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} | 0 | 2022-08-30T00:56:01Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: clinical-finetuned-data3
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. -->
# clinical-finetuned-data3
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5058
- Accuracy: 0.86
- Precision: 0.875
- Recall: 0.9265
- F1: 0.9
## 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:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Cinnamon/electra-small-japanese-discriminator | [
"pytorch",
"electra",
"pretraining",
"ja",
"transformers",
"license:apache-2.0"
] | null | {
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} | 419 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: clinical-finetunedNew
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. -->
# clinical-finetunedNew
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0423
- Accuracy: 0.84
- Precision: 0.8562
- Recall: 0.9191
- F1: 0.8865
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0707 | 1.0 | 50 | 0.9997 | 0.86 | 0.86 | 0.9485 | 0.9021 |
| 0.0593 | 2.0 | 100 | 0.9293 | 0.845 | 0.8777 | 0.8971 | 0.8873 |
| 0.0273 | 3.0 | 150 | 0.9836 | 0.83 | 0.8643 | 0.8897 | 0.8768 |
| 0.039 | 4.0 | 200 | 1.0028 | 0.85 | 0.8732 | 0.9118 | 0.8921 |
| 0.0121 | 5.0 | 250 | 1.0423 | 0.84 | 0.8562 | 0.9191 | 0.8865 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Ciruzzo/DialoGPT-small-hattypotter | [] | null | {
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} | 0 | null | ---
tags:
- Pong-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pong
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-PLE-v0
type: Pong-PLE-v0
metrics:
- type: mean_reward
value: -16.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
Clarianliz30/Caitlyn | [] | null | {
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} | 0 | null | ---
tags:
- conversational
---
# Basil DialoGPT Model |
ClaudeCOULOMBE/RickBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 9 | null | Access to model assayw119/etners-nlp is restricted and you are not in the authorized list. Visit https://huggingface.co/assayw119/etners-nlp to ask for access. |
CleveGreen/FieldClassifier_v2 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
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} | 46 | null | ---
widget:
- text: "How can I protect myself against covid-19?"
context: "Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model and measures of Liao et al. "
- text: "What are the risk factors for covid-19?"
context: "To identify risk factors for hospital deaths from COVID-19, the OpenSAFELY platform examined electronic health records from 17.4 million UK adults. The authors used multivariable Cox proportional hazards model to identify the association of risk of death with older age, lower socio-economic status, being male, non-white ethnic background and certain clinical conditions (diabetes, obesity, cancer, respiratory diseases, heart, kidney, liver, neurological and autoimmune conditions). Notably, asthma was identified as a risk factor, despite prior suggestion of a potential protective role. Interestingly, higher risks due to ethnicity or lower socio-economic status could not be completely attributed to pre-existing health conditions."
--- |
CleveGreen/FieldClassifier_v2_gpt | [
"pytorch",
"gpt2",
"text-classification",
"transformers"
] | text-classification | {
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} | 26 | 2022-08-30T02:28:33Z | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
inference: true
---
# waifu-diffusion v1.4 - Diffusion for Weebs
waifu-diffusion is a latent text-to-image diffusion model that has been conditioned on high-quality anime images through fine-tuning.
<sub>masterpiece, best quality, 1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, watercolor, night, turtleneck</sub>
[Original Weights](https://huggingface.co/hakurei/waifu-diffusion-v1-4)
# Gradio & Colab
We also support a [Gradio](https://github.com/gradio-app/gradio) Web UI and Colab with Diffusers to run Waifu Diffusion:
[](https://huggingface.co/spaces/hakurei/waifu-diffusion-demo)
[](https://colab.research.google.com/drive/1_8wPN7dJO746QXsFnB09Uq2VGgSRFuYE#scrollTo=1HaCauSq546O)
## Model Description
[See here for a full model overview.](https://gist.github.com/harubaru/f727cedacae336d1f7877c4bbe2196e1)
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
## Downstream Uses
This model can be used for entertainment purposes and as a generative art assistant.
## Example Code
```python
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained(
'hakurei/waifu-diffusion',
torch_dtype=torch.float32
).to('cuda')
prompt = "1girl, aqua eyes, baseball cap, blonde hair, closed mouth, earrings, green background, hat, hoop earrings, jewelry, looking at viewer, shirt, short hair, simple background, solo, upper body, yellow shirt"
with autocast("cuda"):
image = pipe(prompt, guidance_scale=6)["sample"][0]
image.save("test.png")
```
## Team Members and Acknowledgements
This project would not have been possible without the incredible work by Stability AI and Novel AI.
- [Haru](https://github.com/harubaru)
- [Salt](https://github.com/sALTaccount/)
- [Sta @ Bit192](https://twitter.com/naclbbr)
In order to reach us, you can join our [Discord server](https://discord.gg/touhouai).
[](https://discord.gg/touhouai) |
CleveGreen/JobClassifier | [
"pytorch",
"bert",
"text-classification",
"transformers"
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} | 31 | 2022-08-30T02:32:05Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="freeagh/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
CleveGreen/JobClassifier_v2 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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} | 37 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="freeagh/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
CoShin/XLM-roberta-large_ko_en_nil_sts | [] | null | {
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} | 0 | null | ---
tags:
- generated_from_trainer
datasets:
- squad_bn
metrics:
- sacrebleu
model-index:
- name: squad-bn-qgen-banglat5
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: squad_bn
type: squad_bn
args: squad_bn
metrics:
- name: Sacrebleu
type: sacrebleu
value: 8.0898
---
<!-- 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. -->
# squad-bn-qgen-banglat5
This model is a fine-tuned version of [csebuetnlp/banglat5](https://huggingface.co/csebuetnlp/banglat5) on the squad_bn dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4808
- Rouge1 Precision: 37.7366
- Rouge1 Recall: 34.2712
- Rouge1 Fmeasure: 34.8738
- Rouge2 Precision: 16.2055
- Rouge2 Recall: 14.568
- Rouge2 Fmeasure: 14.852
- Rougel Precision: 35.4241
- Rougel Recall: 32.2011
- Rougel Fmeasure: 32.7617
- Rougelsum Precision: 35.4167
- Rougelsum Recall: 32.1978
- Rougelsum Fmeasure: 32.7572
- Sacrebleu: 8.0898
- Meteor: 0.1782
- Gen Len: 9.8299
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Rougel Precision | Rougel Recall | Rougel Fmeasure | Rougelsum Precision | Rougelsum Recall | Rougelsum Fmeasure | Sacrebleu | Meteor | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------------------:|:----------------:|:------------------:|:---------:|:------:|:-------:|
| 0.5208 | 1.0 | 16396 | 0.4683 | 38.566 | 35.5094 | 35.9216 | 17.0701 | 15.3916 | 15.6829 | 36.4433 | 33.5298 | 33.958 | 36.4637 | 33.5496 | 33.9913 | 8.6055 | 0.1799 | 9.8340 |
| 0.479 | 2.0 | 32792 | 0.4815 | 40.7475 | 35.8163 | 37.0498 | 17.9002 | 15.2742 | 15.9601 | 38.6977 | 33.8607 | 35.1258 | 38.7261 | 33.8717 | 35.1537 | 9.0561 | 0.1835 | 9.4338 |
| 0.4577 | 3.0 | 49188 | 0.4879 | 40.6712 | 36.2763 | 37.2775 | 18.5942 | 16.0689 | 16.7206 | 38.8546 | 34.5013 | 35.5491 | 38.8633 | 34.5255 | 35.5682 | 9.7947 | 0.1879 | 9.6324 |
| 0.4389 | 4.0 | 65584 | 0.4881 | 41.4251 | 36.2873 | 37.6272 | 18.561 | 15.7067 | 16.5358 | 39.434 | 34.3496 | 35.7457 | 39.533 | 34.4702 | 35.8347 | 9.7612 | 0.1881 | 9.3944 |
| 0.4321 | 5.0 | 81980 | 0.4937 | 41.1197 | 36.0568 | 37.4121 | 18.7179 | 15.8348 | 16.6644 | 39.3386 | 34.3177 | 35.7088 | 39.3171 | 34.3015 | 35.6748 | 9.8263 | 0.1887 | 9.4040 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: tfranklin/bert-a-saurus
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. -->
# tfranklin/bert-a-saurus
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0003
- Validation Loss: 0.0004
- Epoch: 2
## 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': 1202, '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: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.2424 | 0.0004 | 0 |
| 0.0004 | 0.0004 | 1 |
| 0.0003 | 0.0004 | 2 |
### Framework versions
- Transformers 4.22.0.dev0
- TensorFlow 2.9.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
CoffeeAddict93/gpt2-medium-call-of-the-wild | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
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} | 14 | null | ---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-average-no-mask-prompt-d-loob
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8871031746031746
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6871657754010695
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6913946587537092
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.8148971650917176
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.958
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6359649122807017
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6458333333333334
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9153231881874341
- name: F1 (macro)
type: f1_macro
value: 0.909786964934943
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8577464788732394
- name: F1 (macro)
type: f1_macro
value: 0.6952254602767576
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6847237269772481
- name: F1 (macro)
type: f1_macro
value: 0.6742659270266346
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9634137859080476
- name: F1 (macro)
type: f1_macro
value: 0.8926357349234371
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9106863052334692
- name: F1 (macro)
type: f1_macro
value: 0.9093125585829993
---
# relbert/roberta-large-semeval2012-average-no-mask-prompt-d-loob
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-d-loob/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.6871657754010695
- Accuracy on SAT: 0.6913946587537092
- Accuracy on BATS: 0.8148971650917176
- Accuracy on U2: 0.6359649122807017
- Accuracy on U4: 0.6458333333333334
- Accuracy on Google: 0.958
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-d-loob/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9153231881874341
- Micro F1 score on CogALexV: 0.8577464788732394
- Micro F1 score on EVALution: 0.6847237269772481
- Micro F1 score on K&H+N: 0.9634137859080476
- Micro F1 score on ROOT09: 0.9106863052334692
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-d-loob/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8871031746031746
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-semeval2012-average-no-mask-prompt-d-loob")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: average_no_mask
- data: relbert/semeval2012_relational_similarity
- template_mode: manual
- template: I wasn’t aware of this relationship, but I just read in the encyclopedia that <subj> is the <mask> of <obj>
- loss_function: info_loob
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 21
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-d-loob/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
CoffeeAddict93/gpt2-modest-proposal | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
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} | 12 | null | ---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: nllb-200-distilled-600M-finetuned-pan_Guru-to-eng_Latn
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. -->
# nllb-200-distilled-600M-finetuned-pan_Guru-to-eng_Latn
This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8728
- Bleu: 42.5453
- Gen Len: 32.376
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 1.5153 | 0.72 | 500 | 1.0531 | 34.9696 | 32.548 |
| 1.1282 | 1.45 | 1000 | 0.9580 | 38.3648 | 31.832 |
| 1.0299 | 2.18 | 1500 | 0.9235 | 40.1212 | 31.964 |
| 0.942 | 2.9 | 2000 | 0.8963 | 41.2737 | 31.884 |
| 0.8869 | 3.63 | 2500 | 0.8847 | 41.4381 | 31.82 |
| 0.8553 | 4.35 | 3000 | 0.8780 | 42.1548 | 32.136 |
| 0.8306 | 5.08 | 3500 | 0.8733 | 42.3333 | 32.64 |
| 0.8063 | 5.8 | 4000 | 0.8728 | 42.5453 | 32.376 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
CogComp/bart-faithful-summary-detector | [
"pytorch",
"jax",
"bart",
"text-classification",
"en",
"dataset:xsum",
"transformers",
"xsum",
"license:cc-by-sa-4.0"
] | text-classification | {
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],
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} | 234 | null | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-ja
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-ja
split: train
args: en-ja
metrics:
- name: Bleu
type: bleu
value: 37.10979592471087
---
<!-- 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. -->
# marian-finetuned-kde4-en-to-ja
This model is a fine-tuned version of [Helsinki-NLP/opus-tatoeba-en-ja](https://huggingface.co/Helsinki-NLP/opus-tatoeba-en-ja) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9825
- Bleu: 37.1098
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Contrastive-Tension/BERT-Base-NLI-CT | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 9 | null | ---
languages:
- la
- grc
- he
---
This model builds upon [an existing language detection model](https://huggingface.co/papluca/xlm-roberta-base-language-detection). It uses the same dataset, extended with Latin, Ancient Greek and (modern) Hebrew texts. |
Contrastive-Tension/BERT-Base-Swe-CT-STSb | [
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
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} | 126 | 2022-08-30T09:47:44Z | ---
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: lg-en-test-version
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. -->
# lg-en-test-version
This model is a fine-tuned version of [AI-Lab-Makerere/lg_en](https://huggingface.co/AI-Lab-Makerere/lg_en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5803
- Bleu: 31.3111
## 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: 9.687717341785184e-05
- train_batch_size: 15
- 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 | Bleu |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 1.0 | 24 | 1.0100 | 28.5722 |
| No log | 2.0 | 48 | 0.7758 | 27.7506 |
| No log | 3.0 | 72 | 0.6459 | 40.3866 |
| No log | 4.0 | 96 | 0.5803 | 31.3111 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Contrastive-Tension/BERT-Distil-CT | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"DistilBertForMaskedLM"
],
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} | 9 | null | ---
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: 0.9512644448166137
- name: Recall
type: recall
value: 0.9559071019858634
- name: F1
type: f1
value: 0.9535801225551919
- name: Accuracy
type: accuracy
value: 0.9921732019781161
---
<!-- 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: 0.0399
- Precision: 0.9513
- Recall: 0.9559
- F1: 0.9536
- Accuracy: 0.9922
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0548 | 1.0 | 1756 | 0.0438 | 0.9368 | 0.9411 | 0.9390 | 0.9900 |
| 0.021 | 2.0 | 3512 | 0.0395 | 0.9446 | 0.9519 | 0.9482 | 0.9914 |
| 0.0108 | 3.0 | 5268 | 0.0399 | 0.9513 | 0.9559 | 0.9536 | 0.9922 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1745
- F1: 0.8505
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3055 | 1.0 | 835 | 0.1842 | 0.8099 |
| 0.1561 | 2.0 | 1670 | 0.1711 | 0.8452 |
| 0.1016 | 3.0 | 2505 | 0.1745 | 0.8505 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Craftified/Bob | [] | null | {
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} | 0 | null | ---
widget:
- text: "define the method i with an argument self."
- text: "substitute asvar for self.asvar."
- text: "convert host to lowercase."
- text: "for every var in self.vars,"
- text: "call the method parser.delete_first_token."
---
```
```
[](https://paperswithcode.com/sota/code-generation-on-django?p=mariancg-a-code-generation-transformer-model)
```
```
# MarianCG: a code generation transformer model inspired by machine translation
This model is to improve the solving of the code generation problem and implement a transformer model that can work with high accurate results. We implemented MarianCG transformer model which is a code generation model that can be able to generate code from natural language. This work declares the impact of using Marian machine translation model for solving the problem of code generation. In our implementation, we prove that a machine translation model can be operated and working as a code generation model. Finally, we set the new contributors and state-of-the-art on CoNaLa reaching a BLEU score of 30.92 and Exact Match Accuracy of 6.2 in the code generation problem with CoNaLa dataset.
MarianCG model and its implementation with the code of training and the generated output is available at this repository:
https://github.com/AhmedSSoliman/MarianCG-NL-to-Code
DJANGO dataset is available at
https://huggingface.co/datasets/AhmedSSoliman/DJANGO
This model is avialable on the huggingface hub https://huggingface.co/AhmedSSoliman/MarianCG-DJANGO
```python
# Model and Tokenizer
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# model_name = "AhmedSSoliman/MarianCG-NL-to-Code"
model = AutoModelForSeq2SeqLM.from_pretrained("AhmedSSoliman/MarianCG-DJANGO")
tokenizer = AutoTokenizer.from_pretrained("AhmedSSoliman/MarianCG-DJANGO")
# Input (Natural Language) and Output (Python Code)
NL_input = "define the method i with an argument self."
output = model.generate(**tokenizer(NL_input, padding="max_length", truncation=True, max_length=512, return_tensors="pt"))
output_code = tokenizer.decode(output[0], skip_special_tokens=True)
```
This model is available in spaces using gradio at: https://huggingface.co/spaces/AhmedSSoliman/MarianCG-DJANGO
---
Tasks:
- Translation
- Code Generation
- Text2Text Generation
- Text Generation
---
# Citation
We now have a [paper](https://doi.org/10.1186/s44147-022-00159-4) for this work and you can cite:
```
@article{soliman2022mariancg,
title={MarianCG: a code generation transformer model inspired by machine translation},
author={Soliman, Ahmed S and Hadhoud, Mayada M and Shaheen, Samir I},
journal={Journal of Engineering and Applied Science},
volume={69},
number={1},
pages={1--23},
year={2022},
publisher={SpringerOpen}
url={https://doi.org/10.1186/s44147-022-00159-4}
}
```
|
Crispy/dialopt-small-kratos | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 1619.40 +/- 156.98
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
...
```
|
Crumped/imdb-simpleRNN | [
"keras"
] | null | {
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} | 0 | null | ---
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/VioletaMG/ddpm-butterflies-128/tensorboard?#scalars)
|
Cryptikdw/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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}
} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Electra-base-squad-adversarialqa-epoch-1
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. -->
# Electra-base-squad-adversarialqa-epoch-1
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.4884
- Epoch: 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:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 43062, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1104, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.4884 | 0 |
### Framework versions
- Transformers 4.21.2
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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} | 0 | null | ---
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: 0.5079
- Wer: 0.3365
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.4933 | 1.0 | 500 | 1.7711 | 0.9978 |
| 0.8658 | 2.01 | 1000 | 0.6262 | 0.5295 |
| 0.4405 | 3.01 | 1500 | 0.4841 | 0.4845 |
| 0.3062 | 4.02 | 2000 | 0.4897 | 0.4215 |
| 0.233 | 5.02 | 2500 | 0.4326 | 0.4101 |
| 0.1896 | 6.02 | 3000 | 0.4924 | 0.4078 |
| 0.1589 | 7.03 | 3500 | 0.4430 | 0.3896 |
| 0.1391 | 8.03 | 4000 | 0.4334 | 0.3889 |
| 0.1216 | 9.04 | 4500 | 0.4691 | 0.3828 |
| 0.1063 | 10.04 | 5000 | 0.4726 | 0.3705 |
| 0.0992 | 11.04 | 5500 | 0.4333 | 0.3690 |
| 0.0872 | 12.05 | 6000 | 0.4986 | 0.3771 |
| 0.0829 | 13.05 | 6500 | 0.4903 | 0.3685 |
| 0.0713 | 14.06 | 7000 | 0.5293 | 0.3655 |
| 0.068 | 15.06 | 7500 | 0.5039 | 0.3612 |
| 0.0621 | 16.06 | 8000 | 0.5314 | 0.3665 |
| 0.0571 | 17.07 | 8500 | 0.5038 | 0.3572 |
| 0.0585 | 18.07 | 9000 | 0.4718 | 0.3550 |
| 0.0487 | 19.08 | 9500 | 0.5482 | 0.3626 |
| 0.0459 | 20.08 | 10000 | 0.5239 | 0.3545 |
| 0.0419 | 21.08 | 10500 | 0.5096 | 0.3473 |
| 0.0362 | 22.09 | 11000 | 0.5222 | 0.3500 |
| 0.0331 | 23.09 | 11500 | 0.5062 | 0.3489 |
| 0.0352 | 24.1 | 12000 | 0.4913 | 0.3459 |
| 0.0315 | 25.1 | 12500 | 0.4701 | 0.3412 |
| 0.028 | 26.1 | 13000 | 0.5178 | 0.3402 |
| 0.0255 | 27.11 | 13500 | 0.5168 | 0.3405 |
| 0.0228 | 28.11 | 14000 | 0.5154 | 0.3368 |
| 0.0232 | 29.12 | 14500 | 0.5079 | 0.3365 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
DTAI-KULeuven/robbertje-1-gb-bort | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:oscar (NL)",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_compatible"
] | fill-mask | {
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"RobertaForMaskedLM"
],
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} | 6 | null | ---
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/nawage/ddpm-butterflies-128/tensorboard?#scalars)
|
alexandrainst/da-hatespeech-classification-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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}
} | 866 | 2022-08-30T20:51:56Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-base-mse-summarization
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-mse-summarization
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8743
- Rouge1: 45.9597
- Rouge2: 26.8086
- Rougel: 39.935
- Rougelsum: 43.8897
- Bleurt: -0.7132
- Gen Len: 18.464
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleurt | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:|
| 1.2568 | 1.0 | 267 | 1.0472 | 41.6829 | 21.9654 | 35.4264 | 39.5556 | -0.8231 | 18.522 |
| 1.1085 | 2.0 | 534 | 0.9840 | 43.1479 | 23.3351 | 36.9244 | 40.886 | -0.7843 | 18.534 |
| 1.0548 | 3.0 | 801 | 0.9515 | 44.1511 | 24.4912 | 37.9549 | 41.9984 | -0.7702 | 18.528 |
| 1.0251 | 4.0 | 1068 | 0.9331 | 44.426 | 24.9439 | 38.2978 | 42.1731 | -0.7633 | 18.619 |
| 0.9888 | 5.0 | 1335 | 0.9201 | 45.0385 | 25.524 | 38.8681 | 42.8998 | -0.7497 | 18.523 |
| 0.9623 | 6.0 | 1602 | 0.9119 | 44.8648 | 25.469 | 38.9281 | 42.7798 | -0.7496 | 18.537 |
| 0.9502 | 7.0 | 1869 | 0.9015 | 44.9668 | 25.5041 | 38.9463 | 42.9368 | -0.7412 | 18.48 |
| 0.9316 | 8.0 | 2136 | 0.8973 | 45.3028 | 25.7232 | 39.1533 | 43.277 | -0.7318 | 18.523 |
| 0.9191 | 9.0 | 2403 | 0.8921 | 45.2901 | 25.916 | 39.2909 | 43.3022 | -0.7296 | 18.529 |
| 0.9122 | 10.0 | 2670 | 0.8889 | 45.3535 | 26.1369 | 39.4861 | 43.28 | -0.7271 | 18.545 |
| 0.8993 | 11.0 | 2937 | 0.8857 | 45.5345 | 26.1669 | 39.5656 | 43.4664 | -0.7269 | 18.474 |
| 0.8905 | 12.0 | 3204 | 0.8816 | 45.7796 | 26.4145 | 39.8117 | 43.734 | -0.7185 | 18.503 |
| 0.8821 | 13.0 | 3471 | 0.8794 | 45.7163 | 26.4314 | 39.719 | 43.6407 | -0.7211 | 18.496 |
| 0.8789 | 14.0 | 3738 | 0.8784 | 45.9097 | 26.7281 | 39.9071 | 43.8105 | -0.7127 | 18.452 |
| 0.8665 | 15.0 | 4005 | 0.8765 | 46.1148 | 26.8882 | 40.1006 | 43.988 | -0.711 | 18.443 |
| 0.8676 | 16.0 | 4272 | 0.8766 | 45.9119 | 26.7674 | 39.9001 | 43.8237 | -0.718 | 18.491 |
| 0.8637 | 17.0 | 4539 | 0.8758 | 45.9158 | 26.7153 | 39.9463 | 43.8323 | -0.7183 | 18.492 |
| 0.8622 | 18.0 | 4806 | 0.8752 | 45.9508 | 26.75 | 39.9533 | 43.8795 | -0.7144 | 18.465 |
| 0.8588 | 19.0 | 5073 | 0.8744 | 45.9192 | 26.7352 | 39.8921 | 43.8204 | -0.7148 | 18.462 |
| 0.8554 | 20.0 | 5340 | 0.8743 | 45.9597 | 26.8086 | 39.935 | 43.8897 | -0.7132 | 18.464 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
DaisyMak/bert-finetuned-squad-transformerfrozen-testtoken | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 7 | null | ---
license:
- apache-2.0
- bsd-3-clause
tags:
- summarization
- summary
- booksum
- long-document
- long-form
datasets:
- kmfoda/booksum
metrics:
- rouge
inference: false
model-index:
- name: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13
results:
- task:
type: summarization
name: Summarization
dataset:
name: samsum
type: samsum
config: samsum
split: test
metrics:
- type: rouge
value: 24.4101
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjhmM2NiMDQ1NjI3Zjk4YjkyMTVkMmUwZDU2YWMwZjc4ZmIzMjA1OGZiYzRmNjI3NDk3OWNmOTlkZDMxZmViMyIsInZlcnNpb24iOjF9.wS774e7vxQrf2gCcPhySsET3UaiUsj8E7mQmBS84wz86aT9j1yCqVX-8ozuj896K5wMygbL-TpUbydRIyyHTDw
- type: rouge
value: 5.003
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTYyNTFkNWZhOTgwNDg5ZWU5Y2U5NGI4Y2Y2YTMxNjUzOWI0ZWNlNDE1OGYzMjA1YTBmNDE4ZjcyOTZmODE4NiIsInZlcnNpb24iOjF9.AuqDkCgUgDWl8vMyrjTh59QW741UssGxdBqj3GZKy5e5gKadClUA709qgKbpxPIbMEyk38yvXYGplaJf5CnCCA
- type: rouge
value: 17.2544
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTBmODZmNWRhMzBhY2MzOGRkZWQzNjAzMGViOGMxYWYyZjNlZmM4YzgzMjkxNTk3M2E1ODAwZjY1M2I2MDZkYyIsInZlcnNpb24iOjF9.Md52aHjujvkxaW-ubJNquiHHHgi-OfRav0ZElVvYhIpU_k0iKEaQZRcw9JYjtG5vZJbQeiWbMzcCOJ999DhrAA
- type: rouge
value: 20.9183
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDJjNDc1OTZjY2VmNWRhNmYwZjRjY2JmNTAyNmIwZjRhYjMyMTdlNzY2M2Q4OGQwNTEyYTU0NGVhYWI2ZTk3NSIsInZlcnNpb24iOjF9.nlqol0HEeEjU7509-B9eyohf3CP3EZTibJ1lTvOx3wt8rU5LzEdwFazOTHjpWlcK_rik7jcySdUDe4fGjJtKAQ
- type: loss
value: 3.194674015045166
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzRiYmRiYjFkZDhlNGIwYTg3NDUwZTEzZjc5MjllNmJmODQ1YzBjNDM4MzQwNmMzNmNkMzk5N2M2MzZlOWY4MyIsInZlcnNpb24iOjF9._YJqPY9p_N2n7UxAkTeGenH1sVAkC_Z5HzZ6NbzlQoa8-RXTfbEPLw7fSKmlsGNyZxj7L_Bs4COIWzwAMxZSAA
- type: gen_len
value: 58.9951
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDJhYzU2Zjg4ZmIyOGRmNTU4MDM2NGZiNzc0NDk3YzZkOTQwMWMwNjMzZDQzZTZiZjk4ZDdmMmI2ODRkYjk3OCIsInZlcnNpb24iOjF9.MG1rcM_qpUhQmAYrsBxyNpcLUrPZw6V_uzYzDAo01kQyZEwJClWgMRVgpsSEnY93Mlu1445QLxkJEByUrfD3BQ
- task:
type: summarization
name: Summarization
dataset:
name: billsum
type: billsum
config: default
split: test
metrics:
- type: rouge
value: 37.3648
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWU4ZmZmYzllMzQxM2I4YTUxMjkwYjEzNDk1NjRlYjJiZjYyYWNiNzM4ODMxMGJjMzdhYjFhMzhlNTE5YmYyMiIsInZlcnNpb24iOjF9.9NTlO_5zLC8Y3mkwstviPb9WmMqPmXfWfEN0yONA6WYhh1jPy0gECEb5uF0G6wBMhTPDTqGMWOYIAF2vMeNbDA
- type: rouge
value: 12.3316
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTJhZTcxMDc5ODljMDBjYzFmYWIzNTA4M2NiZDUwYTMwNTVjZTUyZTU2M2IwYWE2YjkzMzMzMjg1MDU1OWE1NSIsInZlcnNpb24iOjF9.FRsoRao8qj6A8W7OeIVAoZCEc1HCZEzmKOs0CPkUceF19pk1ngaXt5K6kcPJ-5fYJydtfSuSnuG3aqlOEJeYDQ
- type: rouge
value: 22.075
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2FjNTMxMGYyNjgyNjk2YTQwZjM4MTM4Yjg0MTkyN2RmNDE5YTU5ZDNkZDFhZDM2YWRlNDI4M2JlMWYxNDQ3ZCIsInZlcnNpb24iOjF9.wsLUEYGJyMSJPPclOzb1hcRdE-VrZex2Sd5er_XVbe6bY1cRO5DdIn69sE9hmAcltefu4ikpHu2ihbv7qvj4Aw
- type: rouge
value: 31.1679
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTUyODVkZGIxYzMyZDczNzU5YjVkNTliZmM4ZTdiYWE2ZjJhNGM3ZDgzMWE3ZjA2MDBhZWQ1ZGY1YzNmZDMwNiIsInZlcnNpb24iOjF9.fPgMnnXY5oPdCn1STZ0HwUiil8OlLZ8ZWZZav_chDIQ7Kh1RKeLy0EG2vEhrB6IlyP7uZ3RmdT9VHM1_khrEAw
- type: loss
value: 2.745267391204834
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWQ2NDVmODI2ZTQyNmVjZjRkZDdlMTdiODBkZTlkNTFkODBjNjViMTZhMDVkYTkwYWIyNDFkZWZhZmJhODEwMyIsInZlcnNpb24iOjF9.9JWTqdGEhztS--N8grHY6q2a8taVu65Lr17ocXgudp4imhqr9Bhau2X2G5SLN7c1oYieKtyKcWdDAmVzHyTbDw
- type: gen_len
value: 157.3126
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWNiODFmMWQ1ZTkzZGNjNDkwM2ZiZjJlZmQ3N2ExNWJhYmUxYWM2ZGNiYzlhYTY5Y2RhOGVlZDhmN2ZmODQwYSIsInZlcnNpb24iOjF9.sRA9iBS4vzFDZtwM4Vs6Kevj3eiTkS5akApUWTZBCt58YSW8mpoKqsWcnQFEjDCCec-FfV_451OLIetcmDZiCA
- task:
type: summarization
name: Summarization
dataset:
name: xsum
type: xsum
config: default
split: test
metrics:
- type: rouge
value: 18.2975
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjJhMjQ0Yzc4ZmNkOWI5YjhmOTlmOTA4MTE0NWM4NGRlNjE0NDIwOTY2ZmQyNjA0ZmE5MjM2YjAyZDZiNWFkNiIsInZlcnNpb24iOjF9.2UJ48OcezjnfMC0dGjksZpAiXRGNAOHniHdN-tQmQPo0vXwRYNTyPrVULnVoBZUvSdycTYvjl0jDKNhZmtGfCA
- type: rouge
value: 2.6806
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTlkMmQwZTRmN2JlOTQ0N2I0YjdhOTBmYmU3MzEwNzE2ZjFiOTM4OWMyMWRhNmZjNTBkZWY5OGMwYTZhZDRhYSIsInZlcnNpb24iOjF9.7D-IR1aBxx1goOkbeA3Tzd1Wu0Zfi0yQVSG8HWSboM7J67TBHblFsFCVJE7Z2wZRbBW4WtuDIGAcl1d1_Wu_Aw
- type: rouge
value: 11.9453
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGZjNmY5NmU5ODBmMDQyMjhhNzY3NzBlNDEyMTE3NjY1ZmRkZDZkZWI1YTA0ZTA0NzU1MjMzOTNjZDA3YWM1MCIsInZlcnNpb24iOjF9.SlI42pwrWc_OlcBKOPtrYNzvK_DUk6IJlzrrtjvkZX7k1S7bguekAV-_rWHfn_82k8rJ1FQAReasGHu1dZ0aBw
- type: rouge
value: 14.2121
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2E2MGE0MTQ1YmU0MTJkOWY3ZDhhODIwYWNhNTE3YWJkZTFhYzM1ZjBmNGExODIzYmU2YzE1ODg4ZjdhZWMwMiIsInZlcnNpb24iOjF9.K5FEsZtSph0FqF5zwetkE-X5AKOlj5g_02DPdl-kEe1azKrBBZy9sDiS0WfIGfwHiRdNvKGKi8t3PAGPsfQwCQ
- type: loss
value: 4.836681365966797
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzhlYjA0YzZmYjdmYWQwNDFhNzIzYWNkYzM4OGFlOWJiY2EzYTkxYjk3ZmJmNGQyMGE1ZmYzMDU2MzhhMmVkMiIsInZlcnNpb24iOjF9.uHYwqPBg6K63exBvqt__c82gKi52OhPTRSrcIKHOECCmoXJLJKgFJCuIXGWMJ7UP4HG375e9uqunJB0XwC20DA
- type: gen_len
value: 96.2584
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjNjYzQzNmM5NTY2YzVhNzRkZjMxMzhiYTU1MDBiOGZkYjA4YTg0MmQzYzQ3YTk3N2YwMDA5MWNlM2Y4YTFmZiIsInZlcnNpb24iOjF9.dirG9kG6OdNi-YEMWHv0UMrHTjEt6VS9i6fRbbUeZd1OoP2fl6XcKoDIk6Us-cdiyVnCyyhWsMNsUufMAqLtDA
- task:
type: summarization
name: Summarization
dataset:
name: launch/gov_report
type: launch/gov_report
config: plain_text
split: test
metrics:
- type: rouge
value: 37.3609
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGExYjM5ODRiNThlZTU4ZTdhM2ZlZWRlNTgzNzc3N2ZjODk2ZjdlOGZlMDkzNmU2Yjk1NzQzZjQ5YzkwODllMCIsInZlcnNpb24iOjF9.JQIeaQkG-IlinWoyc6FKJZUgpWfqOsDhludqm5MgVsw68gsjo0nSPp_Y_1q26Y4dulZOLlQLyBAm3mlCA8s5Ag
- type: rouge
value: 8.6943
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWFjNzJkMzViOGM5YWQ0OGQ4ZTg3NTE5MzU1MjZkZjZiZmVkYTk0ZDhkYjAxMjZiZDVkZTYyYjk4MzRjNTQ3YiIsInZlcnNpb24iOjF9.9XJZ2UF6XyZNNrtp-XOEXC6etoDOFLq1xlIoMFEM9Jinisq3kWguXBiqPQWImLKra5WBm7jU_QIX-Fvn8sP-DA
- type: rouge
value: 17.9106
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWQ1MTdmNThiM2FiOGRmZWRmOTNlYWMwYTU1YjRiNTRlMGEwYjBmMmQ0YjQ4MDBhNzMzZmZkNjk3NjU0YzRhMSIsInZlcnNpb24iOjF9.040nGV6pig0Rzq9vkN83ZVWQzyjcVi13L36v0QF-Nhziol_dPPhuvghTlGWXWHwj6amsKzyh8M7rNfwL2TcsAQ
- type: rouge
value: 33.8022
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDYwOGRmYzg4ODc2NDExNjhhMjI5MDg3MjI0YTQ5ZDRhM2NjN2Q2ZjM5YTIwZDIxNmY3Y2JlMmMxYTE5MDE4ZiIsInZlcnNpb24iOjF9.S1nynUjLz7z4gf-0WFfPs-ZuZubhN9kXyVSrYNzOdT2gTJmByQWasKreZkVSWus-HNAHR8DhzL6UUWxuDMmAAQ
- type: loss
value: 3.4974069595336914
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzkyNmU5NTRhMTkxNjA1M2E1MjdiMTE0MzQyMDc4ODBkNmM1NDg1ZDk4OTNjODk2MThlZGZiYzQxOGE1YzgwMiIsInZlcnNpb24iOjF9.H9Oo0VKvcqAHcVNvjeEPEhQe5HP0v614suyCv75tfFGaPSKTIe3UlBNDdGOtqfUxb2zUNaBQ8MkA66C_Fkq6CA
- type: gen_len
value: 243.3453
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWE1MGQzMDc2NDViOGM5ZmVkZjk0NmY0NzliOTBhMmE3NmY5MmUxMTI3NGE2OTQzM2Y1NjdmN2NlZGFlODFlYiIsInZlcnNpb24iOjF9.635fcTp_czTabJUVR_dwpzdkntb4cxEbODAC9MMTKrLKEf9NHqDBJXQ-nBOieW05iCSYzw_tEi8O-QW-sRxDAw
- task:
type: summarization
name: Summarization
dataset:
name: kmfoda/booksum
type: kmfoda/booksum
config: kmfoda--booksum
split: test
metrics:
- type: rouge
value: 35.2043
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTRlZTdjZDRlZGMxYzA2NmRkYjBiMzZkY2Q1ODUyYjJkM2QwOTRmMzA3ZmU5MDI5ZmM1MmZkZDUwNzc0NjhmNyIsInZlcnNpb24iOjF9.zrskApkmkhbfQLtlgjf_n6i3WmZcmkDH7Sd-JTzOYAU3yk1_Zl4paGdmpXvyQY48M71qWsBYtEKkhnzrkvCGBA
- type: rouge
value: 5.746
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2FlMjU2MzU1MTljZjM0ZmFhMmJlZDAxMTcwZDk3YWE5NjVjYjE0YmEyMTgzY2UyMTVmZDY5ZWM1YmM1ZDA5NSIsInZlcnNpb24iOjF9.5nDuOwa98pon3VW1TazB2Vw1uJgh6pfFMorzgLMJFvhgwYz6_MvLR1dDUeffP4eyw7rGZjBmf039AM7CyKEgCg
- type: rouge
value: 15.6794
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjRmYzk3NWFhZDVlODA4YWRiMDU1ZWFhZmMwMWE4MmNkNmNjZWM3ZjUwYzI3MWIxM2Y4MTlhZDk2ZTg5YjkyYSIsInZlcnNpb24iOjF9.TLflM2CYNgz4DNt-TwjgdkTL8ebKckTNnlPVsGLUUGqNI1CvSswzsPedqmntCfKVsH2YAsKsR4ZUb1HtJFsSAw
- type: rouge
value: 32.1129
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzdhNWE1YjRjNGUzYWYyNzM4MjIyYThiODJhODU2OGVlOTYxOGNhZmQ4Mjk2ZDUwNmU0MGQwNjQ5NTk2MzU4ZiIsInZlcnNpb24iOjF9.5yvTmPktBuyzoVNHn7UHcci3OrZLTm7e9d_lQkJq8UwzUuso1wHoy_gdvnvpn2DvUfdcBi5sXgG4mtFnVnGgBw
- type: loss
value: 2.945225238800049
name: loss
verified: true
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- type: gen_len
value: 307.5493
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMmQ1YTgxYmRhYWViYjhhNmYzNjdlYzVhMTNmZTBkY2RiOTRlMTUzNTIzY2RjOTNhMjRmNGRmYjQyNTBmZWRiMiIsInZlcnNpb24iOjF9.7ItU-AQXB4EEj9U9kJceteBQbA5MkZoegeLhCdpZepEaXzqr6Zg3yHLCD9zL_6Svb9uxuin678KOT5Zf-2YWCQ
---
# long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13
> Evaluating some metric results before merging with the "main" wip version
This model is a fine-tuned version of [pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP12](https://huggingface.co/pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP12) on the `kmfoda/booksum`.
The "base" checkpoint that I update when a training session is productive is [here](https://huggingface.co/pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP)
## 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: 0.0006
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1.1
### Framework versions
- Transformers 4.21.2
- Pytorch 1.10.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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} | 0 | 2022-08-30T23:46:07Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_bn
metrics:
- sacrebleu
model-index:
- name: squad-bn-qgen-mt5-all-metric
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: squad_bn
type: squad_bn
args: squad_bn
metrics:
- name: Sacrebleu
type: sacrebleu
value: 6.4143
---
<!-- 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. -->
# squad-bn-qgen-mt5-all-metric
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the squad_bn dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7273
- Rouge1 Precision: 35.8589
- Rouge1 Recall: 29.7041
- Rouge1 Fmeasure: 31.6373
- Rouge2 Precision: 15.4203
- Rouge2 Recall: 12.5155
- Rouge2 Fmeasure: 13.3978
- Rougel Precision: 34.4684
- Rougel Recall: 28.5887
- Rougel Fmeasure: 30.4627
- Rougelsum Precision: 34.4252
- Rougelsum Recall: 28.5362
- Rougelsum Fmeasure: 30.4053
- Sacrebleu: 6.4143
- Meteor: 0.1416
- Gen Len: 16.7199
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Rougel Precision | Rougel Recall | Rougel Fmeasure | Rougelsum Precision | Rougelsum Recall | Rougelsum Fmeasure | Sacrebleu | Meteor | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------------------:|:----------------:|:------------------:|:---------:|:------:|:-------:|
| 0.8449 | 1.0 | 16396 | 0.7340 | 31.6476 | 26.8901 | 28.2871 | 13.621 | 11.3545 | 11.958 | 30.3276 | 25.7754 | 27.1048 | 30.3426 | 25.7489 | 27.0991 | 5.9655 | 0.1336 | 16.8685 |
| 0.7607 | 2.0 | 32792 | 0.7182 | 33.7173 | 28.6115 | 30.1049 | 14.8227 | 12.2059 | 12.9453 | 32.149 | 27.2036 | 28.6617 | 32.2479 | 27.2261 | 28.7272 | 6.6093 | 0.138 | 16.8522 |
| 0.7422 | 3.0 | 49188 | 0.7083 | 34.6128 | 29.0223 | 30.7248 | 14.9888 | 12.3092 | 13.1021 | 33.2507 | 27.8154 | 29.4599 | 33.2848 | 27.812 | 29.5064 | 6.2407 | 0.1416 | 16.5806 |
| 0.705 | 4.0 | 65584 | 0.7035 | 34.156 | 29.0012 | 30.546 | 14.72 | 12.0251 | 12.8161 | 32.7527 | 27.6511 | 29.1955 | 32.7692 | 27.6627 | 29.231 | 6.1784 | 0.1393 | 16.7793 |
| 0.6859 | 5.0 | 81980 | 0.7038 | 35.1405 | 29.6033 | 31.2614 | 15.5108 | 12.6414 | 13.5059 | 33.8335 | 28.4264 | 30.0745 | 33.8782 | 28.4349 | 30.0901 | 6.5896 | 0.144 | 16.6651 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
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} | 0 | null | ---
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/Tsubame/ddpm-butterflies-128/tensorboard?#scalars)
|
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} | 0 | null | ---
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: 0.8648740833380706
---
<!-- 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: 0.1365
- F1: 0.8649
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 |
| 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 |
| 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
DataikuNLP/average_word_embeddings_glove.6B.300d | [
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"license:apache-2.0"
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} | 0 | null |
language:
- "List of ISO 639-1 code for your language"
- lang1
- lang2
thumbnail: "url to a thumbnail used in social sharing"
tags:
- tag1
- tag2
license: "any valid license identifier"
datasets:
- dataset1
- dataset2
metrics:
- metric1
- metric2 |
DataikuNLP/distiluse-base-multilingual-cased-v1 | [
"pytorch",
"distilbert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | {
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"DistilBertModel"
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} | 29 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_10_0
model-index:
- name: wav2vec2-large-xls-r-300m-j-kana-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-j-kana-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_10_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7188
- Wer: 0.1285
## 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 397 | 3.8381 | 0.9571 |
| No log | 2.0 | 794 | 0.8909 | 0.2265 |
| 4.0962 | 3.0 | 1191 | 0.8076 | 0.2054 |
| 4.0962 | 4.0 | 1588 | 0.7300 | 0.1809 |
| 4.0962 | 5.0 | 1985 | 0.7322 | 0.1761 |
| 0.6325 | 6.0 | 2382 | 0.6478 | 0.1524 |
| 0.6325 | 7.0 | 2779 | 0.6559 | 0.1472 |
| 0.408 | 8.0 | 3176 | 0.6925 | 0.1500 |
| 0.408 | 9.0 | 3573 | 0.7567 | 0.1582 |
| 0.408 | 10.0 | 3970 | 0.6687 | 0.1358 |
| 0.29 | 11.0 | 4367 | 0.7223 | 0.1418 |
| 0.29 | 12.0 | 4764 | 0.7082 | 0.1328 |
| 0.2152 | 13.0 | 5161 | 0.7114 | 0.1340 |
| 0.2152 | 14.0 | 5558 | 0.7082 | 0.1280 |
| 0.2152 | 15.0 | 5955 | 0.7188 | 0.1285 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.10.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Davlan/bert-base-multilingual-cased-finetuned-igbo | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 15 | 2022-08-31T03:20:05Z | ---
license: afl-3.0
---
<p align="center">
<br>
<img src="https://expressai-xlab.s3.amazonaws.com/rst/intro_rst.png" width="1000"/>
<br>
</p>
# reStructured Pre-training (RST)
official [repository](https://github.com/ExpressAI/reStructured-Pretraining), [paper](https://arxiv.org/pdf/2206.11147.pdf), [easter eggs](http://expressai.co/peripherals/emoji-eng.html)
#### RST is a new paradigm for language pre-training, which
* unifies **26** different types of signal from **10** data sources (Totten Tomatoes, Dailymail, Wikipedia, Wikidata, Wikihow, Wordnet, arXiv etc ) in the world structurally, being pre-trained with a monolithcal model,
* surpasses strong competitors (e.g., T0) on **52/55** popular datasets from a variety of NLP tasks (classification, IE, retrieval, generation etc)
* achieves superior performance in National College Entrance Examination **(Gaokao-English, 高考-英语)** achieves **40** points higher than the average scores made by students and 15 points higher than GPT3 with **1/16** parameters. In particular, Qin gets a high score of **138.5** (the full mark is 150) in the 2018 English exam
In such a pre-training paradigm,
* Data-centric Pre-training: the role of data will be re-emphasized, and model pre-training and fine-tuning of downstream tasks are viewed as a process of data storing and accessing
* Pre-training over JSON instead of TEXT: a good storage mechanism should not only have the ability to cache a large amount of data but also consider the ease of access.
## Model Description
We release all models introduced in our [paper](https://arxiv.org/pdf/2206.11147.pdf), covering 13 different application scenarios. Each model contains 11 billion parameters.
| Model | Description | Recommended Application
| ----------- | ----------- |----------- |
| **rst-all-11b** | **Trained with all the signals below except signals that are used to train Gaokao models** | **All applications below （specialized models are recommended first if high performance is preferred）** |
| rst-fact-retrieval-11b | Trained with the following signals: WordNet meaning, WordNet part-of-speech, WordNet synonym, WordNet antonym, wikiHow category hierarchy, Wikidata relation, Wikidata entity typing, Paperswithcode entity typing | Knowledge intensive tasks, information extraction tasks,factual checker |
| rst-summarization-11b | Trained with the following signals: DailyMail summary, Paperswithcode summary, arXiv summary, wikiHow summary | Summarization or other general generation tasks, meta-evaluation (e.g., BARTScore) |
| rst-temporal-reasoning-11b | Trained with the following signals: DailyMail temporal information, wikiHow procedure | Temporal reasoning, relation extraction, event-based extraction |
| rst-information-extraction-11b | Trained with the following signals: Paperswithcode entity, Paperswithcode entity typing, Wikidata entity typing, Wikidata relation, Wikipedia entity | Named entity recognition, relation extraction and other general IE tasks in the news, scientific or other domains|
| rst-intent-detection-11b | Trained with the following signals: wikiHow goal-step relation | Intent prediction, event prediction |
| rst-topic-classification-11b | Trained with the following signals: DailyMail category, arXiv category, wikiHow text category, Wikipedia section title | general text classification |
| rst-word-sense-disambiguation-11b | Trained with the following signals: WordNet meaning, WordNet part-of-speech, WordNet synonym, WordNet antonym | Word sense disambiguation, part-of-speech tagging, general IE tasks, common sense reasoning |
| rst-natural-language-inference-11b | Trained with the following signals: ConTRoL dataset, DREAM dataset, LogiQA dataset, RACE & RACE-C dataset, ReClor dataset, DailyMail temporal information | Natural language inference, multiple-choice question answering, reasoning |
| rst-sentiment-classification-11b | Trained with the following signals: Rotten Tomatoes sentiment, Wikipedia sentiment | Sentiment classification, emotion classification |
| rst-gaokao-rc-11b | Trained with multiple-choice QA datasets that are used to train the [T0pp](https://huggingface.co/bigscience/T0pp) model | General multiple-choice question answering|
| rst-gaokao-cloze-11b | Trained with manually crafted cloze datasets | General cloze filling|
| rst-gaokao-writing-11b | Trained with example essays from past Gaokao-English exams and grammar error correction signals | Essay writing, story generation, grammar error correction and other text generation tasks |
## Have a try?
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("XLab/rst-all-11b")
model = AutoModelForSeq2SeqLM.from_pretrained("XLab/rst-all-11b")
inputs = tokenizer.encode("TEXT: this is the best cast iron skillet you will ever buy. QUERY: Is this review \"positive\" or \"negative\"", return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))
```
## Data for reStructure Pre-training
This dataset is a precious treasure, containing a variety of naturally occurring signals. Any downstream task you can think of (e.g., the college entrance exam mentioned in the RST paper) can benefit from being pre-trained on some of our provided signals. We spent several months collecting the following 29 signal types, accounting for a total of 46,926,447 data samples. We hope this dataset will be a valuable asset for everyone in natural language processing research.
We provide collected signals through [DataLab](https://github.com/ExpressAI/DataLab). For efficiency, we only provide 50,000 samples at most for each signal type. If you want all the samples we collected, please fill this [form](https://docs.google.com/forms/d/e/1FAIpQLSdPO50vSdfwoO3D7DQDVlupQnHgrXrwfF3ePE4X1H6BwgTn5g/viewform?usp=sf_link). More specifically, we collected the following signals.
###### We will be happy :smiley: to know if the resource is helpful for your work, and please cite our [work](https://github.com/ExpressAI/reStructured-Pretraining/blob/main/README.md#Bib) :blush:
| Mine | Signal | #Sample | Use in DataLab | Some Applications |
| --- | --- | --- | --- | --- |
| [Rotten Tomatoes](https://www.rottentomatoes.com/) | (review, rating) | 5,311,109 | `load_dataset("rst", "rotten_tomatoes_sentiment")` | Sentiment classification |
| [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (text, category) | 899,904 | `load_dataset("rst", "daily_mail_category")`| Topic classification |
| [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (title, text, summary) | 1,026,616 | `load_dataset("rst", "daily_mail_summary")` | Summarization; Sentence expansion|
| [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (text, events) | 1,006,412 | `load_dataset("rst", "daily_mail_temporal")` | Temporal reasoning|
| [Wikidata](https://www.wikidata.org/wiki/Wikidata:Main_Page) | (entity, entity_type, text) | 2,214,274 | `load_dataset("rst", "wikidata_entity")` | Entity typing|
| [Wikidata](https://www.wikidata.org/wiki/Wikidata:Main_Page) | (subject, object, relation, text) | 1,526,674 | `load_dataset("rst", "wikidata_relation")` | Relation extraction; Fact retrieval|
| [wikiHow](https://www.wikihow.com/Main-Page) | (text, category) | 112,109 | `load_dataset("rst", "wikihow_text_category")` | Topic classification |
| [wikiHow](https://www.wikihow.com/Main-Page) | (low_category, high_category) | 4,868 | `load_dataset("rst", "wikihow_category_hierarchy")` | Relation extraction; Commonsense reasoning|
| [wikiHow](https://www.wikihow.com/Main-Page) | (goal, steps) | 47,956 | `load_dataset("rst", "wikihow_goal_step")` | Intent detection|
| [wikiHow](https://www.wikihow.com/Main-Page) | (text, summary) | 703,278 | `load_dataset("rst", "wikihow_summary")` | Summarization; Sentence expansion |
| [wikiHow](https://www.wikihow.com/Main-Page) | (goal, first_step, second_step) | 47,787 | `load_dataset("rst", "wikihow_procedure")` | Temporal reasoning |
| [wikiHow](https://www.wikihow.com/Main-Page) | (question, description, answer, related_questions) | 47,705 | `load_dataset("rst", "wikihow_question")` | Question generation|
| [Wikipedia](https://www.wikipedia.org/) | (text, entities) |22,231,011 | `load_dataset("rst", "wikipedia_entities")` | Entity recognition|
[Wikipedia](https://www.wikipedia.org/) | (texts, titles) | 3,296,225 | `load_dataset("rst", "wikipedia_sections")` | Summarization|
| [WordNet](https://wordnet.princeton.edu/) | (word, sentence, pos) | 27,123 | `load_dataset("rst", "wordnet_pos")` | Part-of-speech tagging|
| [WordNet](https://wordnet.princeton.edu/) | (word, sentence, meaning, possible_meanings) | 27,123 | `load_dataset("rst", "wordnet_meaning")` | Word sense disambiguation|
| [WordNet](https://wordnet.princeton.edu/) | (word, sentence, synonyms) | 17,804 | `load_dataset("rst", "wordnet_synonym")`| Paraphrasing|
| [WordNet](https://wordnet.princeton.edu/) | (word, sentence, antonyms) | 6,408 | `load_dataset("rst", "wordnet_antonym")` |Negation |
| [ConTRoL]() | (premise, hypothesis, label) | 8,323 | `load_dataset("rst", "qa_control")` | Natural language inference|
|[DREAM](https://transacl.org/ojs/index.php/tacl/article/view/1534)| (context, question, options, answer) | 9,164 | `load_dataset("rst", "qa_dream")` | Reading comprehension|
| [LogiQA](https://doi.org/10.24963/ijcai.2020/501) | (context, question, options, answer) | 7,974 | `load_dataset("rst", "qa_logiqa")` | Reading comprehension|
| [ReClor](https://openreview.net/forum?id=HJgJtT4tvB) | (context, question, options, answer) | 5,138 | `load_dataset("rst", "qa_reclor")` |Reading comprehension |
| [RACE](https://doi.org/10.18653/v1/d17-1082) | (context, question, options, answer) | 44,880 | `load_dataset("rst", "qa_race")` | Reading comprehension|
| [RACE-C](http://proceedings.mlr.press/v101/liang19a.html) | (context, question, options, answer) | 5,093 | `load_dataset("rst", "qa_race_c")` | Reading comprehension|
| [TriviaQA](https://doi.org/10.18653/v1/P17-1147) | (context, question, answer) | 46,636 | `load_dataset("rst", "qa_triviaqa")` |Reading comprehension |
| [Arxiv](https://arxiv.org/) | (text, category) | 1,696,348 | `load_dataset("rst", "arxiv_category")` |Topic classification|
| [Arxiv](https://arxiv.org/) | (text, summary) | 1,696,348 | `load_dataset("rst", "arxiv_summary")` | Summarization; Sentence expansion|
| [Paperswithcode](https://paperswithcode.com/) | (text, entities, datasets, methods, tasks, metrics) | 4,731,233 | `load_dataset("rst", "paperswithcode_entity")` | Entity recognition|
| [Paperswithcode](https://paperswithcode.com/) | (text, summary) | 120,924 | `load_dataset("rst", "paperswithcode_summary")` | Summarization; Sentence expansion|
## Bibtext for Citation Info
```
@article{yuan2022restructured,
title={reStructured Pre-training},
author={Yuan, Weizhe and Liu, Pengfei},
journal={arXiv preprint arXiv:2206.11147},
year={2022}
}
``` |
Davlan/byt5-base-eng-yor-mt | [
"pytorch",
"t5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"T5ForConditionalGeneration"
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} | 11 | null | ---
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/js05212/ddpm-butterflies-128/tensorboard?#scalars)
|
Davlan/byt5-base-yor-eng-mt | [
"pytorch",
"t5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2999
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4977 | 1.0 | 782 | 2.3318 |
| 2.4232 | 2.0 | 1564 | 2.3005 |
| 2.386 | 3.0 | 2346 | 2.2721 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.12.1+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Davlan/m2m100_418M-eng-yor-mt | [
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"M2M100ForConditionalGeneration"
],
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} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-issues-128
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2456
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0986 | 1.0 | 291 | 1.6929 |
| 1.6401 | 2.0 | 582 | 1.4304 |
| 1.4881 | 3.0 | 873 | 1.3916 |
| 1.4 | 4.0 | 1164 | 1.3796 |
| 1.3416 | 5.0 | 1455 | 1.2012 |
| 1.2807 | 6.0 | 1746 | 1.2733 |
| 1.2396 | 7.0 | 2037 | 1.2646 |
| 1.1993 | 8.0 | 2328 | 1.2098 |
| 1.1661 | 9.0 | 2619 | 1.1862 |
| 1.1406 | 10.0 | 2910 | 1.2223 |
| 1.1294 | 11.0 | 3201 | 1.2056 |
| 1.1042 | 12.0 | 3492 | 1.1655 |
| 1.0827 | 13.0 | 3783 | 1.2525 |
| 1.0738 | 14.0 | 4074 | 1.1685 |
| 1.0626 | 15.0 | 4365 | 1.1182 |
| 1.0629 | 16.0 | 4656 | 1.2456 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Davlan/m2m100_418M-yor-eng-mt | [
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"M2M100ForConditionalGeneration"
],
"model_type": "m2m_100",
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} | 6 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: clinical-finetuned-AgitationModel
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. -->
# clinical-finetuned-AgitationModel
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9746
- Accuracy: 0.88
- Precision: 0.9178
- Recall: 0.9178
- F1: 0.9178
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0949 | 1.0 | 50 | 1.0393 | 0.85 | 0.8816 | 0.9178 | 0.8993 |
| 0.0475 | 2.0 | 100 | 1.0619 | 0.85 | 0.8816 | 0.9178 | 0.8993 |
| 0.0149 | 3.0 | 150 | 0.9746 | 0.88 | 0.9178 | 0.9178 | 0.9178 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Tokenizers 0.12.1
|
Davlan/mT5_base_yoruba_adr | [
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2003.10564",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"MT5ForConditionalGeneration"
],
"model_type": "mt5",
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} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4898 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.12.1+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Davlan/mbart50-large-eng-yor-mt | [
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MBartForConditionalGeneration"
],
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} | 5 | null | #Pre-trained-Language-Model-For-Chinese-Patent
ZL-RoBERTa-wwm: MLM with Whole Word Masking
在中文发明专利上进行训练，MLM任务使用了wwm策略 |
Davlan/xlm-roberta-base-finetuned-swahili | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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}
} | 40 | 2022-08-31T07:32:00Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
Davlan/xlm-roberta-base-finetuned-wolof | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
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} | 3 | null | ---
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/livingmagic/ddpm-butterflies-128/tensorboard?#scalars)
|
Davlan/xlm-roberta-base-finetuned-zulu | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: fintuning-sentiment-model-3000-samples
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fintuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3079
- Accuracy: 0.88
- F1: 0.8808
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Tokenizers 0.12.1
|
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} | 0 | null | ---
tags:
- flair
- token-classification
- sequence-tagger-model
---
### Demo: How to use in Flair
Requires:
- **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("osanseviero/flair_test5")
# make example sentence
sentence = Sentence("On September 1st George won 1 dollar while watching Game of Thrones.")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
``` |
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} | 0 | 2022-08-31T11:13:59Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 1612.90 +/- 407.25
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
...
```
|
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- HalfCheetahBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 1647.65 +/- 21.63
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: HalfCheetahBulletEnv-v0
type: HalfCheetahBulletEnv-v0
---
# **A2C** Agent playing **HalfCheetahBulletEnv-v0**
This is a trained model of a **A2C** agent playing **HalfCheetahBulletEnv-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
...
```
|
DeadBeast/mbert-base-cased-finetuned-bengali-fakenews | [
"pytorch",
"bert",
"text-classification",
"bengali",
"dataset:BanFakeNews",
"transformers",
"license:apache-2.0"
] | text-classification | {
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} | 37 | null | ---
license: bigscience-bloom-rail-1.0
language:
- zh
pipeline_tag: text-generation
widget:
- text: "中国的首都是"
---
This model is based on [bigscience/bloom-3b](https://huggingface.co/bigscience/bloom-3b).
We pruned its vocabulary from 250880 to 46145 with Chinese corpus to reduce GPU memory usage. So the total parameter is 2b5 now.
# How to use
```python
from transformers import BloomTokenizerFast, BloomForCausalLM
tokenizer = BloomTokenizerFast.from_pretrained('Langboat/bloom-2b5-zh')
model = BloomForCausalLM.from_pretrained('Langboat/bloom-2b5-zh')
print(tokenizer.batch_decode(model.generate(tokenizer.encode('中国的首都是', return_tensors='pt'))))
``` |
DeadBeast/roberta-base-pretrained-mr-2 | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"RobertaForMaskedLM"
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} | 5 | null | ---
license: bigscience-bloom-rail-1.0
language:
- zh
pipeline_tag: text-generation
widget:
- text: "中国的首都是"
---
This model is based on [bigscience/bloom-7b1](https://huggingface.co/bigscience/bloom-7b1).
We pruned its vocabulary from 250880 to 46145 with Chinese corpus to reduce GPU memory usage. So the total parameter is 6b4 now.
# How to use
```python
from transformers import BloomTokenizerFast, BloomForCausalLM
tokenizer = BloomTokenizerFast.from_pretrained('Langboat/bloom-6b4-zh')
model = BloomForCausalLM.from_pretrained('Langboat/bloom-6b4-zh')
print(tokenizer.batch_decode(model.generate(tokenizer.encode('中国的首都是', return_tensors='pt'))))
```
|
Declan/Breitbart_model_v7 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 5 | 2022-08-31T13:31:39Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 822.42 +/- 48.82
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
...
```
|
Declan/CNN_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 5 | 2022-08-31T14:24:00Z | ---
license: apache-2.0
language:
- hy
pipeline_tag: text-generation
tags:
- multilingual
- PyTorch
- Transformers
- gpt3
- gpt2
- Deepspeed
- Megatron
datasets:
- mc4
- wikipedia
thumbnail: "https://github.com/sberbank-ai/mgpt"
---
# Multilingual GPT model, Armenian language finetune
We introduce a monolingual GPT-3-based model for Armenian language
The model is based on [mGPT](https://huggingface.co/sberbank-ai/mGPT/), a family of autoregressive GPT-like models with 1.3 billion parameters trained on 60 languages from 25 language families using Wikipedia and Colossal Clean Crawled Corpus.
We reproduce the GPT-3 architecture using GPT-2 sources and the sparse attention mechanism, [Deepspeed](https://github.com/microsoft/DeepSpeed) and [Megatron](https://github.com/NVIDIA/Megatron-LM) frameworks allows us to effectively parallelize the training and inference steps. The resulting models show performance on par with the recently released [XGLM](https://arxiv.org/pdf/2112.10668.pdf) models at the same time covering more languages and enhancing NLP possibilities for low resource languages.
## Code
The source code for the mGPT XL model is available on [Github](https://github.com/sberbank-ai/mgpt)
## Paper
mGPT: Few-Shot Learners Go Multilingual
[Abstract](https://arxiv.org/abs/2204.07580) [PDF](https://arxiv.org/pdf/2204.07580.pdf)
```
@misc{https://doi.org/10.48550/arxiv.2204.07580,
doi = {10.48550/ARXIV.2204.07580},
url = {https://arxiv.org/abs/2204.07580},
author = {Shliazhko, Oleh and Fenogenova, Alena and Tikhonova, Maria and Mikhailov, Vladislav and Kozlova, Anastasia and Shavrina, Tatiana},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2; I.2.7, 68-06, 68-04, 68T50, 68T01},
title = {mGPT: Few-Shot Learners Go Multilingual},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
## Training
The model was fine-tuned on 170GB of Armenian texts, including MC4, Archive.org fiction, EANC public data, OpenSubtitles, OSCAR corpus and blog texts.
Val perplexity is 2.046.
The mGPT model was pre-trained for 12 days x 256 GPU (Tesla NVidia V100), 4 epochs, then 9 days x 64 GPU, 1 epoch
The Armenian finetune was around 7 days with 4 Tesla NVidia V100 and has made 160k steps.
What happens on this image? The model is originally trained with sparse attention masks, then fine-tuned with no sparsity on the last steps (perplexity and loss peak). Getting rid of the sparsity in the end of the training helps to integrate the model into the GPT2 HF class.
|
Declan/CNN_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 3 | 2022-08-31T14:33:40Z | ---
library_name: stable-baselines3
tags:
- HalfCheetahBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 1967.35 +/- 44.90
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: HalfCheetahBulletEnv-v0
type: HalfCheetahBulletEnv-v0
---
# **A2C** Agent playing **HalfCheetahBulletEnv-v0**
This is a trained model of a **A2C** agent playing **HalfCheetahBulletEnv-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
...
```
|
Declan/ChicagoTribune_model_v3 | [
"pytorch",
"bert",
"fill-mask",
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"autotrain_compatible"
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} | 3 | 2022-08-31T15:41:14Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: imagefolder
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. -->
# dress-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `imagefolder` 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/iramshiv/dress-128/tensorboard?#scalars)
|
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} | 0 | 2022-08-31T17:04:29Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 139.50 +/- 32.14
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
Declan/NPR_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-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-uncased-finetuned-squad
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: 2.9615
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 97 | 3.2690 |
| No log | 2.0 | 194 | 3.0873 |
| No log | 3.0 | 291 | 2.9615 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Declan/NPR_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 7 | null | ---
language:
- ur
- en
license: apache-2.0
datasets:
- iwslt2017
metrics:
- bleu
library_name: tensorflowtts
pipeline_tag: translation
---
### urd-eng
* source group: Urdu
* target group: English
* OPUS readme: [urd-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/urd-eng/README.md)
* model: transformer-align
* source language(s): urd
* target language(s): eng
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/urd-eng/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/urd-eng/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/urd-eng/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.urd.eng | 23.2 | 0.435 |
### System Info:
- hf_name: urd-eng
- source_languages: urd
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/urd-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ur', 'en']
- src_constituents: {'urd'}
- tgt_constituents: {'eng'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/urd-eng/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/urd-eng/opus-2020-06-17.test.txt
- src_alpha3: urd
- tgt_alpha3: eng
- short_pair: ur-en
- chrF2_score: 0.435
- bleu: 23.2
- brevity_penalty: 0.975
- ref_len: 12029.0
- src_name: Urdu
- tgt_name: English
- train_date: 2020-06-17
- src_alpha2: ur
- tgt_alpha2: en
- prefer_old: False
- long_pair: urd-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Declan/NPR_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 3 | 2022-08-31T17:57:28Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: ClimateBertQA
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. -->
# ClimateBertQA
This model is a fine-tuned version of [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3251
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.1604 | 1.0 | 4081 | 1.1894 |
| 0.8577 | 2.0 | 8162 | 1.1763 |
| 0.6395 | 3.0 | 12243 | 1.1118 |
| 0.5015 | 4.0 | 16324 | 1.3251 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Declan/NewYorkTimes_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 5 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="curt-tigges/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Declan/Politico_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 7 | null | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- AaronCU/autotrain-data-attribute-classification
co2_eq_emissions:
emissions: 0.002847008943614719
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1343651539
- CO2 Emissions (in grams): 0.0028
## Validation Metrics
- Loss: 0.163
- Accuracy: 0.949
- Macro F1: 0.947
- Micro F1: 0.949
- Weighted F1: 0.949
- Macro Precision: 0.943
- Micro Precision: 0.949
- Weighted Precision: 0.951
- Macro Recall: 0.952
- Micro Recall: 0.949
- Weighted Recall: 0.949
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/AaronCU/autotrain-attribute-classification-1343651539
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("AaronCU/autotrain-attribute-classification-1343651539", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("AaronCU/autotrain-attribute-classification-1343651539", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Denver/distilbert-base-uncased-finetuned-squad | [] | null | {
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} | 0 | null | ---
license: cc-by-4.0
language: hi
---
## HindBERT-Scratch
HindBERT is a Hindi BERT model. It is a base-BERT model trained from scratch on publicly available Hindi monolingual datasets.
[project link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can be found in our [paper] (<a href='https://arxiv.org/abs/2211.11418'> link </a>)
The best version of model is shared <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> here </a>
Citing:
```
@article{joshi2022l3cubehind,
author = {Joshi, Raviraj},
year = {2022},
month = {09},
pages = {},
title = {L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages},
doi = {10.13140/RG.2.2.14606.84809}
}
``` |
DeskDown/MarianMixFT_en-my | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- kensho/spgispeech
widget:
- example_title: Finance Speech
src: https://drive.google.com/uc?id=151bzDnN_f0Dfjjrg36nI97tXM39t5Ka8
model-index:
- name: wav2vec2-base-finetuned-spgispeech-dev
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-spgispeech-dev
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [kensho/spgispeech](https://huggingface.co/datasets/kensho/spgispeech) dev dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2897
- Wer: 0.1508
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.8285 | 2.22 | 1500 | 0.3361 | 0.2754 |
| 0.2582 | 4.44 | 3000 | 0.2643 | 0.2205 |
| 0.1697 | 6.66 | 4500 | 0.2467 | 0.2006 |
| 0.1314 | 8.88 | 6000 | 0.2711 | 0.1927 |
| 0.1084 | 11.09 | 7500 | 0.2521 | 0.1872 |
| 0.0922 | 13.31 | 9000 | 0.2588 | 0.1827 |
| 0.0818 | 15.53 | 10500 | 0.2572 | 0.1783 |
| 0.0712 | 17.75 | 12000 | 0.2720 | 0.1766 |
| 0.067 | 19.97 | 13500 | 0.2873 | 0.1751 |
| 0.0594 | 22.19 | 15000 | 0.2753 | 0.1704 |
| 0.0546 | 24.41 | 16500 | 0.2794 | 0.1694 |
| 0.0505 | 26.63 | 18000 | 0.2811 | 0.1665 |
| 0.0467 | 28.85 | 19500 | 0.2906 | 0.1657 |
| 0.0417 | 31.07 | 21000 | 0.3043 | 0.1661 |
| 0.0395 | 33.28 | 22500 | 0.3068 | 0.1627 |
| 0.0368 | 35.5 | 24000 | 0.3096 | 0.1617 |
| 0.0334 | 37.72 | 25500 | 0.3036 | 0.1581 |
| 0.0322 | 39.94 | 27000 | 0.2819 | 0.1564 |
| 0.0286 | 42.16 | 28500 | 0.2936 | 0.1544 |
| 0.0279 | 44.38 | 30000 | 0.2914 | 0.1534 |
| 0.0264 | 46.6 | 31500 | 0.2957 | 0.1519 |
| 0.0241 | 48.82 | 33000 | 0.2897 | 0.1508 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
DimaOrekhov/transformer-method-name | [
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"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
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} | 8 | null | ---
language:
- it
tags:
- Text Classification
datasets:
- TAG-IT
---
Write an italian sentence with the prefix "Classifica Argomento: " to get a topic classification of the sentence.
The dataset used for the task is: [TAG-IT](https://sites.google.com/view/tag-it-2020/).
The model is a fine tuned version of [IT5-base](https://huggingface.co/gsarti/it5-base) of Sarti and Nissim. |
Donghyun/L2_BERT | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned-ViT-human-action-recognition-v1
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. -->
# finetuned-ViT-human-action-recognition-v1
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Human_Action_Recognition dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1427
- Accuracy: 0.0791
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4986 | 0.13 | 100 | 3.1427 | 0.0791 |
| 1.1929 | 0.25 | 200 | 3.4083 | 0.0726 |
| 1.2673 | 0.38 | 300 | 3.4615 | 0.0769 |
| 0.9805 | 0.51 | 400 | 3.9192 | 0.0824 |
| 1.158 | 0.63 | 500 | 4.2648 | 0.0698 |
| 1.2544 | 0.76 | 600 | 4.5536 | 0.0574 |
| 1.0073 | 0.89 | 700 | 4.0310 | 0.0819 |
| 0.9315 | 1.02 | 800 | 4.5154 | 0.0702 |
| 0.9063 | 1.14 | 900 | 4.7162 | 0.0633 |
| 0.6756 | 1.27 | 1000 | 4.6482 | 0.0626 |
| 1.0239 | 1.4 | 1100 | 4.6437 | 0.0635 |
| 0.7634 | 1.52 | 1200 | 4.5625 | 0.0752 |
| 0.8365 | 1.65 | 1300 | 4.9912 | 0.0561 |
| 0.8979 | 1.78 | 1400 | 5.1739 | 0.0356 |
| 0.9448 | 1.9 | 1500 | 4.8946 | 0.0541 |
| 0.697 | 2.03 | 1600 | 4.9516 | 0.0741 |
| 0.7861 | 2.16 | 1700 | 5.0090 | 0.0776 |
| 0.6404 | 2.28 | 1800 | 5.3905 | 0.0643 |
| 0.7939 | 2.41 | 1900 | 4.9159 | 0.1015 |
| 0.6331 | 2.54 | 2000 | 5.3083 | 0.0589 |
| 0.6082 | 2.66 | 2100 | 4.8538 | 0.0857 |
| 0.6229 | 2.79 | 2200 | 5.3086 | 0.0689 |
| 0.6964 | 2.92 | 2300 | 5.3745 | 0.0713 |
| 0.5246 | 3.05 | 2400 | 5.0369 | 0.0796 |
| 0.6097 | 3.17 | 2500 | 5.2935 | 0.0743 |
| 0.5778 | 3.3 | 2600 | 5.5431 | 0.0709 |
| 0.4196 | 3.43 | 2700 | 5.5508 | 0.0759 |
| 0.5495 | 3.55 | 2800 | 5.5728 | 0.0813 |
| 0.5932 | 3.68 | 2900 | 5.7992 | 0.0663 |
| 0.4382 | 3.81 | 3000 | 5.8010 | 0.0643 |
| 0.4827 | 3.93 | 3100 | 5.7529 | 0.0680 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Doohae/q_encoder | [
"pytorch"
] | null | {
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} | 3 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-finetuned-mbti-0901
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-finetuned-mbti-0901
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9470
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.1073 | 1.0 | 9906 | 4.0111 |
| 4.0302 | 2.0 | 19812 | 3.9761 |
| 3.9757 | 3.0 | 29718 | 3.9578 |
| 3.9471 | 4.0 | 39624 | 3.9495 |
| 3.9187 | 5.0 | 49530 | 3.9470 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Doquey/DialoGPT-small-Luisbot1 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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} | 7 | null | ---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: twitter-roberta-base-stance-abortionV3
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. -->
# twitter-roberta-base-stance-abortionV3
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-stance-abortion](https://huggingface.co/cardiffnlp/twitter-roberta-base-stance-abortion) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5095
- F1: 0.7917
## 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8492 | 1.0 | 12 | 0.4862 | 0.7917 |
| 0.7291 | 2.0 | 24 | 0.4264 | 0.7917 |
| 0.5465 | 3.0 | 36 | 0.6450 | 0.7917 |
| 0.5905 | 4.0 | 48 | 0.5857 | 0.7917 |
| 0.4556 | 5.0 | 60 | 0.5095 | 0.7917 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
DoyyingFace/bert-asian-hate-tweets-asian-clean-with-unclean-valid | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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} | 29 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Imene/vit-base-patch16-224-in21k-wwwwwi
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. -->
# Imene/vit-base-patch16-224-in21k-wwwwwi
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.2187
- Train Accuracy: 0.5652
- Train Top-3-accuracy: 0.7611
- Validation Loss: 3.8221
- Validation Accuracy: 0.2540
- Validation Top-3-accuracy: 0.4409
- 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4920, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 5.3476 | 0.0283 | 0.0716 | 5.1306 | 0.0483 | 0.1240 | 0 |
| 4.9357 | 0.0914 | 0.2057 | 4.7998 | 0.1158 | 0.2385 | 1 |
| 4.6155 | 0.1641 | 0.3230 | 4.5616 | 0.1430 | 0.2891 | 2 |
| 4.3325 | 0.2269 | 0.4188 | 4.3480 | 0.1722 | 0.3391 | 3 |
| 4.0702 | 0.2915 | 0.4984 | 4.1662 | 0.2042 | 0.3886 | 4 |
| 3.8262 | 0.3638 | 0.5758 | 4.0416 | 0.2296 | 0.4067 | 5 |
| 3.6117 | 0.4258 | 0.6415 | 3.9451 | 0.2329 | 0.4234 | 6 |
| 3.4324 | 0.4855 | 0.6956 | 3.8690 | 0.2499 | 0.4397 | 7 |
| 3.2991 | 0.5320 | 0.7376 | 3.8351 | 0.2553 | 0.4359 | 8 |
| 3.2187 | 0.5652 | 0.7611 | 3.8221 | 0.2540 | 0.4409 | 9 |
### Framework versions
- Transformers 4.21.2
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-12 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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} | 29 | null | ---
language: en
license: apache-2.0
datasets: climatebert/environmental_claims
tags:
- ClimateBERT
- climate
---
# Model Card for environmental-claims
## Model Description
The environmental-claims model is fine-tuned on the [EnvironmentalClaims](https://huggingface.co/datasets/climatebert/environmental_claims) dataset by using the [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) model as pre-trained language model. The underlying methodology can be found in our [research paper](https://arxiv.org/abs/2209.00507).
## Climate Performance Model Card
| environmental-claims | |
|--------------------------------------------------------------------------|----------------|
| 1. Is the resulting model publicly available? | Yes |
| 2. How much time does the training of the final model take? | < 5 min |
| 3. How much time did all experiments take (incl. hyperparameter search)? | 60 hours |
| 4. What was the power of GPU and CPU? | 0.3 kW |
| 5. At which geo location were the computations performed? | Switzerland |
| 6. What was the energy mix at the geo location? | 89 gCO2eq/kWh |
| 7. How much CO2eq was emitted to train the final model? | 2.2 g |
| 8. How much CO2eq was emitted for all experiments? | 1.6 kg |
| 9. What is the average CO2eq emission for the inference of one sample? | 0.0067 mg |
| 10. Which positive environmental impact can be expected from this work? | This work can help detect and evaluate environmental claims and thus have a positive impact on the environment in the future. |
| 11. Comments | - |
## Citation Information
```bibtex
@misc{stammbach2022environmentalclaims,
title = {A Dataset for Detecting Real-World Environmental Claims},
author = {Stammbach, Dominik and Webersinke, Nicolas and Bingler, Julia Anna and Kraus, Mathias and Leippold, Markus},
year = {2022},
doi = {10.48550/ARXIV.2209.00507},
url = {https://arxiv.org/abs/2209.00507},
publisher = {arXiv},
}
``` |
albert-large-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"AlbertForMaskedLM"
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} | 687 | 2022-09-01T15:43:56Z | ---
license: apache-2.0
---
TRACER with EfficientNet v1 b7 encoder. |
bert-large-cased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"BertForMaskedLM"
],
"model_type": "bert",
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},
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} | 2,316 | 2022-09-01T16:54:26Z | ---
license: afl-3.0
---
<p align="center">
<br>
<img src="https://expressai-xlab.s3.amazonaws.com/rst/intro_rst.png" width="1000"/>
<br>
</p>
# reStructured Pre-training (RST)
official [repository](https://github.com/ExpressAI/reStructured-Pretraining), [paper](https://arxiv.org/pdf/2206.11147.pdf), [easter eggs](http://expressai.co/peripherals/emoji-eng.html)
#### RST is a new paradigm for language pre-training, which
* unifies **26** different types of signal from **10** data sources (Totten Tomatoes, Dailymail, Wikipedia, Wikidata, Wikihow, Wordnet, arXiv etc ) in the world structurally, being pre-trained with a monolithcal model,
* surpasses strong competitors (e.g., T0) on **52/55** popular datasets from a variety of NLP tasks (classification, IE, retrieval, generation etc)
* achieves superior performance in National College Entrance Examination **(Gaokao-English, 高考-英语)** achieves **40** points higher than the average scores made by students and 15 points higher than GPT3 with **1/16** parameters. In particular, Qin gets a high score of **138.5** (the full mark is 150) in the 2018 English exam
In such a pre-training paradigm,
* Data-centric Pre-training: the role of data will be re-emphasized, and model pre-training and fine-tuning of downstream tasks are viewed as a process of data storing and accessing
* Pre-training over JSON instead of TEXT: a good storage mechanism should not only have the ability to cache a large amount of data but also consider the ease of access.
## Model Description
We release all models introduced in our [paper](https://arxiv.org/pdf/2206.11147.pdf), covering 13 different application scenarios. Each model contains 11 billion parameters.
| Model | Description | Recommended Application
| ----------- | ----------- |----------- |
| rst-all-11b | Trained with all the signals below except signals that are used to train Gaokao models | All applications below （specialized models are recommended first if high performance is preferred） |
| rst-fact-retrieval-11b | Trained with the following signals: WordNet meaning, WordNet part-of-speech, WordNet synonym, WordNet antonym, wikiHow category hierarchy, Wikidata relation, Wikidata entity typing, Paperswithcode entity typing | Knowledge intensive tasks, information extraction tasks,factual checker |
| rst-summarization-11b | Trained with the following signals: DailyMail summary, Paperswithcode summary, arXiv summary, wikiHow summary | Summarization or other general generation tasks, meta-evaluation (e.g., BARTScore) |
| rst-temporal-reasoning-11b | Trained with the following signals: DailyMail temporal information, wikiHow procedure | Temporal reasoning, relation extraction, event-based extraction |
| rst-information-extraction-11b | Trained with the following signals: Paperswithcode entity, Paperswithcode entity typing, Wikidata entity typing, Wikidata relation, Wikipedia entity | Named entity recognition, relation extraction and other general IE tasks in the news, scientific or other domains|
| rst-intent-detection-11b | Trained with the following signals: wikiHow goal-step relation | Intent prediction, event prediction |
| rst-topic-classification-11b | Trained with the following signals: DailyMail category, arXiv category, wikiHow text category, Wikipedia section title | general text classification |
| rst-word-sense-disambiguation-11b | Trained with the following signals: WordNet meaning, WordNet part-of-speech, WordNet synonym, WordNet antonym | Word sense disambiguation, part-of-speech tagging, general IE tasks, common sense reasoning |
| rst-natural-language-inference-11b | Trained with the following signals: ConTRoL dataset, DREAM dataset, LogiQA dataset, RACE & RACE-C dataset, ReClor dataset, DailyMail temporal information | Natural language inference, multiple-choice question answering, reasoning |
| rst-sentiment-classification-11b | Trained with the following signals: Rotten Tomatoes sentiment, Wikipedia sentiment | Sentiment classification, emotion classification |
| rst-gaokao-rc-11b | Trained with multiple-choice QA datasets that are used to train the [T0pp](https://huggingface.co/bigscience/T0pp) model | General multiple-choice question answering|
| **rst-gaokao-cloze-11b** | **Trained with manually crafted cloze datasets** | **General cloze filling**|
| rst-gaokao-writing-11b | Trained with example essays from past Gaokao-English exams and grammar error correction signals | Essay writing, story generation, grammar error correction and other text generation tasks |
## Have a try?
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("XLab/rst-all-11b")
model = AutoModelForSeq2SeqLM.from_pretrained("XLab/rst-all-11b")
inputs = tokenizer.encode("TEXT: this is the best cast iron skillet you will ever buy. QUERY: Is this review \"positive\" or \"negative\"", return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))
```
## Data for reStructure Pre-training
This dataset is a precious treasure, containing a variety of naturally occurring signals. Any downstream task you can think of (e.g., the college entrance exam mentioned in the RST paper) can benefit from being pre-trained on some of our provided signals. We spent several months collecting the following 29 signal types, accounting for a total of 46,926,447 data samples. We hope this dataset will be a valuable asset for everyone in natural language processing research.
We provide collected signals through [DataLab](https://github.com/ExpressAI/DataLab). For efficiency, we only provide 50,000 samples at most for each signal type. If you want all the samples we collected, please fill this [form](https://docs.google.com/forms/d/e/1FAIpQLSdPO50vSdfwoO3D7DQDVlupQnHgrXrwfF3ePE4X1H6BwgTn5g/viewform?usp=sf_link). More specifically, we collected the following signals.
###### We will be happy :smiley: to know if the resource is helpful for your work, and please cite our [work](https://github.com/ExpressAI/reStructured-Pretraining/blob/main/README.md#Bib) :blush:
| Mine | Signal | #Sample | Use in DataLab | Some Applications |
| --- | --- | --- | --- | --- |
| [Rotten Tomatoes](https://www.rottentomatoes.com/) | (review, rating) | 5,311,109 | `load_dataset("rst", "rotten_tomatoes_sentiment")` | Sentiment classification |
| [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (text, category) | 899,904 | `load_dataset("rst", "daily_mail_category")`| Topic classification |
| [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (title, text, summary) | 1,026,616 | `load_dataset("rst", "daily_mail_summary")` | Summarization; Sentence expansion|
| [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (text, events) | 1,006,412 | `load_dataset("rst", "daily_mail_temporal")` | Temporal reasoning|
| [Wikidata](https://www.wikidata.org/wiki/Wikidata:Main_Page) | (entity, entity_type, text) | 2,214,274 | `load_dataset("rst", "wikidata_entity")` | Entity typing|
| [Wikidata](https://www.wikidata.org/wiki/Wikidata:Main_Page) | (subject, object, relation, text) | 1,526,674 | `load_dataset("rst", "wikidata_relation")` | Relation extraction; Fact retrieval|
| [wikiHow](https://www.wikihow.com/Main-Page) | (text, category) | 112,109 | `load_dataset("rst", "wikihow_text_category")` | Topic classification |
| [wikiHow](https://www.wikihow.com/Main-Page) | (low_category, high_category) | 4,868 | `load_dataset("rst", "wikihow_category_hierarchy")` | Relation extraction; Commonsense reasoning|
| [wikiHow](https://www.wikihow.com/Main-Page) | (goal, steps) | 47,956 | `load_dataset("rst", "wikihow_goal_step")` | Intent detection|
| [wikiHow](https://www.wikihow.com/Main-Page) | (text, summary) | 703,278 | `load_dataset("rst", "wikihow_summary")` | Summarization; Sentence expansion |
| [wikiHow](https://www.wikihow.com/Main-Page) | (goal, first_step, second_step) | 47,787 | `load_dataset("rst", "wikihow_procedure")` | Temporal reasoning |
| [wikiHow](https://www.wikihow.com/Main-Page) | (question, description, answer, related_questions) | 47,705 | `load_dataset("rst", "wikihow_question")` | Question generation|
| [Wikipedia](https://www.wikipedia.org/) | (text, entities) |22,231,011 | `load_dataset("rst", "wikipedia_entities")` | Entity recognition|
[Wikipedia](https://www.wikipedia.org/) | (texts, titles) | 3,296,225 | `load_dataset("rst", "wikipedia_sections")` | Summarization|
| [WordNet](https://wordnet.princeton.edu/) | (word, sentence, pos) | 27,123 | `load_dataset("rst", "wordnet_pos")` | Part-of-speech tagging|
| [WordNet](https://wordnet.princeton.edu/) | (word, sentence, meaning, possible_meanings) | 27,123 | `load_dataset("rst", "wordnet_meaning")` | Word sense disambiguation|
| [WordNet](https://wordnet.princeton.edu/) | (word, sentence, synonyms) | 17,804 | `load_dataset("rst", "wordnet_synonym")`| Paraphrasing|
| [WordNet](https://wordnet.princeton.edu/) | (word, sentence, antonyms) | 6,408 | `load_dataset("rst", "wordnet_antonym")` |Negation |
| [ConTRoL]() | (premise, hypothesis, label) | 8,323 | `load_dataset("rst", "qa_control")` | Natural language inference|
|[DREAM](https://transacl.org/ojs/index.php/tacl/article/view/1534)| (context, question, options, answer) | 9,164 | `load_dataset("rst", "qa_dream")` | Reading comprehension|
| [LogiQA](https://doi.org/10.24963/ijcai.2020/501) | (context, question, options, answer) | 7,974 | `load_dataset("rst", "qa_logiqa")` | Reading comprehension|
| [ReClor](https://openreview.net/forum?id=HJgJtT4tvB) | (context, question, options, answer) | 5,138 | `load_dataset("rst", "qa_reclor")` |Reading comprehension |
| [RACE](https://doi.org/10.18653/v1/d17-1082) | (context, question, options, answer) | 44,880 | `load_dataset("rst", "qa_race")` | Reading comprehension|
| [RACE-C](http://proceedings.mlr.press/v101/liang19a.html) | (context, question, options, answer) | 5,093 | `load_dataset("rst", "qa_race_c")` | Reading comprehension|
| [TriviaQA](https://doi.org/10.18653/v1/P17-1147) | (context, question, answer) | 46,636 | `load_dataset("rst", "qa_triviaqa")` |Reading comprehension |
| [Arxiv](https://arxiv.org/) | (text, category) | 1,696,348 | `load_dataset("rst", "arxiv_category")` |Topic classification|
| [Arxiv](https://arxiv.org/) | (text, summary) | 1,696,348 | `load_dataset("rst", "arxiv_summary")` | Summarization; Sentence expansion|
| [Paperswithcode](https://paperswithcode.com/) | (text, entities, datasets, methods, tasks, metrics) | 4,731,233 | `load_dataset("rst", "paperswithcode_entity")` | Entity recognition|
| [Paperswithcode](https://paperswithcode.com/) | (text, summary) | 120,924 | `load_dataset("rst", "paperswithcode_summary")` | Summarization; Sentence expansion|
## Bibtext for Citation Info
```
@article{yuan2022restructured,
title={reStructured Pre-training},
author={Yuan, Weizhe and Liu, Pengfei},
journal={arXiv preprint arXiv:2206.11147},
year={2022}
}
``` |
distilbert-base-german-cased | [
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"de",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 43,667 | 2022-09-01T18:23:04Z | ---
license: afl-3.0
---
<p align="center">
<br>
<img src="https://expressai-xlab.s3.amazonaws.com/rst/intro_rst.png" width="1000"/>
<br>
</p>
# reStructured Pre-training (RST)
official [repository](https://github.com/ExpressAI/reStructured-Pretraining), [paper](https://arxiv.org/pdf/2206.11147.pdf), [easter eggs](http://expressai.co/peripherals/emoji-eng.html)
#### RST is a new paradigm for language pre-training, which
* unifies **26** different types of signal from **10** data sources (Totten Tomatoes, Dailymail, Wikipedia, Wikidata, Wikihow, Wordnet, arXiv etc ) in the world structurally, being pre-trained with a monolithcal model,
* surpasses strong competitors (e.g., T0) on **52/55** popular datasets from a variety of NLP tasks (classification, IE, retrieval, generation etc)
* achieves superior performance in National College Entrance Examination **(Gaokao-English, 高考-英语)** achieves **40** points higher than the average scores made by students and 15 points higher than GPT3 with **1/16** parameters. In particular, Qin gets a high score of **138.5** (the full mark is 150) in the 2018 English exam
In such a pre-training paradigm,
* Data-centric Pre-training: the role of data will be re-emphasized, and model pre-training and fine-tuning of downstream tasks are viewed as a process of data storing and accessing
* Pre-training over JSON instead of TEXT: a good storage mechanism should not only have the ability to cache a large amount of data but also consider the ease of access.
## Model Description
We release all models introduced in our [paper](https://arxiv.org/pdf/2206.11147.pdf), covering 13 different application scenarios. Each model contains 11 billion parameters.
| Model | Description | Recommended Application
| ----------- | ----------- |----------- |
| rst-all-11b | Trained with all the signals below except signals that are used to train Gaokao models | All applications below （specialized models are recommended first if high performance is preferred） |
| rst-fact-retrieval-11b | Trained with the following signals: WordNet meaning, WordNet part-of-speech, WordNet synonym, WordNet antonym, wikiHow category hierarchy, Wikidata relation, Wikidata entity typing, Paperswithcode entity typing | Knowledge intensive tasks, information extraction tasks,factual checker |
| rst-summarization-11b | Trained with the following signals: DailyMail summary, Paperswithcode summary, arXiv summary, wikiHow summary | Summarization or other general generation tasks, meta-evaluation (e.g., BARTScore) |
| rst-temporal-reasoning-11b | Trained with the following signals: DailyMail temporal information, wikiHow procedure | Temporal reasoning, relation extraction, event-based extraction |
| rst-information-extraction-11b | Trained with the following signals: Paperswithcode entity, Paperswithcode entity typing, Wikidata entity typing, Wikidata relation, Wikipedia entity | Named entity recognition, relation extraction and other general IE tasks in the news, scientific or other domains|
| rst-intent-detection-11b | Trained with the following signals: wikiHow goal-step relation | Intent prediction, event prediction |
| rst-topic-classification-11b | Trained with the following signals: DailyMail category, arXiv category, wikiHow text category, Wikipedia section title | general text classification |
| rst-word-sense-disambiguation-11b | Trained with the following signals: WordNet meaning, WordNet part-of-speech, WordNet synonym, WordNet antonym | Word sense disambiguation, part-of-speech tagging, general IE tasks, common sense reasoning |
| rst-natural-language-inference-11b | Trained with the following signals: ConTRoL dataset, DREAM dataset, LogiQA dataset, RACE & RACE-C dataset, ReClor dataset, DailyMail temporal information | Natural language inference, multiple-choice question answering, reasoning |
| rst-sentiment-classification-11b | Trained with the following signals: Rotten Tomatoes sentiment, Wikipedia sentiment | Sentiment classification, emotion classification |
| **rst-gaokao-rc-11b** | **Trained with multiple-choice QA datasets that are used to train the [T0pp](https://huggingface.co/bigscience/T0pp) model** | **General multiple-choice question answering**|
| rst-gaokao-cloze-11b | Trained with manually crafted cloze datasets | General cloze filling|
| rst-gaokao-writing-11b | Trained with example essays from past Gaokao-English exams and grammar error correction signals | Essay writing, story generation, grammar error correction and other text generation tasks |
## Have a try?
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("XLab/rst-all-11b")
model = AutoModelForSeq2SeqLM.from_pretrained("XLab/rst-all-11b")
inputs = tokenizer.encode("TEXT: this is the best cast iron skillet you will ever buy. QUERY: Is this review \"positive\" or \"negative\"", return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))
```
## Data for reStructure Pre-training
This dataset is a precious treasure, containing a variety of naturally occurring signals. Any downstream task you can think of (e.g., the college entrance exam mentioned in the RST paper) can benefit from being pre-trained on some of our provided signals. We spent several months collecting the following 29 signal types, accounting for a total of 46,926,447 data samples. We hope this dataset will be a valuable asset for everyone in natural language processing research.
We provide collected signals through [DataLab](https://github.com/ExpressAI/DataLab). For efficiency, we only provide 50,000 samples at most for each signal type. If you want all the samples we collected, please fill this [form](https://docs.google.com/forms/d/e/1FAIpQLSdPO50vSdfwoO3D7DQDVlupQnHgrXrwfF3ePE4X1H6BwgTn5g/viewform?usp=sf_link). More specifically, we collected the following signals.
###### We will be happy :smiley: to know if the resource is helpful for your work, and please cite our [work](https://github.com/ExpressAI/reStructured-Pretraining/blob/main/README.md#Bib) :blush:
| Mine | Signal | #Sample | Use in DataLab | Some Applications |
| --- | --- | --- | --- | --- |
| [Rotten Tomatoes](https://www.rottentomatoes.com/) | (review, rating) | 5,311,109 | `load_dataset("rst", "rotten_tomatoes_sentiment")` | Sentiment classification |
| [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (text, category) | 899,904 | `load_dataset("rst", "daily_mail_category")`| Topic classification |
| [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (title, text, summary) | 1,026,616 | `load_dataset("rst", "daily_mail_summary")` | Summarization; Sentence expansion|
| [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (text, events) | 1,006,412 | `load_dataset("rst", "daily_mail_temporal")` | Temporal reasoning|
| [Wikidata](https://www.wikidata.org/wiki/Wikidata:Main_Page) | (entity, entity_type, text) | 2,214,274 | `load_dataset("rst", "wikidata_entity")` | Entity typing|
| [Wikidata](https://www.wikidata.org/wiki/Wikidata:Main_Page) | (subject, object, relation, text) | 1,526,674 | `load_dataset("rst", "wikidata_relation")` | Relation extraction; Fact retrieval|
| [wikiHow](https://www.wikihow.com/Main-Page) | (text, category) | 112,109 | `load_dataset("rst", "wikihow_text_category")` | Topic classification |
| [wikiHow](https://www.wikihow.com/Main-Page) | (low_category, high_category) | 4,868 | `load_dataset("rst", "wikihow_category_hierarchy")` | Relation extraction; Commonsense reasoning|
| [wikiHow](https://www.wikihow.com/Main-Page) | (goal, steps) | 47,956 | `load_dataset("rst", "wikihow_goal_step")` | Intent detection|
| [wikiHow](https://www.wikihow.com/Main-Page) | (text, summary) | 703,278 | `load_dataset("rst", "wikihow_summary")` | Summarization; Sentence expansion |
| [wikiHow](https://www.wikihow.com/Main-Page) | (goal, first_step, second_step) | 47,787 | `load_dataset("rst", "wikihow_procedure")` | Temporal reasoning |
| [wikiHow](https://www.wikihow.com/Main-Page) | (question, description, answer, related_questions) | 47,705 | `load_dataset("rst", "wikihow_question")` | Question generation|
| [Wikipedia](https://www.wikipedia.org/) | (text, entities) |22,231,011 | `load_dataset("rst", "wikipedia_entities")` | Entity recognition|
[Wikipedia](https://www.wikipedia.org/) | (texts, titles) | 3,296,225 | `load_dataset("rst", "wikipedia_sections")` | Summarization|
| [WordNet](https://wordnet.princeton.edu/) | (word, sentence, pos) | 27,123 | `load_dataset("rst", "wordnet_pos")` | Part-of-speech tagging|
| [WordNet](https://wordnet.princeton.edu/) | (word, sentence, meaning, possible_meanings) | 27,123 | `load_dataset("rst", "wordnet_meaning")` | Word sense disambiguation|
| [WordNet](https://wordnet.princeton.edu/) | (word, sentence, synonyms) | 17,804 | `load_dataset("rst", "wordnet_synonym")`| Paraphrasing|
| [WordNet](https://wordnet.princeton.edu/) | (word, sentence, antonyms) | 6,408 | `load_dataset("rst", "wordnet_antonym")` |Negation |
| [ConTRoL]() | (premise, hypothesis, label) | 8,323 | `load_dataset("rst", "qa_control")` | Natural language inference|
|[DREAM](https://transacl.org/ojs/index.php/tacl/article/view/1534)| (context, question, options, answer) | 9,164 | `load_dataset("rst", "qa_dream")` | Reading comprehension|
| [LogiQA](https://doi.org/10.24963/ijcai.2020/501) | (context, question, options, answer) | 7,974 | `load_dataset("rst", "qa_logiqa")` | Reading comprehension|
| [ReClor](https://openreview.net/forum?id=HJgJtT4tvB) | (context, question, options, answer) | 5,138 | `load_dataset("rst", "qa_reclor")` |Reading comprehension |
| [RACE](https://doi.org/10.18653/v1/d17-1082) | (context, question, options, answer) | 44,880 | `load_dataset("rst", "qa_race")` | Reading comprehension|
| [RACE-C](http://proceedings.mlr.press/v101/liang19a.html) | (context, question, options, answer) | 5,093 | `load_dataset("rst", "qa_race_c")` | Reading comprehension|
| [TriviaQA](https://doi.org/10.18653/v1/P17-1147) | (context, question, answer) | 46,636 | `load_dataset("rst", "qa_triviaqa")` |Reading comprehension |
| [Arxiv](https://arxiv.org/) | (text, category) | 1,696,348 | `load_dataset("rst", "arxiv_category")` |Topic classification|
| [Arxiv](https://arxiv.org/) | (text, summary) | 1,696,348 | `load_dataset("rst", "arxiv_summary")` | Summarization; Sentence expansion|
| [Paperswithcode](https://paperswithcode.com/) | (text, entities, datasets, methods, tasks, metrics) | 4,731,233 | `load_dataset("rst", "paperswithcode_entity")` | Entity recognition|
| [Paperswithcode](https://paperswithcode.com/) | (text, summary) | 120,924 | `load_dataset("rst", "paperswithcode_summary")` | Summarization; Sentence expansion|
## Bibtext for Citation Info
```
@article{yuan2022restructured,
title={reStructured Pre-training},
author={Yuan, Weizhe and Liu, Pengfei},
journal={arXiv preprint arXiv:2206.11147},
year={2022}
}
``` |
distilbert-base-uncased | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"distilbert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
} | 10,887,471 | null | ---
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: 0.8748965566869354
---
<!-- 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: 0.1216
- F1: 0.8749
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2247 | 1.0 | 834 | 0.1429 | 0.8432 |
| 0.1127 | 2.0 | 1668 | 0.1270 | 0.8653 |
| 0.0712 | 3.0 | 2502 | 0.1216 | 0.8749 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu116
- Datasets 2.3.2
- Tokenizers 0.12.1
|
openai-gpt | [
"pytorch",
"tf",
"rust",
"safetensors",
"openai-gpt",
"text-generation",
"en",
"arxiv:1705.11168",
"arxiv:1803.02324",
"arxiv:1910.09700",
"transformers",
"license:mit",
"has_space"
] | text-generation | {
"architectures": [
"OpenAIGPTLMHeadModel"
],
"model_type": "openai-gpt",
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},
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"max_length": 50
},
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}
} | 65,432 | null | Access to model deseipel/medium-LucyClarke_ is restricted and you are not in the authorized list. Visit https://huggingface.co/deseipel/medium-LucyClarke_ to ask for access. |
ARTeLab/mbart-summarization-mlsum | [
"pytorch",
"mbart",
"text2text-generation",
"it",
"dataset:ARTeLab/mlsum-it",
"transformers",
"summarization",
"autotrain_compatible",
"has_space"
] | summarization | {
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
"task_specific_params": {
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},
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}
} | 111 | 2022-09-02T23:16:29Z | ---
tags:
- generated_from_keras_callback
model-index:
- name: monday-custom-model
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. -->
# monday-custom-model
This model was trained from scratch 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 4.21.2
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
AdapterHub/roberta-base-pf-emo | [
"roberta",
"en",
"dataset:emo",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification"
] | text-classification | {
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}
} | 2 | 2022-09-03T15:56:16Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# Pavankalyan/Sentence_embedding_fine-tuned
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('Pavankalyan/Sentence_embedding_fine-tuned')
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
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# 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('Pavankalyan/Sentence_embedding_fine-tuned')
model = AutoModel.from_pretrained('Pavankalyan/Sentence_embedding_fine-tuned')
# 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, cls pooling.
sentence_embeddings = cls_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=Pavankalyan/Sentence_embedding_fine-tuned)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 706 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.DenoisingAutoEncoderLoss.DenoisingAutoEncoderLoss`
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": 3e-05
},
"scheduler": "constantlr",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
AdapterHub/roberta-base-pf-winogrande | [
"roberta",
"en",
"dataset:winogrande",
"arxiv:2104.08247",
"adapter-transformers",
"adapterhub:comsense/winogrande"
] | null | {
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} | 0 | 2022-09-03T20:07:17Z | ---
license: mit
widget:
- text: Bu sene eriğin kilosu kaç lira olacak?"
example_title: "Question"
- text: "Evlilik mükemmel bir kurum ama kim bir kurumda yaşamak ister?"
example_title: "Not Question"
---
# Question Detection Model Fine-Tuned with Tweet Dataset
You can find detailed explanation about dataset [here](https://github.com/izzetkalic/botcuk-dataset-analyze/tree/main/datasets/qd-tweet).
* RQ: Rhetorical Questions
* FK: Factual Knowledge
* OQ: Other Questions
* NQ: Not Question |
AdapterHub/roberta-base-pf-wnut_17 | [
"roberta",
"en",
"dataset:wnut_17",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification"
] | token-classification | {
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} | 4 | 2022-09-03T20:11:18Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilr2-lr2e05-wd0.1-bs64
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. -->
# distilr2-lr2e05-wd0.1-bs64
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2722
- Rmse: 0.5218
- Mse: 0.2722
- Mae: 0.4090
## 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: 512
- eval_batch_size: 512
- 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 | Rmse | Mse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.2771 | 1.0 | 312 | 0.2742 | 0.5237 | 0.2742 | 0.4241 |
| 0.2737 | 2.0 | 624 | 0.2726 | 0.5221 | 0.2726 | 0.4079 |
| 0.2718 | 3.0 | 936 | 0.2727 | 0.5222 | 0.2727 | 0.4149 |
| 0.2696 | 4.0 | 1248 | 0.2722 | 0.5218 | 0.2722 | 0.4090 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Aeskybunnie/Me | [] | null | {
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} | 0 | 2022-09-03T21:51:26Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- go_emotions
metrics:
- accuracy
- f1
model-index:
- name: roberta-large-bne-finetuned-go_emotions-es
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: go_emotions
type: go_emotions
config: simplified
split: train
args: simplified
metrics:
- name: Accuracy
type: accuracy
value: 0.5668425681618294
- name: F1
type: f1
value: 0.5572049178848779
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-bne-finetuned-go_emotions-es
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) on the go_emotions dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2457
- Accuracy: 0.5668
- F1: 0.5572
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 1.5678 | 1.0 | 9077 | 1.5649 | 0.5671 | 0.5197 |
| 1.3898 | 2.0 | 18154 | 1.5005 | 0.5776 | 0.5492 |
| 0.915 | 3.0 | 27231 | 1.8045 | 0.5891 | 0.5692 |
| 0.5424 | 4.0 | 36308 | 2.8463 | 0.5646 | 0.5519 |
| 0.2018 | 5.0 | 45385 | 3.2457 | 0.5668 | 0.5572 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Akashpb13/xlsr_hungarian_new | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hu",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
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}
} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_8_0
model-index:
- name: Fine_Tunning_on_CV_Urdu_dataset
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. -->
# Fine_Tunning_on_CV_Urdu_dataset
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_8_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2389
- Wer: 0.7380
## 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: 100
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 15.2352 | 1.69 | 100 | 4.0555 | 1.0 |
| 3.3873 | 3.39 | 200 | 3.2521 | 1.0 |
| 3.2387 | 5.08 | 300 | 3.2304 | 1.0 |
| 3.1983 | 6.78 | 400 | 3.1712 | 1.0 |
| 3.1224 | 8.47 | 500 | 3.0883 | 1.0 |
| 3.0782 | 10.17 | 600 | 3.0767 | 0.9996 |
| 3.0618 | 11.86 | 700 | 3.0280 | 1.0 |
| 2.9929 | 13.56 | 800 | 2.8994 | 1.0 |
| 2.785 | 15.25 | 900 | 2.4330 | 1.0 |
| 2.1276 | 16.95 | 1000 | 1.7795 | 0.9517 |
| 1.5544 | 18.64 | 1100 | 1.5101 | 0.8266 |
| 1.2651 | 20.34 | 1200 | 1.4037 | 0.7993 |
| 1.0816 | 22.03 | 1300 | 1.3101 | 0.7638 |
| 0.9817 | 23.73 | 1400 | 1.2855 | 0.7542 |
| 0.9019 | 25.42 | 1500 | 1.2737 | 0.7421 |
| 0.8688 | 27.12 | 1600 | 1.2457 | 0.7435 |
| 0.8293 | 28.81 | 1700 | 1.2389 | 0.7380 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.6886160714285715
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4043
- F1: 0.6886
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1347 | 1.0 | 50 | 0.5771 | 0.4880 |
| 0.5066 | 2.0 | 100 | 0.4209 | 0.6582 |
| 0.3631 | 3.0 | 150 | 0.4043 | 0.6886 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AlbertHSU/BertTEST | [
"pytorch"
] | null | {
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} | 8 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/reda_getachew/1662284943859/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/1464982586443370501/jh6Dqife_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">Getachew K Reda</div>
<div style="text-align: center; font-size: 14px;">@reda_getachew</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 Getachew K Reda.
| Data | Getachew K Reda |
| --- | --- |
| Tweets downloaded | 605 |
| Retweets | 73 |
| Short tweets | 9 |
| Tweets kept | 523 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1sf5r66e/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 @reda_getachew's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/jlj5mw14) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/jlj5mw14/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/reda_getachew')
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)
|
AnjanBiswas/distilbert-base-uncased-finetuned-emotion | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | {
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} | 37 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sagemaker-bert-mini-arabic
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. -->
# sagemaker-bert-mini-arabic
This model is a fine-tuned version of [asafaya/bert-mini-arabic](https://huggingface.co/asafaya/bert-mini-arabic) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2531
- Accuracy: 0.8974
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3385 | 1.0 | 1469 | 0.2707 | 0.8840 |
| 0.2416 | 2.0 | 2938 | 0.2531 | 0.8974 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3
|
AnonymousSub/AR_SDR_HF_model_base | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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} | 1 | 2022-09-05T09:26:38Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-xls-r-300m-arabic_speech_commands_10s_one_speaker_all_classes_TTS
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-xls-r-300m-arabic_speech_commands_10s_one_speaker_all_classes_TTS
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2062
- Accuracy: 0.9579
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.6865 | 0.99 | 34 | 3.6873 | 0.025 |
| 3.6155 | 1.99 | 68 | 3.4150 | 0.2188 |
| 2.6933 | 2.99 | 102 | 2.4527 | 0.4625 |
| 1.789 | 3.99 | 136 | 1.5249 | 0.7246 |
| 0.8812 | 4.99 | 170 | 0.7804 | 0.8708 |
| 0.4054 | 5.99 | 204 | 0.6304 | 0.8558 |
| 0.3481 | 6.99 | 238 | 0.5552 | 0.8667 |
| 0.238 | 7.99 | 272 | 0.4142 | 0.9113 |
| 0.1981 | 8.99 | 306 | 0.3007 | 0.9354 |
| 0.1254 | 9.99 | 340 | 0.2556 | 0.9479 |
| 0.1356 | 10.99 | 374 | 0.5148 | 0.8825 |
| 0.1263 | 11.99 | 408 | 0.3228 | 0.9308 |
| 0.1074 | 12.99 | 442 | 0.3085 | 0.9279 |
| 0.0756 | 13.99 | 476 | 0.4546 | 0.9029 |
| 0.0763 | 14.99 | 510 | 0.4045 | 0.9133 |
| 0.0902 | 15.99 | 544 | 0.3123 | 0.9287 |
| 0.1134 | 16.99 | 578 | 0.2054 | 0.9504 |
| 0.0943 | 17.99 | 612 | 0.2871 | 0.93 |
| 0.0511 | 18.99 | 646 | 0.3628 | 0.9292 |
| 0.0525 | 19.99 | 680 | 0.2228 | 0.9471 |
| 0.0769 | 20.99 | 714 | 0.3069 | 0.9329 |
| 0.0564 | 21.99 | 748 | 0.2658 | 0.9358 |
| 0.0319 | 22.99 | 782 | 0.2886 | 0.9387 |
| 0.0485 | 23.99 | 816 | 0.2342 | 0.9467 |
| 0.0542 | 24.99 | 850 | 0.3723 | 0.9287 |
| 0.0478 | 25.99 | 884 | 0.2890 | 0.9396 |
| 0.0373 | 26.99 | 918 | 0.2849 | 0.9383 |
| 0.0437 | 27.99 | 952 | 0.3886 | 0.9237 |
| 0.02 | 28.99 | 986 | 0.2672 | 0.9387 |
| 0.0379 | 29.99 | 1020 | 0.2946 | 0.9363 |
| 0.0253 | 30.99 | 1054 | 0.2499 | 0.9433 |
| 0.0256 | 31.99 | 1088 | 0.2967 | 0.9337 |
| 0.029 | 32.99 | 1122 | 0.2577 | 0.9458 |
| 0.0427 | 33.99 | 1156 | 0.2899 | 0.9396 |
| 0.0167 | 34.99 | 1190 | 0.2984 | 0.9437 |
| 0.0334 | 35.99 | 1224 | 0.4822 | 0.9175 |
| 0.0288 | 36.99 | 1258 | 0.2802 | 0.9417 |
| 0.017 | 37.99 | 1292 | 0.2233 | 0.9504 |
| 0.0064 | 38.99 | 1326 | 0.2657 | 0.9429 |
| 0.0176 | 39.99 | 1360 | 0.2062 | 0.9579 |
| 0.0307 | 40.99 | 1394 | 0.3633 | 0.9275 |
| 0.0208 | 41.99 | 1428 | 0.3059 | 0.9421 |
| 0.0091 | 42.99 | 1462 | 0.2488 | 0.9483 |
| 0.0121 | 43.99 | 1496 | 0.2397 | 0.9496 |
| 0.0106 | 44.99 | 1530 | 0.2958 | 0.9413 |
| 0.0176 | 45.99 | 1564 | 0.2243 | 0.9525 |
| 0.0153 | 46.99 | 1598 | 0.2293 | 0.9537 |
| 0.011 | 47.99 | 1632 | 0.2654 | 0.9496 |
| 0.0237 | 48.99 | 1666 | 0.2252 | 0.9533 |
| 0.0053 | 49.99 | 1700 | 0.2380 | 0.9483 |
| 0.0142 | 50.99 | 1734 | 0.2590 | 0.9467 |
| 0.0259 | 51.99 | 1768 | 0.2363 | 0.9508 |
| 0.0062 | 52.99 | 1802 | 0.2451 | 0.9496 |
| 0.0123 | 53.99 | 1836 | 0.2546 | 0.9479 |
| 0.011 | 54.99 | 1870 | 0.2578 | 0.9487 |
| 0.0143 | 55.99 | 1904 | 0.2770 | 0.945 |
| 0.015 | 56.99 | 1938 | 0.2869 | 0.9421 |
| 0.0099 | 57.99 | 1972 | 0.2922 | 0.9429 |
| 0.0086 | 58.99 | 2006 | 0.2783 | 0.9437 |
| 0.013 | 59.99 | 2040 | 0.2748 | 0.9433 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
AnonymousSub/SR_rule_based_roberta_hier_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 2 | null | # Bert Online Discussions (bert-web-discussions-en)
This model is a fine-tuned version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was introduced in [this paper](https://aclanthology.org/2022.acl-long.379/).
## Model description
The BERT base language model was fine-tuned on the [Webis-CMV-20 corpus](https://zenodo.org/record/3778298#.YxB-HC223RZ) and on the [args.me corpus](https://zenodo.org/record/3734893#.YxB-NC223RY).
The model was trained on a sample of 2,469,026 sentences in total. |
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: cards-demo-model3
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. -->
# cards-demo-model3
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9271
- F1: 0.7505
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.301 | 1.0 | 41 | 0.9127 | 0.7477 |
| 0.318 | 2.0 | 82 | 0.9173 | 0.7574 |
| 0.2757 | 3.0 | 123 | 0.9271 | 0.7505 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Tokenizers 0.12.1
|
Aries/T5_question_answering | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
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} | 5 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: tire-types
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7230769395828247
---
# tire-types
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### all-terrain tire
#### competition tire
#### passenger tire
 |
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-paraphrase-feedback
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. -->
# bart-paraphrase-feedback
This model is a fine-tuned version of [theojolliffe/bart-paraphrase-v4-e1-feedback](https://huggingface.co/theojolliffe/bart-paraphrase-v4-e1-feedback) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3640
- Rouge1: 55.8307
- Rouge2: 49.7983
- Rougel: 51.7379
- Rougelsum: 55.0839
- Gen Len: 19.4385
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.6009 | 1.0 | 521 | 0.3640 | 55.8307 | 49.7983 | 51.7379 | 55.0839 | 19.4385 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Axon/resnet18-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
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} | 0 | null | ---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification-conceptnet-validated
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.856984126984127
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5080213903743316
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5192878338278932
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6653696498054474
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.84
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.45614035087719296
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5393518518518519
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9132138014163026
- name: F1 (macro)
type: f1_macro
value: 0.9101733559621606
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8502347417840377
- name: F1 (macro)
type: f1_macro
value: 0.6852576593859314
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6852654387865655
- name: F1 (macro)
type: f1_macro
value: 0.6694360423727916
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9604228976838005
- name: F1 (macro)
type: f1_macro
value: 0.8826948107609662
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9022250078345346
- name: F1 (macro)
type: f1_macro
value: 0.9002463330589072
---
# relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification-conceptnet-validated
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification-conceptnet-validated/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5080213903743316
- Accuracy on SAT: 0.5192878338278932
- Accuracy on BATS: 0.6653696498054474
- Accuracy on U2: 0.45614035087719296
- Accuracy on U4: 0.5393518518518519
- Accuracy on Google: 0.84
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification-conceptnet-validated/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9132138014163026
- Micro F1 score on CogALexV: 0.8502347417840377
- Micro F1 score on EVALution: 0.6852654387865655
- Micro F1 score on K&H+N: 0.9604228976838005
- Micro F1 score on ROOT09: 0.9022250078345346
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification-conceptnet-validated/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.856984126984127
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification-conceptnet-validated")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: mask
- data: relbert/semeval2012_relational_similarity
- split: train
- data_eval: relbert/conceptnet_high_confidence
- split_eval: full
- template_mode: manual
- template: Today, I finally discovered the relation between <subj> and <obj> : <obj> is <subj>'s <mask>
- loss_function: nce_logout
- classification_loss: True
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 27
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- exclude_relation_eval: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification-conceptnet-validated/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
Axon/resnet34-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_10_0
model-index:
- name: wav2vec2-large-xls-r-300m-j-phoneme-colab-new
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-j-phoneme-colab-new
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_10_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5498
- Wer: 0.3257
## 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 397 | 0.7976 | 0.7045 |
| No log | 2.0 | 794 | 0.5777 | 0.5723 |
| 1.7064 | 3.0 | 1191 | 0.4775 | 0.4706 |
| 1.7064 | 4.0 | 1588 | 0.4755 | 0.4580 |
| 1.7064 | 5.0 | 1985 | 0.4678 | 0.4250 |
| 0.3823 | 6.0 | 2382 | 0.4742 | 0.4196 |
| 0.3823 | 7.0 | 2779 | 0.4419 | 0.3817 |
| 0.2485 | 8.0 | 3176 | 0.4402 | 0.3711 |
| 0.2485 | 9.0 | 3573 | 0.4942 | 0.3703 |
| 0.2485 | 10.0 | 3970 | 0.4877 | 0.3613 |
| 0.1735 | 11.0 | 4367 | 0.5073 | 0.3453 |
| 0.1735 | 12.0 | 4764 | 0.5127 | 0.3354 |
| 0.1238 | 13.0 | 5161 | 0.5545 | 0.3392 |
| 0.1238 | 14.0 | 5558 | 0.5419 | 0.3290 |
| 0.1238 | 15.0 | 5955 | 0.5498 | 0.3257 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.10.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Axon/resnet50-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- coscan-speech
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-coscan-sex
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: Coscan Speech
type: NbAiLab/coscan-speech
args: no
metrics:
- name: Test Accuracy
type: accuracy
value: 0.9993247805536799
- name: Validation Accuracy
type: accuracy
value: 0.9965283657917019
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-coscan-sex
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the coscan-speech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0229
- Accuracy: 0.9965
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0034 | 1.0 | 6644 | 0.0229 | 0.9965 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.10.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Aybars/XLM_Turkish | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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"XLMRobertaForQuestionAnswering"
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} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: afrodp95/distilbert-base-uncased-finetuned-job-skills-ner
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# afrodp95/distilbert-base-uncased-finetuned-job-skills-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0923
- Validation Loss: 0.1313
- Train Precision: 0.3601
- Train Recall: 0.4922
- Train F1: 0.4159
- Train Accuracy: 0.9522
- Epoch: 5
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1386, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.3257 | 0.1935 | 0.3122 | 0.2144 | 0.2542 | 0.9521 | 0 |
| 0.1564 | 0.1464 | 0.3503 | 0.3423 | 0.3463 | 0.9546 | 1 |
| 0.1257 | 0.1365 | 0.3593 | 0.4893 | 0.4143 | 0.9522 | 2 |
| 0.1102 | 0.1318 | 0.3607 | 0.4692 | 0.4079 | 0.9521 | 3 |
| 0.1002 | 0.1305 | 0.3504 | 0.4941 | 0.4100 | 0.9515 | 4 |
| 0.0923 | 0.1313 | 0.3601 | 0.4922 | 0.4159 | 0.9522 | 5 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Ayham/albert_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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"EncoderDecoderModel"
],
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} | 12 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: Dogz
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# Dogz
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Golden Retriever
#### Jack Russell Terrier
#### Pitbull Terrier
 |
Ayham/albert_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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}
} | 6 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-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. -->
# roberta-base-finetuned-squad
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 2 | 5.6504 |
| No log | 2.0 | 4 | 5.0165 |
| No log | 3.0 | 6 | 4.2438 |
| No log | 4.0 | 8 | 3.2047 |
| No log | 5.0 | 10 | 2.3533 |
| No log | 6.0 | 12 | 2.1072 |
| No log | 7.0 | 14 | 1.4145 |
| No log | 8.0 | 16 | 1.0086 |
| No log | 9.0 | 18 | 0.5869 |
| No log | 10.0 | 20 | 0.2890 |
| No log | 11.0 | 22 | 0.1551 |
| No log | 12.0 | 24 | 0.0902 |
| No log | 13.0 | 26 | 0.0503 |
| No log | 14.0 | 28 | 0.0312 |
| No log | 15.0 | 30 | 0.0173 |
| No log | 16.0 | 32 | 0.0113 |
| No log | 17.0 | 34 | 0.0085 |
| No log | 18.0 | 36 | 0.0056 |
| No log | 19.0 | 38 | 0.0035 |
| No log | 20.0 | 40 | 0.0024 |
| No log | 21.0 | 42 | 0.0018 |
| No log | 22.0 | 44 | 0.0012 |
| No log | 23.0 | 46 | 0.0011 |
| No log | 24.0 | 48 | 0.0009 |
| No log | 25.0 | 50 | 0.0007 |
| No log | 26.0 | 52 | 0.0006 |
| No log | 27.0 | 54 | 0.0006 |
| No log | 28.0 | 56 | 0.0005 |
| No log | 29.0 | 58 | 0.0004 |
| No log | 30.0 | 60 | 0.0004 |
| No log | 31.0 | 62 | 0.0004 |
| No log | 32.0 | 64 | 0.0004 |
| No log | 33.0 | 66 | 0.0004 |
| No log | 34.0 | 68 | 0.0003 |
| No log | 35.0 | 70 | 0.0003 |
| No log | 36.0 | 72 | 0.0003 |
| No log | 37.0 | 74 | 0.0003 |
| No log | 38.0 | 76 | 0.0002 |
| No log | 39.0 | 78 | 0.0002 |
| No log | 40.0 | 80 | 0.0002 |
| No log | 41.0 | 82 | 0.0002 |
| No log | 42.0 | 84 | 0.0002 |
| No log | 43.0 | 86 | 0.0002 |
| No log | 44.0 | 88 | 0.0002 |
| No log | 45.0 | 90 | 0.0002 |
| No log | 46.0 | 92 | 0.0002 |
| No log | 47.0 | 94 | 0.0002 |
| No log | 48.0 | 96 | 0.0002 |
| No log | 49.0 | 98 | 0.0002 |
| No log | 50.0 | 100 | 0.0002 |
| No log | 51.0 | 102 | 0.0002 |
| No log | 52.0 | 104 | 0.0002 |
| No log | 53.0 | 106 | 0.0002 |
| No log | 54.0 | 108 | 0.0002 |
| No log | 55.0 | 110 | 0.0002 |
| No log | 56.0 | 112 | 0.0002 |
| No log | 57.0 | 114 | 0.0002 |
| No log | 58.0 | 116 | 0.0002 |
| No log | 59.0 | 118 | 0.0002 |
| No log | 60.0 | 120 | 0.0002 |
| No log | 61.0 | 122 | 0.0001 |
| No log | 62.0 | 124 | 0.0001 |
| No log | 63.0 | 126 | 0.0001 |
| No log | 64.0 | 128 | 0.0001 |
| No log | 65.0 | 130 | 0.0001 |
| No log | 66.0 | 132 | 0.0001 |
| No log | 67.0 | 134 | 0.0001 |
| No log | 68.0 | 136 | 0.0001 |
| No log | 69.0 | 138 | 0.0001 |
| No log | 70.0 | 140 | 0.0001 |
| No log | 71.0 | 142 | 0.0001 |
| No log | 72.0 | 144 | 0.0001 |
| No log | 73.0 | 146 | 0.0001 |
| No log | 74.0 | 148 | 0.0001 |
| No log | 75.0 | 150 | 0.0001 |
| No log | 76.0 | 152 | 0.0001 |
| No log | 77.0 | 154 | 0.0001 |
| No log | 78.0 | 156 | 0.0001 |
| No log | 79.0 | 158 | 0.0001 |
| No log | 80.0 | 160 | 0.0001 |
| No log | 81.0 | 162 | 0.0001 |
| No log | 82.0 | 164 | 0.0001 |
| No log | 83.0 | 166 | 0.0001 |
| No log | 84.0 | 168 | 0.0001 |
| No log | 85.0 | 170 | 0.0001 |
| No log | 86.0 | 172 | 0.0001 |
| No log | 87.0 | 174 | 0.0001 |
| No log | 88.0 | 176 | 0.0001 |
| No log | 89.0 | 178 | 0.0001 |
| No log | 90.0 | 180 | 0.0001 |
| No log | 91.0 | 182 | 0.0001 |
| No log | 92.0 | 184 | 0.0001 |
| No log | 93.0 | 186 | 0.0001 |
| No log | 94.0 | 188 | 0.0001 |
| No log | 95.0 | 190 | 0.0001 |
| No log | 96.0 | 192 | 0.0001 |
| No log | 97.0 | 194 | 0.0001 |
| No log | 98.0 | 196 | 0.0001 |
| No log | 99.0 | 198 | 0.0001 |
| No log | 100.0 | 200 | 0.0001 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Ayham/albert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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}
} | 7 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="slarionne/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Ayham/bert_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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} | 4 | null | Access to model ONVS/Sporadicism is restricted and you are not in the authorized list. Visit https://huggingface.co/ONVS/Sporadicism to ask for access. |
Ayham/bert_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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}
}
} | 4 | null | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: /content/drive/Shareddrives/artGAN S2 2022/sugimori-artwork
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 `/content/drive/Shareddrives/artGAN S2 2022/sugimori-artwork` 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/Tahahah/ddpm-butterflies-128/tensorboard?#scalars)
|
Ayham/bert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
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},
"summarization": {
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"translation_en_to_ro": {
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}
}
} | 6 | null | ---
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
config: plus
split: train
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9503225806451613
---
<!-- 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: 0.2339
- Accuracy: 0.9503
## 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: 12
- eval_batch_size: 12
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.2073 | 1.0 | 1271 | 1.3840 | 0.8542 |
| 0.7452 | 2.0 | 2542 | 0.4053 | 0.9316 |
| 0.1916 | 3.0 | 3813 | 0.2580 | 0.9452 |
| 0.0768 | 4.0 | 5084 | 0.2371 | 0.9477 |
| 0.0455 | 5.0 | 6355 | 0.2339 | 0.9503 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Ayham/bert_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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},
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},
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"prefix": null
}
}
} | 3 | null | ---
language: vi
datasets:
- cc100
tags:
- summarization
license: mit
widget:
- text: "VietAI là tổ chức phi lợi nhuận với sứ mệnh ươm mầm tài năng về trí tuệ nhân tạo và xây dựng một cộng đồng các chuyên gia trong lĩnh vực trí tuệ nhân tạo đẳng cấp quốc tế tại Việt Nam."
---
# ViT5-Base Finetuned on `vietnews` Abstractive Summarization (No prefix needed)
State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.
[](https://paperswithcode.com/sota/abstractive-text-summarization-on-vietnews?p=vit5-pretrained-text-to-text-transformer-for)
## How to use
For more details, do check out [our Github repo](https://github.com/vietai/ViT5) and [eval script](https://github.com/vietai/ViT5/blob/main/eval/Eval_vietnews_sum.ipynb).
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("VietAI/vit5-base-vietnews-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/vit5-base-vietnews-summarization")
model.cuda()
sentence = "VietAI là tổ chức phi lợi nhuận với sứ mệnh ươm mầm tài năng về trí tuệ nhân tạo và xây dựng một cộng đồng các chuyên gia trong lĩnh vực trí tuệ nhân tạo đẳng cấp quốc tế tại Việt Nam."
sentence = sentence + "</s>"
encoding = tokenizer(sentence, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
outputs = model.generate(
input_ids=input_ids, attention_mask=attention_masks,
max_length=256,
early_stopping=True
)
for output in outputs:
line = tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
print(line)
```
## Citation
```
@inproceedings{phan-etal-2022-vit5,
title = "{V}i{T}5: Pretrained Text-to-Text Transformer for {V}ietnamese Language Generation",
author = "Phan, Long and Tran, Hieu and Nguyen, Hieu and Trinh, Trieu H.",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop",
year = "2022",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-srw.18",
pages = "136--142",
}
``` |
Ayham/bertgpt2_cnn | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
}
} | 4 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/funfacts/1662519173108/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/1251284305185255425/TuAMzBHm_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">Funfacts</div>
<div style="text-align: center; font-size: 14px;">@funfacts</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 Funfacts.
| Data | Funfacts |
| --- | --- |
| Tweets downloaded | 2160 |
| Retweets | 7 |
| Short tweets | 3 |
| Tweets kept | 2150 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/j0uu6ccx/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 @funfacts's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2r4x2tam) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2r4x2tam/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/funfacts')
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)
|
Ayham/roberta_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
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},
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"prefix": null
}
}
} | 4 | null | ---
license: cc
widget:
- text: "User: Hey, how are you?"
example_title: "How are you?"
- text: "User: What did you do today?"
example_title: "What did you do today?"
- text: "User: What's your favorite movie?"
example_title: "What's your favorite movie?"
--- |
Ayham/robertagpt2_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
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"prefix": null
}
}
} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9454375
- name: F1
type: f1
value: 0.9458448428504193
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1476
- Accuracy: 0.9454
- F1: 0.9458
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8907 | 1.0 | 250 | 0.2625 | 0.9184 | 0.9157 |
| 0.2315 | 2.0 | 500 | 0.1476 | 0.9454 | 0.9458 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Ayham/robertagpt2_xsum4 | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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}
}
} | 8 | null | ---
tags:
- autotrain
- text-classification
language:
- bn
widget:
- text: "I love AutoTrain 🤗"
datasets:
- neuralspace/autotrain-data-citizen_nlu_bn
co2_eq_emissions:
emissions: 0.08431503532658222
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1370652766
- CO2 Emissions (in grams): 0.0843
## Validation Metrics
- Loss: 0.117
- Accuracy: 0.971
- Macro F1: 0.971
- Micro F1: 0.971
- Weighted F1: 0.971
- Macro Precision: 0.973
- Micro Precision: 0.971
- Weighted Precision: 0.972
- Macro Recall: 0.970
- Micro Recall: 0.971
- Weighted Recall: 0.971
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/neuralspace/autotrain-citizen_nlu_bn-1370652766
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("neuralspace/autotrain-citizen_nlu_bn-1370652766", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("neuralspace/autotrain-citizen_nlu_bn-1370652766", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Ayham/xlmroberta_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 9 | null | Access to model Valkyries15/tf_demo is restricted and you are not in the authorized list. Visit https://huggingface.co/Valkyries15/tf_demo to ask for access. |
Ayham/xlmroberta_large_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 12 | null | ---
tags:
- autotrain
- text-classification
language:
- hi
widget:
- text: "I love AutoTrain 🤗"
datasets:
- neuralspace/autotrain-data-citizen_nlu_hindi
co2_eq_emissions:
emissions: 0.06283545088764929
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1370952776
- CO2 Emissions (in grams): 0.0628
## Validation Metrics
- Loss: 0.101
- Accuracy: 0.974
- Macro F1: 0.974
- Micro F1: 0.974
- Weighted F1: 0.974
- Macro Precision: 0.975
- Micro Precision: 0.974
- Weighted Precision: 0.975
- Macro Recall: 0.973
- Micro Recall: 0.974
- Weighted Recall: 0.974
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/neuralspace/autotrain-citizen_nlu_hindi-1370952776
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("neuralspace/autotrain-citizen_nlu_hindi-1370952776", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("neuralspace/autotrain-citizen_nlu_hindi-1370952776", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
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