics:
- rouge
model-index:
- name: t5-base-finetuned-elif-attempt1
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: 3.9675
---
<!-- 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-elif-attempt1
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: 5.3889
- Rouge1: 3.9675
- Rouge2: 0.248
- Rougel: 3.454
- Rougelsum: 3.765
- Gen Len: 19.0
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 5.8271 | 1.0 | 17040 | 5.3889 | 3.9675 | 0.248 | 3.454 | 3.765 | 19.0 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
CoreyMorris/a2c-AntBulletEnv-v0-old
|
CoreyMorris
| 2022-09-26T00:52:15Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-26T00:51:18Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 951.33 +/- 234.16
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
...
```
|
kkotkar1/t5-small-t5-base
|
kkotkar1
| 2022-09-25T22:49:52Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:eli5",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-25T16:33:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
model-index:
- name: t5-small-t5-base
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-t5-base
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eli5 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
gur509/t5-small-finetuned-eli5
|
gur509
| 2022-09-25T22:23:43Z | 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
| 2022-09-24T23:38:24Z |
---
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: 15.1689
---
<!-- 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: 3.5993
- Rouge1: 15.1689
- Rouge2: 2.1762
- Rougel: 12.7542
- Rougelsum: 14.0214
- Gen Len: 18.9988
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 3.8011 | 1.0 | 17040 | 3.5993 | 15.1689 | 2.1762 | 12.7542 | 14.0214 | 18.9988 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
sd-concepts-library/remert
|
sd-concepts-library
| 2022-09-25T20:50:59Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-25T20:50:05Z |
---
license: mit
---
### remert on Stable Diffusion
This is the `<Remert>` 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`:
|
quecopiones/distillbert-base-spanish-uncased-finetuned-full-suicidios
|
quecopiones
| 2022-09-25T19:52:22Z | 90 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-25T14:14:14Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distillbert-base-spanish-uncased-finetuned-full-suicidios
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. -->
# distillbert-base-spanish-uncased-finetuned-full-suicidios
This model is a fine-tuned version of [CenIA/distillbert-base-spanish-uncased](https://huggingface.co/CenIA/distillbert-base-spanish-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0825
- Accuracy: 0.9814
- F1: 0.9814
## 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: 6
- eval_batch_size: 6
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.2059 | 1.0 | 32058 | 0.1142 | 0.9694 | 0.9694 |
| 0.1229 | 2.0 | 64116 | 0.0825 | 0.9814 | 0.9814 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
monakth/distilbert-base-multilingual-cased-finetuned-squad
|
monakth
| 2022-09-25T19:18:13Z | 121 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-25T16:02:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-multilingual-cased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-cased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1954
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2983 | 1.0 | 5555 | 1.2202 |
| 1.0252 | 2.0 | 11110 | 1.1583 |
| 0.8078 | 3.0 | 16665 | 1.1954 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
eliwill/stoic-generator-10e
|
eliwill
| 2022-09-25T18:37:19Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-25T18:25:15Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: eliwill/stoic-generator-10e
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# eliwill/stoic-generator-10e
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.4753
- Validation Loss: 3.7980
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.0230 | 3.9474 | 0 |
| 3.8580 | 3.8982 | 1 |
| 3.7757 | 3.8721 | 2 |
| 3.7149 | 3.8489 | 3 |
| 3.6640 | 3.8343 | 4 |
| 3.6210 | 3.8152 | 5 |
| 3.5796 | 3.8088 | 6 |
| 3.5429 | 3.8038 | 7 |
| 3.5061 | 3.7967 | 8 |
| 3.4753 | 3.7980 | 9 |
### Framework versions
- Transformers 4.22.1
- TensorFlow 2.8.2
- Datasets 2.5.1
- Tokenizers 0.12.1
|
amirabbas/wav2vec2-large-xls-r-300m-turkish-demo-colab
|
amirabbas
| 2022-09-25T18:23:15Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-25T12:17:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-demo-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-large-xls-r-300m-turkish-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
kevinbram/nyfin
|
kevinbram
| 2022-09-25T17:13:57Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-25T15:28:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: nyfin
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. -->
# nyfin
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2155
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.26 | 1.0 | 5533 | 1.2155 |
### Framework versions
- Transformers 4.22.0
- Pytorch 1.11.0
- Datasets 2.4.0
- Tokenizers 0.12.1
|
simecek/DNADebertaK6_Arabidopsis
|
simecek
| 2022-09-25T14:27:59Z | 178 | 1 |
transformers
|
[
"transformers",
"pytorch",
"deberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-19T07:42:31Z |
---
tags:
- generated_from_trainer
model-index:
- name: DNADebertaK6_Arabidopsis
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. -->
# DNADebertaK6_Arabidopsis
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: 1.7194
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 600001
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:------:|:---------------:|
| 4.6174 | 6.12 | 20000 | 1.9257 |
| 1.8873 | 12.24 | 40000 | 1.8098 |
| 1.8213 | 18.36 | 60000 | 1.7952 |
| 1.8042 | 24.48 | 80000 | 1.7888 |
| 1.7945 | 30.6 | 100000 | 1.7861 |
| 1.7873 | 36.72 | 120000 | 1.7772 |
| 1.782 | 42.84 | 140000 | 1.7757 |
| 1.7761 | 48.96 | 160000 | 1.7632 |
| 1.7714 | 55.08 | 180000 | 1.7685 |
| 1.7677 | 61.2 | 200000 | 1.7568 |
| 1.7637 | 67.32 | 220000 | 1.7570 |
| 1.7585 | 73.44 | 240000 | 1.7442 |
| 1.7554 | 79.56 | 260000 | 1.7556 |
| 1.7515 | 85.68 | 280000 | 1.7505 |
| 1.7483 | 91.8 | 300000 | 1.7463 |
| 1.745 | 97.92 | 320000 | 1.7425 |
| 1.7427 | 104.04 | 340000 | 1.7425 |
| 1.7398 | 110.16 | 360000 | 1.7359 |
| 1.7377 | 116.28 | 380000 | 1.7369 |
| 1.7349 | 122.4 | 400000 | 1.7340 |
| 1.7325 | 128.52 | 420000 | 1.7313 |
| 1.731 | 134.64 | 440000 | 1.7256 |
| 1.7286 | 140.76 | 460000 | 1.7238 |
| 1.7267 | 146.88 | 480000 | 1.7324 |
| 1.7247 | 153.0 | 500000 | 1.7247 |
| 1.7228 | 159.12 | 520000 | 1.7185 |
| 1.7209 | 165.24 | 540000 | 1.7166 |
| 1.7189 | 171.36 | 560000 | 1.7206 |
| 1.7181 | 177.48 | 580000 | 1.7190 |
| 1.7159 | 183.6 | 600000 | 1.7194 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
kevinbram/testarenz
|
kevinbram
| 2022-09-25T14:17:18Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-25T13:44:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: testarenz
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. -->
# testarenz
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2153
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2806 | 1.0 | 5533 | 1.2153 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ganchengguang/RoBERTa-base-janpanese
|
ganchengguang
| 2022-09-25T13:47:39Z | 106 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:1907.11692",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-23T13:38:58Z |
---
license: apache-2.0
widget:
- text: 横浜国立大学は日本の[MASK]県にある。
---
This is RoBERTa model pretrained on texts in the Japanese language.
3.45GB wikipedia text
trained 1.65M step
use the sentencepiece tokenizer.
If you want to fine-tune model. Please use
```python
from transformers import BertTokenizer, RobertaModel
BertTokenizer.from_pretrained('')
RoBERTModel.from_pretrained('')
```
The accuracy in JGLUE-marc_ja-v1.0 binary sentiment classification 95.4%
Contribute by Yokohama Nationaly University Mori Lab
@article{liu2019roberta,
title={Roberta: A robustly optimized bert pretraining approach},
author={Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov,
Veselin},
journal={arXiv preprint arXiv:1907.11692},
year={2019}
}
|
Okyx/fillmaskmodel
|
Okyx
| 2022-09-25T12:36:49Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"xlm-roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-25T12:32:35Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: fillmaskmodel
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# fillmaskmodel
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4400, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
### Framework versions
- Transformers 4.22.1
- TensorFlow 2.8.2
- Tokenizers 0.12.1
|
rram12/Pixelcopter-PLE-v0
|
rram12
| 2022-09-25T11:56:34Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-25T11:56:26Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 7.10 +/- 5.39
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
shmuhammad/distilbert-base-uncased-distilled-clinc
|
shmuhammad
| 2022-09-25T11:06:16Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-18T14:37:40Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9487096774193549
---
<!-- 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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3060
- Accuracy: 0.9487
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.643 | 1.0 | 318 | 1.9110 | 0.7452 |
| 1.4751 | 2.0 | 636 | 0.9678 | 0.8606 |
| 0.7736 | 3.0 | 954 | 0.5578 | 0.9168 |
| 0.4652 | 4.0 | 1272 | 0.4081 | 0.9352 |
| 0.3364 | 5.0 | 1590 | 0.3538 | 0.9442 |
| 0.2801 | 6.0 | 1908 | 0.3294 | 0.9465 |
| 0.2515 | 7.0 | 2226 | 0.3165 | 0.9471 |
| 0.2366 | 8.0 | 2544 | 0.3107 | 0.9487 |
| 0.2292 | 9.0 | 2862 | 0.3069 | 0.9490 |
| 0.2247 | 10.0 | 3180 | 0.3060 | 0.9487 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1.post200
- Datasets 1.16.1
- Tokenizers 0.10.3
|
shmuhammad/distilbert-base-uncased-finetuned-clinc
|
shmuhammad
| 2022-09-25T10:06:15Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-18T12:12:52Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.92
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7758
- Accuracy: 0.92
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.295 | 1.0 | 318 | 3.2908 | 0.7448 |
| 2.6313 | 2.0 | 636 | 1.8779 | 0.8384 |
| 1.5519 | 3.0 | 954 | 1.1600 | 0.8981 |
| 1.0148 | 4.0 | 1272 | 0.8585 | 0.9123 |
| 0.7974 | 5.0 | 1590 | 0.7758 | 0.92 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1.post200
- Datasets 1.16.1
- Tokenizers 0.10.3
|
shed-e/thucnews
|
shed-e
| 2022-09-25T08:29:09Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:load_train",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-25T07:46:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- load_train
metrics:
- accuracy
model-index:
- name: thucnews
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: load_train
type: load_train
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9433
---
<!-- 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. -->
# thucnews
This model is a fine-tuned version of [hfl/rbt6](https://huggingface.co/hfl/rbt6) on the load_train dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3191
- Accuracy: 0.9433
## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2038 | 1.0 | 704 | 0.2018 | 0.9332 |
| 0.1403 | 2.0 | 1408 | 0.1829 | 0.9406 |
| 0.0894 | 3.0 | 2112 | 0.2073 | 0.9419 |
| 0.056 | 4.0 | 2816 | 0.2228 | 0.9408 |
| 0.0321 | 5.0 | 3520 | 0.2689 | 0.9417 |
| 0.0209 | 6.0 | 4224 | 0.2819 | 0.9431 |
| 0.0099 | 7.0 | 4928 | 0.3131 | 0.9421 |
| 0.0057 | 8.0 | 5632 | 0.3191 | 0.9433 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
nikhilsk/t5-base-finetuned-eli5
|
nikhilsk
| 2022-09-25T07:53:22Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:eli5",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-24T23:04:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
model-index:
- name: t5-base-finetuned-eli5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetuned-eli5
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the eli5 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
jamescalam/mpnet-snli
|
jamescalam
| 2022-09-25T07:31:18Z | 4 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:snli",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-20T22:25:09Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language:
- en
license: mit
datasets:
- snli
---
# MPNet NLI
***Note**: The same model trained with negatives yields better performance. [Find it here](https://huggingface.co/jamescalam/mpnet-snli-negatives).*
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. It has been fine-tuned using the **S**tanford **N**atural **L**anguage **I**nference (SNLI) dataset and returns MRR@10 and MAP scores of ~0.92 on the SNLI test set.
Find more info from [James Briggs on YouTube](https://youtube.com/c/jamesbriggs) or in the [**free** NLP for Semantic Search ebook](https://pinecone.io/learn/nlp).
## 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('jamescalam/mpnet-snli')
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('jamescalam/mpnet-snli')
model = AutoModel.from_pretrained('jamescalam/mpnet-snli')
# 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)
```
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 5731 with parameters:
```
{'batch_size': 32}
```
**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": 573,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
|
jamescalam/mpnet-nli-sts
|
jamescalam
| 2022-09-25T07:28:38Z | 5 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:snli",
"dataset:stsb",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-25T07:13:18Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language:
- en
license: mit
datasets:
- snli
- stsb
---
# MPNet NLI and STS
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. It uses the [jamescalam/mpnet-snli-negatives](https://huggingface.co/jamescalam/mpnet-snli-negatives) model as a starting point, and is fine-tuned further on the **S**emantic **T**extual **S**imilarity **b**enchmark (STSb) dataset. Returning evaluation scores of ~0.9 cosine Pearson correlation using the STSb test set.
Find more info from [James Briggs on YouTube](https://youtube.com/c/jamesbriggs) or in the [**free** NLP for Semantic Search ebook](https://pinecone.io/learn/nlp).
<!--- 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('jamescalam/mpnet-nli-sts')
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('jamescalam/mpnet-nli-sts')
model = AutoModel.from_pretrained('jamescalam/mpnet-nli-sts')
# 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)
```
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 180 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": 5,
"evaluation_steps": 25,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 90,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
|
ShadowTwin41/bert-finetuned-ner
|
ShadowTwin41
| 2022-09-25T07:26:43Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-25T07:18:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9127878490935816
- name: Recall
type: recall
value: 0.9405923931336251
- name: F1
type: f1
value: 0.9264815582262743
- name: Accuracy
type: accuracy
value: 0.9841937952551951
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0586
- Precision: 0.9128
- Recall: 0.9406
- F1: 0.9265
- Accuracy: 0.9842
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 293 | 0.0844 | 0.8714 | 0.9123 | 0.8914 | 0.9760 |
| 0.1765 | 2.0 | 586 | 0.0601 | 0.9109 | 0.9357 | 0.9231 | 0.9834 |
| 0.1765 | 3.0 | 879 | 0.0586 | 0.9128 | 0.9406 | 0.9265 | 0.9842 |
### Framework versions
- Transformers 4.22.0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Fadil-1/t5-small-finetuned-ELI5
|
Fadil-1
| 2022-09-25T04:19:32Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:eli5",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-23T22:50:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
model-index:
- name: t5-small-finetuned-ELI5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-ELI5
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eli5 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu102
- Datasets 2.5.1
- Tokenizers 0.12.1
|
neelmehta00/t5-small-finetuned-eli5-neel-final-again
|
neelmehta00
| 2022-09-25T03:34:10Z | 110 | 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
| 2022-09-25T02:21:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
metrics:
- rouge
model-index:
- name: t5-small-finetuned-eli5-neel-final-again
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: 15.1361
---
<!-- 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-neel-final-again
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: 3.5993
- Rouge1: 15.1361
- Rouge2: 2.1584
- Rougel: 12.7499
- Rougelsum: 13.989
- Gen Len: 18.9998
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 3.8014 | 1.0 | 17040 | 3.5993 | 15.1361 | 2.1584 | 12.7499 | 13.989 | 18.9998 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincoln81ParaphraseMedium
|
BigSalmon
| 2022-09-25T02:27:53Z | 173 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-25T02:14:22Z |
data: https://github.com/BigSalmon2/InformalToFormalDataset
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln80Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln80Paraphrase")
```
```
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.
```
```
accustomed to having its name uttered ______, harvard university is weathering a rare spell of reputational tumult
(a) in reverential tones
(b) with great affection
(c) in adulatory fashion
(d) in glowing terms
```
```
informal english: i reached out to accounts who had a lot of followers, helping to make people know about us.
resume english: i partnered with prominent influencers to build brand awareness.
***
```
|
sd-concepts-library/yuji-himukai-style
|
sd-concepts-library
| 2022-09-25T02:06:22Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-25T02:06:16Z |
---
license: mit
---
### Yuji-Himukai-Style on Stable Diffusion
This is the `<Yuji Himukai-Style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
hujiazhen/t5-small-finetuned-eli5
|
hujiazhen
| 2022-09-25T00:54:13Z | 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
| 2022-09-24T21:14:31Z |
---
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: 12.731
---
<!-- 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: 3.6906
- Rouge1: 12.731
- Rouge2: 1.9944
- Rougel: 10.0818
- Rougelsum: 11.7305
- Gen Len: 19.0
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:-------:|:---------:|:-------:|
| 3.8994 | 1.0 | 17040 | 3.6906 | 12.731 | 1.9944 | 10.0818 | 11.7305 | 19.0 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
bguan/lunar_lander_v2_ppo_220924A
|
bguan
| 2022-09-24T23:32:24Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-24T22:49:51Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 283.01 +/- 14.03
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
sd-concepts-library/wire-angels
|
sd-concepts-library
| 2022-09-24T23:25:57Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-24T23:25:43Z |
---
license: mit
---
### wire-angels on Stable Diffusion
This is the `<wire-angels>` 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/sadbutchhours
|
huggingtweets
| 2022-09-24T22:33:30Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-24T22:28:41Z |
---
language: en
thumbnail: http://www.huggingtweets.com/sadbutchhours/1664058806344/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/1333210907959119872/b_LOBjz9_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">pangaea | The McRib™ Is Back!</div>
<div style="text-align: center; font-size: 14px;">@sadbutchhours</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 pangaea | The McRib™ Is Back!.
| Data | pangaea | The McRib™ Is Back! |
| --- | --- |
| Tweets downloaded | 3228 |
| Retweets | 313 |
| Short tweets | 440 |
| Tweets kept | 2475 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2g58d4t3/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 @sadbutchhours's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1worq93s) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1worq93s/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/sadbutchhours')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
sd-concepts-library/kanv1
|
sd-concepts-library
| 2022-09-24T22:14:05Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-24T22:14:01Z |
---
license: mit
---
### KANv1 on Stable Diffusion
This is the `<KAN>` 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`:
|
gokuls/bert-tiny-sst2-KD-BERT_and_distilBERT
|
gokuls
| 2022-09-24T20:16:17Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-24T19:59:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-tiny-sst2-KD-BERT_and_distilBERT
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: sst2
split: train
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.8325688073394495
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-tiny-sst2-KD-BERT_and_distilBERT
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5530
- Accuracy: 0.8326
## 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: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.7317 | 1.0 | 4210 | 1.5887 | 0.8222 |
| 1.0068 | 2.0 | 8420 | 1.5530 | 0.8326 |
| 0.7961 | 3.0 | 12630 | 1.7072 | 0.8245 |
| 0.6852 | 4.0 | 16840 | 1.8794 | 0.8177 |
| 0.6039 | 5.0 | 21050 | 1.8691 | 0.8142 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
gokuls/bert-tiny-sst2-KD-BERT
|
gokuls
| 2022-09-24T19:43:27Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-24T19:26:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-tiny-sst2-KD-BERT
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: sst2
split: train
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.8348623853211009
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-tiny-sst2-KD-BERT
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8257
- Accuracy: 0.8349
## 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: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7521 | 1.0 | 4210 | 0.7345 | 0.8234 |
| 0.4301 | 2.0 | 8420 | 0.7748 | 0.8303 |
| 0.3335 | 3.0 | 12630 | 0.8257 | 0.8349 |
| 0.2831 | 4.0 | 16840 | 0.9145 | 0.8188 |
| 0.2419 | 5.0 | 21050 | 0.9096 | 0.8177 |
| 0.2149 | 6.0 | 25260 | 0.8410 | 0.8234 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
gokuls/bert-tiny-emotion-KD-distilBERT
|
gokuls
| 2022-09-24T19:33:23Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-24T19:21:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: bert-tiny-emotion-KD-distilBERT
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: 0.913
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-tiny-emotion-KD-distilBERT
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5444
- Accuracy: 0.913
## 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: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 4.2533 | 1.0 | 1000 | 2.8358 | 0.7675 |
| 2.3274 | 2.0 | 2000 | 1.5893 | 0.8675 |
| 1.3974 | 3.0 | 3000 | 1.0286 | 0.891 |
| 0.9035 | 4.0 | 4000 | 0.7534 | 0.8955 |
| 0.6619 | 5.0 | 5000 | 0.6350 | 0.905 |
| 0.5482 | 6.0 | 6000 | 0.6180 | 0.899 |
| 0.4937 | 7.0 | 7000 | 0.5448 | 0.91 |
| 0.4013 | 8.0 | 8000 | 0.5493 | 0.906 |
| 0.3839 | 9.0 | 9000 | 0.5481 | 0.9095 |
| 0.3281 | 10.0 | 10000 | 0.5528 | 0.9115 |
| 0.3098 | 11.0 | 11000 | 0.5864 | 0.9095 |
| 0.2762 | 12.0 | 12000 | 0.5566 | 0.9095 |
| 0.2467 | 13.0 | 13000 | 0.5444 | 0.913 |
| 0.2286 | 14.0 | 14000 | 0.5306 | 0.912 |
| 0.2215 | 15.0 | 15000 | 0.5312 | 0.9115 |
| 0.2038 | 16.0 | 16000 | 0.5242 | 0.912 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
kk00165/Jiang
|
kk00165
| 2022-09-24T18:48:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-09-24T18:39:49Z |
Jiangstyle on Stable Diffusion
This is the <Jiangstyle> concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the Stable Conceptualizer notebook. You can also train your own concepts and load them into the concept libraries using this notebook.
Here is the new concept you will be able to use as a style:
|
gokuls/bert-tiny-Massive-intent-KD-BERT
|
gokuls
| 2022-09-24T18:38:58Z | 115 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:massive",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-23T19:19:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
model-index:
- name: bert-tiny-Massive-intent-KD-BERT
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: massive
type: massive
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.853418593212002
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-tiny-Massive-intent-KD-BERT
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8380
- Accuracy: 0.8534
## 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: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 5.83 | 1.0 | 720 | 4.8826 | 0.3050 |
| 4.7602 | 2.0 | 1440 | 3.9904 | 0.4191 |
| 4.0301 | 3.0 | 2160 | 3.3806 | 0.5032 |
| 3.4797 | 4.0 | 2880 | 2.9065 | 0.5967 |
| 3.0352 | 5.0 | 3600 | 2.5389 | 0.6596 |
| 2.6787 | 6.0 | 4320 | 2.2342 | 0.7044 |
| 2.3644 | 7.0 | 5040 | 1.9873 | 0.7354 |
| 2.1145 | 8.0 | 5760 | 1.7928 | 0.7462 |
| 1.896 | 9.0 | 6480 | 1.6293 | 0.7644 |
| 1.7138 | 10.0 | 7200 | 1.5062 | 0.7752 |
| 1.5625 | 11.0 | 7920 | 1.3923 | 0.7885 |
| 1.4229 | 12.0 | 8640 | 1.3092 | 0.7978 |
| 1.308 | 13.0 | 9360 | 1.2364 | 0.8018 |
| 1.201 | 14.0 | 10080 | 1.1759 | 0.8155 |
| 1.1187 | 15.0 | 10800 | 1.1322 | 0.8214 |
| 1.0384 | 16.0 | 11520 | 1.0990 | 0.8234 |
| 0.976 | 17.0 | 12240 | 1.0615 | 0.8308 |
| 0.9163 | 18.0 | 12960 | 1.0377 | 0.8328 |
| 0.8611 | 19.0 | 13680 | 1.0054 | 0.8337 |
| 0.812 | 20.0 | 14400 | 0.9926 | 0.8367 |
| 0.7721 | 21.0 | 15120 | 0.9712 | 0.8382 |
| 0.7393 | 22.0 | 15840 | 0.9586 | 0.8357 |
| 0.7059 | 23.0 | 16560 | 0.9428 | 0.8372 |
| 0.6741 | 24.0 | 17280 | 0.9377 | 0.8396 |
| 0.6552 | 25.0 | 18000 | 0.9229 | 0.8377 |
| 0.627 | 26.0 | 18720 | 0.9100 | 0.8416 |
| 0.5972 | 27.0 | 19440 | 0.9028 | 0.8416 |
| 0.5784 | 28.0 | 20160 | 0.8996 | 0.8406 |
| 0.5595 | 29.0 | 20880 | 0.8833 | 0.8451 |
| 0.5438 | 30.0 | 21600 | 0.8772 | 0.8475 |
| 0.5218 | 31.0 | 22320 | 0.8758 | 0.8451 |
| 0.509 | 32.0 | 23040 | 0.8728 | 0.8480 |
| 0.4893 | 33.0 | 23760 | 0.8640 | 0.8480 |
| 0.4948 | 34.0 | 24480 | 0.8541 | 0.8475 |
| 0.4722 | 35.0 | 25200 | 0.8595 | 0.8495 |
| 0.468 | 36.0 | 25920 | 0.8488 | 0.8495 |
| 0.4517 | 37.0 | 26640 | 0.8460 | 0.8505 |
| 0.4462 | 38.0 | 27360 | 0.8450 | 0.8485 |
| 0.4396 | 39.0 | 28080 | 0.8422 | 0.8490 |
| 0.427 | 40.0 | 28800 | 0.8380 | 0.8534 |
| 0.4287 | 41.0 | 29520 | 0.8385 | 0.8480 |
| 0.4222 | 42.0 | 30240 | 0.8319 | 0.8510 |
| 0.421 | 43.0 | 30960 | 0.8296 | 0.8510 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
gokuls/bert-base-sst2
|
gokuls
| 2022-09-24T18:00:08Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-24T17:29:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: sst2
split: train
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.9036697247706422
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-sst2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3735
- Accuracy: 0.9037
## 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: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.243 | 1.0 | 4210 | 0.3735 | 0.9037 |
| 0.1557 | 2.0 | 8420 | 0.3907 | 0.8922 |
| 0.1248 | 3.0 | 12630 | 0.3690 | 0.8945 |
| 0.1017 | 4.0 | 16840 | 0.5466 | 0.8830 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Chemsseddine/bert-base-cased-finetuned-DOP
|
Chemsseddine
| 2022-09-24T17:47:24Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-24T17:34:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-cased-finetuned-DOP
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-DOP
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1947
- Accuracy: 0.8
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8621 | 1.0 | 20 | 1.4032 | 0.675 |
| 1.4484 | 2.0 | 40 | 1.1947 | 0.8 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
gokuls/distilroberta-sst2
|
gokuls
| 2022-09-24T17:20:50Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-24T16:56:40Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilroberta-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: sst2
split: train
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.908256880733945
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-sst2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3451
- Accuracy: 0.9083
## 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: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2928 | 1.0 | 4210 | 0.3499 | 0.8876 |
| 0.1908 | 2.0 | 8420 | 0.3451 | 0.9083 |
| 0.1489 | 3.0 | 12630 | 0.3440 | 0.9048 |
| 0.119 | 4.0 | 16840 | 0.4963 | 0.8911 |
| 0.0974 | 5.0 | 21050 | 0.4645 | 0.8888 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
azaidi-face/xlm-roberta-base-finetuned-panx-de
|
azaidi-face
| 2022-09-24T17:16:43Z | 125 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-24T17:09:45Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8663101604278075
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1339
- F1: 0.8663
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2581 | 1.0 | 525 | 0.1690 | 0.8303 |
| 0.1305 | 2.0 | 1050 | 0.1352 | 0.8484 |
| 0.0839 | 3.0 | 1575 | 0.1339 | 0.8663 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
robingeibel/led-large-16384-finetuned-big_patent
|
robingeibel
| 2022-09-24T16:03:38Z | 93 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"tensorboard",
"led",
"feature-extraction",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-06-28T10:32:30Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: led-large-16384-finetuned-big_patent
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# led-large-16384-finetuned-big_patent
This model is a fine-tuned version of [robingeibel/led-large-16384-finetuned-big_patent](https://huggingface.co/robingeibel/led-large-16384-finetuned-big_patent) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.22.1
- TensorFlow 2.8.2
- Datasets 2.5.1
- Tokenizers 0.12.1
|
sd-concepts-library/lex
|
sd-concepts-library
| 2022-09-24T15:55:39Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-24T15:55:33Z |
---
license: mit
---
### lex on Stable Diffusion
This is the `<lex>` 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/vi-en-roberta-base
|
truongpdd
| 2022-09-24T15:17:38Z | 37 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-19T02:44:51Z |
```
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('truongpdd/vi-en-roberta-base')
model = AutoModel.from_pretrained('truongpdd/vi-en-roberta-base', from_flax=True)
```
|
masakhane/afrimbart_bam_fr_news
|
masakhane
| 2022-09-24T15:08:14Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"bam",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-09T17:31:01Z |
---
license: afl-3.0
language:
- bam
- fr
---
|
masakhane/afrimbart_fr_bam_news
|
masakhane
| 2022-09-24T15:08:14Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"fr",
"bam",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-09T17:31:31Z |
---
language:
- fr
- bam
license: afl-3.0
---
|
masakhane/byt5_bam_fr_news
|
masakhane
| 2022-09-24T15:08:09Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"bam",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-09T17:37:43Z |
---
language:
- bam
- fr
license: afl-3.0
---
|
masakhane/mt5_bam_fr_news
|
masakhane
| 2022-09-24T15:08:08Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"bam",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-09T17:50:43Z |
---
language:
- bam
- fr
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_bam_rel_news
|
masakhane
| 2022-09-24T15:08:04Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"bam",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-09T17:43:23Z |
---
language:
- fr
- bam
license: afl-3.0
---
|
masakhane/m2m100_418M_bam_fr_rel
|
masakhane
| 2022-09-24T15:08:03Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"bam",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-09T17:47:50Z |
---
language:
- bam
- fr
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_bam_rel_news_ft
|
masakhane
| 2022-09-24T15:08:03Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"bam",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-09T17:44:33Z |
---
language:
- fr
- bam
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_bam_rel_ft
|
masakhane
| 2022-09-24T15:08:02Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"bam",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-09T17:48:57Z |
---
language:
- fr
- bam
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_bam_rel
|
masakhane
| 2022-09-24T15:08:02Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"bam",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-09T17:47:29Z |
---
language:
- fr
- bam
license: afl-3.0
---
|
masakhane/afrimt5_bbj_fr_news
|
masakhane
| 2022-09-24T15:08:00Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"bbj",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-13T16:51:01Z |
---
language:
- bbj
- fr
license: afl-3.0
---
|
masakhane/afrimbart_fr_bbj_news
|
masakhane
| 2022-09-24T15:07:59Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"fr",
"bbj",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-13T16:51:24Z |
---
language:
- fr
- bbj
license: afl-3.0
---
|
masakhane/afribyt5_fr_bbj_news
|
masakhane
| 2022-09-24T15:07:58Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"fr",
"bbj",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-13T16:52:06Z |
---
language:
- fr
- bbj
license: afl-3.0
---
|
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