modelId
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81
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list | pipeline_tag
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values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
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AnonymousSub/bert_triplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 2 | null | ---
tags:
- generated_from_trainer
model-index:
- name: final-squad-bn-qgen-banglat5-all-metric-v3
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. -->
# final-squad-bn-qgen-banglat5-all-metric-v3
This model is a fine-tuned version of [csebuetnlp/banglat5](https://huggingface.co/csebuetnlp/banglat5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4231
- Rouge1 Precision: 39.5263
- Rouge1 Recall: 37.168
- Rouge1 Fmeasure: 37.1806
- Rouge2 Precision: 17.7035
- Rouge2 Recall: 16.508
- Rouge2 Fmeasure: 16.5336
- Rougel Precision: 37.135
- Rougel Recall: 34.9177
- Rougel Fmeasure: 34.9266
- Rougelsum Precision: 37.1205
- Rougelsum Recall: 34.8982
- Rougelsum Fmeasure: 34.9129
- Bleu-1: 36.4356
- Bleu-2: 22.3217
- Bleu-3: 14.7682
- Bleu-4: 10.0865
- Meteor: 0.2051
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### 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 | Bleu-1 | Bleu-2 | Bleu-3 | Bleu-4 | Meteor |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------------------:|:----------------:|:------------------:|:-------:|:-------:|:-------:|:-------:|:------:|
| 0.5901 | 1.0 | 6769 | 0.4756 | 32.2563 | 31.3211 | 30.7652 | 12.2914 | 11.9567 | 11.6739 | 29.1321 | 28.3925 | 27.84 | 29.1291 | 28.3832 | 27.8378 | 31.9366 | 17.9528 | 10.9479 | 6.8658 | 0.1715 |
| 0.5094 | 2.0 | 13538 | 0.4343 | 37.5727 | 35.6711 | 35.4661 | 16.3104 | 15.4196 | 15.3046 | 35.2059 | 33.4452 | 33.2559 | 35.1882 | 33.4395 | 33.24 | 35.1532 | 21.1183 | 13.7297 | 9.2128 | 0.1955 |
| 0.4866 | 3.0 | 20307 | 0.4267 | 38.6402 | 36.2947 | 36.2796 | 16.8569 | 15.7129 | 15.7114 | 36.2902 | 34.0855 | 34.0734 | 36.2733 | 34.0723 | 34.0661 | 35.6286 | 21.554 | 14.098 | 9.5506 | 0.1996 |
| 0.4732 | 4.0 | 27076 | 0.4235 | 39.3469 | 36.7357 | 36.8598 | 17.4835 | 16.2062 | 16.2677 | 36.9883 | 34.5422 | 34.6543 | 36.9783 | 34.5352 | 34.6594 | 35.9917 | 21.9745 | 14.4922 | 9.884 | 0.203 |
| 0.4646 | 5.0 | 33845 | 0.4224 | 39.4223 | 37.0956 | 37.0893 | 17.6277 | 16.4682 | 16.4692 | 37.0994 | 34.896 | 34.8991 | 37.0885 | 34.8691 | 34.8811 | 36.3637 | 22.2704 | 14.7068 | 10.021 | 0.2049 |
| 0.4517 | 6.0 | 40614 | 0.4231 | 39.5263 | 37.168 | 37.1806 | 17.7035 | 16.508 | 16.5336 | 37.135 | 34.9177 | 34.9266 | 37.1205 | 34.8982 | 34.9129 | 36.4356 | 22.3217 | 14.7682 | 10.0865 | 0.2051 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
AnonymousSub/cline_emanuals | [
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"roberta",
"transformers"
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: twitter-climate-sentiment-model
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-climate-sentiment-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2779
- eval_accuracy: 0.8941
- eval_f1: 0.9372
- eval_runtime: 135.2041
- eval_samples_per_second: 39.873
- eval_steps_per_second: 2.493
- epoch: 1.0
- step: 1348
## 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
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cpu
- Datasets 2.5.2
- Tokenizers 0.12.1
|
AnonymousSub/declutr-model_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
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} | 2 | null | ---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: Basque_Dialects_Classification
results: []
widget:
- text: "Gaur eskolara etorri naiz"
example_title: "Example 1"
- text: "Gaur eskolara etorri naz"
example_title: "Example 2"
---
<!-- 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. -->
# Basque_Dialects_Classification
This model is a fine-tuned version of [ixa-ehu/roberta-eus-cc100-base-cased](https://huggingface.co/ixa-ehu/roberta-eus-cc100-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4558
- F1: 0.6846
## 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: 7.2193610624691235e-06
- train_batch_size: 1
- 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: 0.7
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.4126 | 1.0 | 9611 | 1.4626 | 0.5893 |
| 1.3446 | 2.0 | 19222 | 1.5642 | 0.6421 |
| 1.0157 | 3.0 | 28833 | 1.5049 | 0.6596 |
| 1.3012 | 4.0 | 38444 | 1.4939 | 0.6708 |
| 1.0824 | 5.0 | 48055 | 1.4558 | 0.6846 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
AnonymousSub/declutr-model_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
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} | 26 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1578421524804927490/hffLTarW_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">Lost Ark @ TwitchCon</div>
<div style="text-align: center; font-size: 14px;">@playlostark</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 Lost Ark @ TwitchCon.
| Data | Lost Ark @ TwitchCon |
| --- | --- |
| Tweets downloaded | 3248 |
| Retweets | 40 |
| Short tweets | 376 |
| Tweets kept | 2832 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ef4be5e/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 @playlostark's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3qttau89) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3qttau89/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/playlostark')
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)
|
AnonymousSub/declutr-s10-SR | [
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"roberta",
"text-classification",
"transformers"
]
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} | 36 | null | ---
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds
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. -->
# codeparrot-ds
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2520
## 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: 0.0005
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0285 | 0.61 | 5000 | 1.2520 |
### Framework versions
- Transformers 4.12.0
- Pytorch 1.12.1
- Datasets 1.12.1
- Tokenizers 0.10.3
|
AnonymousSub/dummy_1 | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 33 | null | ---
language:
- "th"
tags:
- "thai"
- "token-classification"
- "pos"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "apache-2.0"
pipeline_tag: "token-classification"
widget:
- text: "หลายหัวดีกว่าหัวเดียว"
---
# deberta-base-thai-ud-goeswith
## Model Description
This is a DeBERTa(V2) model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [deberta-base-thai](https://huggingface.co/KoichiYasuoka/deberta-base-thai).
## How to Use
```py
class UDgoeswith(object):
def __init__(self,bert):
from transformers import AutoTokenizer,AutoModelForTokenClassification
self.tokenizer=AutoTokenizer.from_pretrained(bert)
self.model=AutoModelForTokenClassification.from_pretrained(bert)
def __call__(self,text):
import numpy,torch,ufal.chu_liu_edmonds
w=self.tokenizer(text,return_offsets_mapping=True)
v=w["input_ids"]
x=[v[0:i]+[self.tokenizer.mask_token_id]+v[i+1:]+[j] for i,j in enumerate(v[1:-1],1)]
with torch.no_grad():
e=self.model(input_ids=torch.tensor(x)).logits.numpy()[:,1:-2,:]
r=[1 if i==0 else -1 if j.endswith("|root") else 0 for i,j in sorted(self.model.config.id2label.items())]
e+=numpy.where(numpy.add.outer(numpy.identity(e.shape[0]),r)==0,0,numpy.nan)
g=self.model.config.label2id["X|_|goeswith"]
r=numpy.tri(e.shape[0])
for i in range(e.shape[0]):
for j in range(i+2,e.shape[1]):
r[i,j]=r[i,j-1] if numpy.nanargmax(e[i,j-1])==g else 1
e[:,:,g]+=numpy.where(r==0,0,numpy.nan)
m=numpy.full((e.shape[0]+1,e.shape[1]+1),numpy.nan)
m[1:,1:]=numpy.nanmax(e,axis=2).transpose()
p=numpy.zeros(m.shape)
p[1:,1:]=numpy.nanargmax(e,axis=2).transpose()
for i in range(1,m.shape[0]):
m[i,0],m[i,i],p[i,0]=m[i,i],numpy.nan,p[i,i]
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
if [0 for i in h if i==0]!=[0]:
m[:,0]+=numpy.where(m[:,0]==numpy.nanmax(m[[i for i,j in enumerate(h) if j==0],0]),0,numpy.nan)
m[[i for i,j in enumerate(h) if j==0]]+=[0 if i==0 or j==0 else numpy.nan for i,j in enumerate(h)]
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
u="# text = "+text+"\n"
v=[(s,e) for s,e in w["offset_mapping"] if s<e]
for i,(s,e) in enumerate(v,1):
q=self.model.config.id2label[p[i,h[i]]].split("|")
u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n"
return u+"\n"
nlp=UDgoeswith("KoichiYasuoka/deberta-base-thai-ud-goeswith")
print(nlp("หลายหัวดีกว่าหัวเดียว"))
```
with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/).
Or without ufal.chu-liu-edmonds:
```
from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/deberta-base-thai-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple")
print(nlp("หลายหัวดีกว่าหัวเดียว"))
```
|
AnonymousSub/dummy_2 | [
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"bert",
"text-classification",
"transformers"
]
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}
} | 39 | null | ---
license: mit
---
### fox purple on Stable Diffusion
This is the `<foxi-purple>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
AnonymousSub/rule_based_hier_triplet_0.1_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"BertModel"
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} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-model2-0910
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-model2-0910
This model is a fine-tuned version of [theojolliffe/bart-model2-1409](https://huggingface.co/theojolliffe/bart-model2-1409) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 48 | 0.7670 | 38.9302 | 29.5801 | 37.9912 | 35.6634 | 20.0 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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}
} | 6 | null | ---
tags:
- image-classification
- pytorch
metrics:
- accuracy
model-index:
- name: syn-oct-ViT-Base-4Epochs-30c-v2-run
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666746139526
---
# syn-oct-ViT-Base-4Epochs-30c-v2-run |
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
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"prefix": null
}
}
} | 8 | null | ---
tags:
- autotrain
- translation
language:
- en
- de
datasets:
- Tritkoman/autotrain-data-wwwwdsxzaa
co2_eq_emissions:
emissions: 13.980549591928089
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1702359709
- CO2 Emissions (in grams): 13.9805
## Validation Metrics
- Loss: 1.741
- SacreBLEU: 27.270
- Gen len: 13.747 |
AnonymousSub/rule_based_only_classfn_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
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}
}
} | 7 | null | This is a test, do not use it, the results are very bad. |
AnonymousSub/rule_based_only_classfn_epochs_1_shard_1_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
} | 2 | 2022-10-09T13:03:31Z | ---
tags:
- autotrain
- tabular
- classification
- tabular-classification
datasets:
- tejas23/autotrain-data-amx2
co2_eq_emissions:
emissions: 7.7048287301375975
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1702259725
- CO2 Emissions (in grams): 7.7048
## Validation Metrics
- Loss: 0.421
- Accuracy: 0.827
- Macro F1: 0.530
- Micro F1: 0.827
- Weighted F1: 0.805
- Macro Precision: 0.579
- Micro Precision: 0.827
- Weighted Precision: 0.795
- Macro Recall: 0.513
- Micro Recall: 0.827
- Weighted Recall: 0.827
## Usage
```python
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]
predictions = model.predict(data) # or model.predict_proba(data)
``` |
AnonymousSub/rule_based_only_classfn_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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}
}
} | 32 | null | ---
tags:
- autotrain
- tabular
- classification
- tabular-classification
datasets:
- tejas23/autotrain-data-amx2
co2_eq_emissions:
emissions: 0.00824689737605251
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1702259728
- CO2 Emissions (in grams): 0.0082
## Validation Metrics
- Loss: 0.434
- Accuracy: 0.831
- Macro F1: 0.521
- Micro F1: 0.831
- Weighted F1: 0.803
- Macro Precision: 0.590
- Micro Precision: 0.831
- Weighted Precision: 0.794
- Macro Recall: 0.507
- Micro Recall: 0.831
- Weighted Recall: 0.831
## Usage
```python
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]
predictions = model.predict(data) # or model.predict_proba(data)
``` |
AnonymousSub/rule_based_only_classfn_twostage_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 27 | 2022-10-09T13:03:45Z | ---
tags:
- autotrain
- tabular
- classification
- tabular-classification
datasets:
- tejas23/autotrain-data-amx2
co2_eq_emissions:
emissions: 0.002766545033914285
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1702259729
- CO2 Emissions (in grams): 0.0028
## Validation Metrics
- Loss: 6.095
- Accuracy: 0.824
- Macro F1: 0.543
- Micro F1: 0.824
- Weighted F1: 0.808
- Macro Precision: 0.572
- Micro Precision: 0.824
- Weighted Precision: 0.801
- Macro Recall: 0.543
- Micro Recall: 0.824
- Weighted Recall: 0.824
## Usage
```python
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]
predictions = model.predict(data) # or model.predict_proba(data)
``` |
AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
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},
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"translation_en_to_ro": {
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}
}
} | 8 | null | ---
license: mit
---
### Roblox avatar on Stable Diffusion
why am i spending time making these?, anyways.
This is the `<roblox-avatar>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
photos were taken from pinterest.
Here is the new concept you will be able to use as an `object`:
|
AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
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}
}
} | 2 | null | ---
tags:
- image-classification
- pytorch
metrics:
- accuracy
model-index:
- name: syn-oct-ViT-Base-8Epochs-30c-v2-run
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.949999988079071
---
# syn-oct-ViT-Base-8Epochs-30c-v2-run |
AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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"no_repeat_ngram_size": null,
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},
"text-generation": {
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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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}
} | 23 | null | ---
tags:
- generated_from_trainer
datasets:
- ccmatrix
metrics:
- bleu
model-index:
- name: t5-base_ro-finetuned-en-to-it
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: ccmatrix
type: ccmatrix
config: en-it
split: train[3000:12000]
args: en-it
metrics:
- name: Bleu
type: bleu
value: 19.6396
---
<!-- 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_ro-finetuned-en-to-it
This model is a fine-tuned version of [j0hngou/t5-base-finetuned-en-to-ro](https://huggingface.co/j0hngou/t5-base-finetuned-en-to-ro) on the ccmatrix dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4669
- Bleu: 19.6396
- Gen Len: 52.4247
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 282 | 2.0942 | 5.6875 | 73.434 |
| 2.5108 | 2.0 | 564 | 1.9725 | 6.6631 | 72.6607 |
| 2.5108 | 3.0 | 846 | 1.9010 | 7.9227 | 67.01 |
| 2.1659 | 4.0 | 1128 | 1.8452 | 8.9935 | 65.1027 |
| 2.1659 | 5.0 | 1410 | 1.7979 | 9.4164 | 64.9827 |
| 2.0288 | 6.0 | 1692 | 1.7590 | 9.6035 | 66.6933 |
| 2.0288 | 7.0 | 1974 | 1.7264 | 10.7658 | 62.068 |
| 1.9238 | 8.0 | 2256 | 1.6955 | 11.5779 | 59.472 |
| 1.8435 | 9.0 | 2538 | 1.6729 | 12.7588 | 56.584 |
| 1.8435 | 10.0 | 2820 | 1.6541 | 13.3086 | 56.1153 |
| 1.775 | 11.0 | 3102 | 1.6337 | 13.8543 | 55.3307 |
| 1.775 | 12.0 | 3384 | 1.6148 | 14.3566 | 55.2853 |
| 1.7204 | 13.0 | 3666 | 1.5994 | 14.693 | 55.6607 |
| 1.7204 | 14.0 | 3948 | 1.5838 | 15.1284 | 55.5327 |
| 1.6705 | 15.0 | 4230 | 1.5742 | 15.6125 | 55.0087 |
| 1.632 | 16.0 | 4512 | 1.5600 | 15.9616 | 54.052 |
| 1.632 | 17.0 | 4794 | 1.5526 | 16.495 | 53.562 |
| 1.5868 | 18.0 | 5076 | 1.5392 | 16.4252 | 54.4613 |
| 1.5868 | 19.0 | 5358 | 1.5311 | 16.753 | 54.1853 |
| 1.5656 | 20.0 | 5640 | 1.5262 | 17.0308 | 54.2473 |
| 1.5656 | 21.0 | 5922 | 1.5186 | 17.3553 | 53.396 |
| 1.529 | 22.0 | 6204 | 1.5121 | 17.6177 | 53.472 |
| 1.529 | 23.0 | 6486 | 1.5058 | 17.6409 | 53.6847 |
| 1.5071 | 24.0 | 6768 | 1.5038 | 18.2009 | 53.2327 |
| 1.4903 | 25.0 | 7050 | 1.4962 | 18.4838 | 52.9587 |
| 1.4903 | 26.0 | 7332 | 1.4935 | 18.5545 | 52.688 |
| 1.4686 | 27.0 | 7614 | 1.4879 | 18.62 | 53.5 |
| 1.4686 | 28.0 | 7896 | 1.4850 | 19.0099 | 52.34 |
| 1.4511 | 29.0 | 8178 | 1.4813 | 19.0538 | 52.474 |
| 1.4511 | 30.0 | 8460 | 1.4787 | 18.89 | 53.0753 |
| 1.4364 | 31.0 | 8742 | 1.4756 | 19.2582 | 52.3587 |
| 1.4279 | 32.0 | 9024 | 1.4739 | 19.2973 | 52.69 |
| 1.4279 | 33.0 | 9306 | 1.4725 | 19.3624 | 52.694 |
| 1.4172 | 34.0 | 9588 | 1.4704 | 19.5421 | 52.1667 |
| 1.4172 | 35.0 | 9870 | 1.4689 | 19.4807 | 52.5487 |
| 1.4141 | 36.0 | 10152 | 1.4685 | 19.5972 | 52.2733 |
| 1.4141 | 37.0 | 10434 | 1.4676 | 19.5835 | 52.374 |
| 1.4058 | 38.0 | 10716 | 1.4674 | 19.6374 | 52.3447 |
| 1.4058 | 39.0 | 10998 | 1.4671 | 19.6105 | 52.5273 |
| 1.4027 | 40.0 | 11280 | 1.4669 | 19.6396 | 52.4247 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0
|
AnonymousSub/rule_based_roberta_hier_quadruplet_0.1_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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}
} | 6 | null | ---
language:
- pt
tags:
- legal
pipeline_tag: fill-mask
model-index:
- name: checkpoints
results:
- task:
name: Fill Mask
type: fill-mask
metrics:
- name: Loss
type: loss
value: 0.47251805663108826
license: cc-by-4.0
widget:
- text: "Com efeito, se tal fosse possível, o Poder [MASK] – que não dispõe de função legislativa – passaria a desempenhar atribuição que lhe é institucionalmente estranha (a de legislador positivo), usurpando, desse modo, no contexto de um sistema de poderes essencialmente limitados, competência que não lhe pertence, com evidente transgressão ao princípio constitucional da separação de poderes."
---
## (BERT base) Language modeling in the legal domain in Portuguese
**legal-bert-base-cased-ptbr** is a Language Model in the legal domain in Portuguese based on the model [BERTimbau base](https://huggingface.co/neuralmind/bert-base-portuguese-cased) by using a MASK objective.
The model is intended to assist NLP research in the legal field, computer law and legal technology applications. Several legal texts in Portuguese were used (more information below).
**Large version of the model will be available soon**.
## Pre-training corpora
The pre-training corpora of **legal-bert-base-cased-ptbr** include:
* 61309 - Documentos juridicos diversos | (Miscellaneous legal documents)
* 751 - Petições (Recurso Extraordinário JEC) | (Petitions)
* 682 - Sentenças | (Sentences)
* 498 - Acordãos 2º Instancia | (2nd Instance Accords)
* 469 - Agravos Recurso extraordinário | (RE grievances)
* 411 - Despacho de Admissibilidade | (Admissibility Order)
The data used was provided by the BRAZILIAN SUPREME FEDERAL TRIBUNAL, through the terms of use: [LREC 2020](https://ailab.unb.br/victor/lrec2020).
The results of this project do not imply in any way the position of the BRAZILIAN SUPREME FEDERAL TRIBUNAL, all being the sole and exclusive responsibility of the author of the model.
## Load Pretrained Model
````python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("dominguesm/legal-bert-base-cased-ptbr")
model = AutoModel.from_pretrained("dominguesm/legal-bert-base-cased-ptbr")
# OR
from transformers import pipeline
pipe = pipeline('fill-mask', "dominguesm/legal-bert-base-cased-ptbr")
````
## Use **legal-bert-base-cased-ptbr** variants as Language Models
| Text | Masked token | Predictions |
| ---------------------------------- | ------------ | ------------ |
| De ordem, a Secretaria Judiciária do Supremo Tribunal Federal INTIMA a parte abaixo identificada, ou quem as suas vezes fizer, do inteiro teor do(a) despacho/decisão presente nos autos (art. 270 do Código de Processo [MASK] e art 5º da Lei 11.419/2006). | Civil | ('Civil', 0.9999), ('civil', 0.0001), ('Penal', 0.0000), ('eletrônico', 0.0000), ('2015', 0.0000) |
| 2. INTIMAÇÃO da Autarquia: 2.2 Para que apresente em Juízo, com a contestação, cópia do processo administrativo referente ao benefício [MASK] em discussão na lide | previdenciário | ('ora', 0.9424), ('administrativo', 0.0202), ('doença', 0.0117), ('acidente', 0.0037), ('posto', 0.0036) |
| Certifico que, nesta data, os presentes autos foram remetidos ao [MASK] para processar e julgar recurso (Agravo de Instrumento). | STF | ('Tribunal', 0.4278), ('Supremo', 0.1657), ('origem', 0.1538), ('arquivo', 0.1415), ('sistema', 0.0216) |
| TEMA: 810. Validade da correção monetária e dos juros moratórios [MASK] sobre as condenações impostas à Fazenda Pública, conforme previstos no art. 1º-F da Lei 9.494/1997, com a redação dada pela Lei 11.960/2009. | incidentes | ('incidentes', 0.9979), ('incidente', 0.0021), ('aplicados', 0.0000), (',', 0.0000), ('aplicada', 0.0000) |
## Training results
````
Num examples = 353435
Num Epochs = 3
Instantaneous batch size per device = 4
Total train batch size (w. parallel, distributed & accumulation) = 32
Gradient Accumulation steps = 1
Total optimization steps = 33135
TRAIN RESULTS
"epoch": 3.0
"train_loss": 0.6107781137512769
"train_runtime": 10192.1545
"train_samples": 353435
"train_samples_per_second": 104.031
"train_steps_per_second": 3.251
EVAL RESULTS
"epoch": 3.0
"eval_loss": 0.47251805663108826
"eval_runtime": 126.3026
"eval_samples": 17878
"eval_samples_per_second": 141.549
"eval_steps_per_second": 4.426
"perplexity": 1.604028145934512
````
## Citation
```
@misc{domingues2022legal-bert-base-cased-ptbr,
author = {Domingues, Maicon}
title = {Language Model in the legal domain in Portuguese},
year={2022},
howpublished= {\url{https://huggingface.co/dominguesm/legal-bert-base-cased-ptbr/}}
}
```
|
AnonymousSub/rule_based_roberta_hier_triplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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}
} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ccmatrix
metrics:
- bleu
model-index:
- name: t5-small-finetuned-en-to-it-b32
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: ccmatrix
type: ccmatrix
config: en-it
split: train[3000:12000]
args: en-it
metrics:
- name: Bleu
type: bleu
value: 9.6816
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-en-to-it-b32
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the ccmatrix dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1496
- Bleu: 9.6816
- Gen Len: 56.5347
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 282 | 2.9409 | 2.6764 | 69.2487 |
| 3.3809 | 2.0 | 564 | 2.8277 | 2.4974 | 87.428 |
| 3.3809 | 3.0 | 846 | 2.7483 | 2.6851 | 89.7887 |
| 3.1255 | 4.0 | 1128 | 2.6831 | 3.1801 | 85.6927 |
| 3.1255 | 5.0 | 1410 | 2.6293 | 3.6949 | 79.9467 |
| 2.9965 | 6.0 | 1692 | 2.5809 | 4.0149 | 76.852 |
| 2.9965 | 7.0 | 1974 | 2.5403 | 4.3463 | 74.6487 |
| 2.9002 | 8.0 | 2256 | 2.5033 | 4.838 | 72.6053 |
| 2.8229 | 9.0 | 2538 | 2.4694 | 5.2829 | 67.984 |
| 2.8229 | 10.0 | 2820 | 2.4421 | 5.4964 | 68.986 |
| 2.76 | 11.0 | 3102 | 2.4135 | 5.8118 | 66.528 |
| 2.76 | 12.0 | 3384 | 2.3897 | 6.1966 | 65.052 |
| 2.7051 | 13.0 | 3666 | 2.3667 | 6.452 | 64.2273 |
| 2.7051 | 14.0 | 3948 | 2.3465 | 6.6428 | 63.516 |
| 2.6568 | 15.0 | 4230 | 2.3265 | 6.9467 | 61.8673 |
| 2.6183 | 16.0 | 4512 | 2.3101 | 7.2029 | 60.7393 |
| 2.6183 | 17.0 | 4794 | 2.2954 | 7.4982 | 60.0327 |
| 2.5757 | 18.0 | 5076 | 2.2799 | 7.7555 | 59.968 |
| 2.5757 | 19.0 | 5358 | 2.2660 | 7.8406 | 60.0307 |
| 2.5534 | 20.0 | 5640 | 2.2558 | 8.0679 | 59.0793 |
| 2.5534 | 21.0 | 5922 | 2.2426 | 8.3325 | 58.5367 |
| 2.5159 | 22.0 | 6204 | 2.2324 | 8.3538 | 58.6893 |
| 2.5159 | 23.0 | 6486 | 2.2217 | 8.5867 | 57.7627 |
| 2.4983 | 24.0 | 6768 | 2.2135 | 8.8324 | 56.7367 |
| 2.4791 | 25.0 | 7050 | 2.2052 | 8.8113 | 57.4373 |
| 2.4791 | 26.0 | 7332 | 2.1981 | 9.0909 | 57.0173 |
| 2.4529 | 27.0 | 7614 | 2.1908 | 9.0056 | 57.802 |
| 2.4529 | 28.0 | 7896 | 2.1856 | 9.2696 | 56.9773 |
| 2.4395 | 29.0 | 8178 | 2.1780 | 9.2824 | 57.0007 |
| 2.4395 | 30.0 | 8460 | 2.1722 | 9.2106 | 56.9893 |
| 2.4277 | 31.0 | 8742 | 2.1685 | 9.4668 | 56.406 |
| 2.4181 | 32.0 | 9024 | 2.1646 | 9.4992 | 56.2327 |
| 2.4181 | 33.0 | 9306 | 2.1616 | 9.5054 | 56.3033 |
| 2.4071 | 34.0 | 9588 | 2.1578 | 9.5093 | 56.548 |
| 2.4071 | 35.0 | 9870 | 2.1554 | 9.5227 | 56.7807 |
| 2.3991 | 36.0 | 10152 | 2.1532 | 9.5762 | 56.756 |
| 2.3991 | 37.0 | 10434 | 2.1518 | 9.6659 | 56.5913 |
| 2.3955 | 38.0 | 10716 | 2.1506 | 9.7199 | 56.5753 |
| 2.3955 | 39.0 | 10998 | 2.1498 | 9.6715 | 56.558 |
| 2.3913 | 40.0 | 11280 | 2.1496 | 9.6816 | 56.5347 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0
|
AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
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} | 4 | 2022-10-09T14:09:03Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: levit-192_finetuned_on_unlabelled_IA_with_snorkel_labels
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. -->
# levit-192_finetuned_on_unlabelled_IA_with_snorkel_labels
This model is a fine-tuned version of [facebook/levit-192](https://huggingface.co/facebook/levit-192) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.9836
- Recall: 0.9822
- F1: 0.9829
- Accuracy: 0.9873
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 253 | nan | 0.9743 | 0.9791 | 0.9766 | 0.9826 |
| 0.0557 | 2.0 | 506 | nan | 0.9829 | 0.9801 | 0.9815 | 0.9863 |
| 0.0557 | 3.0 | 759 | nan | 0.9836 | 0.9822 | 0.9829 | 0.9873 |
| 0.0543 | 4.0 | 1012 | nan | 0.9839 | 0.9775 | 0.9807 | 0.9858 |
| 0.0543 | 5.0 | 1265 | nan | 0.9616 | 0.9727 | 0.9670 | 0.9752 |
| 0.0457 | 6.0 | 1518 | nan | 0.9563 | 0.9699 | 0.9629 | 0.9720 |
| 0.0457 | 7.0 | 1771 | nan | 0.9822 | 0.9808 | 0.9815 | 0.9863 |
| 0.0418 | 8.0 | 2024 | nan | 0.9735 | 0.9769 | 0.9752 | 0.9815 |
| 0.0418 | 9.0 | 2277 | nan | 0.9832 | 0.9811 | 0.9822 | 0.9868 |
| 0.0396 | 10.0 | 2530 | nan | 0.9843 | 0.9815 | 0.9829 | 0.9873 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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}
} | 23 | 2022-10-09T14:10:20Z | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: tmphgqi7q16
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. -->
# tmphgqi7q16
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.21.2
- TensorFlow 2.9.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Appolo/TestModel | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 272.33 +/- 17.74
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ArvinZhuang/BiTAG-t5-large | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"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: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 4 | 2022-10-10T00:11:26Z | This model is based on [hivemind/gpt-j-6B-8bit](https://huggingface.co/hivemind/gpt-j-6B-8bit) and increased vocabulary size to 91238. Since the existing weights are maintained, add a new vocabulary and use it for fine tuning.
이 모델은 hivemind/gpt-j-6B-8bit를 기반으로 vocabulary 크기를 91238로 늘인 것입니다. 기존 weight가 유지되고 있기 때문에 새로운 vocabulary를 추가 하여 fine tunning 하는데 사용하세요.
|
Ateeb/QA | [
"pytorch",
"distilbert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
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}
} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-finetuned
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. -->
# mt5-base-finetuned
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4443
- Rouge1: 0.2523
- Rouge2: 0.2151
- Rougel: 0.2477
- Rougelsum: 0.2501
## 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: 5.6e-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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 0.8153 | 1.0 | 7131 | 0.4937 | 0.252 | 0.2141 | 0.247 | 0.2496 |
| 0.4935 | 2.0 | 14262 | 0.4474 | 0.253 | 0.2151 | 0.248 | 0.2505 |
| 0.4494 | 3.0 | 21393 | 0.4443 | 0.2523 | 0.2151 | 0.2477 | 0.2501 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
Augustvember/WOKKAWOKKA | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 12 | null | ---
tags:
- generated_from_trainer
model-index:
- name: kobigbird-bert-base-finetuned-klue-v2_epoch64
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. -->
# kobigbird-bert-base-finetuned-klue-v2_epoch64
This model is a fine-tuned version of [monologg/kobigbird-bert-base](https://huggingface.co/monologg/kobigbird-bert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8139
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5026 | 0.55 | 500 | 1.3754 |
| 0.9551 | 1.1 | 1000 | 0.9633 |
| 0.8 | 1.64 | 1500 | 0.8191 |
| 0.6182 | 2.19 | 2000 | 0.7808 |
| 0.5731 | 2.74 | 2500 | 0.7482 |
| 0.3699 | 3.29 | 3000 | 0.8175 |
| 0.3634 | 3.84 | 3500 | 0.8139 |
### Framework versions
- Transformers 4.23.0
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
Augustvember/WokkaBot99 | []
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} | 0 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/emmarkgadgets/1666104626415/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/1577618253312040962/_WR59faP_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">Emmark</div>
<div style="text-align: center; font-size: 14px;">@emmarkgadgets</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 Emmark.
| Data | Emmark |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 200 |
| Short tweets | 2112 |
| Tweets kept | 937 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/zjdekgzp/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 @emmarkgadgets's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/uheep0ve) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/uheep0ve/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/emmarkgadgets')
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)
|
Augustvember/WokkaBotF | []
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} | 0 | null | Access to model sajidhameed63/urdu_fine_tuning_check is restricted and you are not in the authorized list. Visit https://huggingface.co/sajidhameed63/urdu_fine_tuning_check to ask for access. |
Axon/resnet18-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
]
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} | 0 | null | ---
license: unknown
language:
- en
tags:
- mlconsole
library_name: mlconsole
metrics:
- mae
- loss
model-index:
- name: house_price_prediction
results:
- task:
type: regression
name: regression
dataset:
type: house price prediction
name: House price prediction
metrics:
- type: mae
name: Mean absolute error
value: 5.466085910797119
- type: loss
name: Model loss
value: 57.81909942626953
---
# House price prediction (#0)
Trained on [ML Console](https://mlconsole.com).
[Load the model on ML Console](https://mlconsole.com/model/hf/osanseviero/house_price_prediction).
|
Aybars/ModelOnWhole | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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"BertForQuestionAnswering"
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} | 4 | null | ---
license: apache-2.0
tags:
- mlconsole
- tabular-regression
library_name: mlconsole
inference: false
datasets:
- julien-c/kaggle-rounakbanik-pokemon
metrics:
- mae
- loss
model-index:
- name: pokemon-predict-hp
results:
- task:
type: tabular-regression
name: tabular-regression
dataset:
type: julien-c/kaggle-rounakbanik-pokemon
name: pokemon.csv
metrics:
- type: mae
name: Mean absolute error
value: 15.908513069152832
- type: loss
name: Model loss
value: 647.6045532226562
---
# pokemon.csv (#0)
Trained on [ML Console](https://mlconsole.com) on the [julien-c/kaggle-rounakbanik-pokemon](https://huggingface.co/datasets/julien-c/kaggle-rounakbanik-pokemon).
[Load the model on ML Console](https://mlconsole.com/model/hf/julien-c/pokemon-predict-hp).
### Screenshots of training
|
Ayham/distilbert_roberta_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 14 | null | Access to model Datasculptor/portrait-sculpture-in-the-style-of-sigfried-gross is restricted and you are not in the authorized list. Visit https://huggingface.co/Datasculptor/portrait-sculpture-in-the-style-of-sigfried-gross to ask for access. |
Ayran/DialoGPT-medium-harry-potter-1-through-3 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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}
} | 12 | null | ---
language:
- bn
- gu
- hi
- mr
- ne
- or
- pa
- sa
- ur
library_name: transformers
pipeline_tag: fill-mask
---
# IA-Original
IA-Original is a multilingual RoBERTa model pre-trained exclusively on 11 Indian languages from the Indo-Aryan language family. It is pre-trained on the monolingual corpora of these languages and subsequently evaluated on a set of diverse tasks.
The 11 languages covered by IA-Original are: Bhojpuri, Bengali, Gujarati, Hindi, Magahi, Marathi, Nepali, Oriya, Punjabi, Sanskrit, Urdu.
The code can be found [here](https://github.com/IBM/NL-FM-Toolkit). For more information, check-out our [paper](https://aclanthology.org/2021.emnlp-main.675/).
## Pretraining Corpus
We pre-trained IA-Original on the publicly available monolingual corpus. The corpus has the following distribution of languages:
| **Language** | **\# Sentences** | **\# Tokens** | |
| :------------ | ---------------: | ------------: | ------------: |
| | | **\# Total** | **\# Unique** |
| Hindi (hi) | 1552\.89 | 20,098\.73 | 25\.01 |
| Bengali (bn) | 353\.44 | 4,021\.30 | 6\.5 |
| Sanskrit (sa) | 165\.35 | 1,381\.04 | 11\.13 |
| Urdu (ur) | 153\.27 | 2,465\.48 | 4\.61 |
| Marathi (mr) | 132\.93 | 1,752\.43 | 4\.92 |
| Gujarati (gu) | 131\.22 | 1,565\.08 | 4\.73 |
| Nepali (ne) | 84\.21 | 1,139\.54 | 3\.43 |
| Punjabi (pa) | 68\.02 | 945\.68 | 2\.00 |
| Oriya (or) | 17\.88 | 274\.99 | 1\.10 |
| Bhojpuri (bh) | 10\.25 | 134\.37 | 1\.13 |
| Magahi (mag) | 0\.36 | 3\.47 | 0\.15 |
## Evaluation Results
IA-Original is evaluated on IndicGLUE and some additional tasks. For more details about the tasks, refer to the [paper](https://aclanthology.org/2021.emnlp-main.675/).
## Downloads
You can also download it from [Huggingface](https://huggingface.co/ibm/ia-multilingual-original-script-roberta).
## Citing
If you are using any of the resources, please cite the following article:
```
@inproceedings{dhamecha-etal-2021-role,
title = "Role of {L}anguage {R}elatedness in {M}ultilingual {F}ine-tuning of {L}anguage {M}odels: {A} {C}ase {S}tudy in {I}ndo-{A}ryan {L}anguages",
author = "Dhamecha, Tejas and
Murthy, Rudra and
Bharadwaj, Samarth and
Sankaranarayanan, Karthik and
Bhattacharyya, Pushpak",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.675",
doi = "10.18653/v1/2021.emnlp-main.675",
pages = "8584--8595",
}
```
## Contributors
- Tejas Dhamecha
- Rudra Murthy
- Samarth Bharadwaj
- Karthik Sankaranarayanan
- Pushpak Bhattacharyya
## Contact
- Rudra Murthy ([[email protected]](mailto:[email protected])) |
Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6-e18 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 12 | 2022-10-10T12:21:04Z | ---
tags:
- generated_from_keras_callback
model-index:
- name: layoutlm-funsd-tf
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. -->
# layoutlm-funsd-tf
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2280
- Validation Loss: 0.6532
- Train Overall Precision: 0.7218
- Train Overall Recall: 0.7878
- Train Overall F1: 0.7534
- Train Overall Accuracy: 0.8144
- Epoch: 7
## 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': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Train Overall Precision | Train Overall Recall | Train Overall F1 | Train Overall Accuracy | Epoch |
|:----------:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:----------------------:|:-----:|
| 1.6940 | 1.4151 | 0.2686 | 0.2785 | 0.2735 | 0.5128 | 0 |
| 1.1731 | 0.8665 | 0.5771 | 0.6101 | 0.5932 | 0.7267 | 1 |
| 0.7612 | 0.6849 | 0.6362 | 0.7336 | 0.6814 | 0.7784 | 2 |
| 0.5630 | 0.6265 | 0.6748 | 0.7592 | 0.7145 | 0.8017 | 3 |
| 0.4441 | 0.6256 | 0.6935 | 0.7767 | 0.7328 | 0.8036 | 4 |
| 0.3641 | 0.6402 | 0.7115 | 0.7772 | 0.7429 | 0.7940 | 5 |
| 0.2781 | 0.6248 | 0.7176 | 0.7868 | 0.7506 | 0.8141 | 6 |
| 0.2280 | 0.6532 | 0.7218 | 0.7878 | 0.7534 | 0.8144 | 7 |
### Framework versions
- Transformers 4.22.2
- TensorFlow 2.10.0
- Datasets 2.5.2
- Tokenizers 0.12.1
|
AyushPJ/ai-club-inductions-21-nlp-ELECTRA-base-squad | [
"pytorch",
"electra",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 12 | null | ---
license: cc-by-sa-4.0
pipeline_tag: fill-mask
arxiv: 2210.05529
language: en
thumbnail: https://github.com/coastalcph/hierarchical-transformers/raw/main/data/figures/hat_encoder.png
tags:
- long-documents
datasets:
- c4
model-index:
- name: kiddothe2b/hierarchical-transformer-base-4096
results: []
---
# Hierarchical Attention Transformer (HAT) / hierarchical-transformer-base-4096
## Model description
This is a Hierarchical Attention Transformer (HAT) model as presented in [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification (Chalkidis et al., 2022)](https://arxiv.org/abs/2210.05529).
The model has been warm-started re-using the weights of RoBERTa (Liu et al., 2019), and continued pre-trained for MLM in long sequences following the paradigm of Longformer released by Beltagy et al. (2020). It supports sequences of length up to 4,096.
HAT uses hierarchical attention, which is a combination of segment-wise and cross-segment attention operations. You can think of segments as paragraphs or sentences.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
See the [model hub](https://huggingface.co/models?filter=hierarchical-transformer) to look for other versions of HAT or fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole document to make decisions, such as document classification, sequential sentence classification, or question answering.
## How to use
You can use this model directly for masked language modeling:
```python
from transformers import AutoTokenizer, AutoModelForForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hierarchical-transformer-base-4096", trust_remote_code=True)
mlm_model = AutoModelForMaskedLM("kiddothe2b/hierarchical-transformer-base-4096", trust_remote_code=True)
```
You can also fine-tune it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hierarchical-transformer-base-4096", trust_remote_code=True)
doc_classifier = AutoModelForSequenceClassification.from_pretrained("kiddothe2b/hierarchical-transformer-base-4096", trust_remote_code=True)
```
## Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from
neutral. Therefore, the model can have biased predictions.
## Training procedure
### Training and evaluation data
The model has been warm-started from [roberta-base](https://huggingface.co/roberta-base) checkpoint and has been continued pre-trained for additional 50k steps in long sequences (> 1024 subwords) of [C4](https://huggingface.co/datasets/c4) (Raffel et al., 2020).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: tpu
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 50000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.7437 | 0.2 | 10000 | 1.6370 |
| 1.6994 | 0.4 | 20000 | 1.6054 |
| 1.6726 | 0.6 | 30000 | 1.5718 |
| 1.644 | 0.8 | 40000 | 1.5526 |
| 1.6299 | 1.0 | 50000 | 1.5368 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
## Citing
If you use HAT in your research, please cite:
[An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification](https://arxiv.org/abs/2210.05529). Ilias Chalkidis, Xiang Dai, Manos Fergadiotis, Prodromos Malakasiotis, and Desmond Elliott. 2022. arXiv:2210.05529 (Preprint).
```
@misc{chalkidis-etal-2022-hat,
url = {https://arxiv.org/abs/2210.05529},
author = {Chalkidis, Ilias and Dai, Xiang and Fergadiotis, Manos and Malakasiotis, Prodromos and Elliott, Desmond},
title = {An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification},
publisher = {arXiv},
year = {2022},
}
```
|
AyushPJ/ai-club-inductions-21-nlp-distilBERT | [
"pytorch",
"distilbert",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"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": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 8 | null | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- cuad
model-index:
- name: bert-finetuned-cuad-legalbert1
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-finetuned-cuad-legalbert1
This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the cuad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
AyushPJ/test-squad-trained-finetuned-squad | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"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
}
}
} | 8 | null | ---
license: mit
---
<strong>Classifier of event reported in vaccine-related content in Italian language</strong></br>
A monolingual model for classifying the nature of the event reported in vaccine-related content in Italian language. The model was trained on 36,722 and independently tested on 9,299 social media content between Facebook posts, Twitter tweets, Instagram media and YouTube videos. It is a fine-tuned version of bert-base-multilingual-cased.
<strong>Model output</strong></br>
The model classifies each input into one of three distinct classes:</br>
<ul>
<li>Adverse</li>
<li>Neutral</li>
<li>Positive</li>
</ul>
<strong>Citation info and BibTeX entry</strong></br>
<a href="https://arxiv.org/abs/2301.05961" target="_blank">https://arxiv.org/abs/2301.05961</a>
```bibtex
@article{,
title={Unveiling the Hidden Agenda: Biases in News Reporting and Consumption},
author={Galeazzi, Alessandro and Peruzzi, Antonio and Brugnoli, Emanuele and Delmastro, Marco and Zollo, Fabiana},
journal={ArXiv},
year={2023},
volume={abs/2301.05961}
}
``` |
Azizun/Geotrend-10-epochs | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"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
}
}
} | 6 | 2022-10-10T13:38:05Z | ---
tags:
- espnet
- audio
- text-to-speech
language: jp
datasets:
- ErodeesFleurs
license: cc-by-4.0
---
## ESPnet2 TTS model
### `ErodeesFleurs/Amtmp`
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout d5b5ec7b2e77bd3e10707141818b7e6c57ac6b3f
pip install -e .
cd egs2/amadeus/tts1
./run.sh --skip_data_prep false --skip_train true --download_model ErodeesFleurs/Amtmp
```
## TTS config
<details><summary>expand</summary>
```
config: conf/tuning/finetune_vits.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/tts_amadeus_vits_finetune_from_jsut_32_sentence
ngpu: 1
seed: 777
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: true
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: false
collect_stats: false
write_collected_feats: false
max_epoch: 2000
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- total_count
- max
keep_nbest_models: 3
nbest_averaging_interval: 0
grad_clip: -1
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: 50
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: true
wandb_project: amadeus
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param:
- downloads/f3698edf589206588f58f5ec837fa516/exp/tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause/train.total_count.ave_10best.pth:tts:tts
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 5000000
valid_batch_bins: null
train_shape_file:
- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/train/text_shape.phn
- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/train/speech_shape
valid_shape_file:
- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/valid/text_shape.phn
- exp/tts_stats_raw_linear_spectrogram_phn_jaconv_pyopenjtalk_accent_with_pause/valid/speech_shape
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 204800
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/22k/raw/train/text
- text
- text
- - dump/22k/raw/train/wav.scp
- speech
- sound
valid_data_path_and_name_and_type:
- - dump/22k/raw/dev/text
- text
- text
- - dump/22k/raw/dev/wav.scp
- speech
- sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adamw
optim_conf:
lr: 0.0001
betas:
- 0.8
- 0.99
eps: 1.0e-09
weight_decay: 0.0
scheduler: exponentiallr
scheduler_conf:
gamma: 0.999875
optim2: adamw
optim2_conf:
lr: 0.0001
betas:
- 0.8
- 0.99
eps: 1.0e-09
weight_decay: 0.0
scheduler2: exponentiallr
scheduler2_conf:
gamma: 0.999875
generator_first: false
token_list:
- <blank>
- <unk>
- '1'
- '2'
- '0'
- '3'
- '4'
- '-1'
- '5'
- a
- o
- '-2'
- i
- '-3'
- u
- e
- k
- n
- t
- '6'
- r
- '-4'
- s
- N
- m
- pau
- '7'
- sh
- d
- g
- w
- '8'
- U
- '-5'
- I
- cl
- h
- y
- b
- '9'
- j
- ts
- ch
- '-6'
- z
- p
- '-7'
- f
- ky
- ry
- '-8'
- gy
- '-9'
- hy
- ny
- '-10'
- by
- my
- '-11'
- '-12'
- '-13'
- py
- '-14'
- '-15'
- v
- '10'
- '-16'
- '-17'
- '11'
- '-21'
- '-20'
- '12'
- '-19'
- '13'
- '-18'
- '14'
- dy
- '15'
- ty
- '-22'
- '16'
- '18'
- '19'
- '17'
- <sos/eos>
odim: null
model_conf: {}
use_preprocessor: true
token_type: phn
bpemodel: null
non_linguistic_symbols: null
cleaner: jaconv
g2p: pyopenjtalk_accent_with_pause
feats_extract: linear_spectrogram
feats_extract_conf:
n_fft: 1024
hop_length: 256
win_length: null
normalize: null
normalize_conf: {}
tts: vits
tts_conf:
generator_type: vits_generator
generator_params:
hidden_channels: 192
spks: -1
global_channels: -1
segment_size: 32
text_encoder_attention_heads: 2
text_encoder_ffn_expand: 4
text_encoder_blocks: 6
text_encoder_positionwise_layer_type: conv1d
text_encoder_positionwise_conv_kernel_size: 3
text_encoder_positional_encoding_layer_type: rel_pos
text_encoder_self_attention_layer_type: rel_selfattn
text_encoder_activation_type: swish
text_encoder_normalize_before: true
text_encoder_dropout_rate: 0.1
text_encoder_positional_dropout_rate: 0.0
text_encoder_attention_dropout_rate: 0.1
use_macaron_style_in_text_encoder: true
use_conformer_conv_in_text_encoder: false
text_encoder_conformer_kernel_size: -1
decoder_kernel_size: 7
decoder_channels: 512
decoder_upsample_scales:
- 8
- 8
- 2
- 2
decoder_upsample_kernel_sizes:
- 16
- 16
- 4
- 4
decoder_resblock_kernel_sizes:
- 3
- 7
- 11
decoder_resblock_dilations:
- - 1
- 3
- 5
- - 1
- 3
- 5
- - 1
- 3
- 5
use_weight_norm_in_decoder: true
posterior_encoder_kernel_size: 5
posterior_encoder_layers: 16
posterior_encoder_stacks: 1
posterior_encoder_base_dilation: 1
posterior_encoder_dropout_rate: 0.0
use_weight_norm_in_posterior_encoder: true
flow_flows: 4
flow_kernel_size: 5
flow_base_dilation: 1
flow_layers: 4
flow_dropout_rate: 0.0
use_weight_norm_in_flow: true
use_only_mean_in_flow: true
stochastic_duration_predictor_kernel_size: 3
stochastic_duration_predictor_dropout_rate: 0.5
stochastic_duration_predictor_flows: 4
stochastic_duration_predictor_dds_conv_layers: 3
vocabs: 85
aux_channels: 513
discriminator_type: hifigan_multi_scale_multi_period_discriminator
discriminator_params:
scales: 1
scale_downsample_pooling: AvgPool1d
scale_downsample_pooling_params:
kernel_size: 4
stride: 2
padding: 2
scale_discriminator_params:
in_channels: 1
out_channels: 1
kernel_sizes:
- 15
- 41
- 5
- 3
channels: 128
max_downsample_channels: 1024
max_groups: 16
bias: true
downsample_scales:
- 2
- 2
- 4
- 4
- 1
nonlinear_activation: LeakyReLU
nonlinear_activation_params:
negative_slope: 0.1
use_weight_norm: true
use_spectral_norm: false
follow_official_norm: false
periods:
- 2
- 3
- 5
- 7
- 11
period_discriminator_params:
in_channels: 1
out_channels: 1
kernel_sizes:
- 5
- 3
channels: 32
downsample_scales:
- 3
- 3
- 3
- 3
- 1
max_downsample_channels: 1024
bias: true
nonlinear_activation: LeakyReLU
nonlinear_activation_params:
negative_slope: 0.1
use_weight_norm: true
use_spectral_norm: false
generator_adv_loss_params:
average_by_discriminators: false
loss_type: mse
discriminator_adv_loss_params:
average_by_discriminators: false
loss_type: mse
feat_match_loss_params:
average_by_discriminators: false
average_by_layers: false
include_final_outputs: true
mel_loss_params:
fs: 22050
n_fft: 1024
hop_length: 256
win_length: null
window: hann
n_mels: 80
fmin: 0
fmax: null
log_base: null
lambda_adv: 1.0
lambda_mel: 45.0
lambda_feat_match: 2.0
lambda_dur: 1.0
lambda_kl: 1.0
sampling_rate: 22050
cache_generator_outputs: true
pitch_extract: null
pitch_extract_conf: {}
pitch_normalize: null
pitch_normalize_conf: {}
energy_extract: null
energy_extract_conf: {}
energy_normalize: null
energy_normalize_conf: {}
required:
- output_dir
- token_list
version: '202207'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
Azuris/DialoGPT-medium-senorita | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"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": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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},
"translation_en_to_fr": {
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},
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"max_length": null,
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"prefix": null
}
}
} | 14 | 2022-10-10T13:44:57Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad2
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-squad2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3471
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0.5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4671 | 0.5 | 2767 | 1.3471 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
BAHIJA/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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},
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},
"translation_en_to_ro": {
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}
}
} | 36 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/angelicismbj/1667671357876/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/1578378077826330624/CWUsJU25_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">angelicism beijing</div>
<div style="text-align: center; font-size: 14px;">@angelicismbj</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 angelicism beijing.
| Data | angelicism beijing |
| --- | --- |
| Tweets downloaded | 2366 |
| Retweets | 213 |
| Short tweets | 352 |
| Tweets kept | 1801 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/c813g98z/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 @angelicismbj's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/46oix6b0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/46oix6b0/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/angelicismbj')
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)
|
BME-TMIT/foszt2oszt | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"hu",
"transformers",
"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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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 15 | 2022-10-10T14:01:22Z | ---
library_name: stable-baselines3
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 69.30 +/- 7.32
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **PPO** Agent playing **CartPole-v1**
This is a trained model of a **PPO** agent playing **CartPole-v1**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BSC-LT/roberta-base-bne-sqac | [
"pytorch",
"roberta",
"question-answering",
"es",
"dataset:BSC-TeMU/SQAC",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"qa",
"question answering",
"license:apache-2.0",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"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": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 10 | 2022-10-10T14:42:01Z | ---
license: cc-by-sa-4.0
pipeline_tag: fill-mask
language: en
arxiv: 2210.05529
tags:
- long-documents
datasets:
- c4
model-index:
- name: kiddothe2b/adhoc-hierarchical-transformer-base-4096
results: []
---
# Hierarchical Attention Transformer (HAT) / kiddothe2b/adhoc-hierarchical-transformer-base-4096
## Model description
This is a Hierarchical Attention Transformer (HAT) model as presented in [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification (Chalkidis et al., 2022)](https://arxiv.org/abs/2210.05529).
The model has been warm-started re-using the weights of RoBERTa (Liu et al., 2019), BUT has not been continued pre-trained. It supports sequences of length up to 4,096.
HAT uses hierarchical attention, which is a combination of segment-wise and cross-segment attention operations. You can think of segments as paragraphs or sentences.
Note: If you wish to use a fully pre-trained HAT model, you have to use [kiddothe2b/adhoc-hat-base-4096](https://huggingface.co/kiddothe2b/adhoc-hat-base-4096).
## Intended uses & limitations
The model is intended to be fine-tuned on a downstream task.
See the [model hub](https://huggingface.co/models?filter=hierarchical-transformer) to look for other versions of HAT, or fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole document to make decisions, such as document classification, sequential sentence classification, or question answering.
## How to use
You can fine-tune it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/adhoc-hierarchical-transformer-base-4096", trust_remote_code=True)
doc_classifier = AutoModelForSequenceClassification("kiddothe2b/adhoc-hierarchical-transformer-base-4096", trust_remote_code=True)
```
Note: If you wish to use a fully pre-trained HAT model, you have to use [kiddothe2b/hierarchical-transformer-base-4096](https://huggingface.co/kiddothe2b/hierarchical-transformer-base-4096).
## Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from
neutral. Therefore, the model can have biased predictions.
## Training procedure
### Training and evaluation data
The model has been warm-started from [roberta-base](https://huggingface.co/roberta-base) checkpoint.
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
## Citing
If you use HAT in your research, please cite:
[An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification](https://arxiv.org/abs/2210.05529). Ilias Chalkidis, Xiang Dai, Manos Fergadiotis, Prodromos Malakasiotis, and Desmond Elliott. 2022. arXiv:2210.05529 (Preprint).
```
@misc{chalkidis-etal-2022-hat,
url = {https://arxiv.org/abs/2210.05529},
author = {Chalkidis, Ilias and Dai, Xiang and Fergadiotis, Manos and Malakasiotis, Prodromos and Elliott, Desmond},
title = {An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification},
publisher = {arXiv},
year = {2022},
}
```
|
BSC-LT/roberta-large-bne-capitel-pos | [
"pytorch",
"roberta",
"token-classification",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"capitel",
"pos",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 13 | 2022-10-10T14:56:03Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 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="CoenSchouten/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"])
```
|
BSen/wav2vec2-large-xls-r-300m-turkish-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"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
}
}
} | 6 | 2022-10-10T15:10:09Z | ---
license: cc-by-sa-4.0
pipeline_tag: fill-mask
arxiv: 2210.05529
language: en
tags:
- long-documents
datasets:
- c4
model-index:
- name: kiddothe2b/longformer-base-4096
results: []
---
# Longformer / longformer-base-4096
## Model description
[Longformer](https://arxiv.org/abs/2004.05150) is a transformer model for long documents. This version of Longformer presented in [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification (Chalkidis et al., 2022)](https://arxiv.org/abs/2210.05529).
The model has been warm-started re-using the weights of RoBERTa (Liu et al., 2019), and continued pre-trained for MLM in long sequences following the paradigm of original Longformer released by Beltagy et al. (2020). It supports sequences of length up to 4,096.
Longformer uses a combination of a sliding window (local) attention and global attention. Global attention is user-configured based on the task to allow the model to learn task-specific representations.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
See the [model hub](https://huggingface.co/models?filter=longformer) to look for fine-tuned versions on a task that
interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole document to make decisions, such as document classification, sequential sentence classification or question answering.
## How to use
You can use this model directly with a pipeline for masked language modeling:
```python
from transformers import pipeline
mlm_model = pipeline('fill-mask', model='kiddothe2b/longformer-base-4096', trust_remote_code=True)
mlm_model("Hello I'm a <mask> model.")
```
You can also fine-tune it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks:
```python
from transformers import AutoTokenizer, AutoModelforSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/longformer-base-4096", trust_remote_code=True)
doc_classifier = AutoModelforSequenceClassification("kiddothe2b/longformer-base-4096", trust_remote_code=True)
```
## Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from
neutral. Therefore, the model can have biased predictions.
## Training procedure
### Training and evaluation data
The model has been warm-started from [roberta-base](https://huggingface.co/roberta-base) checkpoint and has been continued pre-trained for additional 50k steps in long sequences (> 1024 subwords) of [C4](https://huggingface.co/datasets/c4) (Raffel et al., 2020).
### Training hyperparameters
TThe following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- 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
- training_steps: 50000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.7067 | 0.2 | 10000 | 1.5923 | 0.6714 |
| 1.6532 | 0.4 | 20000 | 1.5494 | 0.6784 |
| 1.622 | 0.6 | 30000 | 1.5208 | 0.6830 |
| 1.588 | 0.8 | 40000 | 1.4880 | 0.6876 |
| 1.5682 | 1.0 | 50000 | 1.4680 | 0.6908 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.11.6
## Citing
If you use HAT in your research, please cite:
[An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification](https://arxiv.org/abs/2210.05529). Ilias Chalkidis, Xiang Dai, Manos Fergadiotis, Prodromos Malakasiotis, and Desmond Elliott. 2022. arXiv:2210.05529 (Preprint).
```
@misc{chalkidis-etal-2022-hat,
url = {https://arxiv.org/abs/2210.05529},
author = {Chalkidis, Ilias and Dai, Xiang and Fergadiotis, Manos and Malakasiotis, Prodromos and Elliott, Desmond},
title = {An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification},
publisher = {arXiv},
year = {2022},
}
```
Also cite the original work: [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150).
```
@article{Beltagy2020Longformer,
title={Longformer: The Long-Document Transformer},
author={Iz Beltagy and Matthew E. Peters and Arman Cohan},
journal={arXiv:2004.05150},
year={2020},
}
```
|
Backedman/DialoGPT-small-Anika | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"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": {
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"num_beams": null,
"prefix": null
}
}
} | 6 | 2022-10-10T15:31:26Z | ---
license: mit
---
### wojak on Stable Diffusion
This is the `wojak` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
Bagus/ser-japanese | []
| null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 0 | 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: 7.48 +/- 2.69
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="Alt41r/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"])
```
|
Bagus/wav2vec2-xlsr-japanese-speech-emotion-recognition | [
"pytorch",
"wav2vec2",
"audio-classification",
"ja",
"dataset:jtes",
"transformers",
"audio",
"speech",
"speech-emotion-recognition",
"has_space"
]
| audio-classification | {
"architectures": [
"HubertForSequenceClassification"
],
"model_type": "wav2vec2",
"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": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 26 | null | ---
license: mit
---
This is a test readme
doddodododo
mlm |
BatuhanYilmaz/distilbert-base-uncased-finetuned-squad-d5716d28 | [
"pytorch",
"distilbert",
"fill-mask",
"en",
"dataset:squad",
"arxiv:1910.01108",
"transformers",
"question-answering",
"license:apache-2.0",
"autotrain_compatible"
]
| question-answering | {
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],
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} | 18 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: t5-small-finetuned-en-to-it-lrs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-en-to-it-lrs
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1483
- Bleu: 10.4962
- Gen Len: 51.8247
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.9618 | 1.0 | 1125 | 2.8717 | 4.6688 | 66.512 |
| 1.7256 | 2.0 | 2250 | 2.7638 | 6.5673 | 56.7267 |
| 1.6133 | 3.0 | 3375 | 2.6703 | 7.4218 | 55.1753 |
| 1.5132 | 4.0 | 4500 | 2.6096 | 7.9581 | 54.5387 |
| 1.4558 | 5.0 | 5625 | 2.5603 | 8.5191 | 52.41 |
| 1.4392 | 6.0 | 6750 | 2.5109 | 8.976 | 52.1867 |
| 1.4113 | 7.0 | 7875 | 2.4768 | 9.2615 | 51.8907 |
| 1.3669 | 8.0 | 9000 | 2.4447 | 9.3001 | 52.6 |
| 1.3575 | 9.0 | 10125 | 2.4262 | 9.5818 | 51.774 |
| 1.3315 | 10.0 | 11250 | 2.3906 | 9.584 | 52.3213 |
| 1.3231 | 11.0 | 12375 | 2.3740 | 9.7574 | 51.63 |
| 1.2917 | 12.0 | 13500 | 2.3475 | 9.8298 | 51.6367 |
| 1.282 | 13.0 | 14625 | 2.3269 | 9.8176 | 52.06 |
| 1.2841 | 14.0 | 15750 | 2.3121 | 9.9668 | 51.9487 |
| 1.2548 | 15.0 | 16875 | 2.2993 | 9.9941 | 51.708 |
| 1.2487 | 16.0 | 18000 | 2.2816 | 10.0288 | 52.364 |
| 1.2462 | 17.0 | 19125 | 2.2697 | 10.1991 | 51.3893 |
| 1.232 | 18.0 | 20250 | 2.2581 | 10.2667 | 51.6693 |
| 1.2227 | 19.0 | 21375 | 2.2428 | 10.3357 | 51.5373 |
| 1.2279 | 20.0 | 22500 | 2.2350 | 10.3646 | 51.4633 |
| 1.2159 | 21.0 | 23625 | 2.2275 | 10.3489 | 51.472 |
| 1.2036 | 22.0 | 24750 | 2.2186 | 10.3756 | 51.444 |
| 1.2089 | 23.0 | 25875 | 2.2082 | 10.3555 | 51.7133 |
| 1.1957 | 24.0 | 27000 | 2.2016 | 10.4624 | 51.4293 |
| 1.1828 | 25.0 | 28125 | 2.1953 | 10.4474 | 51.4287 |
| 1.1885 | 26.0 | 29250 | 2.1887 | 10.3417 | 51.4227 |
| 1.1817 | 27.0 | 30375 | 2.1844 | 10.4777 | 51.5787 |
| 1.1769 | 28.0 | 31500 | 2.1759 | 10.4044 | 51.5907 |
| 1.1831 | 29.0 | 32625 | 2.1728 | 10.4434 | 51.6587 |
| 1.1842 | 30.0 | 33750 | 2.1706 | 10.4136 | 51.7653 |
| 1.1828 | 31.0 | 34875 | 2.1689 | 10.5099 | 51.5893 |
| 1.1673 | 32.0 | 36000 | 2.1613 | 10.4957 | 51.646 |
| 1.1603 | 33.0 | 37125 | 2.1570 | 10.4438 | 51.6633 |
| 1.1718 | 34.0 | 38250 | 2.1564 | 10.5364 | 51.7113 |
| 1.1651 | 35.0 | 39375 | 2.1538 | 10.4444 | 51.6593 |
| 1.1756 | 36.0 | 40500 | 2.1501 | 10.4497 | 51.7393 |
| 1.1595 | 37.0 | 41625 | 2.1499 | 10.4701 | 51.7313 |
| 1.1603 | 38.0 | 42750 | 2.1499 | 10.4611 | 51.7533 |
| 1.1586 | 39.0 | 43875 | 2.1487 | 10.4776 | 51.836 |
| 1.161 | 40.0 | 45000 | 2.1483 | 10.4962 | 51.8247 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0
|
BatuhanYilmaz/dummy-model | [
"tf",
"camembert",
"fill-mask",
"transformers",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible"
]
| fill-mask | {
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"CamembertForMaskedLM"
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} | 6 | null | ---
license: unknown
inference: false
tags:
- mlconsole
- tabular-classification
library_name: mlconsole
metrics:
- accuracy
- loss
datasets:
- julien-c/kaggle-rounakbanik-pokemon
model-index:
- name: pokemon_is_legendary
results:
- task:
type: tabular-classification
name: tabular-classification
dataset:
type: julien-c/kaggle-rounakbanik-pokemon
name: pokemon.csv
metrics:
- type: accuracy
name: Accuracy
value: 1
- type: loss
name: Model loss
value: 0.314619243144989
---
# pokemon.csv (#0)
Trained on [ML Console](https://mlconsole.com).
[Load the model on ML Console](https://mlconsole.com/model/hf/halflings/pokemon_is_legendary).
|
BatuhanYilmaz/dummy | []
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} | 0 | null | ---
license: unknown
inference: false
tags:
- mlconsole
- tabular-regression
library_name: mlconsole
metrics:
- mae
- loss
datasets:
- julien-c/kaggle-rounakbanik-pokemon
model-index:
- name: pokemon_hp
results:
- task:
type: tabular-regression
name: tabular-regression
dataset:
type: julien-c/kaggle-rounakbanik-pokemon
name: julien-c/kaggle-rounakbanik-pokemon
metrics:
- type: mae
name: Mean absolute error
value: 4.479848861694336
- type: loss
name: Model loss
value: 51.252288818359375
---
# pokemon.csv (1) (#1)
Trained on [ML Console](https://mlconsole.com).
[Load the model on ML Console](https://mlconsole.com/model/hf/halflings/pokemon_hp).
|
BatuhanYilmaz/marian-finetuned-kde4-en-to-fr | []
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} | 0 | null | ---
language: en
tags:
- timelms
- twitter
license: mit
datasets:
- twitter-api
---
# Twitter June 2022 (RoBERTa-base, 154M)
This is a RoBERTa-base model trained on 153.86M tweets until the end of June 2022 (15M tweets increment).
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the [TimeLMs repository](https://github.com/cardiffnlp/timelms).
For other models trained until different periods, check this [table](https://github.com/cardiffnlp/timelms#released-models).
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed [here](https://github.com/cardiffnlp/timelms/tree/main/data).
```python
def preprocess(text):
preprocessed_text = []
for t in text.split():
if len(t) > 1:
t = '@user' if t[0] == '@' and t.count('@') == 1 else t
t = 'http' if t.startswith('http') else t
preprocessed_text.append(t)
return ' '.join(preprocessed_text)
```
## Example Masked Language Model
```python
from transformers import pipeline, AutoTokenizer
MODEL = "cardiffnlp/twitter-roberta-base-mar2022-15M-incr"
fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
tokenizer = AutoTokenizer.from_pretrained(MODEL)
def pprint(candidates, n):
for i in range(n):
token = tokenizer.decode(candidates[i]['token'])
score = candidates[i]['score']
print("%d) %.5f %s" % (i+1, score, token))
texts = [
"So glad I'm <mask> vaccinated.",
"I keep forgetting to bring a <mask>.",
"Looking forward to watching <mask> Game tonight!",
]
for text in texts:
t = preprocess(text)
print(f"{'-'*30}\n{t}")
candidates = fill_mask(t)
pprint(candidates, 5)
```
Output:
```
------------------------------
So glad I'm <mask> vaccinated.
1) 0.35668 not
2) 0.27636 fully
3) 0.18418 getting
4) 0.03197 still
5) 0.02259 triple
------------------------------
I keep forgetting to bring a <mask>.
1) 0.04261 book
2) 0.04233 backpack
3) 0.04161 charger
4) 0.03892 mask
5) 0.03636 lighter
------------------------------
Looking forward to watching <mask> Game tonight!
1) 0.55292 the
2) 0.17813 The
3) 0.03052 this
4) 0.01565 Championship
5) 0.01391 End
```
## Example Tweet Embeddings
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
from scipy.spatial.distance import cosine
from collections import Counter
def get_embedding(text): # naive approach for demonstration
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
return np.mean(features[0], axis=0)
MODEL = "cardiffnlp/twitter-roberta-base-mar2022-15M-incr"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModel.from_pretrained(MODEL)
query = "The book was awesome"
tweets = ["I just ordered fried chicken 🐣",
"The movie was great",
"What time is the next game?",
"Just finished reading 'Embeddings in NLP'"]
sims = Counter()
for tweet in tweets:
sim = 1 - cosine(get_embedding(query), get_embedding(tweet))
sims[tweet] = sim
print('Most similar to: ', query)
print(f"{'-'*30}")
for idx, (tweet, sim) in enumerate(sims.most_common()):
print("%d) %.5f %s" % (idx+1, sim, tweet))
```
Output:
```
Most similar to: The book was awesome
------------------------------
1) 0.98951 The movie was great
2) 0.96042 Just finished reading 'Embeddings in NLP'
3) 0.95454 I just ordered fried chicken 🐣
4) 0.95148 What time is the next game?
```
## Example Feature Extraction
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
MODEL = "cardiffnlp/twitter-roberta-base-mar2022-15M-incr"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
text = "Good night 😊"
text = preprocess(text)
# Pytorch
model = AutoModel.from_pretrained(MODEL)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
features_mean = np.mean(features[0], axis=0)
#features_max = np.max(features[0], axis=0)
# # Tensorflow
# model = TFAutoModel.from_pretrained(MODEL)
# encoded_input = tokenizer(text, return_tensors='tf')
# features = model(encoded_input)
# features = features[0].numpy()
# features_mean = np.mean(features[0], axis=0)
# #features_max = np.max(features[0], axis=0)
``` |
BatuhanYilmaz/mt5-small-finetuned-amazonbooks-en-es | []
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} | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 20,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 40,
"warmup_steps": 4,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Bee-Garbs/DialoGPT-real-cartman-small | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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"GPT2LMHeadModel"
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} | 10 | 2022-10-10T18:28:25Z | ---
license: unknown
inference: false
tags:
- mlconsole
- tabular-classification
library_name: mlconsole
metrics:
- accuracy
- loss
datasets:
- train.csv
model-index:
- name: titanic-survival-with-ml-console
results:
- task:
type: tabular-classification
name: tabular-classification
dataset:
type: train.csv
name: train.csv
metrics:
- type: accuracy
name: Accuracy
value: 0.7882882952690125
- type: loss
name: Model loss
value: 0.5075606107711792
---
# train.csv (#2)
Trained on [ML Console](https://mlconsole.com).
[Load the model on ML Console](https://mlconsole.com/model/hf/victor/titanic-survival-with-ml-console).
|
BenWitter/DialoGPT-small-Tyrion | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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} | 11 | 2022-10-10T19:02:20Z | ---
tags:
- autotrain
- translation
language:
- en
- nl
datasets:
- Tritkoman/autotrain-data-wdwssqddwd
co2_eq_emissions:
emissions: 0.642110734276787
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1716860020
- CO2 Emissions (in grams): 0.6421
## Validation Metrics
- Loss: 0.741
- SacreBLEU: 31.314
- Gen len: 14.605 |
Bharathdamu/wav2vec2-large-xls-r-300m-hindi-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
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} | 4 | 2022-10-10T19:21:36Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: flaubert_base_cased-finetuned-DOP6
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. -->
# flaubert_base_cased-finetuned-DOP6
This model is a fine-tuned version of [flaubert/flaubert_base_cased](https://huggingface.co/flaubert/flaubert_base_cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4109
- Accuracy: 0.8591
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9735 | 1.0 | 110 | 0.6360 | 0.7955 |
| 0.6305 | 2.0 | 220 | 0.4801 | 0.8227 |
| 0.4992 | 3.0 | 330 | 0.4163 | 0.8545 |
| 0.4172 | 4.0 | 440 | 0.4109 | 0.8591 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
|
Bharathdamu/wav2vec2-large-xls-r-300m-hindi2-colab | []
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} | 0 | 2022-10-10T19:28:20Z | ---
language: nl
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- phoneme-recognition
model-index:
- name: wav2vec2-base-960h-phoneme-reco-dutch
results:
- task:
name: Automatic Phoneme Recognition
type: automatic-phoneme-recognition
dataset:
name: CommonVoice (clean)
type: librispeech_asr
config: clean
split: test
args:
language: nl
metrics:
- name: Test PER
type: per
value: 20.83
- name: Val PER
type: per
value: 16.18
---
# Model Description
The Wav2vec2 base model [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) fine tuned on phoneme recognition task for the dutch language.
# Usage
To transcribe in phonemes audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
# load model and tokenizer
processor = Wav2Vec2Processor.from_pretrained("Clementapa/wav2vec2-base-960h-phoneme-reco-dutch")
model = Wav2Vec2ForCTC.from_pretrained("Clementapa/wav2vec2-base-960h-phoneme-reco-dutch")
# load dummy dataset and read soundfiles
ds = load_dataset("common_voice", "nl", split="validation")
# tokenize
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
``` |
BigSalmon/GPTNeo350MInformalToFormalLincoln5 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
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}
} | 11 | 2022-10-11T01:08:51Z | Pre-trained evaluator in EMNLP 2022 paper
*[Towards a Unified Multi-Dimensional Evaluator for Text Generation](https://arxiv.org/abs/2210.07197)*
## Introduction
**Multi-dimensional evaluation** is the dominant paradigm for human evaluation in Natural Language Generation (NLG), i.e., evaluating the generated text from multiple explainable dimensions, such as coherence and fluency.
However, automatic evaluation in NLG is still dominated by similarity-based metrics (e.g., ROUGE, BLEU), but they are not sufficient to portray the difference between the advanced generation models.
Therefore, we propose **UniEval** to bridge this gap so that a more comprehensive and fine-grained evaluation of NLG systems can be achieved.
## Pre-trained Evaluator
**unieval-dialog** is the pre-trained evaluator for the dialogue response generation task. It can evaluate the model output from five dimensions:
- *naturalness*
- *coherence*
- *engagingness*
- *groundedness*
- *understandability*
## Usage
Please refer to [our GitHub repository](https://github.com/maszhongming/UniEval). |
BigSalmon/InfillFormalLincoln | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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}
} | 8 | 2022-10-11T01:51:32Z | ---
license: mit
---
### muxoyara on Stable Diffusion
This is the `<muxoyara>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
BigSalmon/MrLincoln3 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 17 | 2022-10-11T04:02:18Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 1859.98 +/- 54.45
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
...
```
|
BigSalmon/MrLincoln4 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
} | 10 | 2022-10-11T04:24:49Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 197.81 +/- 75.44
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BigSalmon/MrLincoln6 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 9 | 2022-10-11T04:37:26Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
model-index:
- name: testmodel
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: sentiment
split: train
args: sentiment
metrics:
- name: Accuracy
type: accuracy
value: 0.697
- name: F1
type: f1
value: 0.697
---
<!-- 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. -->
# testmodel
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7132
- Accuracy: 0.697
- F1: 0.697
## 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 4.23.0
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
BigSalmon/MrLincoln7 | []
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} | 0 | 2022-10-11T04:38:01Z | ---
language:
- as
tags:
- Assamese pos tagger
- pos tagger for Assamese
- flair based pos tagging model
metrics:
- F1 score
---
# AsPOS: Pre-trained model for Assamese POS tagging
AsPOS is a pre-trained POS tagging model focusing on Assamese language. Stacked embedding (MuRIL + FlairEmbedding) and BiLSTM-CRF model are used to train the model. It achieves an F1-score of 74.62% in POS tagging with 41 POS tagset.
## Annotated Assamese POS tagged dataset
The dataset has been annotated by an automatic POS tagger, of which the accuracy is 74.62%. After that, it is manually corrected. The dataset is split into three parts for model training, those are train.txt, dev.txt, and test.txt.
## Requirements
- It requires python 3.6+
- Install [Flair](https://github.com/flairNLP/flair) (Version: 0.9.0) preferably in virtual environment,
## How to run
Download the pre-trained model from the link- [AsPOS](https://huggingface.co/dpathak/aspos_assamese_pos_tagger/blob/main/AsPOS.pt).
```
from flair.models import SequenceTagger
from flair.data import Sentence, Token
# Load the tagger
model = SequenceTagger.load('AsPOS.pt')
# create example sentence
sen='ফুকন বসুমতাৰী এজন অধ্য়াপক । তেওঁ বৰ্তমান কোকৰাঝাৰত থাকে ।'
sentence = Sentence(sen)
# predict tags and print
model.predict(sentence)
print(sentence.to_tagged_string())
ফুকন <N_NNP> বসুমতাৰী <N_NN> এজন <QT_QTF> অধ্য়াপক <N_NN> । <RD_PUNC> তেওঁ <PR_PRP> বৰ্তমান <RB>
কোকৰাঝাৰত <N_NNP> থাকে <V_VM> । <RD_PUNC>
# create example sentence
sen='মাতৃভাষাৰ সমান্তৰালকৈ সংস্কৃত, ইংৰাজী ভাষাৰ চৰ্চা অত্যন্ত জৰুৰী ৷'
sentence = Sentence(sen)
# predict tags and print
model.predict(sentence)
print(sentence.to_tagged_string()
মাতৃভাষাৰ <N_NN> সমান্তৰালকৈ <N_NN> সংস্কৃত <N_NNP> , <RD_PUNC> ইংৰাজী <N_NNP> ভাষাৰ <N_ANN> চৰ্চা <N_NN> অত্যন্ত <RP_INTF>
জৰুৰী <N_NN> ৷ <RD_PUNC>
```
-----
```
# If you use our model, please cite this paper:
@INPROCEEDINGS{10017934,
author={Pathak, Dhrubajyoti and Nandi, Sukumar and Sarmah, Priyankoo},
booktitle={2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA)},
title={AsPOS: Assamese Part of Speech Tagger using Deep Learning Approach},
year={2022},
volume={},
number={},
pages={1-8},
doi={10.1109/AICCSA56895.2022.10017934}}
|
BigSalmon/SimplifyText | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
],
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}
} | 17 | 2022-10-11T05:59:59Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 573.50 +/- 171.74
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga hezzze -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga hezzze -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga hezzze
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
BigSalmon/T5Salmon2 | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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"prefix": "translate English to German: "
},
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"prefix": "translate English to French: "
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"prefix": "translate English to Romanian: "
}
}
} | 13 | null | ---
datasets:
- bigscience/P3
language: en
license: apache-2.0
---
**Official repository**: [seonghyeonye/Flipped-Learning](https://github.com/seonghyeonye/Flipped-Learning)
# Model Description
FLIPPED uses a unique meta-learning method to show zero-shot task generalization on classification natural language prompts, outperforming GPT-3 and T0-11B on many tasks with a 4x smaller scale.
It is a series of encoder-decoder model trained on a numerous classification dataset. We show inputs and its corresponding outputs of each instances in each dataset to FLIPPED, and train it to generate its possible instruction. We add unlikelihood loss in order **not** to generate the instruction when given the same input, but a wrong output. To obtain FLIPPED, we fine-tune a T5 model in a given scale on a multitask mixture covering many different classification NLP tasks.
# Intended uses
You can use the models to perform inference on tasks by specifying your input-output NLP query in a "input: {input}\noutput: {output}" form , and the model will predict the instruction. For example, You can try
*"input: <extra_id_0> this is the best cast iron skillet you will ever buy<extra_id_1>\noutput: Positive"*
as an input, and the model will hopefully generate *"Title: Review:"*.
# How to use
Our overall explanation models along with ablations can be found in our [paper](https://arxiv.org/abs/2210.02969). We recommend using the [FLIPPED-11B](seonghyeonye/flipped_11B) checkpoint as it leads (on average) to the best performances on a variety of NLP tasks.
|Model|Number of parameters|
|-|-|
|[Flipped_11B](https://huggingface.co/seonghyeonye/flipped_11B)|11 billion|
|[Flipped_3B](https://huggingface.co/seonghyeonye/flipped_3B)|3 billion|
Here is how to download the model in PyTorch:
```python
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
model = T5ForConditionalGeneration.from_pretrained("seonghyeonye/flipped_11B")
tokenizer = T5Tokenizer.from_pretrained("seonghyeonye/flipped_11B")
```
If you want to use another checkpoint, please replace the path in `T5Tokenizer` and `T5ForConditionalGeneration`.
We also provide a quick [Jupyter Notebook](https://github.com/seonghyeonye/Flipped-Learning/blob/master/flipped_inference.ipynb) where you can inference with our method.
**Note: the model was trained with bfloat16 activations. As such, we highly discourage running inference with fp16.**
# Training procedure
FLIPPED models are based on [T5](https://huggingface.co/google/t5-v1_1-xxl), a Transformer-based encoder-decoder language model pre-trained with a masked language modeling-style objective on [C4](https://huggingface.co/datasets/c4).
At a high level, the input text along with output label is fed to the encoder and the instruction text is produced by the decoder. The model is fine-tuned to autoregressively generate the target. We also feed input text along with a wrong input, adding an unlikelihood loss in order not to make model produce the proper instruction in that case. Here are our training details.
Training details:
- Fine-tuning steps: 5'000
- Input sequence length: 384
- Target sequence length: 64
- Batch size: 240
- Optimizer: Adafactor
- Learning rate: 5e-5
- Dropout: 0.1
- Sampling strategy: proportional to the number of examples in each dataset (we randomly sampled any dataset if it has over 500'000 examples so that it has at most 500'000 examples. Also, we randomly choose which instruction to generate for each training steps, so ideally each instruction appears *num_examples/num_templates* while training.)
# Training data
We trained different variants T0 with different mixtures of datasets.
|Model|Training datasets|
|--|--|
|FLIPPED-11B|- Multiple-Choice QA: CommonsenseQA, DREAM, QUAIL, QuaRTz, Social IQA, WiQA, Cosmos, QASC, Quarel, SciQ<br>- Sentiment: Amazon, App Reviews, IMDB, Rotten Tomatoes, Yelp<br>- Topic Classification: AG News, DBPedia<br>- Paraphrase Identification: MRPC, PAWS, QQP|
|FLIPPED_3B|Same as FLIPPED-11B|
We only choose prompts examples that has output lables, which can be found on the dataset page.
# Evaluation data
We evaluate our models on following datasets:
|Task category|Datasets|
|-|-|
|Natural language inference|ANLI(R1, R2, R3), CB, RTE|
|Coreference resolution|WSC, Winogrande|
|Word sense disambiguation|WiC|
|Sentence completion|COPA, HellaSwag, Story Cloze|
|QA|PIQA, ARC-Challenge, OpenbookQA|
We also evaluate FLIPPED on a subset of [BIG-bench benchmark](https://github.com/google/BIG-bench):
- Code description task
- Conceptual combinations
- Hindu knowledge json
- Known unknowns
- Language identification
- Logic grid puzzle task
- Logical deduction
- Common misconceptions
- Movie dialog same or different
- Novel concepts
- Strategyqa
- Formal fallacies syllogisms negation
- VitaminC
- Winowhy multiple choice
# Label generalization
We evaluate the robustness of models on following datasets with changing the output label of the datasets. The substitute words can be found in our [paper](https://arxiv.org/abs/2210.02969).
|Task category|(Datasets, Template name)|
|-|-|
|Unseen tasks|(WSC, does the pronoun refer to), (CB, can we infer), (RTE, MNLI crowdsource)|
|Seen tasks|(IMDB, Reviewer Enjoyment Yes No), (PAWS, Meaning) |
The template name we used can be found in the [promptsource template library](https://github.com/bigscience-workshop/promptsource/tree/main/promptsource/templates).
# BibTeX entry and citation info
```bibtex
@article{ye2022guess,
title={Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners},
author={Ye, Seonghyeon and Kim, Doyoung and Jang, Joel and Shin, Joongbo and Seo, Minjoon},
journal={arXiv preprint arXiv:2210.02969},
year={2022}
}
``` |
BillelBenoudjit/jplu-wikiann | [
"fr",
"dataset:wikiann",
"model-index"
]
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} | 0 | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: latihanyuk
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. -->
# latihanyuk
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 8.4344
- Validation Loss: 8.8852
- 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': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 8.4344 | 8.8852 | 0 |
### Framework versions
- Transformers 4.23.0
- TensorFlow 2.8.2
- Datasets 2.5.2
- Tokenizers 0.13.1
|
Blabla/Pipipopo | []
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} | 0 | null | a beautiful girl,lovely,ACG,street art,fashion,callous girl |
Blaine-Mason/hackMIT-finetuned-sst2 | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer"
]
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}
} | 36 | null | ---
language:
- bs
- en
- hr
- sh
- sr
language_bcp47:
- bs_Latn
- sr_Cyrl
- sr_Latn
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-base-en-sh
results:
- task:
name: Translation eng-hrv
type: translation
args: eng-hrv
dataset:
name: flores200-dev
type: flores200-dev
args: eng-hrv
metrics:
- name: BLEU
type: bleu
value: 28.1
- name: chr-F
type: chrf
value: 0.57963
- task:
name: Translation eng-srp_Cyrl
type: translation
args: eng-srp_Cyrl
dataset:
name: flores200-dev
type: flores200-dev
args: eng-srp_Cyrl
metrics:
- name: BLEU
type: bleu
value: 32.2
- name: chr-F
type: chrf
value: 0.60096
- task:
name: Translation eng-hrv
type: translation
args: eng-hrv
dataset:
name: flores200-devtest
type: flores200-devtest
args: eng-hrv
metrics:
- name: BLEU
type: bleu
value: 28.9
- name: chr-F
type: chrf
value: 0.58652
- task:
name: Translation eng-srp_Cyrl
type: translation
args: eng-srp_Cyrl
dataset:
name: flores200-devtest
type: flores200-devtest
args: eng-srp_Cyrl
metrics:
- name: BLEU
type: bleu
value: 31.7
- name: chr-F
type: chrf
value: 0.59874
- task:
name: Translation eng-hrv
type: translation
args: eng-hrv
dataset:
name: flores101-devtest
type: flores_101
args: eng hrv devtest
metrics:
- name: BLEU
type: bleu
value: 28.7
- name: chr-F
type: chrf
value: 0.586
- task:
name: Translation eng-srp_Cyrl
type: translation
args: eng-srp_Cyrl
dataset:
name: flores101-devtest
type: flores_101
args: eng srp_Cyrl devtest
metrics:
- name: BLEU
type: bleu
value: 31.7
- name: chr-F
type: chrf
value: 0.59874
- task:
name: Translation eng-bos_Latn
type: translation
args: eng-bos_Latn
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-bos_Latn
metrics:
- name: BLEU
type: bleu
value: 46.3
- name: chr-F
type: chrf
value: 0.666
- task:
name: Translation eng-hbs
type: translation
args: eng-hbs
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-hbs
metrics:
- name: BLEU
type: bleu
value: 42.1
- name: chr-F
type: chrf
value: 0.631
- task:
name: Translation eng-hrv
type: translation
args: eng-hrv
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-hrv
metrics:
- name: BLEU
type: bleu
value: 49.7
- name: chr-F
type: chrf
value: 0.691
- task:
name: Translation eng-srp_Cyrl
type: translation
args: eng-srp_Cyrl
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-srp_Cyrl
metrics:
- name: BLEU
type: bleu
value: 45.1
- name: chr-F
type: chrf
value: 0.645
- task:
name: Translation eng-srp_Latn
type: translation
args: eng-srp_Latn
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-srp_Latn
metrics:
- name: BLEU
type: bleu
value: 39.8
- name: chr-F
type: chrf
value: 0.613
---
# opus-mt-tc-base-en-sh
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Model Details
Neural machine translation model for translating from English (en) to Serbo-Croatian (sh).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-align)
- **Release**: 2021-04-20
- **License:** CC-BY-4.0
- **Language(s):**
- Source Language(s): eng
- Target Language(s): bos_Latn hbs hrv srp_Cyrl srp_Latn
- Language Pair(s): eng-bos_Latn eng-hbs eng-hrv eng-srp_Cyrl eng-srp_Latn
- Valid Target Language Labels: >>bos_Cyrl<< >>bos_Latn<< >>cnr<< >>cnr_Latn<< >>hbs<< >>hbs_Cyrl<< >>hrv<< >>srp_Cyrl<< >>srp_Latn<<
- **Original Model**: [opus+bt-2021-04-20.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hbs/opus+bt-2021-04-20.zip)
- **Resources for more information:**
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- More information about released models for this language pair: [OPUS-MT eng-hbs README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-hbs/README.md)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>bos_Latn<<`
## Uses
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## How to Get Started With the Model
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>hrv<< You're about to make a very serious mistake.",
">>hbs<< I've just been too busy."
]
model_name = "pytorch-models/opus-mt-tc-base-en-sh"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Ti si o tome napraviti vrlo ozbiljnu pogrešku.
# [4]
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-base-en-sh")
print(pipe(">>hrv<< You're about to make a very serious mistake."))
# expected output: Ti si o tome napraviti vrlo ozbiljnu pogrešku.
```
## Training
- **Data**: opus+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-align
- **Original MarianNMT Model**: [opus+bt-2021-04-20.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hbs/opus+bt-2021-04-20.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* test set translations: [opus+bt-2021-04-20.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hbs/opus+bt-2021-04-20.test.txt)
* test set scores: [opus+bt-2021-04-20.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hbs/opus+bt-2021-04-20.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| eng-bos_Latn | tatoeba-test-v2021-08-07 | 0.666 | 46.3 | 301 | 1650 |
| eng-hbs | tatoeba-test-v2021-08-07 | 0.631 | 42.1 | 10017 | 63927 |
| eng-hrv | tatoeba-test-v2021-08-07 | 0.691 | 49.7 | 1480 | 9396 |
| eng-srp_Cyrl | tatoeba-test-v2021-08-07 | 0.645 | 45.1 | 1580 | 9152 |
| eng-srp_Latn | tatoeba-test-v2021-08-07 | 0.613 | 39.8 | 6656 | 43729 |
| eng-hrv | flores101-devtest | 0.586 | 28.7 | 1012 | 22423 |
| eng-hrv | flores200-dev | 0.57963 | 28.1 | 997 | 21567 |
| eng-hrv | flores200-devtest | 0.58652 | 28.9 | 1012 | 22423 |
| eng-srp_Cyrl | flores101-devtest | 0.59874 | 31.7 | 1012 | 23456 |
| eng-srp_Cyrl | flores200-dev | 0.60096 | 32.2 | 997 | 22384 |
| eng-srp_Cyrl | flores200-devtest | 0.59874 | 31.7 | 1012 | 23456 |
## Citation Information
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: e2a6299
* port time: Tue Oct 11 10:14:32 CEST 2022
* port machine: LM0-400-22516.local
|
Blerrrry/Kkk | []
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: gpt2-finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.6466666666666666
- name: F1
type: f1
value: 0.6159420289855072
---
<!-- 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-finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7442
- Accuracy: 0.6467
- F1: 0.6159
## 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: 2
### Training results
### Framework versions
- Transformers 4.23.0
- Pytorch 1.12.1+cu116
- Datasets 2.5.2
- Tokenizers 0.13.1
|
Bloodwarrior/Chikfalay | []
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} | 0 | null | ---
license: bigscience-bloom-rail-1.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: bloom-finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8633333333333333
- name: F1
type: f1
value: 0.8655737704918034
---
<!-- 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. -->
# bloom-finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2018
- Accuracy: 0.8633
- F1: 0.8656
## 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: 2
### Training results
### Framework versions
- Transformers 4.23.0
- Pytorch 1.12.1+cu116
- Datasets 2.5.2
- Tokenizers 0.13.1
|
BlueGamerBeast/DialoGPT-small-joshua | []
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} | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 200 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 200,
"warmup_steps": 20,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Bman/DialoGPT-medium-harrypotter | []
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} | 0 | null | ---
license: cc-by-nc-sa-4.0
pipeline_tag: fill-mask
language: en
arxiv: 2210.05529
tags:
- long-documents
datasets:
- wikipedia
model-index:
- name: kiddothe2b/hierarchical-transformer-I3-mini-1024
results: []
---
# Hierarchical Attention Transformer (HAT) / hierarchical-transformer-I3-mini-1024
## Model description
This is a Hierarchical Attention Transformer (HAT) model as presented in [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification (Chalkidis et al., 2022)](https://arxiv.org/abs/2210.05529).
The model has been warm-started re-using the weights of miniature BERT (Turc et al., 2019), and continued pre-trained for MLM following the paradigm of Longformer released by Beltagy et al. (2020). It supports sequences of length up to 1,024.
HAT uses hierarchical attention, which is a combination of segment-wise and cross-segment attention operations. You can think of segments as paragraphs or sentences.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
See the [model hub](https://huggingface.co/models?filter=hierarchical-transformer) to look for other versions of HAT or fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole document to make decisions, such as document classification, sequential sentence classification, or question answering.
## How to use
You can use this model directly for masked language modeling:
```python
from transformers import AutoTokenizer, AutoModelforForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hierarchical-transformer-I3-mini-1024", trust_remote_code=True)
mlm_model = AutoModelforForMaskedLM("kiddothe2b/hierarchical-transformer-I3-mini-1024", trust_remote_code=True)
```
You can also fine-tune it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks:
```python
from transformers import AutoTokenizer, AutoModelforSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hierarchical-transformer-I3-mini-1024", trust_remote_code=True)
doc_classifier = AutoModelforSequenceClassification("kiddothe2b/hierarchical-transformer-I3-mini-1024", trust_remote_code=True)
```
## Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from
neutral. Therefore, the model can have biased predictions.
## Training procedure
### Training and evaluation data
The model has been warm-started from [google/bert_uncased_L-6_H-256_A-4](https://huggingface.co/google/bert_uncased_L-6_H-256_A-4) checkpoint and has been continued pre-trained for additional 50k steps on English [Wikipedia](https://huggingface.co/datasets/wikipedia).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: tpu
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 50000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.7353 | 0.2 | 10000 | 2.5067 |
| 2.6081 | 0.4 | 20000 | 2.3966 |
| 2.5552 | 0.6 | 30000 | 2.3446 |
| 2.5105 | 0.8 | 40000 | 2.3117 |
| 2.4978 | 1.14 | 50000 | 2.2954 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
## Citing
If you use HAT in your research, please cite:
[An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification](https://arxiv.org/abs/2210.05529). Ilias Chalkidis, Xiang Dai, Manos Fergadiotis, Prodromos Malakasiotis, and Desmond Elliott. 2022. arXiv:2210.05529 (Preprint).
```
@misc{chalkidis-etal-2022-hat,
url = {https://arxiv.org/abs/2210.05529},
author = {Chalkidis, Ilias and Dai, Xiang and Fergadiotis, Manos and Malakasiotis, Prodromos and Elliott, Desmond},
title = {An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification},
publisher = {arXiv},
year = {2022},
}
```
|
BobBraico/bert-finetuned-ner | []
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} | 0 | null | ---
license: cc-by-sa-4.0
pipeline_tag: fill-mask
language: en
arxiv: 2210.05529
tags:
- long-documents
datasets:
- wikipedia
model-index:
- name: kiddothe2b/hierarchical-transformer-LC1-mini-1024
results: []
---
# Hierarchical Attention Transformer (HAT) / hierarchical-transformer-LC1-mini-1024
## Model description
This is a Hierarchical Attention Transformer (HAT) model as presented in [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification (Chalkidis et al., 2022)](https://arxiv.org/abs/2210.05529).
The model has been warm-started re-using the weights of miniature BERT (Turc et al., 2019), and continued pre-trained for MLM following the paradigm of Longformer released by Beltagy et al. (2020). It supports sequences of length up to 1,024.
HAT uses hierarchical attention, which is a combination of segment-wise and cross-segment attention operations. You can think of segments as paragraphs or sentences.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
See the [model hub](https://huggingface.co/models?other=hierarchical-transformer) to look for other versions of HAT, or fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole document to make decisions, such as document classification, sequential sentence classification, or question answering.
## How to use
You can use this model directly for masked language modeling:
```python
from transformers import AutoTokenizer, AutoModelforForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hierarchical-transformer-LC1-mini-1024", trust_remote_code=True)
mlm_model = AutoModelforForMaskedLM("kiddothe2b/hierarchical-transformer-LC1-mini-1024", trust_remote_code=True)
```
You can also fine-tun it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks:
```python
from transformers import AutoTokenizer, AutoModelforSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hierarchical-transformer-LC1-mini-1024", trust_remote_code=True)
doc_classifier = AutoModelforSequenceClassification("kiddothe2b/hierarchical-transformer-LC1-mini-1024", trust_remote_code=True)
```
## Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from
neutral. Therefore, the model can have biased predictions.
## Training procedure
### Training and evaluation data
The model has been warm-started from [google/bert_uncased_L-6_H-256_A-4](https://huggingface.co/google/bert_uncased_L-6_H-256_A-4) checkpoint and has been continued pre-trained for additional 50k steps on English [Wikipedia](https://huggingface.co/datasets/wikipedia).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: tpu
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 50000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.3959 | 0.2 | 10000 | 2.2258 |
| 2.3395 | 0.4 | 20000 | 2.1738 |
| 2.3082 | 0.6 | 30000 | 2.1404 |
| 2.273 | 0.8 | 40000 | 2.1145 |
| 2.262 | 1.14 | 50000 | 2.1004 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
## Citing
If you use HAT in your research, please cite:
[An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification](https://arxiv.org/abs/2210.05529). Ilias Chalkidis, Xiang Dai, Manos Fergadiotis, Prodromos Malakasiotis, and Desmond Elliott. 2022. arXiv:2210.05529 (Preprint).
```
@misc{chalkidis-etal-2022-hat,
url = {https://arxiv.org/abs/2210.05529},
author = {Chalkidis, Ilias and Dai, Xiang and Fergadiotis, Manos and Malakasiotis, Prodromos and Elliott, Desmond},
title = {An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification},
publisher = {arXiv},
year = {2022},
}
```
|
BobBraico/distilbert-base-uncased-finetuned-imdb-accelerate | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 259.18 +/- 18.31
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BobBraico/distilbert-base-uncased-finetuned-imdb | []
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} | 0 | null | ---
license: cc-by-sa-4.0
pipeline_tag: fill-mask
language: en
arxiv:
tags:
- long_documents
datasets:
- c4
model-index:
- name: kiddothe2b/longformer-mini-1024
results: []
---
# Longformer / longformer-mini-1024
## Model description
[Longformer](https://arxiv.org/abs/2004.05150) is a transformer model for long documents. This version of Longformer is presented in [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification (Chalkidis et al., 2022)](https://arxiv.org/abs/2210.05529).
The model has been warm-started re-using the weights of miniature BERT (Turc et al., 2019), and continued pre-trained for MLM following the paradigm of Longformer released by [Beltagy et al. (2020)](](https://arxiv.org/abs/1908.08962)). It supports sequences of length up to 1,024.
Longformer uses a combination of a sliding window (local) attention and global attention. Global attention is user-configured based on the task to allow the model to learn task-specific representations.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
See the [model hub](https://huggingface.co/models?filter=longformer) to look for fine-tuned versions on a task that
interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole document to make decisions, such as document classification, sequential sentence classification, or question answering.
## How to use
You can use this model directly with a pipeline for masked language modeling:
```python
from transformers import pipeline
mlm_model = pipeline('fill-mask', model='kiddothe2b/longformer-mini-1024', trust_remote_code=True)
mlm_model("Hello I'm a <mask> model.")
```
You can also fine-tune it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks:
```python
from transformers import AutoTokenizer, AutoModelforSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/longformer-mini-1024", trust_remote_code=True)
doc_classifier = AutoModelforSequenceClassification("kiddothe2b/longformer-mini-1024", trust_remote_code=True)
```
## Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from
neutral. Therefore, the model can have biased predictions.
## Training procedure
### Training and evaluation data
The model has been warm-started from [google/bert_uncased_L-6_H-256_A-4](https://huggingface.co/google/bert_uncased_L-6_H-256_A-4) checkpoint and has been continued pre-trained for additional 50k steps on English [Wikipedia](https://huggingface.co/datasets/wikipedia).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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
- training_steps: 50000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.7067 | 0.2 | 10000 | 1.5923 | 0.6714 |
| 1.6532 | 0.4 | 20000 | 1.5494 | 0.6784 |
| 1.622 | 0.6 | 30000 | 1.5208 | 0.6830 |
| 1.588 | 0.8 | 40000 | 1.4880 | 0.6876 |
| 1.5682 | 1.0 | 50000 | 1.4680 | 0.6908 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.11.6
## Citing
If you use HAT in your research, please cite:
[An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification](https://arxiv.org/abs/2210.05529). Ilias Chalkidis, Xiang Dai, Manos Fergadiotis, Prodromos Malakasiotis, and Desmond Elliott. 2022. arXiv:2210.05529 (Preprint).
```
@misc{chalkidis-etal-2022-hat,
url = {https://arxiv.org/abs/2210.05529},
author = {Chalkidis, Ilias and Dai, Xiang and Fergadiotis, Manos and Malakasiotis, Prodromos and Elliott, Desmond},
title = {An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification},
publisher = {arXiv},
year = {2022},
}
```
Also cite the original work: [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150).
```
@article{Beltagy2020Longformer,
title={Longformer: The Long-Document Transformer},
author={Iz Beltagy and Matthew E. Peters and Arman Cohan},
journal={arXiv:2004.05150},
year={2020},
}
```
|
BogdanKuloren/continual-learning-paper-embeddings-model | [
"pytorch",
"mpnet",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"MPNetModel"
],
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}
}
} | 11 | null | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: camembert-ner-finetuned-ner
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. -->
# camembert-ner-finetuned-ner
This model is a fine-tuned version of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0947
- Precision: 0.9851
- Recall: 0.9887
- F1: 0.9869
- Accuracy: 0.9860
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1092 | 1.0 | 1355 | 0.0916 | 0.9872 | 0.9780 | 0.9826 | 0.9815 |
| 0.0668 | 2.0 | 2710 | 0.0799 | 0.9835 | 0.9887 | 0.9861 | 0.9850 |
| 0.0373 | 3.0 | 4065 | 0.0925 | 0.9822 | 0.9885 | 0.9853 | 0.9843 |
| 0.0262 | 4.0 | 5420 | 0.0898 | 0.9886 | 0.9815 | 0.9850 | 0.9847 |
| 0.0192 | 5.0 | 6775 | 0.0947 | 0.9851 | 0.9887 | 0.9869 | 0.9860 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
BonjinKim/dst_kor_bert | [
"pytorch",
"jax",
"bert",
"pretraining",
"transformers"
]
| null | {
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"BertForPreTraining"
],
"model_type": "bert",
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} | 5 | null | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: greek_legal_bert_v2-finetuned-ner-V3
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. -->
# greek_legal_bert_v2-finetuned-ner-V3
This model is a fine-tuned version of [alexaapo/greek_legal_bert_v2](https://huggingface.co/alexaapo/greek_legal_bert_v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0907
- Precision: 0.9023
- Recall: 0.9265
- F1: 0.9142
- Accuracy: 0.9828
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.19 | 25 | 0.0661 | 0.8895 | 0.9229 | 0.9059 | 0.9813 |
| No log | 2.38 | 50 | 0.0820 | 0.9091 | 0.9319 | 0.9204 | 0.9838 |
| No log | 3.57 | 75 | 0.0791 | 0.8924 | 0.9211 | 0.9065 | 0.9825 |
| No log | 4.76 | 100 | 0.0824 | 0.8950 | 0.9319 | 0.9131 | 0.9841 |
| No log | 5.95 | 125 | 0.0820 | 0.8830 | 0.9194 | 0.9008 | 0.9812 |
| No log | 7.14 | 150 | 0.0862 | 0.9059 | 0.9319 | 0.9187 | 0.9817 |
| No log | 8.33 | 175 | 0.0915 | 0.9021 | 0.9247 | 0.9133 | 0.9826 |
| No log | 9.52 | 200 | 0.0905 | 0.9023 | 0.9265 | 0.9142 | 0.9828 |
### Framework versions
- Transformers 4.23.0
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1 |
BossLee/t5-gec | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
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"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"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: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 6 | 2022-10-11T09:31:12Z | ---
license: cc-by-sa-4.0
pipeline_tag: fill-mask
language: en
arxiv: 2210.05529
tags:
- long-documents
datasets:
- wikipedia
model-index:
- name: kiddothe2b/hierarchical-transformer-EC2-mini-1024
results: []
---
# Hierarchical Attention Transformer (HAT) / hierarchical-transformer-EC2-mini-1024
## Model description
This is a Hierarchical Attention Transformer (HAT) model as presented in [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification (Chalkidis et al., 2022)](https://arxiv.org/abs/2210.05529).
The model has been warm-started re-using the weights of miniature BERT (Turc et al., 2019), and continued pre-trained for MLM following the paradigm of Longformer released by Beltagy et al. (2020). It supports sequences of length up to 1,024.
HAT uses hierarchical attention, which is a combination of segment-wise and cross-segment attention operations. You can think of segments as paragraphs or sentences.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
See the [model hub](https://huggingface.co/models?other=hierarchical-transformer) to look for other versions of HAT or fine-tuned versions of a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole document to make decisions, such as document classification, sequential sentence classification, or question answering.
## How to use
You can use this model directly for masked language modeling:
```python
from transformers import AutoTokenizer, AutoModelforForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hierarchical-transformer-EC2-mini-1024", trust_remote_code=True)
mlm_model = AutoModelforForMaskedLM(kiddothe2b/hierarchical-transformer-EC2-mini-1024", trust_remote_code=True)
```
You can also fine-tune it for SequenceClassification, SequentialSentenceClassification, and MultipleChoice down-stream tasks:
```python
from transformers import AutoTokenizer, AutoModelforSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/hierarchical-transformer-EC2-mini-1024", trust_remote_code=True)
doc_classifier = AutoModelforSequenceClassification("kiddothe2b/hierarchical-transformer-EC2-mini-1024", trust_remote_code=True)
```
## Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from
neutral. Therefore, the model can have biased predictions.
## Training procedure
### Training and evaluation data
The model has been warm-started from [google/bert_uncased_L-6_H-256_A-4](https://huggingface.co/google/bert_uncased_L-6_H-256_A-4) checkpoint and has been continued pre-trained for additional 50k steps on English [Wikipedia](https://huggingface.co/datasets/wikipedia).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: tpu
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 50000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.3798 | 0.2 | 10000 | 2.2014 |
| 2.3267 | 0.4 | 20000 | 2.1535 |
| 2.2976 | 0.6 | 30000 | 2.1234 |
| 2.2649 | 0.8 | 40000 | 2.1010 |
| 2.254 | 1.14 | 50000 | 2.0870 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
## Citing
If you use HAT in your research, please cite:
[An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification](https://arxiv.org/abs/2210.05529). Ilias Chalkidis, Xiang Dai, Manos Fergadiotis, Prodromos Malakasiotis, and Desmond Elliott. 2022. arXiv:2210.05529 (Preprint).
```
@misc{chalkidis-etal-2022-hat,
url = {https://arxiv.org/abs/2210.05529},
author = {Chalkidis, Ilias and Dai, Xiang and Fergadiotis, Manos and Malakasiotis, Prodromos and Elliott, Desmond},
title = {An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification},
publisher = {arXiv},
year = {2022},
}
```
|
BotterHax/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
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}
} | 8 | 2022-10-11T09:36:19Z | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: greek_legal_bert_v2-finetuned-ner-V3
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. -->
# greek_legal_bert_v2-finetuned-ner-V4
This model is a fine-tuned version of [amichailidis/greek_legal_bert_v2-finetuned-ner](https://huggingface.co/amichailidis/greek_legal_bert_v2-finetuned-ner) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0659
- Precision: 0.9033
- Recall: 0.9373
- F1: 0.9200
- Accuracy: 0.9845
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.19 | 25 | 0.1246 | 0.8264 | 0.8190 | 0.8227 | 0.9680 |
| No log | 2.38 | 50 | 0.0729 | 0.8716 | 0.9247 | 0.8974 | 0.9809 |
| No log | 3.57 | 75 | 0.0553 | 0.8978 | 0.9444 | 0.9205 | 0.9851 |
| No log | 4.76 | 100 | 0.0591 | 0.8990 | 0.9409 | 0.9194 | 0.9852 |
| No log | 5.95 | 125 | 0.0598 | 0.9017 | 0.9373 | 0.9192 | 0.9849 |
| No log | 7.14 | 150 | 0.0628 | 0.9064 | 0.9373 | 0.9216 | 0.9842 |
| No log | 8.33 | 175 | 0.0636 | 0.9031 | 0.9355 | 0.9190 | 0.9843 |
| No log | 9.52 | 200 | 0.0659 | 0.9033 | 0.9373 | 0.9200 | 0.9845 |
### Framework versions
- Transformers 4.23.0
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
Branex/gpt-neo-2.7B | []
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} | 0 | null | ---
language: en
license: apache-2.0
datasets:
- sst2
- glue
tags:
- openvino
---
## [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) exported to the OpenVINO IR.
## Model Details
**Model Description:** This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2. This model reaches an accuracy of 91.3 on the dev set.
## Usage example
You can use this model with Transformers *pipeline*.
```python
from transformers import AutoTokenizer, pipeline
from optimum.intel.openvino import OVModelForSequenceClassification
model_id = "echarlaix/distilbert-base-uncased-finetuned-sst-2-english-openvino"
model = OVModelForSequenceClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
cls_pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "He's a dreadful magician."
outputs = cls_pipe(text)
```
|
Brayan/CNN_Brain_Tumor | []
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}
} | 0 | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: ajinkyaT/albert-japanese-v2-finetuned-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. -->
# ajinkyaT/albert-japanese-v2-finetuned-ner
This model is a fine-tuned version of [ajinkyaT/albert-japanese-v2-finetuned-ner](https://huggingface.co/ajinkyaT/albert-japanese-v2-finetuned-ner) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1292
- Validation Loss: 0.1499
- Train Precision: 0.6817
- Train Recall: 0.6951
- Train F1: 0.6883
- Train Accuracy: 0.9594
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1320, '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 |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.1299 | 0.1499 | 0.6817 | 0.6951 | 0.6883 | 0.9594 | 4 |
| 0.1306 | 0.1499 | 0.6817 | 0.6951 | 0.6883 | 0.9594 | 5 |
| 0.1296 | 0.1499 | 0.6817 | 0.6951 | 0.6883 | 0.9594 | 6 |
| 0.1292 | 0.1499 | 0.6817 | 0.6951 | 0.6883 | 0.9594 | 7 |
| 0.1306 | 0.1499 | 0.6817 | 0.6951 | 0.6883 | 0.9594 | 8 |
| 0.1292 | 0.1499 | 0.6817 | 0.6951 | 0.6883 | 0.9594 | 9 |
### Framework versions
- Transformers 4.23.1
- TensorFlow 2.8.2
- Datasets 2.5.2
- Tokenizers 0.13.1
|
BrianTin/MTBERT | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
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} | 11 | 2022-10-11T10:05:41Z | See https://github.com/Askannz/gundam-stable-diffusion |
Bryanwong/wangchanberta-ner | []
| null | {
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} | 0 | null | ---
license: openrail
---
technological
strange
special
male
dark
green
blue |
Brykee/DialoGPT-medium-Morty | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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"model_type": "gpt2",
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} | 10 | 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
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-ner | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
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}
}
} | 229 | 2022-10-11T14:03:17Z | ---
license: apache-2.0
language: en
datasets:
- wikipedia
- bookcorpus
model-index:
- name: asi/albert-act-base
results:
- task:
type: text-classification
name: CoLA
dataset:
type: glue
name: CoLA # General Language Understanding Evaluation benchmark (GLUE)
split: cola
metrics:
- type: matthews_correlation
value: 27.5
name: Matthew's Corr
- task:
type: text-classification
name: SST-2
dataset:
type: glue
name: SST-2 # The Stanford Sentiment Treebank
split: sst2
metrics:
- type: accuracy
value: 87.6
name: Accuracy
- task:
type: text-classification
name: MRPC
dataset:
type: glue
name: MRPC # Microsoft Research Paraphrase Corpus
split: mrpc
metrics:
- type: accuracy
value: 78.7
name: Accuracy
- type: f1
value: 84.7
name: F1
- task:
type: text-similarity
name: STS-B
dataset:
type: glue
name: STS-B # Semantic Textual Similarity Benchmark
split: stsb
metrics:
- type: spearmanr
value: 79.7
name: Spearman Corr
- type: pearsonr
value: 81.8
name: Pearson Corr
- task:
type: text-classification
name: QQP
dataset:
type: glue
name: QQP # Quora Question Pairs
split: qqp
metrics:
- type: f1
value: 67.8
name: F1
- type: accuracy
value: 87.5
name: Accuracy
- task:
type: text-classification
name: MNLI-m
dataset:
type: glue
name: MNLI-m # MultiNLI Matched
split: mnli_matched
metrics:
- type: accuracy
value: 77.0
name: Accuracy
- task:
type: text-classification
name: MNLI-mm
dataset:
type: glue
name: MNLI-mm # MultiNLI Matched
split: mnli_mismatched
metrics:
- type: accuracy
value: 76.8
name: Accuracy
- task:
type: text-classification
name: QNLI
dataset:
type: glue
name: QNLI # Question NLI
split: qnli
metrics:
- type: accuracy
value: 86.4
name: Accuracy
- task:
type: text-classification
name: RTE
dataset:
type: glue
name: RTE # Recognizing Textual Entailment
split: rte
metrics:
- type: accuracy
value: 62.0
name: Accuracy
- task:
type: text-classification
name: WNLI
dataset:
type: glue
name: WNLI # Winograd NLI
split: wnli
metrics:
- type: accuracy
value: 65.1
name: Accuracy
---
# Adaptive Depth Transformers
Implementation of the paper "How Many Layers and Why? An Analysis of the Model Depth in Transformers". In this study, we investigate the role of the multiple layers in deep transformer models. We design a variant of ALBERT that dynamically adapts the number of layers for each token of the input.
## Model architecture
We augment a multi-layer transformer encoder with a halting mechanism, which dynamically adjusts the number of layers for each token.
We directly adapted this mechanism from Graves ([2016](#graves-2016)). At each iteration, we compute a probability for each token to stop updating its state.
## Model use
The architecture is not yet directly included in the Transformers library. The code used for pre-training is available in the following [github repository](https://github.com/AntoineSimoulin/adaptive-depth-transformers). So you should install the code implementation first:
```bash
!pip install git+https://github.com/AntoineSimoulin/adaptive-depth-transformers$
```
Then you can use the model directly.
```python
from act import AlbertActConfig, AlbertActModel, TFAlbertActModel
from transformers import AlbertTokenizer
tokenizer = AlbertTokenizer.from_pretrained('asi/albert-act-base')
model = AlbertActModel.from_pretrained('asi/albert-act-base')
_ = model.eval()
inputs = tokenizer("a lump in the middle of the monkeys stirred and then fell quiet .", return_tensors="pt")
outputs = model(**inputs)
outputs.updates
# tensor([[[[15., 9., 10., 7., 3., 8., 5., 7., 12., 10., 6., 8., 8., 9., 5., 8.]]]])
```
## Citations
### BibTeX entry and citation info
If you use our iterative transformer model for your scientific publication or your industrial applications, please cite the following [paper](https://aclanthology.org/2021.acl-srw.23/):
```bibtex
@inproceedings{simoulin-crabbe-2021-many,
title = "How Many Layers and Why? {A}n Analysis of the Model Depth in Transformers",
author = "Simoulin, Antoine and
Crabb{\'e}, Benoit",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-srw.23",
doi = "10.18653/v1/2021.acl-srw.23",
pages = "221--228",
}
```
### References
><div id="graves-2016">Alex Graves. 2016. Adaptive computation time for recurrent neural networks. CoRR, abs/1603.08983.</div>
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
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},
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}
} | 25 | 2022-10-11T14:04:00Z | ---
license: apache-2.0
language: en
datasets:
- wikipedia
- bookcorpus
model-index:
- name: asi/albert-act-base
results:
- task:
type: text-classification
name: CoLA
dataset:
type: glue
name: CoLA # General Language Understanding Evaluation benchmark (GLUE)
split: cola
metrics:
- type: matthews_correlation
value: 33.8
name: Matthew's Corr
- task:
type: text-classification
name: SST-2
dataset:
type: glue
name: SST-2 # The Stanford Sentiment Treebank
split: sst2
metrics:
- type: accuracy
value: 88.6
name: Accuracy
- task:
type: text-classification
name: MRPC
dataset:
type: glue
name: MRPC # Microsoft Research Paraphrase Corpus
split: mrpc
metrics:
- type: accuracy
value: 79.4
name: Accuracy
- type: f1
value: 85.2
name: F1
- task:
type: text-similarity
name: STS-B
dataset:
type: glue
name: STS-B # Semantic Textual Similarity Benchmark
split: stsb
metrics:
- type: spearmanr
value: 81.2
name: Spearman Corr
- type: pearsonr
value: 82.7
name: Pearson Corr
- task:
type: text-classification
name: QQP
dataset:
type: glue
name: QQP # Quora Question Pairs
split: qqp
metrics:
- type: f1
value: 67.8
name: F1
- type: accuracy
value: 87.4
name: Accuracy
- task:
type: text-classification
name: MNLI-m
dataset:
type: glue
name: MNLI-m # MultiNLI Matched
split: mnli_matched
metrics:
- type: accuracy
value: 79.5
name: Accuracy
- task:
type: text-classification
name: MNLI-mm
dataset:
type: glue
name: MNLI-mm # MultiNLI Matched
split: mnli_mismatched
metrics:
- type: accuracy
value: 78.5
name: Accuracy
- task:
type: text-classification
name: QNLI
dataset:
type: glue
name: QNLI # Question NLI
split: qnli
metrics:
- type: accuracy
value: 88.3
name: Accuracy
- task:
type: text-classification
name: RTE
dataset:
type: glue
name: RTE # Recognizing Textual Entailment
split: rte
metrics:
- type: accuracy
value: 61.9
name: Accuracy
- task:
type: text-classification
name: WNLI
dataset:
type: glue
name: WNLI # Winograd NLI
split: wnli
metrics:
- type: accuracy
value: 65.1
name: Accuracy
---
# Adaptive Depth Transformers
Implementation of the paper "How Many Layers and Why? An Analysis of the Model Depth in Transformers". In this study, we investigate the role of the multiple layers in deep transformer models. We design a variant of ALBERT that dynamically adapts the number of layers for each token of the input.
## Model architecture
We augment a multi-layer transformer encoder with a halting mechanism, which dynamically adjusts the number of layers for each token.
We directly adapted this mechanism from Graves ([2016](#graves-2016)). At each iteration, we compute a probability for each token to stop updating its state.
## Model use
The architecture is not yet directly included in the Transformers library. The code used for pre-training is available in the following [github repository](https://github.com/AntoineSimoulin/adaptive-depth-transformers). So you should install the code implementation first:
```bash
!pip install git+https://github.com/AntoineSimoulin/adaptive-depth-transformers$
```
Then you can use the model directly.
```python
from act import AlbertActConfig, AlbertActModel, TFAlbertActModel
from transformers import AlbertTokenizer
tokenizer = AlbertTokenizer.from_pretrained('asi/albert-act-base')
model = AlbertActModel.from_pretrained('asi/albert-act-base')
_ = model.eval()
inputs = tokenizer("a lump in the middle of the monkeys stirred and then fell quiet .", return_tensors="pt")
outputs = model(**inputs)
outputs.updates
# tensor([[[[15., 9., 10., 7., 3., 8., 5., 7., 12., 10., 6., 8., 8., 9., 5., 8.]]]])
```
## Citations
### BibTeX entry and citation info
If you use our iterative transformer model for your scientific publication or your industrial applications, please cite the following [paper](https://aclanthology.org/2021.acl-srw.23/):
```bibtex
@inproceedings{simoulin-crabbe-2021-many,
title = "How Many Layers and Why? {A}n Analysis of the Model Depth in Transformers",
author = "Simoulin, Antoine and
Crabb{\'e}, Benoit",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-srw.23",
doi = "10.18653/v1/2021.acl-srw.23",
pages = "221--228",
}
```
### References
><div id="graves-2016">Alex Graves. 2016. Adaptive computation time for recurrent neural networks. CoRR, abs/1603.08983.</div>
|
CAMeL-Lab/bert-base-arabic-camelbert-msa | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 2,967 | null | Access to model Ogaabi/Wamba is restricted and you are not in the authorized list. Visit https://huggingface.co/Ogaabi/Wamba to ask for access. |
CLAck/indo-mixed | [
"pytorch",
"marian",
"text2text-generation",
"en",
"id",
"dataset:ALT",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
]
| translation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
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}
}
} | 15 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-hatexplain-label-all-tokens-False
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-hatexplain-label-all-tokens-False
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1722
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 174 | 0.1750 |
| No log | 2.0 | 348 | 0.1704 |
| 0.1846 | 3.0 | 522 | 0.1722 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
CLS/WubiBERT_models | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5512772054945002
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8076
- Matthews Correlation: 0.5513
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5264 | 1.0 | 535 | 0.5380 | 0.4135 |
| 0.3486 | 2.0 | 1070 | 0.5007 | 0.4923 |
| 0.2404 | 3.0 | 1605 | 0.5373 | 0.5358 |
| 0.1757 | 4.0 | 2140 | 0.7435 | 0.5414 |
| 0.122 | 5.0 | 2675 | 0.8076 | 0.5513 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
CLTL/MedRoBERTa.nl | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"transformers",
"license:mit",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"RobertaForMaskedLM"
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}
} | 2,988 | null | ---
license: mit
---
### Ayush Spider SPR on Stable Diffusion
This is the `<spr-mn>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
CLTL/gm-ner-xlmrbase | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"nl",
"transformers",
"dighum",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
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}
} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-base-intent-classification-ori
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-intent-classification-ori
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [intent-dataset](https://huggingface.co/datasets/MuhammadIqbalBazmi/intent-dataset) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4928
- Accuracy: 0.9167
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 45
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1867 | 1.0 | 28 | 2.1745 | 0.2708 |
| 2.1177 | 2.0 | 56 | 2.1165 | 0.2708 |
| 2.1012 | 3.0 | 84 | 2.0553 | 0.2708 |
| 1.9851 | 4.0 | 112 | 1.9551 | 0.375 |
| 1.9092 | 5.0 | 140 | 1.9765 | 0.2917 |
| 1.6848 | 6.0 | 168 | 1.8461 | 0.2917 |
| 1.6576 | 7.0 | 196 | 1.5223 | 0.5 |
| 1.4492 | 8.0 | 224 | 1.4500 | 0.4792 |
| 1.2193 | 9.0 | 252 | 1.5349 | 0.4792 |
| 1.1149 | 10.0 | 280 | 1.2159 | 0.5833 |
| 1.0615 | 11.0 | 308 | 1.1469 | 0.6875 |
| 1.0584 | 12.0 | 336 | 1.2778 | 0.6042 |
| 0.8237 | 13.0 | 364 | 1.1774 | 0.5625 |
| 0.6699 | 14.0 | 392 | 0.9661 | 0.6875 |
| 0.7414 | 15.0 | 420 | 1.2787 | 0.5208 |
| 0.5324 | 16.0 | 448 | 0.8592 | 0.7292 |
| 0.3753 | 17.0 | 476 | 0.6860 | 0.7917 |
| 0.3274 | 18.0 | 504 | 0.6210 | 0.8333 |
| 0.3667 | 19.0 | 532 | 0.7310 | 0.75 |
| 0.2347 | 20.0 | 560 | 0.6801 | 0.7292 |
| 0.2036 | 21.0 | 588 | 0.9876 | 0.6875 |
| 0.1711 | 22.0 | 616 | 0.6323 | 0.7917 |
| 0.205 | 23.0 | 644 | 0.4414 | 0.8958 |
| 0.0892 | 24.0 | 672 | 0.4253 | 0.8958 |
| 0.0777 | 25.0 | 700 | 0.4703 | 0.8958 |
| 0.0717 | 26.0 | 728 | 0.4883 | 0.8958 |
| 0.041 | 27.0 | 756 | 0.6224 | 0.8542 |
| 0.0493 | 28.0 | 784 | 0.5839 | 0.875 |
| 0.0405 | 29.0 | 812 | 0.6454 | 0.8542 |
| 0.04 | 30.0 | 840 | 0.6102 | 0.875 |
| 0.0333 | 31.0 | 868 | 0.6080 | 0.875 |
| 0.0303 | 32.0 | 896 | 0.5539 | 0.875 |
| 0.025 | 33.0 | 924 | 0.5799 | 0.8958 |
| 0.0246 | 34.0 | 952 | 0.5766 | 0.8958 |
| 0.0209 | 35.0 | 980 | 0.5700 | 0.8958 |
| 0.0225 | 36.0 | 1008 | 0.5709 | 0.8958 |
| 0.0225 | 37.0 | 1036 | 0.5582 | 0.8958 |
| 0.0217 | 38.0 | 1064 | 0.5258 | 0.875 |
| 0.0207 | 39.0 | 1092 | 0.5058 | 0.8958 |
| 0.0234 | 40.0 | 1120 | 0.4981 | 0.8958 |
| 0.021 | 41.0 | 1148 | 0.4928 | 0.9167 |
| 0.0224 | 42.0 | 1176 | 0.4962 | 0.9167 |
| 0.0212 | 43.0 | 1204 | 0.5329 | 0.8958 |
| 0.0208 | 44.0 | 1232 | 0.5727 | 0.8958 |
| 0.0206 | 45.0 | 1260 | 0.5733 | 0.8958 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
CLTL/icf-levels-adm | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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},
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},
"text-generation": {
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"max_length": null
},
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},
"translation_en_to_fr": {
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},
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}
}
} | 33 | null | ---
pipeline_tag: image-to-image
tags:
- art
---
a hyper network trained by よし男's artwork.
(reference: https://www.pixiv.net/users/3584828)
only for study and self use
please do not publish or use for business.
请勿发表或商用
Author: Tongfan Wei ([email protected])
an example by base model anything v4.5, upscale model CUGAN
 |
CLTL/icf-levels-enr | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
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},
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},
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},
"translation_en_to_fr": {
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},
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}
} | 30 | null | data: https://github.com/BigSalmon2/InformalToFormalDataset
Text Generation Informal Formal
Trained on this model: https://huggingface.co/CarperAI/FIM-NeoX-1.3B, which is geared toward filling in the blank. Check out their model and give them a like!
```
from transformers import GPTNeoXForCausalLM, GPTNeoXTokenizerFast
tokenizer = GPTNeoXTokenizerFast.from_pretrained("CarperAI/FIM-NeoX-1.3B")
model = GPTNeoXForCausalLM.from_pretrained("BigSalmon/FormalInformalConcise-FIM-NeoX-1.3B")
```
To load model, you may need to do:
```
pip install git+https://github.com/huggingface/transformers
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/GPT2Mask
```
```
prompt = """<|SUF|> into relaxation <|PRE|> music before bedtime <|MID|>"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
How To Make Prompts:
Infill Phrase Masking In-Fill
```
<|SUF|> into relaxation <|PRE|> music before bedtime <|MID|>
```
Informal To Formal
```
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
- penny has practically no value
- should be taken out of circulation
- just as other coins have been in us history
- lost use
- value not enough
- to make environmental consequences worthy
text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
```
first: ( was complicit in / was involved in ).
antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ).
***
first: ( have no qualms about / see no issue with ).
antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ).
***
first: ( do not see eye to eye / disagree often ).
antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ).
***
first:
```
```
stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground.
***
languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo.
***
dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia.
***
embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons.
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
```
Essay Intro (National Parks):
text: tourists are at ease in the national parks, ( swept up in the beauty of their natural splendor ).
***
Essay Intro (D.C. Statehood):
washington, d.c. is a city of outsize significance, ( ground zero for the nation's political life / center stage for the nation's political machinations ).
```
```
topic: the Golden State Warriors.
characterization 1: the reigning kings of the NBA.
characterization 2: possessed of a remarkable cohesion.
characterization 3: helmed by superstar Stephen Curry.
characterization 4: perched atop the league’s hierarchy.
characterization 5: boasting a litany of hall-of-famers.
***
topic: emojis.
characterization 1: shorthand for a digital generation.
characterization 2: more versatile than words.
characterization 3: the latest frontier in language.
characterization 4: a form of self-expression.
characterization 5: quintessentially millennial.
characterization 6: reflective of a tech-centric world.
***
topic:
```
```
regular: illinois went against the census' population-loss prediction by getting more residents.
VBG: defying the census' prediction of population loss, illinois experienced growth.
***
regular: microsoft word’s high pricing increases the likelihood of competition.
VBG: extortionately priced, microsoft word is inviting competition.
***
regular:
```
```
source: badminton should be more popular in the US.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) games played with racquets are popular, (2) just look at tennis and ping pong, (3) but badminton underappreciated, (4) fun, fast-paced, competitive, (5) needs to be marketed more
text: the sporting arena is dominated by games that are played with racquets. tennis and ping pong, in particular, are immensely popular. somewhat curiously, however, badminton is absent from this pantheon. exciting, fast-paced, and competitive, it is an underappreciated pastime. all that it lacks is more effective marketing.
***
source: movies in theaters should be free.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) movies provide vital life lessons, (2) many venues charge admission, (3) those without much money
text: the lessons that movies impart are far from trivial. the vast catalogue of cinematic classics is replete with inspiring sagas of friendship, bravery, and tenacity. it is regrettable, then, that admission to theaters is not free. in their current form, the doors of this most vital of institutions are closed to those who lack the means to pay.
***
source:
```
```
in the private sector, { transparency } is vital to the business’s credibility. the { disclosure of information } can be the difference between success and failure.
***
the labor market is changing, with { remote work } now the norm. this { flexible employment } allows the individual to design their own schedule.
***
the { cubicle } is the locus of countless grievances. many complain that the { enclosed workspace } restricts their freedom of movement.
***
```
```
it would be natural to assume that americans, as a people whose ancestors { immigrated to this country }, would be sympathetic to those seeking to do likewise.
question: what does “do likewise” mean in the above context?
(a) make the same journey
(b) share in the promise of the american dream
(c) start anew in the land of opportunity
(d) make landfall on the united states
***
in the private sector, { transparency } is vital to the business’s credibility. this orientation can be the difference between success and failure.
question: what does “this orientation” mean in the above context?
(a) visible business practices
(b) candor with the public
(c) open, honest communication
(d) culture of accountability
```
```
example: suppose you are a teacher. further suppose you want to tell an accurate telling of history. then suppose a parent takes offense. they do so in the name of name of their kid. this happens a lot.
text: educators' responsibility to remain true to the historical record often clashes with the parent's desire to shelter their child from uncomfortable realities.
***
example: suppose you are a student at college. now suppose you have to buy textbooks. that is going to be worth hundreds of dollars. given how much you already spend on tuition, that is going to hard cost to bear.
text: the exorbitant cost of textbooks, which often reaches hundreds of dollars, imposes a sizable financial burden on the already-strapped college student.
```
```
accustomed to having its name uttered ______, harvard university is weathering a rare spell of reputational tumult
(a) in reverential tones
(b) with great affection
(c) in adulatory fashion
(d) in glowing terms
```
```
clarify: international ( {working together} / cooperation ) is called for when ( {issue go beyond lots of borders} / an issue transcends borders / a given matter has transnational implications ).
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
```
music before bedtime [makes for being able to relax] -> is a recipe for relaxation.
```
```
[people wanting entertainment love traveling new york city] -> travelers flock to new york city in droves, drawn to its iconic entertainment scene. [cannot blame them] -> one cannot fault them [broadway so fun] -> when it is home to such thrilling fare as Broadway.
```
```
in their ( ‖ when you are rushing because you want to get there on time ‖ / haste to arrive punctually / mad dash to be timely ), morning commuters are too rushed to whip up their own meal.
***
politicians prefer to author vague plans rather than ( ‖ when you can make a plan without many unknowns ‖ / actionable policies / concrete solutions ).
``` |
CLTL/icf-levels-etn | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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} | 31 | null | ---
license: mit
---
### Natasha Johnston on Stable Diffusion
This is the `<natasha-johnston>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
CLTL/icf-levels-fac | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
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} | 32 | null | Access to model ccw/real is restricted and you are not in the authorized list. Visit https://huggingface.co/ccw/real to ask for access. |
CLTL/icf-levels-ins | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
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} | 32 | null | ---
tags:
- conversational
---
# Ned Flanders DialoGPT |
CLTL/icf-levels-mbw | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
]
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} | 30 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 50.90 +/- 14.29
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
|
CM-CA/Cartman | []
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} | 0 | null | ---
license: bsd-3-clause
---
Copyright 2018-2022, UT-Battelle
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
CNT-UPenn/Bio_ClinicalBERT_for_seizureFreedom_classification | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 28 | null | ---
license: bsd-3-clause
---
Copyright 2018-2022, UT-Battelle
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
CNT-UPenn/RoBERTa_for_seizureFrequency_QA | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
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} | 5 | null | ---
license: bsd-3-clause
---
Copyright 2018-2022, UT-Battelle
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
CSResearcher/TestModel | [
"license:mit"
]
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: train[:5000]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.916
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1671
- Accuracy: 0.916
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7213 | 0.99 | 62 | 1.6647 | 0.885 |
| 1.2902 | 1.99 | 124 | 1.2744 | 0.918 |
| 1.1288 | 2.99 | 186 | 1.1671 | 0.916 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1
|
CSZay/bart | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: EdBianchi/T5-finetuned-abstracts
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. -->
# EdBianchi/T5-finetuned-abstracts
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.9469
- Train Lr: 0.0004
- Validation Loss: 1.8462
- Validation Lr: 0.0002
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 0.00015378147, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Lr | Validation Loss | Validation Lr | Epoch |
|:----------:|:--------:|:---------------:|:-------------:|:-----:|
| 2.2534 | 0.0005 | 1.9839 | 0.0007 | 0 |
| 1.9469 | 0.0004 | 1.8462 | 0.0002 | 1 |
### Framework versions
- Transformers 4.21.3
- TensorFlow 2.10.0
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Caddy/UD | []
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} | 0 | null | ---
license: bsd-3-clause
---
Copyright 2018-2022, UT-Battelle
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
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