modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-06 12:28:13
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 543
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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chrisvinsen/wav2vec2-4
|
chrisvinsen
| 2022-05-22T16:29:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-22T13:37:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-4
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. -->
# wav2vec2-4
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1442
- Wer: 1.0
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 5.1303 | 1.37 | 200 | 3.2783 | 1.0 |
| 2.8798 | 2.74 | 400 | 3.1233 | 1.0 |
| 2.8586 | 4.11 | 600 | 3.1612 | 1.0 |
| 2.8613 | 5.48 | 800 | 3.1354 | 1.0 |
| 2.8588 | 6.85 | 1000 | 3.2634 | 1.0 |
| 2.8572 | 8.22 | 1200 | 3.0905 | 1.0 |
| 2.8573 | 9.59 | 1400 | 3.2315 | 1.0 |
| 2.8532 | 10.96 | 1600 | 3.0999 | 1.0 |
| 2.8567 | 12.33 | 1800 | 3.1496 | 1.0 |
| 2.8556 | 13.7 | 2000 | 3.1081 | 1.0 |
| 2.8551 | 15.07 | 2200 | 3.1139 | 1.0 |
| 2.8545 | 16.44 | 2400 | 3.1621 | 1.0 |
| 2.8547 | 17.81 | 2600 | 3.1124 | 1.0 |
| 2.8551 | 19.18 | 2800 | 3.1612 | 1.0 |
| 2.854 | 20.55 | 3000 | 3.1052 | 1.0 |
| 2.8542 | 21.92 | 3200 | 3.1558 | 1.0 |
| 2.8544 | 23.29 | 3400 | 3.1370 | 1.0 |
| 2.8546 | 24.66 | 3600 | 3.1616 | 1.0 |
| 2.8563 | 26.03 | 3800 | 3.1366 | 1.0 |
| 2.8514 | 27.4 | 4000 | 3.1434 | 1.0 |
| 2.8543 | 28.77 | 4200 | 3.1442 | 1.0 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
saeedHedayatian/q-FrozenLake-v1-4x4
|
saeedHedayatian
| 2022-05-22T16:26:29Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T16:26:23Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4
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="saeedHedayatian/q-FrozenLake-v1-4x4", 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"])
```
|
spasis/bert-finetuned-squad
|
spasis
| 2022-05-22T15:56:08Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-22T14:03:14Z |
---
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: 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
|
aakorolyova/outcome_similarity
|
aakorolyova
| 2022-05-22T15:50:25Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-18T08:43:29Z |
<h1>Model description</h1>
This is a fine-tuned BioBERT model for text pair classification, namely for identifying pairs of clinical trial outcomes' mentions that refeer to the same outcome (e.g. "overall survival in patients with oesophageal squamous cell carcinoma and PD-L1 combined positive score (CPS) of 10 or more" and "overall survival" can be considered to refer to the same outcome, while "overall survival" and "progression-free survival" refer to different outcomes).
This is the second version of the model; the original model development was reported in:
Anna Koroleva, Patrick Paroubek. Measuring semantic similarity of clinical trial outcomes using deep pre-trained language representations. Journal of Biomedical Informatics – X, 2019 https://www.sciencedirect.com/science/article/pii/S2590177X19300575
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 be used for any task requiring identification of pairs of outcome mentions referring to the same outcome.
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 possiblw 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:
```
from transformers import AutoTokenizer
from transformers import AutoModelForTokenClassification
from transformers import AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('dmis-lab/biobert-v1.1')
model = AutoModelForSequenceClassification.from_pretrained(r'aakorolyova/outcome_similarity')
out1 = 'overall survival'
out2 = 'overall survival in patients with oesophageal squamous cell carcinoma and PD-L1 combined positive score (CPS) of 10 or more'
tokenized_input = tokenizer(out1, out2, padding="max_length", truncation=True, return_tensors='pt')
output = model_similarity(**tokenized_input)['logits']
output = np.argmax(output.detach().numpy(), axis=1)
print(output)
```
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_similarity
<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: 86.67%
Recall: 92.86%
F1: 89.66%
|
diskshima/deep-rl-class-unit02-FrozenLake-v1-4x4-slippery
|
diskshima
| 2022-05-22T15:42:25Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T13:32:38Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: deep-rl-class-unit02-FrozenLake-v1-4x4-slippery
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="diskshima/deep-rl-class-unit02-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"])
```
|
stevemobs/distilbert-base-uncased-combined-squad-adversarial
|
stevemobs
| 2022-05-22T15:35:53Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-22T12:37:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-combined-squad-adversarial
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-combined-squad-adversarial
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7273
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.574 | 1.0 | 10130 | 1.5529 |
| 1.2707 | 2.0 | 20260 | 1.6522 |
| 1.0196 | 3.0 | 30390 | 1.7273 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
diskshima/deep-rl-class-unit02-Taxi-v3
|
diskshima
| 2022-05-22T13:45:40Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T13:45:31Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: deep-rl-class-unit02-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="diskshima/deep-rl-class-unit02-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"])
```
|
chrisvinsen/wav2vec2-3
|
chrisvinsen
| 2022-05-22T13:15:00Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-22T11:48:20Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: 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. -->
# wav2vec2-3
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1124
- Wer: 1.0
## 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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 3.7797 | 0.34 | 200 | 3.0703 | 1.0 |
| 2.8701 | 0.69 | 400 | 3.3128 | 1.0 |
| 2.8695 | 1.03 | 600 | 3.1333 | 1.0 |
| 2.8634 | 1.38 | 800 | 3.1634 | 1.0 |
| 2.8629 | 1.72 | 1000 | 3.0432 | 1.0 |
| 2.8652 | 2.07 | 1200 | 3.0300 | 1.0 |
| 2.8602 | 2.41 | 1400 | 3.1894 | 1.0 |
| 2.8622 | 2.75 | 1600 | 3.1950 | 1.0 |
| 2.8606 | 3.1 | 1800 | 3.0656 | 1.0 |
| 2.8605 | 3.44 | 2000 | 3.0614 | 1.0 |
| 2.8595 | 3.79 | 2200 | 3.0697 | 1.0 |
| 2.8504 | 4.13 | 2400 | 3.1404 | 1.0 |
| 2.8553 | 4.48 | 2600 | 3.0682 | 1.0 |
| 2.8585 | 4.82 | 2800 | 3.1393 | 1.0 |
| 2.8567 | 5.16 | 3000 | 3.1013 | 1.0 |
| 2.8539 | 5.51 | 3200 | 3.0740 | 1.0 |
| 2.8588 | 5.85 | 3400 | 3.0616 | 1.0 |
| 2.8509 | 6.2 | 3600 | 3.1032 | 1.0 |
| 2.8589 | 6.54 | 3800 | 3.1348 | 1.0 |
| 2.8505 | 6.88 | 4000 | 3.1514 | 1.0 |
| 2.8548 | 7.23 | 4200 | 3.1319 | 1.0 |
| 2.8466 | 7.57 | 4400 | 3.1412 | 1.0 |
| 2.8549 | 7.92 | 4600 | 3.1235 | 1.0 |
| 2.8532 | 8.26 | 4800 | 3.0751 | 1.0 |
| 2.8548 | 8.61 | 5000 | 3.0946 | 1.0 |
| 2.8513 | 8.95 | 5200 | 3.0840 | 1.0 |
| 2.845 | 9.29 | 5400 | 3.0896 | 1.0 |
| 2.8592 | 9.64 | 5600 | 3.1055 | 1.0 |
| 2.8453 | 9.98 | 5800 | 3.1124 | 1.0 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
ak987/distilbert-base-uncased-finetuned-squad
|
ak987
| 2022-05-22T13:07:19Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-22T10:03:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1576
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2253 | 1.0 | 5533 | 1.1728 |
| 0.9685 | 2.0 | 11066 | 1.1400 |
| 0.7604 | 3.0 | 16599 | 1.1576 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Leizhang/xlm-roberta-base-finetuned-panx-de
|
Leizhang
| 2022-05-22T12:51:10Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-21T13:16:07Z |
---
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: 0.8620945214069894
---
<!-- 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: 0.1372
- F1: 0.8621
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 |
| 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 |
| 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
lucaordronneau/lo-ppo-LunarLander-v2_1
|
lucaordronneau
| 2022-05-22T12:46:35Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T12:46:03Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 254.78 +/- 12.14
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
...
```
|
9dogs/LunarLander-v2_PPO-MLP
|
9dogs
| 2022-05-22T12:34:30Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T12:34:02Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 149.42 +/- 111.62
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
...
```
|
moghis/xlm-roberta-base-finetuned-panx-it
|
moghis
| 2022-05-22T12:33:39Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-22T12:18:19Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
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: 0.2380
- F1 Score: 0.8289
## 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 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7058 | 1.0 | 70 | 0.3183 | 0.7480 |
| 0.2808 | 2.0 | 140 | 0.2647 | 0.8070 |
| 0.1865 | 3.0 | 210 | 0.2380 | 0.8289 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
stevemobs/distilbert-base-uncased-finetuned-squad-finetuned-squad_adversarial
|
stevemobs
| 2022-05-22T12:13:03Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:adversarial_qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-22T11:40:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- adversarial_qa
model-index:
- name: distilbert-base-uncased-finetuned-squad-finetuned-squad_adversarial
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad-finetuned-squad_adversarial
This model is a fine-tuned version of [stevemobs/distilbert-base-uncased-finetuned-squad](https://huggingface.co/stevemobs/distilbert-base-uncased-finetuned-squad) on the adversarial_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3121
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6352 | 1.0 | 1896 | 2.2623 |
| 2.1121 | 2.0 | 3792 | 2.2465 |
| 1.7932 | 3.0 | 5688 | 2.3121 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Asklepije/q-FrozenLake-v1-4x4-non-slippery
|
Asklepije
| 2022-05-22T11:43:16Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T11:43:08Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-non-slippery
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="Asklepije/q-FrozenLake-v1-4x4-non-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"])
```
|
vyang/TEST2ppo-LunarLander-v2
|
vyang
| 2022-05-22T11:26:35Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-19T13:06:45Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 289.34 +/- 23.86
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
|
subhasisj/de-adapter-32
|
subhasisj
| 2022-05-22T11:00:43Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-22T08:24:31Z |
---
tags:
- generated_from_trainer
model-index:
- name: de-adapter-32
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. -->
# de-adapter-32
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.4347
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 335 | 5.7031 |
| 5.7592 | 2.0 | 670 | 5.5706 |
| 5.5647 | 3.0 | 1005 | 5.4899 |
| 5.5647 | 4.0 | 1340 | 5.4481 |
| 5.4865 | 5.0 | 1675 | 5.4347 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
moghis/xlm-roberta-base-finetuned-panx-fr-de
|
moghis
| 2022-05-22T09:56:59Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-22T09:30:47Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-finetuned-panx-fr-de
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1631
- F1 Score: 0.8579
## 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 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2878 | 1.0 | 715 | 0.1840 | 0.8247 |
| 0.1456 | 2.0 | 1430 | 0.1596 | 0.8473 |
| 0.0925 | 3.0 | 2145 | 0.1631 | 0.8579 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
mmartu/q-FrozenLake-v1-8x8-slippery
|
mmartu
| 2022-05-22T09:54:19Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T09:54:12Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery
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="mmartu/q-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"])
```
|
sanjay-m1/grammar-corrector
|
sanjay-m1
| 2022-05-22T09:49:54Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-22T09:36:58Z |
## Model description
T5 model trained for Grammar Correction. This model corrects grammatical mistakes in input sentences
### Dataset Description
The T5-base model has been trained on C4_200M dataset.
### Model in Action 🚀
```
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name = 'deep-learning-analytics/GrammarCorrector'
torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name).to(torch_device)
def correct_grammar(input_text,num_return_sequences):
batch = tokenizer([input_text],truncation=True,padding='max_length',max_length=64, return_tensors="pt").to(torch_device)
translated = model.generate(**batch,max_length=64,num_beams=num_beams, num_return_sequences=num_return_sequences, temperature=1.5)
tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
return tgt_text
```
### Example Usage
```
text = 'He are moving here.'
print(correct_grammar(text, num_return_sequences=2))
['He is moving here.', 'He is moving here now.']
```
Another example
```
text = 'Cat drinked milk'
print(correct_grammar(text, num_return_sequences=2))
['Cat drank milk.', 'Cat drink milk.']
```
Model Developed by [Priya-Dwivedi](https://www.linkedin.com/in/priyanka-dwivedi-6864362)
|
rmn0ff/q-Taxi-v3
|
rmn0ff
| 2022-05-22T09:40:42Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T09:14:32Z |
---
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="rmn0ff/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"])
```
|
chrisvinsen/wav2vec2-2
|
chrisvinsen
| 2022-05-22T09:19:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-22T05:54:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-2
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. -->
# wav2vec2-2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9253
- Wer: 0.8133
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 8.4469 | 0.34 | 200 | 3.7440 | 1.0 |
| 3.1152 | 0.69 | 400 | 3.3755 | 1.0 |
| 2.9228 | 1.03 | 600 | 3.0427 | 1.0 |
| 2.8661 | 1.38 | 800 | 2.9406 | 1.0 |
| 2.8402 | 1.72 | 1000 | 2.9034 | 1.0 |
| 2.8301 | 2.07 | 1200 | 2.8850 | 1.0 |
| 2.8088 | 2.41 | 1400 | 2.8479 | 1.0 |
| 2.6892 | 2.75 | 1600 | 2.5800 | 1.0 |
| 2.3249 | 3.1 | 1800 | 2.1310 | 1.0 |
| 1.9687 | 3.44 | 2000 | 1.7652 | 0.9982 |
| 1.7338 | 3.79 | 2200 | 1.5430 | 0.9974 |
| 1.5698 | 4.13 | 2400 | 1.3927 | 0.9985 |
| 1.4475 | 4.48 | 2600 | 1.3186 | 0.9911 |
| 1.3764 | 4.82 | 2800 | 1.2406 | 0.9647 |
| 1.3022 | 5.16 | 3000 | 1.1954 | 0.9358 |
| 1.2409 | 5.51 | 3200 | 1.1450 | 0.8990 |
| 1.1989 | 5.85 | 3400 | 1.1107 | 0.8794 |
| 1.1478 | 6.2 | 3600 | 1.0839 | 0.8667 |
| 1.106 | 6.54 | 3800 | 1.0507 | 0.8573 |
| 1.0792 | 6.88 | 4000 | 1.0179 | 0.8463 |
| 1.0636 | 7.23 | 4200 | 0.9974 | 0.8355 |
| 1.0224 | 7.57 | 4400 | 0.9757 | 0.8343 |
| 1.0166 | 7.92 | 4600 | 0.9641 | 0.8261 |
| 0.9925 | 8.26 | 4800 | 0.9553 | 0.8183 |
| 0.9934 | 8.61 | 5000 | 0.9466 | 0.8199 |
| 0.9741 | 8.95 | 5200 | 0.9353 | 0.8172 |
| 0.9613 | 9.29 | 5400 | 0.9331 | 0.8133 |
| 0.9714 | 9.64 | 5600 | 0.9272 | 0.8144 |
| 0.9593 | 9.98 | 5800 | 0.9253 | 0.8133 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
robert1003/q-Taxi-v3
|
robert1003
| 2022-05-22T09:10:06Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T09:09:53Z |
---
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="robert1003/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"])
```
|
robert1003/q-FrozenLake-v1-4x4-not_slippery
|
robert1003
| 2022-05-22T09:03:38Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T08:59:44Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-not_slippery
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-v1-4x4-no_slippery**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1-4x4-no_slippery** .
## Usage
```python
model = load_from_hub(repo_id="robert1003/q-FrozenLake-v1-4x4-not_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"])
```
|
himanshubeniwal/distilbert-base-uncased-finetuned-cola
|
himanshubeniwal
| 2022-05-22T08:48:32Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-22T06:20:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5383825234212567
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8011
- Matthews Correlation: 0.5384
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5215 | 1.0 | 535 | 0.5279 | 0.4360 |
| 0.3478 | 2.0 | 1070 | 0.5187 | 0.4925 |
| 0.2348 | 3.0 | 1605 | 0.5646 | 0.5341 |
| 0.1741 | 4.0 | 2140 | 0.7430 | 0.5361 |
| 0.1253 | 5.0 | 2675 | 0.8011 | 0.5384 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
shintaro/q-FrozenLake-v1-8x8-slippery
|
shintaro
| 2022-05-22T08:43:01Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T08:42:50Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery
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="shintaro/q-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"])
```
|
pglauner/xlm-roberta-base-finetuned-panx-de
|
pglauner
| 2022-05-22T08:35:58Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-22T08:13:32Z |
---
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: 0.8620945214069894
---
<!-- 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: 0.1372
- F1: 0.8621
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 |
| 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 |
| 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
NeutralBlaster/q-Taxi-v3
|
NeutralBlaster
| 2022-05-22T07:21:18Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T07:21:09Z |
---
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="NeutralBlaster/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"])
```
|
ericw0530/bert-finetuned-squad
|
ericw0530
| 2022-05-22T06:27:50Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-20T15:43:12Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ericw0530/bert-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. -->
# ericw0530/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.1800
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-06, 'decay_steps': 2565, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 4.9079 | 0 |
| 3.5422 | 1 |
| 2.5645 | 2 |
| 2.2832 | 3 |
| 2.1800 | 4 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
astremo/JAINU
|
astremo
| 2022-05-22T05:51:12Z | 15 | 4 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"japanese",
"ainu",
"ja",
"ain",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-30T13:57:31Z |
---
language:
- ja
- ain
license: cc-by-4.0
tags:
- japanese
- ainu
---
# JAINU-Model (T5 fine-tuned model)
JAINU is a Japanese - Ainu language machine translation model.
⚠️ Attention! The model is still experimental and needs to be refined!
# Examples
| input | output|
|---|---|
|こんにちは|イランカラプテ|
|ありがとうございます|イヤイライケレ|
|熊は神ですか|キムンカムイアナクカムイネヤ？|
|熊は怖いのか|キムンカムイアナクアシトマプネヤ？|
|フクロウは鳥です|イソサンケカムイアナクチカプネ|
|分かりません！|ケラムシカレ!|
|勉強した？|ヤイホノッカエキプネヤ?|
|してないです|クキカソモキ|
|さようなら|アプンノオカヤン|
# References
t5 japanese pre-trained model: sonoisa t5-base-japanese (https://huggingface.co/sonoisa/t5-base-japanese)
# License
Shield: [![CC BY 4.0][cc-by-shield]][cc-by]
This work is licensed under a
[Creative Commons Attribution 4.0 International License][cc-by].
[![CC BY 4.0][cc-by-image]][cc-by]
[cc-by]: http://creativecommons.org/licenses/by/4.0/
[cc-by-image]: https://i.creativecommons.org/l/by/4.0/88x31.png
[cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg
|
epsil/q-Taxi-v3
|
epsil
| 2022-05-22T05:09:47Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T05:09:40Z |
---
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="epsil/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"])
```
|
epsil/q-FrozenLake-v1-8x8-slippery
|
epsil
| 2022-05-22T05:02:00Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T05:01:52Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery
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="epsil/q-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"])
```
|
Yuriky/q-FrozenLake-v1-8x8-slippery
|
Yuriky
| 2022-05-22T04:12:40Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T04:12:29Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery
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="Yuriky/q-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"])
```
|
Abhinandan/Q-Learning-Frozen-Lake-v1
|
Abhinandan
| 2022-05-22T03:50:13Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T03:49:06Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Q-Learning-Frozen-Lake-v1
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="Abhinandan/Q-Learning-Frozen-Lake-v1", 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"])
```
|
stevemobs/bert-base-spanish-wwm-uncased-finetuned-squad_es
|
stevemobs
| 2022-05-22T03:38:07Z | 416 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_es",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-21T22:57:12Z |
---
tags:
- generated_from_trainer
datasets:
- squad_es
model-index:
- name: bert-base-spanish-wwm-uncased-finetuned-squad_es
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-spanish-wwm-uncased-finetuned-squad_es
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the squad_es dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7747
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.5377 | 1.0 | 8259 | 1.4632 |
| 1.1928 | 2.0 | 16518 | 1.5536 |
| 0.9486 | 3.0 | 24777 | 1.7747 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
DavidCollier/q-FrozenLake-v1-8x8-slippery
|
DavidCollier
| 2022-05-22T02:38:19Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T02:38:12Z |
---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery
results:
- metrics:
- type: mean_reward
value: 0.03 +/- 0.17
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="DavidCollier/q-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"])
```
|
ruselkomp/sber-framebank-hidesize-1
|
ruselkomp
| 2022-05-22T01:57:09Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-21T22:10:53Z |
---
tags:
- generated_from_trainer
model-index:
- name: sber-framebank-hidesize-1
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. -->
# sber-framebank-hidesize-1
This model is a fine-tuned version of [sberbank-ai/sbert_large_nlu_ru](https://huggingface.co/sberbank-ai/sbert_large_nlu_ru) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4154
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.053 | 1.0 | 11307 | 1.0655 |
| 0.835 | 2.0 | 22614 | 1.2487 |
| 0.6054 | 3.0 | 33921 | 1.4154 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
|
Forkits/q-Taxi-v3
|
Forkits
| 2022-05-22T01:05:05Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T01:04:49Z |
---
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="Forkits/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"])
```
|
Forkits/q-FrozenLake-v1-4x4-no-slippery
|
Forkits
| 2022-05-22T00:58:04Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T00:51:52Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-no-slippery
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="Forkits/q-FrozenLake-v1-4x4-no-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"])
```
|
dalvarez/q-Taxi-v3-2
|
dalvarez
| 2022-05-22T00:17:54Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-22T00:16:17Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-2
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="dalvarez/q-Taxi-v3-2", 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"])
```
|
sandrokim/two_tower_sentence_snoobert
|
sandrokim
| 2022-05-22T00:02:17Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-05-22T00:00:32Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sandrokim/two_tower_sentence_snoobert
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('sandrokim/two_tower_sentence_snoobert')
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('sandrokim/two_tower_sentence_snoobert')
model = AutoModel.from_pretrained('sandrokim/two_tower_sentence_snoobert')
# 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=sandrokim/two_tower_sentence_snoobert)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 719 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 992,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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 -->
|
ptaylour/LunarLander-v2
|
ptaylour
| 2022-05-21T23:00:02Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T22:24:47Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 257.95 +/- 18.69
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
...
```
|
subhasisj/vi-adapter-32
|
subhasisj
| 2022-05-21T22:30:44Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-21T08:14:49Z |
---
tags:
- generated_from_trainer
model-index:
- name: vi-adapter-32
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. -->
# vi-adapter-32
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.4211
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 356 | 5.6984 |
| 5.7565 | 2.0 | 712 | 5.5596 |
| 5.5609 | 3.0 | 1068 | 5.4781 |
| 5.5609 | 4.0 | 1424 | 5.4349 |
| 5.4654 | 5.0 | 1780 | 5.4211 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
dalvarez/q-Taxi-v3
|
dalvarez
| 2022-05-21T22:19:49Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T22:19:42Z |
---
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="dalvarez/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"])
```
|
dalvarez/q-FrozenLake-v1-4x4-no-slippery
|
dalvarez
| 2022-05-21T22:07:11Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T22:07:03Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-no-slippery
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="dalvarez/q-FrozenLake-v1-4x4-no-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"])
```
|
subhasisj/ar-adapter-32
|
subhasisj
| 2022-05-21T20:22:40Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-21T18:21:11Z |
---
tags:
- generated_from_trainer
model-index:
- name: ar-adapter-32
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. -->
# ar-adapter-32
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.3886
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 352 | 5.6861 |
| 5.7356 | 2.0 | 704 | 5.5388 |
| 5.5308 | 3.0 | 1056 | 5.4493 |
| 5.5308 | 4.0 | 1408 | 5.4030 |
| 5.4304 | 5.0 | 1760 | 5.3886 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
turhancan97/q-FrozenLake-v1
|
turhancan97
| 2022-05-21T19:43:25Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T19:43:18Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1
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="turhancan97/q-FrozenLake-v1", 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"])
```
|
ThePixOne/gptcb
|
ThePixOne
| 2022-05-21T19:11:35Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-21T11:57:18Z |
GPT2 345M trained on 30 years of CentraBank's speeches
|
Ambiwlans/qtab-FrozenLake-v1-4x4-nslippery
|
Ambiwlans
| 2022-05-21T18:41:17Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T18:41:10Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: qtab-FrozenLake-v1-4x4-nslippery
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="Ambiwlans/qtab-FrozenLake-v1-4x4-nslippery", 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"])
```
|
amrahmed/q-Taxi-v3
|
amrahmed
| 2022-05-21T16:53:42Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T16:53:35Z |
---
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="amrahmed/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"])
```
|
amrahmed/q-FrozenLake-v1-4x4-non-slippery
|
amrahmed
| 2022-05-21T16:01:45Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T16:01:35Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-non-slippery
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="amrahmed/q-FrozenLake-v1-4x4-non-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"])
```
|
hugoguh/q-FrozenLake-v1-4x4-no_slippery
|
hugoguh
| 2022-05-21T15:46:33Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T15:46:26Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-no_slippery
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="hugoguh/q-FrozenLake-v1-4x4-no_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"])
```
|
BioGeek/PPO-LunarLander-v2
|
BioGeek
| 2022-05-21T15:34:26Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T15:27:53Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 271.97 +/- 16.91
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)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
```
pip install stable-baselines3
pip install huggingface_sb3
```
Then, you can use the model like this:
```python
import gym
from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
# Retrieve the model from the hub
## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
## filename = name of the model zip file from the repository
checkpoint = load_from_hub(repo_id="BioGeek/PPO-LunarLander-v2", filename="ppo-LunarLander-v2.zip")
model = PPO.load(checkpoint)
# Evaluate the agent
eval_env = gym.make('LunarLander-v2')
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
# Watch the agent play
obs = eval_env.reset()
for i in range(1000):
action, _state = model.predict(obs)
obs, reward, done, info = eval_env.step(action)
eval_env.render()
if done:
obs = eval_env.reset()
eval_env.close()
```
|
rmn0ff/q-FrozenLake-v1-4x4
|
rmn0ff
| 2022-05-21T15:14:58Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T15:14:49Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4
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="rmn0ff/q-FrozenLake-v1-4x4", 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"])
```
|
rmn0ff/q-FrozenLake-v1-4x4-slippery
|
rmn0ff
| 2022-05-21T15:06:45Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T14:54:50Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-slippery
results:
- metrics:
- type: mean_reward
value: 0.80 +/- 0.40
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="rmn0ff/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"])
```
|
KrusHan/DQN-LunarLander-v2
|
KrusHan
| 2022-05-21T14:30:50Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-15T15:57:14Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 225.63 +/- 80.78
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **DQN** Agent playing **LunarLander-v2**
This is a trained model of a **DQN** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
NeutralBlaster/q-FrozenLake-v1-8x8-no_slippery
|
NeutralBlaster
| 2022-05-21T14:29:37Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T14:29:29Z |
---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-no_slippery
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="NeutralBlaster/q-FrozenLake-v1-8x8-no_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"])
```
|
domcermak/TEST2ppo-LunarLander-v2
|
domcermak
| 2022-05-21T14:19:30Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T13:34:58Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 272.21 +/- 10.56
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
...
```
|
forsc/unit12ppo-LunarLander-v2
|
forsc
| 2022-05-21T14:09:17Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T14:08:46Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 278.19 +/- 17.91
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
...
```
|
DBusAI/q-Taxi-v3-v5
|
DBusAI
| 2022-05-21T14:04:42Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T14:04:32Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-v5
results:
- metrics:
- type: mean_reward
value: 11.75 +/- 1.44
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="DBusAI/q-Taxi-v3-v5", 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"])
```
|
Photons/q-FrozenLake-v1-8x8-slippery
|
Photons
| 2022-05-21T14:00:32Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T14:00:26Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery
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="Photons/q-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"])
```
|
DBusAI/q-Taxi-v3-v4
|
DBusAI
| 2022-05-21T13:43:58Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T13:43:49Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-v4
results:
- metrics:
- type: mean_reward
value: 10.53 +/- 1.39
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="DBusAI/q-Taxi-v3-v4", 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"])
```
|
padmalaya/padmalaya
|
padmalaya
| 2022-05-21T13:41:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-05-21T13:41:28Z |
Ароматерапия София. Аромамасаж. Масаж с етерични масла.
Ароматерапия. Аромамасаж София. Ароматерапия в София. АРОМА Масаж с натурални етерични масла. [https://www.padmalaya.bg/masaji-v-sofia/aromaterapiya-masaj-eterichni-masla-8/](https://www.padmalaya.bg/masaji-v-sofia/aromaterapiya-masaj-eterichni-masla-8/)
|
DBusAI/q-Taxi-v3-v2
|
DBusAI
| 2022-05-21T13:32:59Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T13:32:41Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-v2
results:
- metrics:
- type: mean_reward
value: 9.12 +/- 2.89
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="DBusAI/q-Taxi-v3-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"])
```
|
DBusAI/q-Taxi-v3-v1
|
DBusAI
| 2022-05-21T13:30:58Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T13:30:51Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-v1
results:
- metrics:
- type: mean_reward
value: 7.80 +/- 2.82
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="DBusAI/q-Taxi-v3-v1", 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"])
```
|
DBusAI/q-FrozenLake-v1-8x8-slippery-v3
|
DBusAI
| 2022-05-21T12:45:08Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T12:45:01Z |
---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery-v3
results:
- metrics:
- type: mean_reward
value: 0.93 +/- 0.25
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="DBusAI/q-FrozenLake-v1-8x8-slippery-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"])
```
|
DBusAI/q-FrozenLake-v1-4x4-slippery-v3
|
DBusAI
| 2022-05-21T12:37:36Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T12:37:29Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-slippery-v3
results:
- metrics:
- type: mean_reward
value: 0.81 +/- 0.39
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="DBusAI/q-FrozenLake-v1-4x4-slippery-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"])
```
|
DBusAI/q-FrozenLake-v1-4x4-slippery-v1
|
DBusAI
| 2022-05-21T12:32:54Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T12:31:24Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-slippery-v1
results:
- metrics:
- type: mean_reward
value: 0.83 +/- 0.37
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="DBusAI/q-FrozenLake-v1-4x4-slippery-v1", 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"])
```
|
DBusAI/q-FrozenLake-v1-4x4-slippery
|
DBusAI
| 2022-05-21T12:29:11Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T12:29:04Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-slippery
results:
- metrics:
- type: mean_reward
value: 0.00 +/- 0.00
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="DBusAI/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"])
```
|
DBusAI/q-FrozenLake-v1-8x8-no_slippery
|
DBusAI
| 2022-05-21T12:23:33Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T12:23:26Z |
---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-no_slippery
results:
- metrics:
- type: mean_reward
value: nan +/- nan
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="DBusAI/q-FrozenLake-v1-8x8-no_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"])
```
|
ruselkomp/deep-pavlov-framebank-hidesize-1
|
ruselkomp
| 2022-05-21T12:19:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-21T08:04:30Z |
---
tags:
- generated_from_trainer
model-index:
- name: deep-pavlov-framebank-hidesize-1
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. -->
# deep-pavlov-framebank-hidesize-1
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0967
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.073 | 1.0 | 2827 | 1.0101 |
| 0.7856 | 2.0 | 5654 | 1.0367 |
| 0.5993 | 3.0 | 8481 | 1.0967 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
|
imamnurby/rob2rand_chen_w_prefix_tc
|
imamnurby
| 2022-05-21T12:14:38Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-21T12:11:26Z |
---
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: rob2rand_chen_w_prefix_tc
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. -->
# rob2rand_chen_w_prefix_tc
This model is a fine-tuned version of [imamnurby/rob2rand_chen_w_prefix](https://huggingface.co/imamnurby/rob2rand_chen_w_prefix) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2749
- Bleu: 83.9120
- Em: 86.2159
- Bleu Em: 85.0639
## 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-06
- 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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Em | Bleu Em |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|
| 0.6922 | 0.71 | 500 | 0.2425 | 68.5819 | 79.7927 | 74.1873 |
| 0.086 | 1.42 | 1000 | 0.2480 | 70.9791 | 79.5855 | 75.2823 |
| 0.0865 | 2.13 | 1500 | 0.2567 | 68.7037 | 78.8256 | 73.7646 |
| 0.0758 | 2.84 | 2000 | 0.2483 | 69.4605 | 80.2418 | 74.8512 |
| 0.0683 | 3.55 | 2500 | 0.2662 | 68.3732 | 78.4456 | 73.4094 |
| 0.0643 | 4.26 | 3000 | 0.2700 | 66.5413 | 78.3765 | 72.4589 |
| 0.0596 | 4.97 | 3500 | 0.2611 | 67.4313 | 78.9637 | 73.1975 |
| 0.0519 | 5.68 | 4000 | 0.2697 | 68.3717 | 79.1019 | 73.7368 |
| 0.0478 | 6.39 | 4500 | 0.2914 | 69.7507 | 77.7202 | 73.7354 |
| 0.0461 | 7.1 | 5000 | 0.2776 | 68.5387 | 79.1019 | 73.8203 |
| 0.04 | 7.81 | 5500 | 0.2975 | 67.6316 | 78.1693 | 72.9004 |
| 0.0373 | 8.52 | 6000 | 0.2922 | 68.0161 | 79.4473 | 73.7317 |
| 0.0345 | 9.23 | 6500 | 0.3032 | 69.4580 | 79.2401 | 74.3490 |
| 0.032 | 9.94 | 7000 | 0.3104 | 67.2595 | 79.0328 | 73.1462 |
| 0.0294 | 10.65 | 7500 | 0.3077 | 65.8142 | 78.4801 | 72.1472 |
| 0.0269 | 11.36 | 8000 | 0.3092 | 70.2072 | 78.8601 | 74.5337 |
| 0.026 | 12.07 | 8500 | 0.3117 | 70.4504 | 79.4473 | 74.9489 |
| 0.0229 | 12.78 | 9000 | 0.3114 | 69.4635 | 79.2401 | 74.3518 |
| 0.0215 | 13.49 | 9500 | 0.3143 | 67.3601 | 79.3092 | 73.3346 |
| 0.0205 | 14.2 | 10000 | 0.3176 | 68.4031 | 78.9983 | 73.7007 |
| 0.0195 | 14.91 | 10500 | 0.3253 | 66.5673 | 78.9637 | 72.7655 |
| 0.0173 | 15.62 | 11000 | 0.3377 | 68.7553 | 78.7219 | 73.7386 |
| 0.0164 | 16.34 | 11500 | 0.3377 | 69.2474 | 79.1364 | 74.1919 |
| 0.0161 | 17.05 | 12000 | 0.3371 | 69.0846 | 79.6200 | 74.3523 |
| 0.0148 | 17.76 | 12500 | 0.3457 | 70.8330 | 79.3782 | 75.1056 |
| 0.0137 | 18.47 | 13000 | 0.3516 | 69.5576 | 79.2401 | 74.3988 |
| 0.0135 | 19.18 | 13500 | 0.3573 | 70.3232 | 79.1364 | 74.7298 |
| 0.0127 | 19.89 | 14000 | 0.3574 | 70.2481 | 79.1019 | 74.6750 |
| 0.0115 | 20.6 | 14500 | 0.3694 | 65.7587 | 78.3765 | 72.0676 |
| 0.0107 | 21.31 | 15000 | 0.3696 | 68.7923 | 78.5838 | 73.6880 |
| 0.0107 | 22.02 | 15500 | 0.3607 | 69.4452 | 78.8256 | 74.1354 |
| 0.0101 | 22.73 | 16000 | 0.3770 | 68.6731 | 78.5492 | 73.6112 |
| 0.0095 | 23.44 | 16500 | 0.3648 | 69.8402 | 79.7237 | 74.7819 |
| 0.0088 | 24.15 | 17000 | 0.3822 | 69.6238 | 79.0328 | 74.3283 |
| 0.0088 | 24.86 | 17500 | 0.3816 | 68.5422 | 79.1364 | 73.8393 |
| 0.0079 | 25.57 | 18000 | 0.3822 | 69.1359 | 79.2401 | 74.1880 |
| 0.0073 | 26.28 | 18500 | 0.3742 | 69.8331 | 79.6891 | 74.7611 |
| 0.007 | 26.99 | 19000 | 0.3849 | 69.5048 | 79.2746 | 74.3897 |
| 0.0072 | 27.7 | 19500 | 0.3881 | 69.6135 | 79.2055 | 74.4095 |
| 0.0059 | 28.41 | 20000 | 0.3922 | 70.2656 | 79.2746 | 74.7701 |
| 0.0069 | 29.12 | 20500 | 0.3936 | 68.2044 | 78.7910 | 73.4977 |
| 0.0059 | 29.83 | 21000 | 0.3983 | 69.6257 | 79.4473 | 74.5365 |
| 0.0055 | 30.54 | 21500 | 0.3973 | 70.4039 | 79.5509 | 74.9774 |
| 0.0057 | 31.25 | 22000 | 0.3960 | 70.3015 | 79.6546 | 74.9780 |
| 0.0056 | 31.96 | 22500 | 0.3945 | 69.9785 | 79.5855 | 74.7820 |
| 0.0049 | 32.67 | 23000 | 0.3947 | 70.1822 | 79.6546 | 74.9184 |
| 0.0049 | 33.38 | 23500 | 0.3957 | 69.1207 | 79.3437 | 74.2322 |
| 0.0048 | 34.09 | 24000 | 0.4097 | 68.8815 | 78.9292 | 73.9053 |
| 0.0043 | 34.8 | 24500 | 0.4039 | 70.0982 | 79.4473 | 74.7727 |
| 0.0044 | 35.51 | 25000 | 0.4080 | 69.3472 | 79.5164 | 74.4318 |
| 0.0042 | 36.22 | 25500 | 0.4066 | 69.0213 | 79.0674 | 74.0443 |
| 0.0038 | 36.93 | 26000 | 0.4128 | 69.1452 | 79.3092 | 74.2272 |
| 0.0037 | 37.64 | 26500 | 0.4134 | 69.2672 | 79.5164 | 74.3918 |
| 0.0034 | 38.35 | 27000 | 0.4161 | 69.7751 | 79.5509 | 74.6630 |
| 0.0038 | 39.06 | 27500 | 0.4037 | 69.4092 | 79.6546 | 74.5319 |
| 0.0031 | 39.77 | 28000 | 0.4041 | 69.3912 | 79.6546 | 74.5229 |
| 0.0032 | 40.48 | 28500 | 0.4185 | 69.1159 | 79.4473 | 74.2816 |
| 0.0031 | 41.19 | 29000 | 0.4245 | 68.6867 | 78.9983 | 73.8425 |
| 0.003 | 41.9 | 29500 | 0.4202 | 69.4091 | 79.3092 | 74.3591 |
| 0.0027 | 42.61 | 30000 | 0.4249 | 68.7400 | 79.0328 | 73.8864 |
| 0.0026 | 43.32 | 30500 | 0.4175 | 69.9729 | 79.8273 | 74.9001 |
| 0.0027 | 44.03 | 31000 | 0.4189 | 69.6688 | 79.5855 | 74.6271 |
| 0.0027 | 44.74 | 31500 | 0.4203 | 69.4071 | 79.5855 | 74.4963 |
| 0.0025 | 45.45 | 32000 | 0.4265 | 69.3197 | 79.1019 | 74.2108 |
| 0.0023 | 46.16 | 32500 | 0.4255 | 69.7513 | 79.3437 | 74.5475 |
| 0.0023 | 46.88 | 33000 | 0.4227 | 69.2893 | 79.5509 | 74.4201 |
| 0.0023 | 47.59 | 33500 | 0.4233 | 69.6060 | 79.5509 | 74.5785 |
| 0.002 | 48.3 | 34000 | 0.4239 | 69.0113 | 79.4819 | 74.2466 |
| 0.0024 | 49.01 | 34500 | 0.4239 | 68.9754 | 79.4128 | 74.1941 |
| 0.0019 | 49.72 | 35000 | 0.4228 | 68.9220 | 79.3782 | 74.1501 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.7.1
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Tobias/bert-base-uncased_German_MultiLable_classification
|
Tobias
| 2022-05-21T12:05:42Z | 7 | 1 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"de",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-21T12:00:43Z |
---
language: de
tags:
- bert
license: apache-2.0
widget:
- text: "Das Frühstück ist sehr gut, es gibt auch Laktosefreie Produkte."
example_title: "Example 1"
- text: "Das Personal ist sehr kompetent und sehr freundlich."
example_title: "Example 2"
- text: "Die Zimmer sind wie beschrieben sehr klein, vergleichbar mit einer Kreuzfahrtschiffkabine. "
example_title: "Example 3"
- text: "Scheinwerfer vor dem Zimmer ganze Nacht an und zu hell"
example_title: "Example 4"
---
# German Hotel Review Sentiment Classification
A model trained on German Hotel Reviews from Switzerland. The base model is the [bert-base-german-cased](https://huggingface.co/bert-base-german-cased). The last hidden layer of the base model was extracted and a classification layer was added. The entire model was then trained for 5 epochs on our dataset.
# Model Performance
| Classes | Precision | Recall | F1 Score |
| :--- | :---: | :---: |:---: |
| Room | 84.62% | 88.00% | 86.27% |
| Food | 79.17% | 82.61% | 80.85% |
| Staff | 63.64% | 70.00% | 66.67% |
| Location | 83.33% | 62.50% | 71.43% |
| GeneralUtilitys | 76.92% | 76.92% | 76.92% |
| HotelOrganisation | 26.67% | 30.77% | 28.57% |
| Unknown | 25.00% | 16.67% | 20.00% |
| ReasonForStay | 100.00% | 50.00% | 66.67% |
| Accuracy | | | 69.00% |
| Macro Average | 67.42% | 59.68% | 62.17% |
| Weighted Average | 69.36% | 69.00% | 68.79% |
## Confusion Matrix
|
DBusAI/q-Taxi-v3
|
DBusAI
| 2022-05-21T11:57:31Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T11:57:25Z |
---
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="DBusAI/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"])
```
|
CWhy/q-FrozenLake-v1-8x8-slippery
|
CWhy
| 2022-05-21T11:44:52Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T11:44:44Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery
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="CWhy/q-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"])
```
|
GideonFr/q-FrozenLake-v1-4x4-non_slippery
|
GideonFr
| 2022-05-21T11:29:29Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T11:29:18Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-non_slippery
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="GideonFr/q-FrozenLake-v1-4x4-non_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"])
```
|
questgen/msmarco-distilbert-base-v4-feature-extraction-pipeline
|
questgen
| 2022-05-21T11:15:42Z | 11 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-05-21T11:11:17Z |
---
pipeline_tag: feature-extraction
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/msmarco-distilbert-base-v4
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.
## 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('sentence-transformers/msmarco-distilbert-base-v4')
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('sentence-transformers/msmarco-distilbert-base-v4')
model = AutoModel.from_pretrained('sentence-transformers/msmarco-distilbert-base-v4')
# 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, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/msmarco-distilbert-base-v4)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, '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})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
```
|
Riverdayspa/bodymassagechennai
|
Riverdayspa
| 2022-05-21T10:53:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-05-21T10:53:01Z |
Riverdayspa™ is one of the Top Luxury Massage Center in Chennai. We offer Quality massage therapy all over the bustling city of Chennai.
https://www.riverdayspa.com/
|
linker81/q-learning-Taxi-v3
|
linker81
| 2022-05-21T09:20:26Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T09:20:18Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-learning-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="linker81/q-learning-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"])
```
|
linker81/q-learning-FrozenLake-v1-4x4-no-slippery
|
linker81
| 2022-05-21T09:16:57Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T09:15:44Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-learning-FrozenLake-v1-4x4-no-slippery
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-v1-4x4-no-slippery**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1-4x4-no-slippery** .
## Usage
```python
model = load_from_hub(repo_id="linker81/q-learning-FrozenLake-v1-4x4-no-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"])
```
|
linker81/QLearning-FrozenLake-v1
|
linker81
| 2022-05-21T09:09:08Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-21T09:09:00Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: QLearning-FrozenLake-v1
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="linker81/QLearning-FrozenLake-v1", 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"])
```
|
kabelomalapane/nso_en_ukuxhumana_model
|
kabelomalapane
| 2022-05-21T01:15:15Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-05-20T11:20:16Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: nso_en_ukuxhumana_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# nso_en_ukuxhumana_model
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-nso-en](https://huggingface.co/Helsinki-NLP/opus-mt-nso-en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9349
- Bleu (before training): 9.3297
- Bleu: 18.1161
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
huggingtweets/darcywubot
|
huggingtweets
| 2022-05-21T00:27:43Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-21T00:27:13Z |
---
language: en
thumbnail: http://www.huggingtweets.com/darcywubot/1653092857463/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/1520965807374835712/oz5XZFva_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">Darcy Bot</div>
<div style="text-align: center; font-size: 14px;">@darcywubot</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 Darcy Bot.
| Data | Darcy Bot |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 6 |
| Short tweets | 413 |
| Tweets kept | 2831 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ou05gm6/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 @darcywubot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3p4xvqb6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3p4xvqb6/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/darcywubot')
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)
|
ruselkomp/deep-pavlov-framebank-5epochs-3
|
ruselkomp
| 2022-05-20T23:45:45Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-20T16:18:32Z |
---
tags:
- generated_from_trainer
model-index:
- name: deep-pavlov-framebank-5epochs-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. -->
# deep-pavlov-framebank-5epochs-3
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4532
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0722 | 1.0 | 2827 | 1.0156 |
| 0.797 | 2.0 | 5654 | 1.0431 |
| 0.587 | 3.0 | 8481 | 1.1751 |
| 0.4144 | 4.0 | 11308 | 1.2978 |
| 0.3173 | 5.0 | 14135 | 1.4532 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2.dev0
- Tokenizers 0.12.1
|
anas-awadalla/albert-xl-v2-finetuned-squad
|
anas-awadalla
| 2022-05-20T23:29:59Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-20T18:16:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: albert-xl-v2-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. -->
# albert-xl-v2-finetuned-squad
This model is a fine-tuned version of [albert-xlarge-v2](https://huggingface.co/albert-xlarge-v2) 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
|
fmcurti/q-FrozenLake-v1-8x8-non-slippery
|
fmcurti
| 2022-05-20T23:14:25Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-20T23:14:19Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-non-slippery
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="fmcurti/q-FrozenLake-v1-8x8-non-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"])
```
|
arampacha/roberta-tiny
|
arampacha
| 2022-05-20T22:07:50Z | 220 | 2 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-20T21:57:19Z |
# roberta-tiny
Tiny untrained model for testing purposes
|
btsas/q-Taxi-v3
|
btsas
| 2022-05-20T21:47:36Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-20T21:47:30Z |
---
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="btsas/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"])
```
|
huggingtweets/woxxy
|
huggingtweets
| 2022-05-20T21:22:47Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/woxxy/1653081762754/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/1474540394956394498/6o13ikqu_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">Woxxy</div>
<div style="text-align: center; font-size: 14px;">@woxxy</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 Woxxy.
| Data | Woxxy |
| --- | --- |
| Tweets downloaded | 3239 |
| Retweets | 308 |
| Short tweets | 374 |
| Tweets kept | 2557 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ekkjj88/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 @woxxy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3aueqdru) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3aueqdru/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/woxxy')
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)
|
dmitry-np/q-FrozenLake-v1-8x8-slippery
|
dmitry-np
| 2022-05-20T20:53:41Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-20T20:50:33Z |
---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery
results:
- metrics:
- type: mean_reward
value: 0.12 +/- 0.32
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="dmitry-np/q-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"])
```
|
dmitry-np/q-Taxi-v3
|
dmitry-np
| 2022-05-20T20:44:07Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-20T20:32:09Z |
---
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="dmitry-np/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"])
```
|
Ukhushn/ukhushn
|
Ukhushn
| 2022-05-20T19:28:31Z | 14 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-05-20T19:28:21Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# Ukhushn/ukhushn
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('Ukhushn/ukhushn')
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('Ukhushn/ukhushn')
model = AutoModel.from_pretrained('Ukhushn/ukhushn')
# 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=Ukhushn/ukhushn)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 6661 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 4,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 2665,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, '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})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
subhasisj/xlm-roberta-base-squad-32
|
subhasisj
| 2022-05-20T19:13:21Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-20T14:05:13Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: xlm-roberta-base-squad-32
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-squad-32
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0083
## 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: 1.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 350 | 1.2339 |
| 2.3864 | 2.0 | 700 | 1.0571 |
| 1.0541 | 3.0 | 1050 | 1.0246 |
| 1.0541 | 4.0 | 1400 | 0.9947 |
| 0.9214 | 5.0 | 1750 | 1.0083 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
tamarab/bert-emotion
|
tamarab
| 2022-05-20T19:12:14Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-20T16:45:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: Precision
type: precision
value: 0.7462955517135084
- name: Recall
type: recall
value: 0.7095634380533169
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1347
- Precision: 0.7463
- Recall: 0.7096
- Fscore: 0.7209
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8385 | 1.0 | 815 | 0.8366 | 0.7865 | 0.5968 | 0.6014 |
| 0.5451 | 2.0 | 1630 | 0.9301 | 0.7301 | 0.6826 | 0.6947 |
| 0.2447 | 3.0 | 2445 | 1.1347 | 0.7463 | 0.7096 | 0.7209 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
Battu007/V3_PPO_LunarLander_v2
|
Battu007
| 2022-05-20T18:05:48Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-20T18:05:31Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 216.14 +/- 67.68
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
...
```
|
umangchaudhry/bert-emotion
|
umangchaudhry
| 2022-05-20T16:56:12Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-20T15:59:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: Precision
type: precision
value: 0.7081377380103309
- name: Recall
type: recall
value: 0.709386945441909
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2350
- Precision: 0.7081
- Recall: 0.7094
- Fscore: 0.7082
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8442 | 1.0 | 815 | 0.8653 | 0.7642 | 0.6192 | 0.6363 |
| 0.5488 | 2.0 | 1630 | 0.9330 | 0.7116 | 0.6838 | 0.6912 |
| 0.2713 | 3.0 | 2445 | 1.2350 | 0.7081 | 0.7094 | 0.7082 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
sana-ngu/HaT5
|
sana-ngu
| 2022-05-20T16:53:35Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2202.05690",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
### HaT5(T5-base)
This is a fine-tuned model of T5 (base) on the hate speech detection dataset. It is intended to be used as a classification model for identifying Tweets (0 - HOF(hate/offensive); 1 - NOT). The task prefix we used for the T5 model is 'classification: '.
More information about the original pre-trained model can be found [here](https://huggingface.co/t5-base)
Classification examples:
|Prediction|Tweet|
|-----|--------|
|0 |Why the fuck I got over 1000 views on my story 😂😂 nothing new over here |
|1. |first of all there is no vaccine to cure , whthr it is capsules, tablets, or injections, they just support to fight with d virus. I do not support people taking any kind of home remedies n making fun of an ayurvedic medicine..😐 |
# More Details
For more details about the datasets and eval results, see [our paper for this work here](https://arxiv.org/abs/2202.05690)
The paper was accepted at the International Joint Conference on Neural Networks (IJCNN) conference 2022.
# How to use
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch
model = T5ForConditionalGeneration.from_pretrained("sana-ngu/HaT5")
tokenizer = T5Tokenizer.from_pretrained("t5-base")
tokenizer.pad_token = tokenizer.eos_token
input_ids = tokenizer("Old lions in the wild lay down and die with dignity when they can't hunt anymore. If a government is having 'teething problems' handling aid supplies one full year into a pandemic, maybe it should take a cue and get the fuck out of the way? ", padding=True, truncation=True, return_tensors='pt').input_ids
outputs = model.generate(input_ids)
pred = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(pred)
```
|
Abhinandan/LunarLander
|
Abhinandan
| 2022-05-20T16:46:26Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-20T16:11:10Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 271.49 +/- 16.50
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
...
```
|
HueyNemud/das22-41-camembert_pretrained_finetuned_ref
|
HueyNemud
| 2022-05-20T16:27:58Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-20T16:26:14Z |
---
tags:
- generated_from_trainer
model-index:
- name: CamemBERT pretrained on french trade directories from the XIXth century
results: []
---
# CamemBERT pretrained and trained for NER on french trade directories from the XIXth century [GOLD training set]
This mdoel is part of the material of the paper
> Abadie, N., Carlinet, E., Chazalon, J., Duménieu, B. (2022). A
> Benchmark of Named Entity Recognition Approaches in Historical
> Documents Application to 19𝑡ℎ Century French Directories. In: Uchida,
> S., Barney, E., Eglin, V. (eds) Document Analysis Systems. DAS 2022.
> Lecture Notes in Computer Science, vol 13237. Springer, Cham.
> https://doi.org/10.1007/978-3-031-06555-2_30
The source code to train this model is available on the [GitHub repository](https://github.com/soduco/paper-ner-bench-das22) of the paper as a Jupyter notebook in `src/ner/40_experiment_2.ipynb`.
## Model description
This model adapts the pre-trained model [das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for NER on 6004 manually annotated directory entries referred as the "reference dataset" in the paper.
Trade directory entries are short and strongly structured texts that giving the name, activity and location of a person or business, e.g:
```
Peynaud, R. de la Vieille Bouclerie, 18. Richard, Joullain et comp., (commission- —Phéâtre Français. naire, (entrepôt), au port de la Rapée-
```
## Intended uses & limitations
This model is intended for reproducibility of the NER evaluation published in the DAS2022 paper.
Several derived models trained for NER on trade directories are available on HuggingFace, each trained on a different dataset :
- [das22-10-camembert_pretrained_finetuned_ref](): trained for NER on ~6000 directory entries manually corrected.
- [das22-10-camembert_pretrained_finetuned_pero](): trained for NER on ~6000 directory entries extracted with PERO-OCR.
- [das22-10-camembert_pretrained_finetuned_tess](): trained for NER on ~6000 directory entries extracted with Tesseract.
### Training hyperparameters
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
HueyNemud/das22-42-camembert_finetuned_ref
|
HueyNemud
| 2022-05-20T16:25:01Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-20T16:22:44Z |
---
tags:
- generated_from_trainer
model-index:
- name: CamemBERT pretrained on french trade directories from the XIXth century
results: []
---
# CamemBERT trained for NER on french trade directories from the XIXth century [GOLD training set]
This mdoel is part of the material of the paper
> Abadie, N., Carlinet, E., Chazalon, J., Duménieu, B. (2022). A
> Benchmark of Named Entity Recognition Approaches in Historical
> Documents Application to 19𝑡ℎ Century French Directories. In: Uchida,
> S., Barney, E., Eglin, V. (eds) Document Analysis Systems. DAS 2022.
> Lecture Notes in Computer Science, vol 13237. Springer, Cham.
> https://doi.org/10.1007/978-3-031-06555-2_30
The source code to train this model is available on the [GitHub repository](https://github.com/soduco/paper-ner-bench-das22) of the paper as a Jupyter notebook in `src/ner/40_experiment_2.ipynb`.
## Model description
This model adapts the model [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for NER on 6004 manually annotated directory entries referred as the "reference dataset" in the paper.
Trade directory entries are short and strongly structured texts that giving the name, activity and location of a person or business, e.g:
```
Peynaud, R. de la Vieille Bouclerie, 18. Richard, Joullain et comp., (commission- —Phéâtre Français. naire, (entrepôt), au port de la Rapée-
```
## Intended uses & limitations
This model is intended for reproducibility of the NER evaluation published in the DAS2022 paper.
Several derived models trained for NER on trade directories are available on HuggingFace, each trained on a different dataset :
- [das22-10-camembert_pretrained_finetuned_ref](): trained for NER on ~6000 directory entries manually corrected.
- [das22-10-camembert_pretrained_finetuned_pero](): trained for NER on ~6000 directory entries extracted with PERO-OCR.
- [das22-10-camembert_pretrained_finetuned_tess](): trained for NER on ~6000 directory entries extracted with Tesseract.
### Training hyperparameters
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
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