modelId
stringlengths 4
81
| tags
sequence | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
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Chaewon/mnmt_decoder_en_gpt2 | [] | null | {
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} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: vocab2-bert-base-multilingual-uncased-udm-tsa
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. -->
# vocab2-bert-base-multilingual-uncased-udm-tsa
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8497
## 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: 20
- eval_batch_size: 20
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 7.3112 | 1.0 | 6419 | 6.1814 |
| 5.8524 | 2.0 | 12838 | 5.4075 |
| 5.3392 | 3.0 | 19257 | 5.0810 |
| 5.0958 | 4.0 | 25676 | 4.9015 |
| 4.9897 | 5.0 | 32095 | 4.8497 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ChaitanyaU/FineTuneLM | [] | null | {
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} | 0 | null | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ismail-lucifer011/autotrain-data-name_all
co2_eq_emissions: 0.8375653425894861
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 904029577
- CO2 Emissions (in grams): 0.8375653425894861
## Validation Metrics
- Loss: 0.0035200684797018766
- Accuracy: 0.9989316041363876
- Precision: 0.9877899024589919
- Recall: 0.9933336010601984
- F1: 0.9905539954046464
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/ismail-lucifer011/autotrain-name_all-904029577
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("ismail-lucifer011/autotrain-name_all-904029577", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("ismail-lucifer011/autotrain-name_all-904029577", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Chakita/Friends | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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}
} | 8 | null | ---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_tenarch_aspects
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.714922049
- name: NER Recall
type: recall
value: 0.7213483146
- name: NER F Score
type: f_score
value: 0.7181208054
---
| Feature | Description |
| --- | --- |
| **Name** | `en_tenarch_aspects` |
| **Version** | `0.2.1` |
| **spaCy** | `>=3.5.0,<3.6.0` |
| **Default Pipeline** | `tok2vec`, `ner`, `entity_to_skill` |
| **Components** | `tok2vec`, `ner`, `entity_to_skill` |
| **Vectors** | 86498 keys, 86498 unique vectors (100 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (6 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `Database`, `Framework`, `Language`, `Library`, `Platform`, `Tool` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 71.81 |
| `ENTS_P` | 71.49 |
| `ENTS_R` | 72.13 |
| `TOK2VEC_LOSS` | 40403.16 |
| `NER_LOSS` | 11205.88 | |
Chakita/KNUBert | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 20 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- adversarial_qa
model-index:
- name: deberta-base-finetuned-aqa
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. -->
# deberta-base-finetuned-aqa
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the adversarial_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6394
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1054 | 1.0 | 2527 | 1.6947 |
| 1.5387 | 2.0 | 5054 | 1.6394 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Chakita/KROBERT | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"masked-lm",
"fill-in-the-blanks",
"autotrain_compatible"
] | fill-mask | {
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"RobertaForMaskedLM"
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} | 7 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **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="KhariotnovKK/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"])
```
|
Chakita/Kalbert | [
"pytorch",
"tensorboard",
"albert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
] | fill-mask | {
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"AlbertForMaskedLM"
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}
} | 5 | null | ---
tags:
- automatic-speech-recognition
- generated_from_trainer
license: mit
language:
- lb
metrics:
- wer
pipeline_tag: automatic-speech-recognition
model-index:
- name: Lemswasabi/wav2vec2-large-xlsr-53-842h-luxembourgish-4h-with-lm
results:
- task:
type: automatic-speech-recognition # Required. Example: automatic-speech-recognition
name: Speech Recognition # Optional. Example: Speech Recognition
metrics:
- type: wer
value: 16.11
name: Dev WER
- type: wer
value: 15.10
name: Test WER
- type: cer
value: 6.63
name: Dev CER
- type: cer
value: 5.79
name: Test CER
---
<!-- 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. -->
#
## Model description
We fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech
collected from [RTL.lu](https://www.rtl.lu/). Then the model was fine-tuned on 4h of labelled
Luxembourgish speech from the same domain. Additionally, we rescore the output transcription
with a 5-gram language model trained on text corpora from RTL.lu and the Luxembourgish parliament.
## 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: 7.5e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
## Citation
This model is a result of our paper `IMPROVING LUXEMBOURGISH SPEECH RECOGNITION WITH CROSS-LINGUAL SPEECH REPRESENTATIONS` submitted to the [IEEE SLT 2022 workshop](https://slt2022.org/)
```
@misc{lb-wav2vec2,
author = {Nguyen, Le Minh and Nayak, Shekhar and Coler, Matt.},
keywords = {Luxembourgish, multilingual speech recognition, language modelling, wav2vec 2.0 XLSR-53, under-resourced language},
title = {IMPROVING LUXEMBOURGISH SPEECH RECOGNITION WITH CROSS-LINGUAL SPEECH REPRESENTATIONS},
year = {2022},
copyright = {2023 IEEE}
}
``` |
Chakita/KannadaBERT | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"masked-lm",
"fill-in-the-blanks",
"autotrain_compatible"
] | fill-mask | {
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}
} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-massive-intent-detection-english
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: massive
type: massive
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.886684599865501
---
<!-- 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-massive-intent-detection-english
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4873
- Accuracy: 0.8867
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.5849 | 1.0 | 360 | 1.3826 | 0.7359 |
| 1.0662 | 2.0 | 720 | 0.7454 | 0.8357 |
| 0.5947 | 3.0 | 1080 | 0.5668 | 0.8642 |
| 0.3824 | 4.0 | 1440 | 0.5007 | 0.8770 |
| 0.2649 | 5.0 | 1800 | 0.4829 | 0.8824 |
| 0.1877 | 6.0 | 2160 | 0.4843 | 0.8824 |
| 0.1377 | 7.0 | 2520 | 0.4858 | 0.8834 |
| 0.1067 | 8.0 | 2880 | 0.4924 | 0.8864 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Chakita/gpt2_mwp | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
],
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} | 6 | 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: -754.84 +/- 269.00
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
...
```
|
Champion/test_upload_vox2_wavlm_epoch8 | [
"sidekit",
"audio"
] | null | {
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} | 0 | null | ---
tags:
- automatic-speech-recognition
- generated_from_trainer
license: mit
language:
- lb
metrics:
- wer
pipeline_tag: automatic-speech-recognition
model-index:
- name: Lemswasabi/wav2vec2-base-librispeech-LS960h-LB842h-luxembourgish-4h-with-lm
results:
- task:
type: automatic-speech-recognition # Required. Example: automatic-speech-recognition
name: Speech Recognition # Optional. Example: Speech Recognition
metrics:
- type: wer
value: 18.40
name: Dev WER
- type: wer
value: 17.75
name: Test WER
- type: cer
value: 7.15
name: Dev CER
- type: cer
value: 6.74
name: Test CER
---
<!-- 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. -->
#
## Model description
We fine-tuned a wav2vec 2.0 base checkpoint pre-trained on LibriSpeech with 842h of unlabelled Luxembourgish speech
collected from [RTL.lu](https://www.rtl.lu/). Then the model was fine-tuned on 4h of labelled
Luxembourgish Speech from the same domain. Additionally, we rescore the output transcription
with a 5-gram language model trained on text corpora from RTL.lu and the Luxembourgish parliament.
## 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: 7.5e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
## Citation
This model is a result of our paper `IMPROVING LUXEMBOURGISH SPEECH RECOGNITION WITH CROSS-LINGUAL SPEECH REPRESENTATIONS` submitted to the [IEEE SLT 2022 workshop](https://slt2022.org/)
```
@misc{lb-wav2vec2,
author = {Nguyen, Le Minh and Nayak, Shekhar and Coler, Matt.},
keywords = {Luxembourgish, multilingual speech recognition, language modelling, wav2vec 2.0 XLSR-53, under-resourced language},
title = {IMPROVING LUXEMBOURGISH SPEECH RECOGNITION WITH CROSS-LINGUAL SPEECH REPRESENTATIONS},
year = {2022},
copyright = {2023 IEEE}
}
``` |
Chan/distilgpt2-finetuned-wikitext2 | [] | null | {
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} | 0 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **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="GideonFr/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"])
```
|
Chan/distilroberta-base-finetuned-wikitext2 | [] | null | {
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} | 0 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/respctclub-utsavsingla/1653404081829/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/1500685428755623941/jT40-aBp_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1482271276077305859/n-xPut5M_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Utsav Singla | Respct.co 🙏🙏 & Respct</div>
<div style="text-align: center; font-size: 14px;">@respctclub-utsavsingla</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 Utsav Singla | Respct.co 🙏🙏 & Respct.
| Data | Utsav Singla | Respct.co 🙏🙏 | Respct |
| --- | --- | --- |
| Tweets downloaded | 365 | 157 |
| Retweets | 109 | 22 |
| Short tweets | 21 | 5 |
| Tweets kept | 235 | 130 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1tvesvyp/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 @respctclub-utsavsingla's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3t9huyws) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3t9huyws/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/respctclub-utsavsingla')
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)
|
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} | 0 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="heriosousa/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"])
```
|
Charlotte77/model_test | [] | null | {
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} | 0 | null | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- vreese2414/autotrain-data-test-frank
co2_eq_emissions: 20.85550802376653
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 896929583
- CO2 Emissions (in grams): 20.85550802376653
## Validation Metrics
- Loss: 0.8998094797134399
- Accuracy: 0.717983651226158
- Macro F1: 0.6850466044284794
- Micro F1: 0.717983651226158
- Weighted F1: 0.7093970537930665
- Macro Precision: 0.692166692035814
- Micro Precision: 0.717983651226158
- Weighted Precision: 0.7181745683190863
- Macro Recall: 0.6985625924834511
- Micro Recall: 0.717983651226158
- Weighted Recall: 0.717983651226158
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/vreese2414/autotrain-test-frank-896929583
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("vreese2414/autotrain-test-frank-896929583", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("vreese2414/autotrain-test-frank-896929583", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
ChaseBread/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- qg_squad
model-index:
- name: t5-small-finetuned-qgsquad-qgen
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-qgsquad-qgen
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the qg_squad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4039
- Rouge4 Precision: 0.0931
- Rouge4 Recall: 0.0834
- Rouge4 Fmeasure: 0.0843
## 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
- 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 | Rouge4 Precision | Rouge4 Recall | Rouge4 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.4325 | 1.0 | 4733 | 0.3960 | 0.0984 | 0.0867 | 0.0889 |
| 0.4137 | 2.0 | 9466 | 0.3863 | 0.1061 | 0.0946 | 0.0963 |
| 0.3914 | 3.0 | 14199 | 0.3806 | 0.1051 | 0.0938 | 0.0955 |
| 0.3946 | 4.0 | 18932 | 0.3786 | 0.1084 | 0.097 | 0.0986 |
| 0.3857 | 5.0 | 23665 | 0.3784 | 0.1101 | 0.0991 | 0.1007 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ChauhanVipul/BERT | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO (Gamma 0.999)
results:
- metrics:
- type: mean_reward
value: 281.70 +/- 22.40
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
...
```
|
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} | 0 | 2022-05-24T15:49:28Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **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="heriosousa/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"])
```
|
Cheatham/xlm-roberta-base-finetuned | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] | text-classification | {
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"XLMRobertaForSequenceClassification"
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} | 20 | null | ---
language: en
tags:
- generative qa
datasets:
- eli5
- stackexchange(pets, cooking, gardening, diy, crafts)
---
Work by [Frederico Vicente](https://huggingface.co/mrvicente) & [Diogo Tavares](https://huggingface.co/d-c-t). We finetuned BART Large for the task of generative question answering. It was trained on eli5, askScience and stackexchange using the following forums: pets, cooking, gardening, diy, crafts.
### Usage
```python
from transformers import (
BartForConditionalGeneration,
BartTokenizer
)
import torch
import json
def read_json_file_2_dict(filename, store_dir='.'):
with open(f'{store_dir}/{filename}', 'r', encoding='utf-8') as file:
return json.load(file)
def get_device():
# If there's a GPU available...
if torch.cuda.is_available():
device = torch.device("cuda")
n_gpus = torch.cuda.device_count()
first_gpu = torch.cuda.get_device_name(0)
print(f'There are {n_gpus} GPU(s) available.')
print(f'GPU gonna be used: {first_gpu}')
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu")
return device
model_name = 'unlisboa/bart_qa_assistant'
tokenizer = BartTokenizer.from_pretrained(model_name)
device = get_device()
model = BartForConditionalGeneration.from_pretrained(model_name).to(device)
model.eval()
model_input = tokenizer(question, truncation=True, padding=True, return_tensors="pt")
generated_answers_encoded = model.generate(input_ids=model_input["input_ids"].to(device),attention_mask=model_input["attention_mask"].to(device),
force_words_ids=None,
min_length=1,
max_length=100,
do_sample=True,
early_stopping=True,
num_beams=4,
temperature=1.0,
top_k=None,
top_p=None,
# eos_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=2,
num_return_sequences=1,
return_dict_in_generate=True,
output_scores=True)
response = tokenizer.batch_decode(generated_answers_encoded['sequences'], skip_special_tokens=True,clean_up_tokenization_spaces=True)
print(response)
```
Have fun! |
Cheatham/xlm-roberta-large-finetuned-d12 | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] | text-classification | {
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} | 20 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ftorres/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"])
```
|
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} | 0 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **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="/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"])
```
|
Cheatham/xlm-roberta-large-finetuned-d1r01 | [
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"xlm-roberta",
"text-classification",
"transformers"
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} | 21 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **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="mgfrantz/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"])
```
|
Cheatham/xlm-roberta-large-finetuned-r01 | [
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"xlm-roberta",
"text-classification",
"transformers"
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} | 23 | 2022-05-24T16:36:08Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 268.90 +/- 26.59
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
...
```
|
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} | 20 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: deberta-base-finetuned-aqa-squad1
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. -->
# deberta-base-finetuned-aqa-squad1
This model is a fine-tuned version of [stevemobs/deberta-base-finetuned-aqa](https://huggingface.co/stevemobs/deberta-base-finetuned-aqa) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7790
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.7662 | 1.0 | 7380 | 0.7575 |
| 0.5586 | 2.0 | 14760 | 0.7790 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
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} | 20 | null | ---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery_param3
results:
- metrics:
- type: mean_reward
value: 0.79 +/- 0.41
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="IvanTi/q-FrozenLake-v1-4x4-Slippery_param3", 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"])
```
|
CheonggyeMountain-Sherpa/kogpt-trinity-poem | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
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} | 15 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="GKPro/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"])
```
|
CheonggyeMountain-Sherpa/kogpt-trinity-punct-wrapper | [
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"gpt2",
"license:cc-by-nc-sa-4.0"
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} | 0 | null | <h1>Model description</h1>
This is a fine-tuned BioBERT model for extracting the relation between clinical trial outcome and its significance level. The task is framed as sentence classification:
- you first need to extract the entities - outcomes and significance levels. For outcomes, you could use the model https://huggingface.co/aakorolyova/reported_outcome_extraction. For significance levels, we have previously used a rule-based approach that worked well; we plan to make the code available in https://github.com/aakorolyova/DeSpin-2.0 soon.
- then, for each pair of outcome and significance level, you mask the entity texts as @OUTCOME$ and @SIGNIFICANCE$
- you run the prediction on the sentence with the masked outcome-significance level pair to get the label (0 if the entities are unrelated, 1 if they are related).
For example, the sentence "Intubation conditions (succinylcholine 8.3 ± 0.8; rocuronium 8.2 ± 0.9; P = 0.7) and failed first intubation attempts (succinylcholine 32/200; rocuronium 36/201; P = 1.0) did not differ between the groups." contains several outcomes ("Intubation conditions", "failed first intubation attempts") and significance levels ("P = 0.7", "P = 1.0"). Masked sentence for each pair and the expected label are as follows:
```
@OUTCOME$ (succinylcholine 8.3 ± 0.8; rocuronium 8.2 ± 0.9; @SIGNIFICANCE$) and failed first intubation attempts (succinylcholine 32/200; rocuronium 36/201; P = 1.0) did not differ between the groups. 1
@OUTCOME$ (succinylcholine 8.3 ± 0.8; rocuronium 8.2 ± 0.9; P = 0.7) and failed first intubation attempts (succinylcholine 32/200; rocuronium 36/201; @SIGNIFICANCE$) did not differ between the groups. 0
Intubation conditions (succinylcholine 8.3 ± 0.8; rocuronium 8.2 ± 0.9; P = 0.7) and @OUTCOME$ (succinylcholine 32/200; rocuronium 36/201; @SIGNIFICANCE$) did not differ between the groups. 1
Intubation conditions (succinylcholine 8.3 ± 0.8; rocuronium 8.2 ± 0.9; @SIGNIFICANCE$) and @OUTCOME$ (succinylcholine 32/200; rocuronium 36/201; P = 1.0) did not differ between the groups. 0
```
This is the second version of the model; the original model development was reported in:
Anna Koroleva, Patrick Paroubek. Extracting relations between outcome and significance level in Randomized Controlled Trials (RCTs) publications. Proceedings of ACL BioNLP workshop, 2019 https://aclanthology.org/W19-5038/
The original work was conducted within the scope of the Assisted authoring for avoiding inadequate claims in scientific reporting PhD project of the Methods for Research on Research (MiRoR, http://miror-ejd.eu/) program.
Model creator: Anna Koroleva
<h1>Intended uses & limitations</h1>
The model was originally intended to be used as a part of spin (unjustified presentation of trial results) detection pipeline in articles reporting Randomised controlled trials (see Anna Koroleva, Sanjay Kamath, Patrick MM Bossuyt, Patrick Paroubek. DeSpin: a prototype system for detecting spin in biomedical publications. Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing. https://aclanthology.org/2020.bionlp-1.5/). It can also be used separately, for predicting outcome - significance level relation.
The main limitation is that the model was trained on a fairly small sample of data annotated by a single annotator. Annotating more data or involvig more annotators was not possible within the PhD project.
<h1>How to use</h1>
The model should be used with the BioBERT tokeniser. A sample code for getting model predictions is below:
```
import numpy as np
from transformers import AutoModelForTokenClassification
from transformers import AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('dmis-lab/biobert-v1.1')
model = AutoModelForSequenceClassification.from_pretrained("aakorolyova/outcome_significance_relation")
text1 = "@OUTCOME$ (succinylcholine 8.3 ± 0.8; rocuronium 8.2 ± 0.9; @SIGNIFICANCE$) and failed first intubation attempts (succinylcholine 32/200; rocuronium 36/201; P = 1.0) did not differ between the groups."
text2 = "@OUTCOME$ (succinylcholine 8.3 ± 0.8; rocuronium 8.2 ± 0.9; P = 0.7) and failed first intubation attempts (succinylcholine 32/200; rocuronium 36/201; @SIGNIFICANCE$) did not differ between the groups."
tokenized_input1 = tokenizer(text1, padding="max_length", truncation=True, return_tensors='pt')
output1 = model(**tokenized_input1)['logits']
output1 = np.argmax(output1.detach().numpy(), axis=1)
print(output1)
tokenized_input2 = tokenizer(text2, padding="max_length", truncation=True, return_tensors='pt')
output2 = model(**tokenized_input2)['logits']
output2 = np.argmax(output2.detach().numpy(), axis=1)
print(output2)
```
Some more useful functions can be found in or Github repository: https://github.com/aakorolyova/DeSpin-2.0
<h1>Training data</h1>
Training data can be found in https://github.com/aakorolyova/DeSpin-2.0/tree/main/data/Outcome_significance_relation
<h1>Training procedure</h1>
The model was fine-tuned using Huggingface Trainer API. Training scripts can be found in https://github.com/aakorolyova/DeSpin-2.0
<h1>Evaluation</h1>
Precision: 94.96%
Recall: 96.35%
F1: 95.65%
|
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} | 0 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="arkadip/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"])
```
|
Chester/traffic-rec | [] | null | {
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} | 0 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **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="GKPro/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"])
```
|
Chikita1/www_stash_stock | [
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} | 0 | null | ---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: huggingfaceclass-qtable-FrozenLake-v1-8x8-slip3
results:
- metrics:
- type: mean_reward
value: 0.53 +/- 0.50
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8
type: FrozenLake-v1-8x8
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="turnip/huggingfaceclass-qtable-FrozenLake-v1-8x8-slip3", 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"])
```
|
Chinat/test-classifier | [] | null | {
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} | 0 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **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="arkadip/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"])
```
|
Ching/negation_detector | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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"RobertaForQuestionAnswering"
],
"model_type": "roberta",
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},
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}
}
} | 9 | null | This model is a non-finetuned RAG-Token model and was created as follows:
```python
from transformers import RagTokenizer, RagTokenForGeneration, AutoTokenizer
model = RagTokenForGeneration.from_pretrained_question_encoder_generator(
"facebook/dpr-question_encoder-single-nq-base",
"facebook/bart-base"
)
question_encoder_tokenizer = AutoTokenizer.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
generator_tokenizer = AutoTokenizer.from_pretrained("facebook/bart-base")
tokenizer = RagTokenizer(question_encoder_tokenizer, generator_tokenizer)
model.config.use_dummy_dataset = True
model.config.index_name = "exact"
model.save_pretrained("./")
tokenizer.save_pretrained("./")
```
|
Chinmay/mlindia | [] | null | {
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} | 0 | null | ---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="GKPro/q-FrozenLake-v1-8x8-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"])
```
|
Chiuchiyin/DialoGPT-small-Donald | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
}
} | 7 | null | ---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- metrics:
- type: mean_reward
value: 0.58 +/- 0.49
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="RustBucket/q-FrozenLake-v1-4x4-Slippery", 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"])
```
|
Chiuchiyin/Donald | [] | null | {
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}
}
} | 0 | null | ---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="arkadip/q-FrozenLake-v1-8x8-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"])
```
|
ChoboAvenger/DialoGPT-small-DocBot | [] | null | {
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}
} | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# ronanki/ml_use_13
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 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('ronanki/ml_use_13')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ronanki/ml_use_13)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8 with parameters:
```
{'batch_size': 4}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 0,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
ChoboAvenger/DialoGPT-small-joshua | [] | null | {
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} | 0 | null | This model is a non-finetuned RAG-Token model and was created as follows:
```python
from transformers import RagTokenizer, RagSequenceForGeneration, AutoTokenizer
model = RagSequenceForGeneration.from_pretrained_question_encoder_generator(
"facebook/dpr-question_encoder-single-nq-base",
"facebook/bart-base"
)
question_encoder_tokenizer = AutoTokenizer.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
generator_tokenizer = AutoTokenizer.from_pretrained("facebook/bart-base")
tokenizer = RagTokenizer(question_encoder_tokenizer, generator_tokenizer)
model.config.use_dummy_dataset = True
model.config.index_name = "exact"
model.save_pretrained("./")
tokenizer.save_pretrained("./")
```
|
ChrisP/xlm-roberta-base-finetuned-marc-en | [] | null | {
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}
} | 0 | null | Hugging Face's logo
---
language:
- om
- am
- rw
- rn
- ha
- ig
- pcm
- so
- sw
- ti
- yo
- multilingual
tags:
- T5
---
# afriteva_small
## Model desription
AfriTeVa small is a sequence to sequence model pretrained on 10 African languages
## Languages
Afaan Oromoo(orm), Amharic(amh), Gahuza(gah), Hausa(hau), Igbo(igb), Nigerian Pidgin(pcm), Somali(som), Swahili(swa), Tigrinya(tig), Yoruba(yor)
### More information on the model, dataset:
### The model
- 64M parameters encoder-decoder architecture (T5-like)
- 6 layers, 8 attention heads and 512 token sequence length
### The dataset
- Multilingual: 10 African languages listed above
- 143 Million Tokens (1GB of text data)
- Tokenizer Vocabulary Size: 70,000 tokens
## Intended uses & limitations
`afriteva_small` is pre-trained model and primarily aimed at being fine-tuned on multilingual sequence-to-sequence tasks.
```python
>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("castorini/afriteva_small")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("castorini/afriteva_small")
>>> src_text = "Ó hùn ọ́ láti di ara wa bí?"
>>> tgt_text = "Would you like to be?"
>>> model_inputs = tokenizer(src_text, return_tensors="pt")
>>> with tokenizer.as_target_tokenizer():
labels = tokenizer(tgt_text, return_tensors="pt").input_ids
>>> model(**model_inputs, labels=labels) # forward pass
```
## Training Procedure
For information on training procedures, please refer to the AfriTeVa [paper](#) or [repository](https://github.com/castorini/afriteva)
## BibTex entry and Citation info
coming soon ...
|
ChrisVCB/DialoGPT-medium-cmjs | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
} | 7 | null | ---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-slippery
results:
- metrics:
- type: mean_reward
value: 0.78 +/- 0.41
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="GKPro/q-FrozenLake-v1-4x4-slippery", 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"])
```
|
ChrisVCB/DialoGPT-medium-ej | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 1000
},
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}
}
} | 13 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-init
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **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="RustBucket/q-Taxi-v3-init", 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"])
```
|
ChristianOrr/madnet_keras | [
"tensorboard",
"dataset:flyingthings-3d",
"dataset:kitti",
"arxiv:1810.05424",
"vision",
"deep-stereo",
"depth-estimation",
"Tensorflow2",
"Keras",
"license:apache-2.0"
] | depth-estimation | {
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}
} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-cnn-pubmed-arxiv-pubmed-v3-e43
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-cnn-pubmed-arxiv-pubmed-v3-e43
This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0837
- Rouge1: 58.1526
- Rouge2: 46.0425
- Rougel: 49.5624
- Rougelsum: 56.9295
- Gen Len: 142.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 43
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 1.2542 | 1.0 | 795 | 0.9354 | 51.4655 | 31.6464 | 34.2376 | 48.9765 | 141.963 |
| 0.7019 | 2.0 | 1590 | 0.8119 | 53.3066 | 34.683 | 36.4262 | 50.907 | 142.0 |
| 0.5251 | 3.0 | 2385 | 0.7839 | 52.4248 | 32.8685 | 36.0084 | 49.9957 | 142.0 |
| 0.3449 | 4.0 | 3180 | 0.7673 | 52.716 | 34.7869 | 38.4201 | 50.8384 | 142.0 |
| 0.2666 | 5.0 | 3975 | 0.7647 | 54.6433 | 37.1337 | 40.1459 | 52.4288 | 141.7778 |
| 0.1805 | 6.0 | 4770 | 0.8400 | 53.5747 | 36.001 | 39.5984 | 51.1935 | 141.8148 |
| 0.1413 | 7.0 | 5565 | 0.7925 | 53.9875 | 37.01 | 40.6532 | 51.9353 | 142.0 |
| 0.113 | 8.0 | 6360 | 0.7665 | 56.395 | 41.5764 | 44.327 | 54.7845 | 142.0 |
| 0.0907 | 9.0 | 7155 | 0.8442 | 55.1407 | 39.4113 | 43.0628 | 53.6503 | 142.0 |
| 0.0824 | 10.0 | 7950 | 0.8469 | 55.7103 | 40.6761 | 43.3754 | 53.8227 | 142.0 |
| 0.0639 | 11.0 | 8745 | 0.8892 | 56.0839 | 40.6204 | 43.2455 | 54.4412 | 142.0 |
| 0.0504 | 12.0 | 9540 | 0.8613 | 56.9634 | 42.8236 | 45.4255 | 55.4026 | 142.0 |
| 0.0447 | 13.0 | 10335 | 0.9341 | 57.7216 | 44.104 | 47.1429 | 56.4299 | 142.0 |
| 0.0396 | 14.0 | 11130 | 0.9203 | 56.2073 | 42.9575 | 45.8068 | 54.8089 | 142.0 |
| 0.036 | 15.0 | 11925 | 0.9253 | 58.5212 | 45.6047 | 49.1205 | 57.0551 | 142.0 |
| 0.0302 | 16.0 | 12720 | 0.9187 | 58.8046 | 46.0106 | 48.0442 | 57.2799 | 142.0 |
| 0.0261 | 17.0 | 13515 | 0.9578 | 57.3405 | 43.8227 | 46.6317 | 55.7836 | 142.0 |
| 0.0231 | 18.0 | 14310 | 0.9578 | 57.7604 | 44.6164 | 47.8902 | 56.2309 | 141.8148 |
| 0.0198 | 19.0 | 15105 | 0.9662 | 57.774 | 44.6407 | 47.5489 | 56.1936 | 142.0 |
| 0.0165 | 20.0 | 15900 | 0.9509 | 59.6297 | 46.5076 | 48.3507 | 58.083 | 142.0 |
| 0.0145 | 21.0 | 16695 | 0.9915 | 58.2245 | 45.1804 | 48.1191 | 56.889 | 142.0 |
| 0.0128 | 22.0 | 17490 | 0.9945 | 58.2646 | 46.2782 | 49.4411 | 56.992 | 142.0 |
| 0.0129 | 23.0 | 18285 | 1.0069 | 57.0055 | 44.1866 | 46.9101 | 55.5056 | 141.9444 |
| 0.0116 | 24.0 | 19080 | 0.9967 | 58.1091 | 45.5303 | 48.2208 | 56.4496 | 142.0 |
| 0.0093 | 25.0 | 19875 | 1.0188 | 56.59 | 43.677 | 45.8956 | 55.0954 | 142.0 |
| 0.008 | 26.0 | 20670 | 0.9976 | 58.5408 | 46.7019 | 48.9235 | 57.2562 | 142.0 |
| 0.0077 | 27.0 | 21465 | 1.0123 | 57.7909 | 45.7619 | 48.3412 | 56.3796 | 142.0 |
| 0.0075 | 28.0 | 22260 | 1.0258 | 58.1694 | 45.03 | 48.282 | 56.7303 | 142.0 |
| 0.0056 | 29.0 | 23055 | 1.0100 | 58.0406 | 45.37 | 48.0125 | 56.5288 | 142.0 |
| 0.0049 | 30.0 | 23850 | 1.0235 | 56.419 | 43.248 | 46.3448 | 54.8467 | 142.0 |
| 0.0042 | 31.0 | 24645 | 1.0395 | 57.7232 | 45.6305 | 48.4531 | 56.3343 | 141.9444 |
| 0.0034 | 32.0 | 25440 | 1.0605 | 58.9049 | 46.8049 | 49.9103 | 57.6751 | 141.5 |
| 0.0032 | 33.0 | 26235 | 1.0362 | 57.8681 | 45.9028 | 48.8624 | 56.5616 | 141.8704 |
| 0.0025 | 34.0 | 27030 | 1.0521 | 58.8985 | 46.8547 | 49.8485 | 57.4249 | 142.0 |
| 0.0021 | 35.0 | 27825 | 1.0639 | 58.9324 | 46.656 | 49.1907 | 57.4836 | 142.0 |
| 0.0023 | 36.0 | 28620 | 1.0624 | 58.5734 | 46.6774 | 49.6377 | 57.3825 | 142.0 |
| 0.0019 | 37.0 | 29415 | 1.0636 | 58.9899 | 46.8217 | 49.4829 | 57.8683 | 142.0 |
| 0.0018 | 38.0 | 30210 | 1.0640 | 58.793 | 46.7964 | 49.7845 | 57.6379 | 142.0 |
| 0.0013 | 39.0 | 31005 | 1.0692 | 57.7124 | 45.5948 | 49.0482 | 56.4246 | 142.0 |
| 0.0012 | 40.0 | 31800 | 1.0746 | 58.1789 | 46.458 | 49.547 | 57.1007 | 141.6296 |
| 0.0008 | 41.0 | 32595 | 1.0815 | 57.7392 | 45.6404 | 48.4845 | 56.6464 | 142.0 |
| 0.0009 | 42.0 | 33390 | 1.0853 | 58.317 | 46.2661 | 49.0466 | 57.0971 | 142.0 |
| 0.0005 | 43.0 | 34185 | 1.0837 | 58.1526 | 46.0425 | 49.5624 | 56.9295 | 142.0 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Chuah/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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} | 9 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# ronanki/ml_mpnet_768_MNR_15
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('ronanki/ml_mpnet_768_MNR_15')
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('ronanki/ml_mpnet_768_MNR_15')
model = AutoModel.from_pretrained('ronanki/ml_mpnet_768_MNR_15')
# 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=ronanki/ml_mpnet_768_MNR_15)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8 with parameters:
```
{'batch_size': 4}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 0,
"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 --> |
ChukSamuels/DialoGPT-small-Dr.FauciBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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}
} | 13 | null | ---
tags:
- huggingnft
- nft
- huggan
- gan
- image
- images
- unconditional-image-generation
datasets:
- huggingnft/hedgies
license: mit
---
# Hugging NFT: hedgies
## Disclaimer
All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright
holder.
## Model description
LightWeight GAN model for unconditional generation.
NFT collection available [here](https://opensea.io/collection/hedgies).
Dataset is available [here](https://huggingface.co/datasets/huggingnft/hedgies).
Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft).
Project repository: [link](https://github.com/AlekseyKorshuk/huggingnft).
[](https://github.com/AlekseyKorshuk/huggingnft)
## Intended uses & limitations
#### How to use
Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft).
#### Limitations and bias
Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft).
## Training data
Dataset is available [here](https://huggingface.co/datasets/huggingnft/hedgies).
## Training procedure
Training script is available [here](https://github.com/AlekseyKorshuk/huggingnft).
## Generated Images
Check results with Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft).
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingnft)
### BibTeX entry and citation info
```bibtex
@InProceedings{huggingnft,
author={Aleksey Korshuk}
year=2022
}
```
|
Chun/DialoGPT-medium-dailydialog | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
],
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}
}
} | 15 | null | ---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: FrozenLake-v1-8x8-slippery
results:
- metrics:
- type: mean_reward
value: 0.52 +/- 0.50
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8
type: FrozenLake-v1-8x8
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="GKPro/FrozenLake-v1-8x8-slippery", 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"])
```
|
Chun/w-en2zh-mtm | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MBartForConditionalGeneration"
],
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}
} | 7 | null | Hugging Face's logo
---
language:
- om
- am
- rw
- rn
- ha
- ig
- pcm
- so
- sw
- ti
- yo
- multilingual
tags:
- T5
---
# afriteva_large
## Model desription
AfriTeVa large is a sequence to sequence model pretrained on 10 African languages
## Languages
Afaan Oromoo(orm), Amharic(amh), Gahuza(gah), Hausa(hau), Igbo(igb), Nigerian Pidgin(pcm), Somali(som), Swahili(swa), Tigrinya(tig), Yoruba(yor)
### More information on the model, dataset:
### The model
- 745M parameters encoder-decoder architecture (T5-like)
- 12 layers, 12 attention heads and 512 token sequence length
### The dataset
- Multilingual: 10 African languages listed above
- 143 Million Tokens (1GB of text data)
- Tokenizer Vocabulary Size: 70,000 tokens
## Intended uses & limitations
`afriteva_large` is pre-trained model and primarily aimed at being fine-tuned on multilingual sequence-to-sequence tasks.
```python
>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("castorini/afriteva_large")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("castorini/afriteva_large")
>>> src_text = "Ó hùn ọ́ láti di ara wa bí?"
>>> tgt_text = "Would you like to be?"
>>> model_inputs = tokenizer(src_text, return_tensors="pt")
>>> with tokenizer.as_target_tokenizer():
labels = tokenizer(tgt_text, return_tensors="pt").input_ids
>>> model(**model_inputs, labels=labels) # forward pass
```
## Training Procedure
For information on training procedures, please refer to the AfriTeVa [paper](#) or [repository](https://github.com/castorini/afriteva)
## BibTex entry and Citation info
coming soon ...
|
Chun/w-en2zh-otm | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"MBartForConditionalGeneration"
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} | 7 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/bladeecity-jerma985/1653418745528/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/1501634135378391044/6FiRJ7RP_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/803601382943162368/F36Z7ypy_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Aim Nothyng & Jerma</div>
<div style="text-align: center; font-size: 14px;">@bladeecity-jerma985</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 Aim Nothyng & Jerma.
| Data | Aim Nothyng | Jerma |
| --- | --- | --- |
| Tweets downloaded | 1620 | 2695 |
| Retweets | 322 | 100 |
| Short tweets | 492 | 286 |
| Tweets kept | 806 | 2309 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3g5k759s/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 @bladeecity-jerma985's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2wj5tjlg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2wj5tjlg/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/bladeecity-jerma985')
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)
|
Chun/w-zh2en-hsk | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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} | 3 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="DeniSSio/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"])
```
|
Chun/w-zh2en-mto | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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} | 7 | 2022-05-24T19:13:26Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="/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"])
```
|
Chungu424/DATA | [] | null | {
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} | 0 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **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="/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"])
```
|
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-finetuned-language-identification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-language-detection-new
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [Language Identification dataset](https://huggingface.co/datasets/papluca/language-identification).
It achieves the following results on the evaluation set:
- Loss: 0.0436
- Accuracy: 0.9959
## Model description
The model used in this task is XLM-RoBERTa, a transformer model with a classification head on top.
## Intended uses & limitations
It identifies the language a document is written in and it supports 20 different langauges:
Arabic (ar), Bulgarian (bg), German (de), Modern greek (el), English (en), Spanish (es), French (fr), Hindi (hi), Italian (it), Japanese (ja), Dutch (nl), Polish (pl), Portuguese (pt), Russian (ru), Swahili (sw), Thai (th), Turkish (tr), Urdu (ur), Vietnamese (vi), Chinese (zh)
## Training and evaluation data
The model is fine-tuned on the [Language Identification dataset](https://huggingface.co/datasets/papluca/language-identification), a corpus consists of text from 20 different languages. The dataset is split with 7000 sentences for training, 1000 for validating, and 1000 for testing. The accuracy on the test set is 99.5%.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0493 | 1.0 | 35000 | 0.0407 | 0.9955 |
| 0.018 | 2.0 | 70000 | 0.0436 | 0.9959 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Chuu/Chumar | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9333333333333333
- name: Recall
type: recall
value: 0.9495119488387749
- name: F1
type: f1
value: 0.9413531325602736
- name: Accuracy
type: accuracy
value: 0.9857243774651204
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0669
- Precision: 0.9333
- Recall: 0.9495
- F1: 0.9414
- Accuracy: 0.9857
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0867 | 1.0 | 1756 | 0.0647 | 0.9227 | 0.9377 | 0.9301 | 0.9838 |
| 0.0383 | 2.0 | 3512 | 0.0603 | 0.9308 | 0.9500 | 0.9403 | 0.9854 |
| 0.0184 | 3.0 | 5268 | 0.0669 | 0.9333 | 0.9495 | 0.9414 | 0.9857 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Cinnamon/electra-small-japanese-discriminator | [
"pytorch",
"electra",
"pretraining",
"ja",
"transformers",
"license:apache-2.0"
] | null | {
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} | 419 | null | ---
tags:
- image-classification
- pytorch
metrics:
- accuracy
- Cohen's Kappa
model-index:
- name: PANDA_ConvNeXT
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.5491307377815247
- name: Quadratic Cohen's Kappa
type: Quadratic Cohen's Kappa
value: 0.6630877256393433
---
# PANDA_ConvNeXT
An attempt to use a ConvNeXT for medical image classification (ISUP grading in prostate histopathology images). Currently uses a tiled and concatenated WSI as input
Example Images (1152,1152,3) 36 WSI patches:
ISUP 0:
<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/0c02d3bb3a62519b31c63d0301c6843e_0.jpeg">
ISUP 1:
<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/0cee71ab57422e04f76e09ef2186fcd5_1.jpeg">
ISUP 2:
<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/00bbc1482301d16de3ff63238cfd0b34_2.jpeg">
ISUP 3:
<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/0c5c2d16c0f2e399b7be641e7e7f66d9_3.jpeg">
ISUP 4:
<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/0c88d7c7033e2048b1068e208b105270_4.jpeg">
ISUP 5:
<img width="256" height="256" src="https://huggingface.co/smc/PANDA_ViT/resolve/main/00c15b23b30a5ba061358d9641118904_5.jpeg"> |
CoachCarter/distilbert-base-uncased | [] | null | {
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} | 0 | 2022-05-25T00:24:34Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **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="micheljperez/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"])
```
|
D3xter1922/electra-base-discriminator-finetuned-mnli | [] | null | {
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} | 0 | null | ---
tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets: squad
---
# oBERT-12-downstream-pruned-unstructured-80-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 1 - 30 Epochs - oBERT - SQuADv1 80%`.
```
Pruning method: oBERT downstream unstructured
Paper: https://arxiv.org/abs/2203.07259
Dataset: SQuADv1
Sparsity: 80%
Number of layers: 12
```
The dev-set performance reported in the paper is averaged over three seeds, and we release the best model (marked with `(*)`):
```
| oBERT 80% | F1 | EM |
| ------------ | ----- | ----- |
| seed=42 | 88.95 | 82.08 |
| seed=3407 (*)| 89.16 | 82.05 |
| seed=54321 | 89.01 | 82.12 |
| ------------ | ----- | ----- |
| mean | 89.04 | 82.08 |
| stdev | 0.108 | 0.035 |
```
Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT)
If you find the model useful, please consider citing our work.
## Citation info
```bibtex
@article{kurtic2022optimal,
title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
journal={arXiv preprint arXiv:2203.07259},
year={2022}
}
``` |
DCU-NLP/bert-base-irish-cased-v1 | [
"pytorch",
"tf",
"bert",
"fill-mask",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
] | fill-mask | {
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} | 1,244 | null | ---
tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets: mnli
---
# MNLI teacher
This model is used as a teacher for all runs on the MNLI downstream task in the paper [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
MNLI dev-set:
```
matched accuracy = 84.54
mismatched accuracy = 85.06
```
Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT)
If you find the model useful, please consider citing our work.
## Citation info
```bibtex
@article{kurtic2022optimal,
title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
journal={arXiv preprint arXiv:2203.07259},
year={2022}
}
``` |
DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support | [
"pytorch",
"jax",
"bert",
"text-classification",
"multilingual",
"nl",
"fr",
"en",
"arxiv:2104.09947",
"transformers",
"Tweets",
"Sentiment analysis"
] | text-classification | {
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} | 29 | null | ---
tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets: squad
---
# oBERT-12-upstream-pruned-unstructured-97-finetuned-squadv1
This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259).
It corresponds to the model presented in the `Table 2 - oBERT - SQuADv1 97%`.
```
Pruning method: oBERT upstream unstructured + sparse-transfer to downstream
Paper: https://arxiv.org/abs/2203.07259
Dataset: SQuADv1
Sparsity: 97%
Number of layers: 12
```
The dev-set performance reported in the paper is averaged over three seeds, and we release the best model (marked with `(*)`):
```
| oBERT 97% | F1 | EM |
| ------------ | ----- | ----- |
| seed=42 | 84.11 | 76.02 |
| seed=3407 (*)| 84.71 | 76.61 |
| seed=54321 | 84.35 | 76.44 |
| ------------ | ----- | ----- |
| mean | 84.39 | 76.36 |
| stdev | 0.301 | 0.303 |
```
Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT)
If you find the model useful, please consider citing our work.
## Citation info
```bibtex
@article{kurtic2022optimal,
title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
journal={arXiv preprint arXiv:2203.07259},
year={2022}
}
``` |
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} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-arxiv
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-arxiv
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3852
- Rouge1: 18.0722
- Rouge2: 6.8453
- Rougel: 14.3659
- Rougelsum: 16.4137
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.5169 | 1.0 | 12500 | 2.3852 | 18.0722 | 6.8453 | 14.3659 | 16.4137 | 19.0 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Davlan/byt5-base-yor-eng-mt | [
"pytorch",
"t5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.92
- name: F1
type: f1
value: 0.9200387095502811
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2156
- Accuracy: 0.92
- F1: 0.9200
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8096 | 1.0 | 250 | 0.3081 | 0.9005 | 0.8974 |
| 0.2404 | 2.0 | 500 | 0.2156 | 0.92 | 0.9200 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Declan/CNN_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 7 | 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: 238.77 +/- 14.32
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
...
```
|
Declan/Politico_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 7 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Against61/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"])
```
|
Declan/Politico_model_v6 | [
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"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 3 | 2022-05-26T16:14:35Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: BobBraico/rlb-cyber-finetuned-imdb
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. -->
# BobBraico/rlb-cyber-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.7869
- Validation Loss: 2.4354
- 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -719, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.7869 | 2.4354 | 0 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Declan/Reuters_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 5 | null | ---
license: mit
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-booksum-finetuned-booksum-test
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-booksum-finetuned-booksum-test
This model is a fine-tuned version of [cnicu/t5-small-booksum](https://huggingface.co/cnicu/t5-small-booksum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2739
- Rouge1: 22.7829
- Rouge2: 4.8349
- Rougel: 18.2465
- Rougelsum: 19.2417
## 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: 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 3.5123 | 1.0 | 8750 | 3.2816 | 21.7712 | 4.3046 | 17.4053 | 18.4707 |
| 3.2347 | 2.0 | 17500 | 3.2915 | 22.2938 | 4.7828 | 17.8567 | 18.9135 |
| 3.0892 | 3.0 | 26250 | 3.2568 | 22.4966 | 4.825 | 18.0344 | 19.1306 |
| 2.9837 | 4.0 | 35000 | 3.2952 | 22.6913 | 5.0322 | 18.176 | 19.2751 |
| 2.9028 | 5.0 | 43750 | 3.2626 | 22.3548 | 4.7521 | 17.8681 | 18.7815 |
| 2.8441 | 6.0 | 52500 | 3.2691 | 22.6279 | 4.932 | 18.1051 | 19.0763 |
| 2.8006 | 7.0 | 61250 | 3.2753 | 22.8911 | 4.8954 | 18.1204 | 19.1464 |
| 2.7742 | 8.0 | 70000 | 3.2739 | 22.7829 | 4.8349 | 18.2465 | 19.2417 |
### Framework versions
- Transformers 4.19.1
- Pytorch 1.7.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
Declan/WallStreetJournal_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
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} | 0 | null | ---
tags:
- conversational
---
#Audrey Hepburn DialoGPT Model |
DeltaHub/adapter_t5-3b_cola | [
"pytorch",
"transformers"
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} | 3 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **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="alefarasin/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"])
```
|
DeltaHub/adapter_t5-3b_mrpc | [
"pytorch",
"transformers"
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} | 3 | null | ---
library_name: keras
tags:
- image-classification
- keras
---
## Model description
It's pretty simple, the model tries to differentiate between dogs/cats.
## Intended uses & limitations
Only JPGs can be used and they are auto resized. **Meant for dogs and cats only.**
## Training and evaluation data
Combined training data from multiple sources. Downloaded around 3000 cat and dog photos from r/dogs and r/cats. Curated kaggle datasets into one large dataset.
## Training procedure
Trained using an early stopping for overfitting
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
|
DeskDown/MarianMixFT_en-th | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
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} | 3 | null | ---
tags:
- conversational
---
#Audrey Hepburn DialoGPT Model |
DeskDown/MarianMix_en-zh-10 | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
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} | 3 | 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: 0.8627004891366169
---
<!-- 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: 0.1363
- F1: 0.8627
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2539 | 1.0 | 525 | 0.1697 | 0.8179 |
| 0.1317 | 2.0 | 1050 | 0.1327 | 0.8516 |
| 0.0819 | 3.0 | 1575 | 0.1363 | 0.8627 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Devid/DialoGPT-small-Miku | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 10 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2424
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4921 | 1.0 | 479 | 2.3047 |
| 2.3893 | 2.0 | 958 | 2.2607 |
| 2.3571 | 3.0 | 1437 | 2.2481 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1
- Datasets 2.2.2
- Tokenizers 0.10.3
|
Devrim/prism-default | [
"license:mit"
] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 28.2621
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4782
- Rouge1: 28.2621
- Rouge2: 7.6583
- Rougel: 22.1971
- Rougelsum: 22.2
- Gen Len: 18.8243
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.7138 | 1.0 | 12753 | 2.4782 | 28.2621 | 7.6583 | 22.1971 | 22.2 | 18.8243 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
DevsIA/Devs_IA | [] | null | {
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} | 0 | null | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Rebreak/autotrain-data-News
co2_eq_emissions: 62.61326668998836
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 916530070
- CO2 Emissions (in grams): 62.61326668998836
## Validation Metrics
- Loss: 0.0855042040348053
- Accuracy: 0.9773220921733938
- Precision: 0.673469387755102
- Recall: 0.014864864864864866
- AUC: 0.8605107881181646
- F1: 0.029087703834288235
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Rebreak/autotrain-News-916530070
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Rebreak/autotrain-News-916530070", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Rebreak/autotrain-News-916530070", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Dhritam/Zova-bot | [] | null | {
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} | 0 | 2022-05-27T06:00:11Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-hindi1-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hindi1-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Dhruva/Interstellar | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-issues-128
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2337
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3389 | 1.0 | 73 | 1.7400 |
| 1.8014 | 2.0 | 146 | 1.4690 |
| 1.634 | 3.0 | 219 | 1.4783 |
| 1.5461 | 4.0 | 292 | 1.3912 |
| 1.4706 | 5.0 | 365 | 1.3109 |
| 1.4161 | 6.0 | 438 | 1.3405 |
| 1.3664 | 7.0 | 511 | 1.3459 |
| 1.332 | 8.0 | 584 | 1.2745 |
| 1.3029 | 9.0 | 657 | 1.2633 |
| 1.2871 | 10.0 | 730 | 1.2336 |
| 1.2807 | 11.0 | 803 | 1.2966 |
| 1.2569 | 12.0 | 876 | 1.1508 |
| 1.2392 | 13.0 | 949 | 1.2530 |
| 1.237 | 14.0 | 1022 | 1.2485 |
| 1.2169 | 15.0 | 1095 | 1.2592 |
| 1.2272 | 16.0 | 1168 | 1.2337 |
### Framework versions
- Transformers 4.19.1
- Pytorch 1.12.0.dev20220513+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
Dilmk2/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 13 | null | CommonVoice Dataset 8.0 --> Train + Test + Validation
WER : 0.216
WER with LM: 0.123 |
DingleyMaillotUrgell/homer-bot | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"conversational"
] | conversational | {
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} | 12 | 2022-05-27T07:25:29Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: xlsr-wav2vec2-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlsr-wav2vec2-3
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4201
- Wer: 0.3998
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.0117 | 0.68 | 400 | 3.0284 | 0.9999 |
| 2.6502 | 1.35 | 800 | 1.0868 | 0.9374 |
| 0.9362 | 2.03 | 1200 | 0.5216 | 0.6491 |
| 0.6675 | 2.7 | 1600 | 0.4744 | 0.5837 |
| 0.5799 | 3.38 | 2000 | 0.4400 | 0.5802 |
| 0.5196 | 4.05 | 2400 | 0.4266 | 0.5314 |
| 0.4591 | 4.73 | 2800 | 0.3808 | 0.5190 |
| 0.4277 | 5.41 | 3200 | 0.3987 | 0.5036 |
| 0.4125 | 6.08 | 3600 | 0.3902 | 0.5040 |
| 0.3797 | 6.76 | 4000 | 0.4105 | 0.5025 |
| 0.3606 | 7.43 | 4400 | 0.3975 | 0.4823 |
| 0.3554 | 8.11 | 4800 | 0.3733 | 0.4747 |
| 0.3373 | 8.78 | 5200 | 0.3737 | 0.4726 |
| 0.3252 | 9.46 | 5600 | 0.3795 | 0.4736 |
| 0.3192 | 10.14 | 6000 | 0.3935 | 0.4736 |
| 0.3012 | 10.81 | 6400 | 0.3974 | 0.4648 |
| 0.2972 | 11.49 | 6800 | 0.4497 | 0.4724 |
| 0.2873 | 12.16 | 7200 | 0.4645 | 0.4843 |
| 0.2849 | 12.84 | 7600 | 0.4461 | 0.4709 |
| 0.274 | 13.51 | 8000 | 0.4002 | 0.4695 |
| 0.2709 | 14.19 | 8400 | 0.4188 | 0.4627 |
| 0.2619 | 14.86 | 8800 | 0.3987 | 0.4646 |
| 0.2545 | 15.54 | 9200 | 0.4083 | 0.4668 |
| 0.2477 | 16.22 | 9600 | 0.4525 | 0.4728 |
| 0.2455 | 16.89 | 10000 | 0.4148 | 0.4515 |
| 0.2281 | 17.57 | 10400 | 0.4304 | 0.4514 |
| 0.2267 | 18.24 | 10800 | 0.4077 | 0.4446 |
| 0.2136 | 18.92 | 11200 | 0.4209 | 0.4445 |
| 0.2032 | 19.59 | 11600 | 0.4543 | 0.4534 |
| 0.1999 | 20.27 | 12000 | 0.4184 | 0.4373 |
| 0.1898 | 20.95 | 12400 | 0.4044 | 0.4424 |
| 0.1846 | 21.62 | 12800 | 0.4098 | 0.4288 |
| 0.1796 | 22.3 | 13200 | 0.4047 | 0.4262 |
| 0.1715 | 22.97 | 13600 | 0.4077 | 0.4189 |
| 0.1641 | 23.65 | 14000 | 0.4162 | 0.4248 |
| 0.1615 | 24.32 | 14400 | 0.4392 | 0.4222 |
| 0.1575 | 25.0 | 14800 | 0.4296 | 0.4185 |
| 0.1456 | 25.68 | 15200 | 0.4363 | 0.4129 |
| 0.1461 | 26.35 | 15600 | 0.4305 | 0.4124 |
| 0.1422 | 27.03 | 16000 | 0.4237 | 0.4086 |
| 0.1378 | 27.7 | 16400 | 0.4294 | 0.4051 |
| 0.1326 | 28.38 | 16800 | 0.4311 | 0.4051 |
| 0.1286 | 29.05 | 17200 | 0.4153 | 0.3992 |
| 0.1283 | 29.73 | 17600 | 0.4201 | 0.3998 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
DivyanshuSheth/T5-Seq2Seq-Final | [] | null | {
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}
} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: language-detection-RoBerta-base-additional
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. -->
# language-detection-RoBerta-base-additional
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1367
- Accuracy: 0.9874
## 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: 50
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
Doiman/DialoGPT-medium-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 13 | null | ---
language: "rw"
thumbnail:
pipeline_tag: automatic-speech-recognition
tags:
- Coqui
- Deepspeech
- LSTM
license: "apache-2.0"
datasets:
- commonvoice
metrics:
- wer
---
**Model card - Kinyarwanda coqui STT model**
**Model details**
- Kinyarwanda Speech to text model
- Developed by [Digital Umuganda](digitalumuganda.com)
- Model based from: Baidu Deepspeech end to end RNN model
- paper: [deepspeech end to end STT](https://arxiv.org/pdf/1412.5567.pdf)
- Documentation on model: [deepspeech documentation](https://deepspeech.readthedocs.io/)
- License: Mozilla 2.0 License
- Feedback on the model: [email protected]
**Intended use cases**
- Intended to be used for
- simple keyword spotting
- simple transcribing
- transfer learning for better kinyarwanda and african language models
- Intended to be used by:
- App developpers
- various organizations who want to transcribe kinyarwanda recordings
- ML researchers
- other researchers in Kinyarwanda and tech usage in kinyarwanda (e.g. Linguists, journalists)
- Not intended to be used as:
- a fully fledged voice assistant
- voice recognition application
- Multiple languages STT
- language detection
**Factors**
- Anti-bias: these are bias that can influence the accuracy of the model
- Gender
- accents and dialects
- age
- Voice quality: factors that can influence the accuracy of the model
- Background noise
- short sentences
- Voice format: voices must be converted to the wav format
- wav format
**Metrics**
- word error rate on the Common Voice Kinyarwanda test set
|Test Corpus|WER|
|-----------|---|
|Common Voice|39.1\%|
**Training data**
- [common voice crowdsource website](https://commonvoice.mozilla.org/en/datasets)
**Evaluation data**
- [common voice crowdsource website](https://commonvoice.mozilla.org/en/datasets)
|
DongHyoungLee/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | {
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}
} | 27 | null | ---
tags:
- conversational
---
# Potaru DiabloGPT model |
DongHyoungLee/kogpt2-base-v2-finetuned-kogpt2_nsmc_single_sentence_classification | [] | null | {
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} | 0 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/terrybroad/1653641199493/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/1445695092325380098/Zk0H0J37_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">Terence Broad</div>
<div style="text-align: center; font-size: 14px;">@terrybroad</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 Terence Broad.
| Data | Terence Broad |
| --- | --- |
| Tweets downloaded | 2248 |
| Retweets | 1230 |
| Short tweets | 231 |
| Tweets kept | 787 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2v3f7i92/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 @terrybroad's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3fxvoi41) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3fxvoi41/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/terrybroad')
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)
|
Donghyun/L2_BERT | [] | null | {
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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: 259.44 +/- 19.25
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
...
```
|
Waynehillsdev/Waynehills_summary_tensorflow | [
"tf",
"t5",
"text2text-generation",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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},
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}
} | 5 | 2022-05-27T10:08:36Z | ---
library_name: stable-baselines3
tags:
- seals/Walker2d-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 1429.13 +/- 411.75
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Walker2d-v0
type: seals/Walker2d-v0
---
# **PPO** Agent playing **seals/Walker2d-v0**
This is a trained model of a **PPO** agent playing **seals/Walker2d-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo ppo --env seals/Walker2d-v0 -orga ernestumorga -f logs/
python enjoy.py --algo ppo --env seals/Walker2d-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo ppo --env seals/Walker2d-v0 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo ppo --env seals/Walker2d-v0 -f logs/ -orga ernestumorga
```
## Hyperparameters
```python
OrderedDict([('batch_size', 8),
('clip_range', 0.4),
('ent_coef', 0.00013057334805552262),
('gae_lambda', 0.92),
('gamma', 0.98),
('learning_rate', 3.791707778339674e-05),
('max_grad_norm', 0.6),
('n_envs', 1),
('n_epochs', 5),
('n_steps', 2048),
('n_timesteps', 1000000.0),
('normalize', True),
('policy', 'MlpPolicy'),
('policy_kwargs',
'dict(activation_fn=nn.ReLU, net_arch=[dict(pi=[256, 256], '
'vf=[256, 256])])'),
('vf_coef', 0.6167177795726859),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
|
Waynehillsdev/wav2vec2-base-timit-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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}
} | 5 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/isaac_a_arthur/1653649231789/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/1301946586331836421/at9dHQeU_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">Isaac Arthur</div>
<div style="text-align: center; font-size: 14px;">@isaac_a_arthur</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 Isaac Arthur.
| Data | Isaac Arthur |
| --- | --- |
| Tweets downloaded | 2697 |
| Retweets | 212 |
| Short tweets | 26 |
| Tweets kept | 2459 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/24wggcyw/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 @isaac_a_arthur's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2yxg71s3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2yxg71s3/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/isaac_a_arthur')
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)
|
Waynehillsdev/waynehills_sentimental_kor | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | {
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"ElectraForSequenceClassification"
],
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}
} | 33 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/campbellclaret/1653647611538/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/1441638351052881920/13PTOAD0_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">ALASTAIR CAMPBELL</div>
<div style="text-align: center; font-size: 14px;">@campbellclaret</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 ALASTAIR CAMPBELL.
| Data | ALASTAIR CAMPBELL |
| --- | --- |
| Tweets downloaded | 3239 |
| Retweets | 1921 |
| Short tweets | 112 |
| Tweets kept | 1206 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1psic63j/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 @campbellclaret's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2bq64fuz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2bq64fuz/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/campbellclaret')
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)
|
Doohae/q_encoder | [
"pytorch"
] | null | {
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}
} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: checkpoints
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. -->
# checkpoints
This model is a fine-tuned version of [vumichien/wav2vec2-large-xlsr-japanese-hiragana](https://huggingface.co/vumichien/wav2vec2-large-xlsr-japanese-hiragana) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4134
- Wer: 0.1884
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 3
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 75
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.4299 | 1.0 | 247 | 0.7608 | 0.4853 |
| 0.8045 | 2.0 | 494 | 0.6603 | 0.4449 |
| 0.6061 | 3.0 | 741 | 0.5527 | 0.4233 |
| 0.4372 | 4.0 | 988 | 0.6262 | 0.4029 |
| 0.3226 | 5.0 | 1235 | 0.4528 | 0.3462 |
| 0.2581 | 6.0 | 1482 | 0.4961 | 0.3226 |
| 0.2147 | 7.0 | 1729 | 0.4856 | 0.3075 |
| 0.1736 | 8.0 | 1976 | 0.4372 | 0.3063 |
| 0.1488 | 9.0 | 2223 | 0.3771 | 0.2761 |
| 0.1286 | 10.0 | 2470 | 0.4373 | 0.2590 |
| 0.1118 | 11.0 | 2717 | 0.3840 | 0.2594 |
| 0.1037 | 12.0 | 2964 | 0.4241 | 0.2590 |
| 0.0888 | 13.0 | 3211 | 0.4150 | 0.2410 |
| 0.0923 | 14.0 | 3458 | 0.3811 | 0.2524 |
| 0.0813 | 15.0 | 3705 | 0.4164 | 0.2459 |
| 0.0671 | 16.0 | 3952 | 0.3498 | 0.2288 |
| 0.0669 | 17.0 | 4199 | 0.3697 | 0.2247 |
| 0.0586 | 18.0 | 4446 | 0.3550 | 0.2251 |
| 0.0533 | 19.0 | 4693 | 0.4024 | 0.2231 |
| 0.0542 | 20.0 | 4940 | 0.4130 | 0.2121 |
| 0.0532 | 21.0 | 5187 | 0.3464 | 0.2231 |
| 0.0451 | 22.0 | 5434 | 0.3346 | 0.1966 |
| 0.0413 | 23.0 | 5681 | 0.4599 | 0.2088 |
| 0.0401 | 24.0 | 5928 | 0.4031 | 0.2162 |
| 0.0345 | 25.0 | 6175 | 0.3726 | 0.2084 |
| 0.033 | 26.0 | 6422 | 0.4619 | 0.2076 |
| 0.0366 | 27.0 | 6669 | 0.4071 | 0.2202 |
| 0.0343 | 28.0 | 6916 | 0.4114 | 0.2088 |
| 0.0319 | 29.0 | 7163 | 0.3605 | 0.2015 |
| 0.0304 | 30.0 | 7410 | 0.4097 | 0.2015 |
| 0.0253 | 31.0 | 7657 | 0.4152 | 0.1970 |
| 0.0235 | 32.0 | 7904 | 0.3829 | 0.2043 |
| 0.0255 | 33.0 | 8151 | 0.3976 | 0.2011 |
| 0.0201 | 34.0 | 8398 | 0.4247 | 0.2088 |
| 0.022 | 35.0 | 8645 | 0.3831 | 0.1945 |
| 0.0175 | 36.0 | 8892 | 0.3838 | 0.2007 |
| 0.0201 | 37.0 | 9139 | 0.4377 | 0.1986 |
| 0.0176 | 38.0 | 9386 | 0.4546 | 0.2043 |
| 0.021 | 39.0 | 9633 | 0.4341 | 0.2039 |
| 0.0191 | 40.0 | 9880 | 0.4043 | 0.1937 |
| 0.0159 | 41.0 | 10127 | 0.4098 | 0.2064 |
| 0.0148 | 42.0 | 10374 | 0.4027 | 0.1905 |
| 0.0129 | 43.0 | 10621 | 0.4104 | 0.1933 |
| 0.0123 | 44.0 | 10868 | 0.3738 | 0.1925 |
| 0.0159 | 45.0 | 11115 | 0.3946 | 0.1933 |
| 0.0091 | 46.0 | 11362 | 0.3971 | 0.1880 |
| 0.0082 | 47.0 | 11609 | 0.4042 | 0.1986 |
| 0.0108 | 48.0 | 11856 | 0.4092 | 0.1884 |
| 0.0123 | 49.0 | 12103 | 0.3674 | 0.1941 |
| 0.01 | 50.0 | 12350 | 0.3750 | 0.1876 |
| 0.0094 | 51.0 | 12597 | 0.3781 | 0.1831 |
| 0.008 | 52.0 | 12844 | 0.4051 | 0.1852 |
| 0.0079 | 53.0 | 13091 | 0.3981 | 0.1937 |
| 0.0068 | 54.0 | 13338 | 0.4425 | 0.1929 |
| 0.0061 | 55.0 | 13585 | 0.4183 | 0.1986 |
| 0.0074 | 56.0 | 13832 | 0.3502 | 0.1880 |
| 0.0071 | 57.0 | 14079 | 0.3908 | 0.1892 |
| 0.0079 | 58.0 | 14326 | 0.3908 | 0.1913 |
| 0.0042 | 59.0 | 14573 | 0.3801 | 0.1864 |
| 0.0049 | 60.0 | 14820 | 0.4065 | 0.1839 |
| 0.0063 | 61.0 | 15067 | 0.4170 | 0.1900 |
| 0.0049 | 62.0 | 15314 | 0.3903 | 0.1856 |
| 0.0031 | 63.0 | 15561 | 0.4042 | 0.1896 |
| 0.0054 | 64.0 | 15808 | 0.3890 | 0.1839 |
| 0.0061 | 65.0 | 16055 | 0.3831 | 0.1847 |
| 0.0052 | 66.0 | 16302 | 0.3898 | 0.1847 |
| 0.0032 | 67.0 | 16549 | 0.4230 | 0.1831 |
| 0.0017 | 68.0 | 16796 | 0.4241 | 0.1823 |
| 0.0022 | 69.0 | 17043 | 0.4360 | 0.1856 |
| 0.0026 | 70.0 | 17290 | 0.4233 | 0.1815 |
| 0.0028 | 71.0 | 17537 | 0.4225 | 0.1835 |
| 0.0018 | 72.0 | 17784 | 0.4163 | 0.1856 |
| 0.0034 | 73.0 | 18031 | 0.4120 | 0.1876 |
| 0.0019 | 74.0 | 18278 | 0.4129 | 0.1876 |
| 0.0023 | 75.0 | 18525 | 0.4134 | 0.1884 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
Doquey/DialoGPT-small-Michaelbot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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} | 10 | 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/535525386872832001/NQn2b8OA_400x400.jpeg')">
</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">普京</div>
<div style="text-align: center; font-size: 14px;">@dlputin</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 普京.
| Data | 普京 |
| --- | --- |
| Tweets downloaded | 3200 |
| Retweets | 0 |
| Short tweets | 586 |
| Tweets kept | 2614 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2t4wvbm9/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 @dlputin's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2vcew5d1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2vcew5d1/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/dlputin')
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)
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-75 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
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}
} | 37 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/mrbean/1653651025192/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/521655203011899392/pxOndDc7_400x400.png')">
</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">Mr Bean</div>
<div style="text-align: center; font-size: 14px;">@mrbean</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 Mr Bean.
| Data | Mr Bean |
| --- | --- |
| Tweets downloaded | 2324 |
| Retweets | 156 |
| Short tweets | 271 |
| Tweets kept | 1897 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1nqdk593/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 @mrbean's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/27zl3ib7) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/27zl3ib7/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/mrbean')
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)
|
DoyyingFace/bert-asian-hate-tweets-asonam-clean | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
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} | 27 | 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/1446231256052731905/octqXaR9_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">Emily Thornberry</div>
<div style="text-align: center; font-size: 14px;">@emilythornberry</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 Emily Thornberry.
| Data | Emily Thornberry |
| --- | --- |
| Tweets downloaded | 3234 |
| Retweets | 1153 |
| Short tweets | 274 |
| Tweets kept | 1807 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/gag2yg4r/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 @emilythornberry's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2zsqk4sk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2zsqk4sk/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/emilythornberry')
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)
|
DoyyingFace/bert-asian-hate-tweets-concat-clean-with-unclean-valid | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"prefix": null
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"translation_en_to_fr": {
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"translation_en_to_ro": {
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}
}
} | 25 | null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: ja
datasets:
- lmqg/qg_jaquad
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: "ゾフィーは貴族出身ではあったが王族出身ではなく、ハプスブルク家の皇位継承者であるフランツ・フェルディナントとの結婚は貴賤結婚となった。皇帝フランツ・ヨーゼフは、2人の間に生まれた子孫が皇位を継がないことを条件として結婚を承認していた。視察が予定されている<hl>6月28日<hl>は2人の14回目の結婚記念日であった。"
example_title: "Question Generation Example 1"
- text: "『クマのプーさん』の物語はまず1925年12月24日、『イヴニング・ニュース』紙のクリスマス特集号に短編作品として掲載された。これは『クマのプーさん』の第一章にあたる作品で、このときだけは挿絵をJ.H.ダウドがつけている。その後作品10話と挿絵が整い、刊行に先駆けて「イーヨーの誕生日」のエピソードが1926年8月に『ロイヤルマガジン』に、同年10月9日に『ニューヨーク・イヴニング・ポスト』紙に掲載されたあと、同年10月14日にロンドンで(メシュエン社)、21日にニューヨークで(ダットン社)『クマのプーさん』が刊行された。前著『ぼくたちがとてもちいさかったころ』がすでに大きな成功を収めていたこともあり、イギリスでは初版は前著の7倍に当たる<hl>3万5000部<hl>が刷られた。他方のアメリカでもその年の終わりまでに15万部を売り上げている。ただし依然として人気のあった前著を売り上げで追い越すには数年の時間を要した。"
example_title: "Question Generation Example 2"
- text: "フェルメールの作品では、17世紀のオランダの画家、ヨハネス・フェルメールの作品について記述する。フェルメールの作品は、疑問作も含め<hl>30数点<hl>しか現存しない。現存作品はすべて油彩画で、版画、下絵、素描などは残っていない。以下には若干の疑問作も含め、37点の基本情報を記載し、各作品について略説する。収録順序、推定制作年代は『「フェルメールとその時代展」図録』による。日本語の作品タイトルについては、上掲図録のほか、『「フェルメール展」図録』、『フェルメール生涯と作品』による。便宜上「1650年代の作品」「1660年代の作品」「1670年代の作品」の3つの節を設けたが、フェルメールの作品には制作年代不明のものが多く、推定制作年代については研究者や文献によって若干の差がある。"
example_title: "Question Generation Example 3"
model-index:
- name: lmqg/mt5-base-jaquad-qg
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_jaquad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 32.54
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 52.67
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 30.58
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 81.77
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 59.68
- name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer
value: 86.66
- name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer
value: 86.65
- name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer
value: 86.67
- name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer
value: 62.8
- name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer
value: 62.78
- name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_gold_answer
value: 62.82
- name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer]
type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
value: 80.31
- name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer]
type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
value: 83.89
- name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer]
type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer
value: 77.14
- name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer]
type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
value: 56.36
- name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer]
type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer
value: 59.12
- name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer]
type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer
value: 54.0
---
# Model Card of `lmqg/mt5-base-jaquad-qg`
This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base)
- **Language:** ja
- **Training data:** [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="ja", model="lmqg/mt5-base-jaquad-qg")
# model prediction
questions = model.generate_q(list_context="フェルメールの作品では、17世紀のオランダの画家、ヨハネス・フェルメールの作品について記述する。フェルメールの作品は、疑問作も含め30数点しか現存しない。現存作品はすべて油彩画で、版画、下絵、素描などは残っていない。", list_answer="30数点")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mt5-base-jaquad-qg")
output = pipe("ゾフィーは貴族出身ではあったが王族出身ではなく、ハプスブルク家の皇位継承者であるフランツ・フェルディナントとの結婚は貴賤結婚となった。皇帝フランツ・ヨーゼフは、2人の間に生まれた子孫が皇位を継がないことを条件として結婚を承認していた。視察が予定されている<hl>6月28日<hl>は2人の14回目の結婚記念日であった。")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-jaquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_jaquad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore | 81.77 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_1 | 57.89 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_2 | 46.06 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_3 | 38.25 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| Bleu_4 | 32.54 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| METEOR | 30.58 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| MoverScore | 59.68 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| ROUGE_L | 52.67 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
- ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/mt5-base-jaquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_jaquad.default.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 86.66 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| QAAlignedF1Score (MoverScore) | 62.8 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| QAAlignedPrecision (BERTScore) | 86.67 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| QAAlignedPrecision (MoverScore) | 62.82 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| QAAlignedRecall (BERTScore) | 86.65 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| QAAlignedRecall (MoverScore) | 62.78 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
- ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/mt5-base-jaquad-ae`](https://huggingface.co/lmqg/mt5-base-jaquad-ae). [raw metric file](https://huggingface.co/lmqg/mt5-base-jaquad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_jaquad.default.lmqg_mt5-base-jaquad-ae.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 80.31 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| QAAlignedF1Score (MoverScore) | 56.36 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| QAAlignedPrecision (BERTScore) | 77.14 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| QAAlignedPrecision (MoverScore) | 54 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| QAAlignedRecall (BERTScore) | 83.89 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
| QAAlignedRecall (MoverScore) | 59.12 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_jaquad
- dataset_name: default
- input_types: ['paragraph_answer']
- output_types: ['question']
- prefix_types: None
- model: google/mt5-base
- max_length: 512
- max_length_output: 32
- epoch: 30
- batch: 32
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 2
- label_smoothing: 0.0
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-jaquad-qg/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
albert-base-v1 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
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"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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},
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}
}
} | 38,156 | 2022-05-27T11:40:10Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-MLM
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-MLM
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0265
- Accuracy: 0.6009
## 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
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
|
albert-large-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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"num_beams": null,
"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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},
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}
}
} | 26,792 | 2022-05-27T12:33:44Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-stars
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-stars
This model is a fine-tuned version of [onewithnickelcoins/roberta-base-MLM](https://huggingface.co/onewithnickelcoins/roberta-base-MLM) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2914
- Accuracy: 0.6857
## 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
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
|
albert-xlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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},
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"prefix": null
},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
} | 341 | 2022-05-27T12:35:18Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="YaYaB/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"])
```
|
albert-xxlarge-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"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": {
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},
"translation_en_to_ro": {
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}
}
} | 42,640 | 2022-05-27T12:49:48Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **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="/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"])
```
|
bert-base-german-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"BertForMaskedLM"
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} | 175,983 | 2022-05-27T13:18:06Z | ---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-slippery-v2
results:
- metrics:
- type: mean_reward
value: 0.76 +/- 0.43
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="vbertret/q-FrozenLake-v1-4x4-slippery-v2", 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"])
```
|
bert-base-german-dbmdz-cased | [
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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} | 1,814 | 2022-05-27T13:21:34Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="srini98/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"])
```
|
bert-base-german-dbmdz-uncased | [
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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}
} | 68,305 | 2022-05-27T13:27:06Z | ---
tags:
- generated_from_keras_callback
model-index:
- name: Intent-Classification-Bert-Base-Cased
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. -->
# Intent-Classification-Bert-Base-Cased
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.9.1
- Datasets 2.2.2
- Tokenizers 0.10.3
|
bert-base-multilingual-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
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"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
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"it",
"ja",
"jv",
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"la",
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"lt",
"roa",
"nds",
"lm",
"mk",
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"mr",
"mn",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"th",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"BertForMaskedLM"
],
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}
} | 4,749,504 | 2022-05-27T13:27:19Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **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="srini98/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"])
```
|
bert-base-multilingual-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
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"pms",
"pl",
"pt",
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"sco",
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"su",
"sw",
"sv",
"tl",
"tg",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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} | 328,585 | 2022-05-27T13:34:57Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: bart-large-cnn-pubmed1o3-pubmed2o3
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: scientific_papers
type: scientific_papers
args: pubmed
metrics:
- name: Rouge1
type: rouge
value: 37.4586
---
<!-- 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-large-cnn-pubmed1o3-pubmed2o3
This model is a fine-tuned version of [theojolliffe/bart-large-cnn-pubmed1o3](https://huggingface.co/theojolliffe/bart-large-cnn-pubmed1o3) on the scientific_papers dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8817
- Rouge1: 37.4586
- Rouge2: 15.5572
- Rougel: 23.0686
- Rougelsum: 34.1522
- Gen Len: 138.379
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.9586 | 1.0 | 19988 | 1.8817 | 37.4586 | 15.5572 | 23.0686 | 34.1522 | 138.379 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
bert-large-cased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 8,214 | 2022-05-27T13:58:04Z | # The model
Pytorch resnet34
# Intended use
Image classification
# Training parameters
pretrained = True
---
language:
- eng
thumbnail:
- "https://pytorch.org/vision/stable/models.html#id10"
tags:
- pytorch
- image classification
license:
- "bsd-2-clause"
metrics:
- acc@1 (on ImageNet-1K): 73.314
- acc@5 (on ImageNet-1K): 91.42
--- |
bert-large-cased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 2,316 | 2022-05-27T14:02:02Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="/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"])
```
|
bert-large-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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} | 388,769 | 2022-05-27T14:03:33Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-kinyarwanda
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-kinyarwanda
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3917
- Wer: 0.3246
## 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: 7e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 9.0634 | 0.12 | 400 | 3.0554 | 1.0 |
| 2.8009 | 0.24 | 800 | 1.5927 | 0.9554 |
| 0.9022 | 0.36 | 1200 | 0.7328 | 0.6445 |
| 0.6213 | 0.48 | 1600 | 0.6138 | 0.5510 |
| 0.5299 | 0.6 | 2000 | 0.6072 | 0.5223 |
| 0.4999 | 0.72 | 2400 | 0.5449 | 0.4969 |
| 0.4731 | 0.84 | 2800 | 0.5261 | 0.4828 |
| 0.458 | 0.96 | 3200 | 0.5058 | 0.4607 |
| 0.4158 | 1.09 | 3600 | 0.4892 | 0.4463 |
| 0.4037 | 1.21 | 4000 | 0.4759 | 0.4429 |
| 0.4021 | 1.33 | 4400 | 0.4615 | 0.4330 |
| 0.3934 | 1.45 | 4800 | 0.4593 | 0.4315 |
| 0.3808 | 1.57 | 5200 | 0.4736 | 0.4344 |
| 0.3838 | 1.69 | 5600 | 0.4569 | 0.4249 |
| 0.3726 | 1.81 | 6000 | 0.4473 | 0.4140 |
| 0.3623 | 1.93 | 6400 | 0.4403 | 0.4097 |
| 0.3517 | 2.05 | 6800 | 0.4389 | 0.4061 |
| 0.333 | 2.17 | 7200 | 0.4383 | 0.4104 |
| 0.3354 | 2.29 | 7600 | 0.4360 | 0.3955 |
| 0.3257 | 2.41 | 8000 | 0.4226 | 0.3942 |
| 0.3275 | 2.53 | 8400 | 0.4206 | 0.4040 |
| 0.3262 | 2.65 | 8800 | 0.4172 | 0.3875 |
| 0.3206 | 2.77 | 9200 | 0.4209 | 0.3877 |
| 0.323 | 2.89 | 9600 | 0.4177 | 0.3825 |
| 0.3099 | 3.01 | 10000 | 0.4101 | 0.3691 |
| 0.3008 | 3.14 | 10400 | 0.4055 | 0.3709 |
| 0.2918 | 3.26 | 10800 | 0.4085 | 0.3800 |
| 0.292 | 3.38 | 11200 | 0.4089 | 0.3713 |
| 0.292 | 3.5 | 11600 | 0.4092 | 0.3730 |
| 0.2785 | 3.62 | 12000 | 0.4151 | 0.3687 |
| 0.2941 | 3.74 | 12400 | 0.4004 | 0.3639 |
| 0.2838 | 3.86 | 12800 | 0.4108 | 0.3703 |
| 0.2854 | 3.98 | 13200 | 0.3911 | 0.3596 |
| 0.2683 | 4.1 | 13600 | 0.3944 | 0.3575 |
| 0.2647 | 4.22 | 14000 | 0.3836 | 0.3538 |
| 0.2704 | 4.34 | 14400 | 0.4006 | 0.3540 |
| 0.2664 | 4.46 | 14800 | 0.3974 | 0.3553 |
| 0.2662 | 4.58 | 15200 | 0.3890 | 0.3470 |
| 0.2615 | 4.7 | 15600 | 0.3856 | 0.3507 |
| 0.2553 | 4.82 | 16000 | 0.3814 | 0.3497 |
| 0.2587 | 4.94 | 16400 | 0.3837 | 0.3440 |
| 0.2522 | 5.06 | 16800 | 0.3834 | 0.3486 |
| 0.2451 | 5.19 | 17200 | 0.3897 | 0.3414 |
| 0.2423 | 5.31 | 17600 | 0.3864 | 0.3481 |
| 0.2434 | 5.43 | 18000 | 0.3808 | 0.3416 |
| 0.2525 | 5.55 | 18400 | 0.3795 | 0.3408 |
| 0.2427 | 5.67 | 18800 | 0.3841 | 0.3411 |
| 0.2411 | 5.79 | 19200 | 0.3804 | 0.3366 |
| 0.2404 | 5.91 | 19600 | 0.3800 | 0.3328 |
| 0.2372 | 6.03 | 20000 | 0.3749 | 0.3335 |
| 0.2244 | 6.15 | 20400 | 0.3820 | 0.3327 |
| 0.2381 | 6.27 | 20800 | 0.3789 | 0.3325 |
| 0.2294 | 6.39 | 21200 | 0.3867 | 0.3298 |
| 0.2378 | 6.51 | 21600 | 0.3843 | 0.3281 |
| 0.2312 | 6.63 | 22000 | 0.3813 | 0.3277 |
| 0.2411 | 6.75 | 22400 | 0.3780 | 0.3268 |
| 0.2315 | 6.87 | 22800 | 0.3790 | 0.3280 |
| 0.241 | 6.99 | 23200 | 0.3776 | 0.3281 |
| 0.2313 | 7.11 | 23600 | 0.3929 | 0.3283 |
| 0.2423 | 7.24 | 24000 | 0.3905 | 0.3280 |
| 0.2337 | 7.36 | 24400 | 0.3979 | 0.3249 |
| 0.2368 | 7.48 | 24800 | 0.3980 | 0.3257 |
| 0.2409 | 7.6 | 25200 | 0.3937 | 0.3229 |
| 0.2416 | 7.72 | 25600 | 0.3867 | 0.3237 |
| 0.2364 | 7.84 | 26000 | 0.3912 | 0.3253 |
| 0.234 | 7.96 | 26400 | 0.3917 | 0.3246 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
bert-large-uncased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
"early_stopping": null,
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},
"translation_en_to_ro": {
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"max_length": null,
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}
}
} | 480,510 | 2022-05-27T14:04:07Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: rafaelmgr/distilbert-base-uncased-finetuned-squad
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. -->
# rafaelmgr/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9684
- Validation Loss: 1.1232
- 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11064, '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}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.5078 | 1.1743 | 0 |
| 0.9684 | 1.1232 | 1 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
bert-large-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"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
}
}
} | 1,058,496 | 2022-05-27T14:07:32Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **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="/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"])
```
|
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