modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
Cheatham/xlm-roberta-base-finetuned
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] |
text-classification
|
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| 20 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.71
- name: F1
type: f1
value: 0.7010309278350516
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5802
- Accuracy: 0.71
- F1: 0.7010
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Cheatham/xlm-roberta-large-finetuned-d12
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] |
text-classification
|
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| 20 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="GesturingMan/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Cheatham/xlm-roberta-large-finetuned-d1r01
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] |
text-classification
|
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| 21 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.75 +/- 0.73
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
Cheatham/xlm-roberta-large-finetuned-r01
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] |
text-classification
|
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| 23 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: FineTune_Vit5_LR0_00001
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. -->
# FineTune_Vit5_LR0_00001
This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9405
- Validation Loss: 0.7156
- Train Rouge1: 48.1785
- Train Rouge2: 25.7772
- Train Rougel: 39.5071
- Train Rougelsum: 39.5644
- Train Gen Len: 14.0508
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 0.9405 | 0.7156 | 48.1785 | 25.7772 | 39.5071 | 39.5644 | 14.0508 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CheonggyeMountain-Sherpa/kogpt-trinity-poem
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPT2LMHeadModel"
],
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}
| 15 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxiv3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="GesturingMan/Taxiv3", 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"])
```
|
Chester/traffic-rec
|
[] | null |
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| 0 | null |
---
language:
- ur
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_11_0
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: wavlm-common_voice-ur
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: MOZILLA-FOUNDATION/COMMON_VOICE_11_0 - UR
type: common_voice_11_0
config: ur
split: test
args: 'Config: ur, Training split: train+validation, Eval split: test'
metrics:
- name: Wer
type: wer
value: 0.37805822235986375
---
<!-- 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. -->
# wavlm-common_voice-ur
This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the MOZILLA-FOUNDATION/COMMON_VOICE_11_0 - UR dataset.
It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 0.3781
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 8
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.9073 | 0.11 | 100 | inf | 1.0 |
| 3.3187 | 0.22 | 200 | inf | 1.0 |
| 2.9683 | 0.32 | 300 | inf | 0.9991 |
| 2.454 | 0.43 | 400 | inf | 0.9915 |
| 1.1169 | 0.54 | 500 | inf | 0.7906 |
| 1.5943 | 0.65 | 600 | inf | 0.7260 |
| 0.9991 | 0.75 | 700 | inf | 0.7305 |
| 1.0608 | 0.86 | 800 | inf | 0.6655 |
| 1.4739 | 0.97 | 900 | inf | 0.6120 |
| 0.8682 | 1.08 | 1000 | inf | 0.6087 |
| 0.8025 | 1.18 | 1100 | inf | 0.5991 |
| 0.8468 | 1.29 | 1200 | inf | 0.5605 |
| 0.5896 | 1.4 | 1300 | inf | 0.5550 |
| 0.6304 | 1.51 | 1400 | inf | 0.5441 |
| 0.6533 | 1.61 | 1500 | inf | 0.5297 |
| 0.7636 | 1.72 | 1600 | inf | 0.5210 |
| 0.5155 | 1.83 | 1700 | inf | 0.5331 |
| 0.6266 | 1.94 | 1800 | inf | 0.5182 |
| 0.4286 | 2.05 | 1900 | inf | 0.4956 |
| 0.527 | 2.15 | 2000 | inf | 0.4935 |
| 0.4919 | 2.26 | 2100 | inf | 0.4933 |
| 0.3977 | 2.37 | 2200 | inf | 0.5015 |
| 0.5349 | 2.48 | 2300 | inf | 0.4942 |
| 0.5066 | 2.58 | 2400 | inf | 0.4684 |
| 0.6734 | 2.69 | 2500 | inf | 0.4870 |
| 0.5411 | 2.8 | 2600 | inf | 0.4919 |
| 0.3451 | 2.91 | 2700 | inf | 0.4607 |
| 0.3913 | 3.01 | 2800 | inf | 0.4558 |
| 0.3046 | 3.12 | 2900 | inf | 0.4685 |
| 0.2954 | 3.23 | 3000 | inf | 0.4638 |
| 0.5469 | 3.34 | 3100 | inf | 0.4495 |
| 0.2334 | 3.44 | 3200 | inf | 0.4547 |
| 0.3119 | 3.55 | 3300 | inf | 0.4619 |
| 0.6393 | 3.66 | 3400 | inf | 0.4541 |
| 0.4133 | 3.77 | 3500 | inf | 0.4456 |
| 0.4946 | 3.88 | 3600 | inf | 0.4369 |
| 0.3484 | 3.98 | 3700 | inf | 0.4335 |
| 0.3996 | 4.09 | 3800 | inf | 0.4717 |
| 0.2754 | 4.2 | 3900 | inf | 0.4414 |
| 0.3141 | 4.31 | 4000 | inf | 0.4390 |
| 0.2231 | 4.41 | 4100 | inf | 0.4353 |
| 0.2673 | 4.52 | 4200 | inf | 0.4410 |
| 0.2911 | 4.63 | 4300 | inf | 0.4337 |
| 0.3643 | 4.74 | 4400 | inf | 0.4362 |
| 0.2706 | 4.84 | 4500 | inf | 0.4359 |
| 0.2464 | 4.95 | 4600 | inf | 0.4249 |
| 0.1453 | 5.06 | 4700 | inf | 0.4293 |
| 0.2619 | 5.17 | 4800 | inf | 0.4201 |
| 0.1888 | 5.27 | 4900 | inf | 0.4222 |
| 0.2571 | 5.38 | 5000 | inf | 0.4333 |
| 0.1653 | 5.49 | 5100 | inf | 0.4192 |
| 0.2102 | 5.6 | 5200 | inf | 0.4232 |
| 0.1456 | 5.71 | 5300 | inf | 0.4198 |
| 0.3314 | 5.81 | 5400 | inf | 0.4169 |
| 0.1703 | 5.92 | 5500 | inf | 0.4118 |
| 0.1546 | 6.03 | 5600 | inf | 0.4147 |
| 0.2065 | 6.14 | 5700 | inf | 0.4291 |
| 0.1792 | 6.24 | 5800 | inf | 0.4175 |
| 0.2433 | 6.35 | 5900 | inf | 0.4157 |
| 0.352 | 6.46 | 6000 | inf | 0.4083 |
| 0.2406 | 6.57 | 6100 | inf | 0.4341 |
| 0.2397 | 6.67 | 6200 | inf | 0.4185 |
| 0.2145 | 6.78 | 6300 | inf | 0.4147 |
| 0.1733 | 6.89 | 6400 | inf | 0.4150 |
| 0.1867 | 7.0 | 6500 | inf | 0.4154 |
| 0.612 | 7.1 | 6600 | inf | 0.4159 |
| 0.1413 | 7.21 | 6700 | inf | 0.4162 |
| 0.2074 | 7.32 | 6800 | inf | 0.4146 |
| 0.1362 | 7.43 | 6900 | inf | 0.4087 |
| 0.2971 | 7.53 | 7000 | inf | 0.4061 |
| 0.1443 | 7.64 | 7100 | inf | 0.4132 |
| 0.3066 | 7.75 | 7200 | inf | 0.4059 |
| 0.2163 | 7.86 | 7300 | inf | 0.4026 |
| 0.1251 | 7.97 | 7400 | inf | 0.4022 |
| 0.154 | 8.07 | 7500 | inf | 0.3980 |
| 0.1809 | 8.18 | 7600 | inf | 0.4030 |
| 0.0985 | 8.29 | 7700 | inf | 0.3992 |
| 0.1672 | 8.4 | 7800 | inf | 0.4049 |
| 0.1508 | 8.5 | 7900 | inf | 0.3985 |
| 0.1893 | 8.61 | 8000 | inf | 0.3999 |
| 0.1045 | 8.72 | 8100 | inf | 0.4014 |
| 0.2569 | 8.83 | 8200 | inf | 0.3976 |
| 0.2654 | 8.93 | 8300 | inf | 0.4021 |
| 0.0641 | 9.04 | 8400 | inf | 0.3964 |
| 0.1145 | 9.15 | 8500 | inf | 0.3995 |
| 0.1808 | 9.26 | 8600 | inf | 0.3960 |
| 0.0766 | 9.36 | 8700 | inf | 0.3938 |
| 0.1537 | 9.47 | 8800 | inf | 0.3909 |
| 0.2864 | 9.58 | 8900 | inf | 0.4028 |
| 0.1372 | 9.69 | 9000 | inf | 0.3970 |
| 0.06 | 9.8 | 9100 | inf | 0.3911 |
| 0.0831 | 9.9 | 9200 | inf | 0.3954 |
| 0.1469 | 10.01 | 9300 | inf | 0.3952 |
| 0.0683 | 10.12 | 9400 | inf | 0.3899 |
| 0.0694 | 10.23 | 9500 | inf | 0.3918 |
| 0.0919 | 10.33 | 9600 | inf | 0.3895 |
| 0.1842 | 10.44 | 9700 | inf | 0.3945 |
| 0.0581 | 10.55 | 9800 | inf | 0.3979 |
| 0.1397 | 10.66 | 9900 | inf | 0.3911 |
| 0.0657 | 10.76 | 10000 | inf | 0.3886 |
| 0.1316 | 10.87 | 10100 | inf | 0.3877 |
| 0.1434 | 10.98 | 10200 | inf | 0.3858 |
| 0.05 | 11.09 | 10300 | inf | 0.3842 |
| 0.0565 | 11.19 | 10400 | inf | 0.3873 |
| 0.1696 | 11.3 | 10500 | inf | 0.3873 |
| 0.0819 | 11.41 | 10600 | inf | 0.3901 |
| 0.0631 | 11.52 | 10700 | inf | 0.3927 |
| 0.1276 | 11.63 | 10800 | inf | 0.3868 |
| 0.1002 | 11.73 | 10900 | inf | 0.3848 |
| 0.081 | 11.84 | 11000 | inf | 0.3873 |
| 0.1745 | 11.95 | 11100 | inf | 0.3895 |
| 0.097 | 12.06 | 11200 | inf | 0.4021 |
| 0.0875 | 12.16 | 11300 | inf | 0.3876 |
| 0.027 | 12.27 | 11400 | inf | 0.3873 |
| 0.0859 | 12.38 | 11500 | inf | 0.3863 |
| 0.1192 | 12.49 | 11600 | inf | 0.3799 |
| 0.1055 | 12.59 | 11700 | inf | 0.3795 |
| 0.0603 | 12.7 | 11800 | inf | 0.3785 |
| 0.111 | 12.81 | 11900 | inf | 0.3783 |
| 0.0313 | 12.92 | 12000 | inf | 0.3800 |
| 0.0241 | 13.02 | 12100 | inf | 0.3796 |
| 0.1072 | 13.13 | 12200 | inf | 0.3803 |
| 0.1758 | 13.24 | 12300 | inf | 0.3809 |
| 0.1334 | 13.35 | 12400 | inf | 0.3794 |
| 0.1372 | 13.46 | 12500 | inf | 0.3798 |
| 0.1919 | 13.56 | 12600 | inf | 0.3791 |
| 0.1753 | 13.67 | 12700 | inf | 0.3781 |
| 0.294 | 13.78 | 12800 | inf | 0.3788 |
| 0.3132 | 13.89 | 12900 | inf | 0.3786 |
| 0.0486 | 13.99 | 13000 | inf | 0.3778 |
| 0.1199 | 14.1 | 13100 | inf | 0.3777 |
| 0.0381 | 14.21 | 13200 | inf | 0.3808 |
| 0.0875 | 14.32 | 13300 | inf | 0.3795 |
| 0.0122 | 14.42 | 13400 | inf | 0.3797 |
| 0.1417 | 14.53 | 13500 | inf | 0.3780 |
| 0.1754 | 14.64 | 13600 | inf | 0.3788 |
| 0.0426 | 14.75 | 13700 | inf | 0.3780 |
| 0.0309 | 14.85 | 13800 | inf | 0.3787 |
| 0.1447 | 14.96 | 13900 | inf | 0.3796 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Chinat/test-classifier
|
[] | null |
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| 0 | null |
---
widget:
- text: "Brad Pitt is en Schauspeler. He hett speelt"
example_title: "Brad Pitt"
inference:
parameters:
max_length: 100
no_repeat_ngram_size: 1
---
|
Ching/negation_detector
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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"RobertaForQuestionAnswering"
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| 9 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 140 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 140,
"warmup_steps": 14,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Chungu424/DATA
|
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| 0 | null |
---
pipeline_tag: summarization
---
The facebook/bart-large-cnn model fine-tuned on a dataset of Khan Academy's Transcripts. Goal is to summarize classroom lecture transcripts into short texts similar to those seen under the "About" section on any Khan Academy video.
|
Ciruzzo/DialoGPT-medium-harrypotter
|
[] | null |
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}
| 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: mjschock/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Clint/clinton
|
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}
| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: ner-bert-multilingual-uncased-geocite
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. -->
# ner-bert-multilingual-uncased-geocite
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Cloudy/DialoGPT-CJ-large
|
[
"pytorch",
"conversational"
] |
conversational
|
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| 1 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 140 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 140,
"warmup_steps": 14,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
CoffeeAddict93/gpt1-call-of-the-wild
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
],
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},
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}
| 8 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -4.13 +/- 2.27
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
CoffeeAddict93/gpt2-modest-proposal
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"text-generation": {
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"max_length": 50
},
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}
| 12 | null |
---
pipeline_tag: fill-mask
---
## XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models
converted checkpoint of XLM-V from fairseq to huggingface
## Fairseq
if original model is needed, please check, model checkpoint:
```
https://dl.fbaipublicfiles.com/fairseq/xlmv/xlmv.base.tar.gz
```
and how to use it
```
https://github.com/facebookresearch/fairseq/blob/main/examples/xlmr/README.md
```
**Note: please use official checkpoints, if they will be added to transformers** (this repo is for personal usage/experiments)
Citation
--------
```
@misc{https://doi.org/10.48550/arxiv.2301.10472,
doi = {10.48550/ARXIV.2301.10472},
url = {https://arxiv.org/abs/2301.10472},
author = {Liang, Davis and Gonen, Hila and Mao, Yuning and Hou, Rui and Goyal, Naman and Ghazvininejad, Marjan and Zettlemoyer, Luke and Khabsa, Madian},
keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models},
publisher = {arXiv},
year = {2023},
copyright = {Creative Commons Attribution Share Alike 4.0 International}
}
```
|
Connor/DialoGPT-small-rick
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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| 7 | null |
---
license: mit
pipeline_tag: text-generation
tags:
- medical
widget:
- text: Bicalutamide
---
## BioGPT
|
Connorvr/BrightBot-small
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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| 7 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -4.94 +/- 0.93
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
Connorvr/TeachingGen
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit"
] |
text-generation
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
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| 4 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 57.20 +/- 27.11
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ConstellationBoi/Oop
|
[] | null |
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| 0 | null |
---
tags:
- TensorRT
- Text2Image
- Stable Diffusion
- Image2Image
- SDA
---
# andite/pastel-mix converted into TensorRT
<a href="https://github.com/chavinlo/sda-node/"><img src="https://i.imgur.com/fQS926g.png"></a>
Model converted from diffusers into TensorRT for accelerated inference up to 4x faster.
For how to use the model check https://github.com/chavinlo/sda-node
This model was automatically converted by SDA-node
Compilation configuration:
```json
{
"_class_name": "StableDiffusionAccelerated_Base",
"_sda_version": "0.1.2",
"_trt_version": "8.5.3",
"_cuda_version": "none",
"_cudnn_version": "none",
"_onnx2trt_version": "8.5.3",
"unet": {
"precision": "fp16",
"path": "engine/unet.plan"
},
"clip": {
"path": "engine/clip.plan"
},
"de_vae": {
"path": "engine/de_vae.plan"
}
}
```
|
Contrastive-Tension/BERT-Distil-CT-STSb
|
[
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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}
| 1 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 513.50 +/- 196.35
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Galiess -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Galiess -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Galiess
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Contrastive-Tension/BERT-Large-CT-STSb
|
[
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"BertModel"
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}
| 7 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### JeffStewart3 Dreambooth model trained by BotsOne with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
Coyotl/DialoGPT-test-last-arthurmorgan
|
[
"conversational"
] |
conversational
|
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| 0 | null |
---
thumbnail: https://www.google.com/url?sa=i&url=https%3A%2F%2Fkagerouproject.fandom.com%2Fwiki%2FHeadphone_Actor%2FGallery&psig=AOvVaw1qa1_iobTskl2YdPAOw_ni&ust=1675739142503000&source=images&cd=vfe&ved=0CA8QjRxqFwoTCLC-8vf0__wCFQAAAAAdAAAAABAI
tags:
- conversational
license: mit
---
# DialoGPT Trained on the Speech of a Light Novel Character
Chat with the model:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("MarinHinawa/DialoGPT-medium-Ene")
model = AutoModelWithLMHead.from_pretrained("MarinHinawa/DialoGPT-medium-Ene")
# Let's chat for 4 lines
for step in range(4):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# print(new_user_input_ids)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature=0.8
)
# pretty print last ouput tokens from bot
print("EneBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```
|
Coyotl/DialoGPT-test2-arthurmorgan
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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"GPT2LMHeadModel"
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| 7 | null |
---
license: openrail
tags:
- stable-diffusion
- text-to-image
---
# th-diffusion
このモデルは、[SD2-1(768)](https://huggingface.co/stabilityai/stable-diffusion-2-1)からアニメスタイルの画像を学習させたものです。SDからアニメスタイルを自力で作ってみたかっただけです。
学習方法は[WD1-4](https://huggingface.co/hakurei/waifu-diffusion-v1-4)とほとんど同じであり、データセットも学習ステップ数も負けてるので劣化版でしかないです。
diffusers用のとwebui用のsafetensorsを置いてます。
このモデルの学習は三段階に分かれます。
1. 13万枚の画像を１０エポック学習:A100 80GBで20時間くらい
2. 39万枚の画像をText endoderを含めて1エポック・含めずに追加で3エポック学習:RTX3090で80時間くらい
4. 61万枚の画像を3エポック学習：RTX3090で100時間くらい
学習設定：
+ 解像度 768×768を基準にしたAspect ratio bucketing.半分くらいが縦896横640です。
+ 学習率 5e-6のconstant
+ batch size 1.は20 2.3.は16
+ タグ [WD1-4tagger](https://huggingface.co/SmilingWolf/wd-v1-4-vit-tagger)を利用しました。
2.3.は[WD1-4のタグ付け規則](https://gist.github.com/harubaru/313eec09026bb4090f4939d01f79a7e7)に従っています。
そのため機能するプロンプトやネガティブプロンプトはWD1-4と変わりません。
右にある変な奴で試せますが、ネガティブプロンプトがないのであんまりいい画像はでないよ。
# 生成例
てきとう、ネガティブプロンプトはworst quality, low quality, medium quality, deleted, lowres, comic, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry
CFG_scaleは10、高めにしたほうがいいかも。
masterpiece,best quality,1girl,solo,sitting,blush,red eyes,blonde hair,twintails,hair ribbon,school uniform,blue sailor collar,blue skirt,black thighhighs
masterpiece,best quality,absurdres,safe,1girl,solo,one eye closed, brown hair, side ponytail, maid, maid headdress, white thighhighs
masterpiece,best quality,hakurei reimu,touhou,solo,blush,brown hair,red eyes,frills,navel , yellow ascot, detached sleeves,hair bow,hair tubes
|
Craftified/Bob
|
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| 0 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: dhanush2
---
### dhanush2 Dreambooth model trained by Prajeevan with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
dhanush2 (use that on your prompt)
|
Craig/paraphrase-MiniLM-L6-v2
|
[
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] |
feature-extraction
|
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"BertModel"
],
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| 1,026 | null |
---
license: creativeml-openrail-m
language:
- en
---
|
Crasher222/kaggle-comp-test
|
[
"pytorch",
"bert",
"text-classification",
"en",
"dataset:Crasher222/autonlp-data-kaggle-test",
"transformers",
"autonlp",
"co2_eq_emissions"
] |
text-classification
|
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"BertForSequenceClassification"
],
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}
}
| 29 | null |
---
language:
- en
tags:
- stable-diffusion
- text-to-image
- lora
license: creativeml-openrail-m
inference: false
---
# ChonkyLotus Character LoRA
## Usage
To use this LoRA you have to download the file, as well as drop it into the "\stable-diffusion-webui\models\Lora" folder
To use it in a prompt, please refer to the extra networks panel in your Automatic1111 webui.
I highly recommend using it at around 0.8 strength for the best results.
If you'd like to support the amazing VTuber on whose model this LoRA was trained, I'd highly recommend you check out [ChonkyLotus](https://www.youtube.com/@ChonkyLotus).
Have fun :)
## Example Pictures
<table>
<tr>
<td><img src=https://i.imgur.com/TEyaCSQ.png width=50% height=100%/></td>
</tr>
<tr>
<td><img src=https://i.imgur.com/PAlVGfg.png width=50% height=100%/></td>
</tr>
<tr>
<td><img src=https://i.imgur.com/LB2uVVo.png width=50% height=100%/></td>
</tr>
</table>
## License
This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
Crisblair/Wkwk
|
[] | null |
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| 0 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-copter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 54.80 +/- 54.94
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Crives/distilbert-base-uncased-finetuned-emotion
|
[
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] |
text-classification
|
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"DistilBertForSequenceClassification"
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}
| 31 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: jason1i/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Crumped/imdb-simpleRNN
|
[
"keras"
] | null |
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nandysoham16/IPod-clustered
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. -->
# nandysoham16/IPod-clustered
This model is a fine-tuned version of [nandysoham16/15-clustered_aug](https://huggingface.co/nandysoham16/15-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5099
- Train End Logits Accuracy: 0.8472
- Train Start Logits Accuracy: 0.8229
- Validation Loss: 0.2496
- Validation End Logits Accuracy: 0.9091
- Validation Start Logits Accuracy: 0.8636
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.5099 | 0.8472 | 0.8229 | 0.2496 | 0.9091 | 0.8636 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CrypticT1tan/DialoGPT-medium-harrypotter
|
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: ishaankul67/IPod-clustered
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. -->
# ishaankul67/IPod-clustered
This model is a fine-tuned version of [nandysoham16/15-clustered_aug](https://huggingface.co/nandysoham16/15-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4234
- Train End Logits Accuracy: 0.8854
- Train Start Logits Accuracy: 0.8542
- Validation Loss: 0.2005
- Validation End Logits Accuracy: 0.9545
- Validation Start Logits Accuracy: 0.9091
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.4234 | 0.8854 | 0.8542 | 0.2005 | 0.9545 | 0.9091 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Cryptikdw/DialoGPT-small-rick
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
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| 7 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nandysoham/IPod-clustered
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. -->
# nandysoham/IPod-clustered
This model is a fine-tuned version of [nandysoham16/15-clustered_aug](https://huggingface.co/nandysoham16/15-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5208
- Train End Logits Accuracy: 0.8229
- Train Start Logits Accuracy: 0.8160
- Validation Loss: 0.1699
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 0.9091
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.5208 | 0.8229 | 0.8160 | 0.1699 | 1.0 | 0.9091 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Crystal/distilbert-base-uncased-finetuned-squad
|
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: sachinsahu/IPod-clustered
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. -->
# sachinsahu/IPod-clustered
This model is a fine-tuned version of [nandysoham16/15-clustered_aug](https://huggingface.co/nandysoham16/15-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4611
- Train End Logits Accuracy: 0.8715
- Train Start Logits Accuracy: 0.875
- Validation Loss: 0.4468
- Validation End Logits Accuracy: 0.8182
- Validation Start Logits Accuracy: 0.8182
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.4611 | 0.8715 | 0.875 | 0.4468 | 0.8182 | 0.8182 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Cthyllax/DialoGPT-medium-PaladinDanse
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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| 10 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nandysoham/2008_Sichuan_earthquake-clustered
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. -->
# nandysoham/2008_Sichuan_earthquake-clustered
This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4560
- Train End Logits Accuracy: 0.8854
- Train Start Logits Accuracy: 0.7882
- Validation Loss: 0.3706
- Validation End Logits Accuracy: 0.8947
- Validation Start Logits Accuracy: 0.8947
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.4560 | 0.8854 | 0.7882 | 0.3706 | 0.8947 | 0.8947 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Culmenus/IceBERT-finetuned-ner
|
[
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"dataset:mim_gold_ner",
"transformers",
"generated_from_trainer",
"license:gpl-3.0",
"model-index",
"autotrain_compatible"
] |
token-classification
|
{
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| 5 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: sachinsahu/2008_Sichuan_earthquake-clustered
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. -->
# sachinsahu/2008_Sichuan_earthquake-clustered
This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3953
- Train End Logits Accuracy: 0.9132
- Train Start Logits Accuracy: 0.7986
- Validation Loss: 0.6470
- Validation End Logits Accuracy: 0.8947
- Validation Start Logits Accuracy: 0.7368
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.3953 | 0.9132 | 0.7986 | 0.6470 | 0.8947 | 0.7368 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Culmenus/XLMR-ENIS-finetuned-ner
|
[
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:mim_gold_ner",
"transformers",
"generated_from_trainer",
"license:agpl-3.0",
"model-index",
"autotrain_compatible"
] |
token-classification
|
{
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"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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}
| 6 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: sachinsahu/Wayback_Machine-clustered
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. -->
# sachinsahu/Wayback_Machine-clustered
This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2612
- Train End Logits Accuracy: 0.9410
- Train Start Logits Accuracy: 0.9132
- Validation Loss: 0.1570
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.2612 | 0.9410 | 0.9132 | 0.1570 | 1.0 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Culmenus/checkpoint-168500-finetuned-de-to-is_nr2
|
[] | null |
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nandysoham/Wayback_Machine-clustered
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. -->
# nandysoham/Wayback_Machine-clustered
This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3070
- Train End Logits Accuracy: 0.9410
- Train Start Logits Accuracy: 0.8924
- Validation Loss: 0.4163
- Validation End Logits Accuracy: 0.6667
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.3070 | 0.9410 | 0.8924 | 0.4163 | 0.6667 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Culmenus/opus-mt-de-is-finetuned-de-to-is
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MarianMTModel"
],
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| 1 | null |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
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[More Information Needed]
## Results
[More Information Needed]
### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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# Technical Specifications [optional]
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# Citation [optional]
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# Glossary [optional]
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|
Culmenus/opus-mt-de-is-finetuned-de-to-is_35g65cc
|
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: sachinsahu/Canadian_Armed_Forces-clustered
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. -->
# sachinsahu/Canadian_Armed_Forces-clustered
This model is a fine-tuned version of [nandysoham16/0-clustered_aug](https://huggingface.co/nandysoham16/0-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5473
- Train End Logits Accuracy: 0.8472
- Train Start Logits Accuracy: 0.8056
- Validation Loss: 0.5142
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 0.4000
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.5473 | 0.8472 | 0.8056 | 0.5142 | 1.0 | 0.4000 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_35g65cc_1
|
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Deep98/IPod-clustered
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. -->
# Deep98/IPod-clustered
This model is a fine-tuned version of [nandysoham16/15-clustered_aug](https://huggingface.co/nandysoham16/15-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4336
- Train End Logits Accuracy: 0.8819
- Train Start Logits Accuracy: 0.8819
- Validation Loss: 0.3193
- Validation End Logits Accuracy: 0.8636
- Validation Start Logits Accuracy: 0.8636
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.4336 | 0.8819 | 0.8819 | 0.3193 | 0.8636 | 0.8636 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_35g65cc_2
|
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: sachinsahu/Cardinal__Catholicism_-clustered
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. -->
# sachinsahu/Cardinal__Catholicism_-clustered
This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2749
- Train End Logits Accuracy: 0.9271
- Train Start Logits Accuracy: 0.9410
- Validation Loss: 0.2860
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 0.75
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.2749 | 0.9271 | 0.9410 | 0.2860 | 1.0 | 0.75 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_ancc
|
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nandysoham/Canadian_Armed_Forces-clustered
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. -->
# nandysoham/Canadian_Armed_Forces-clustered
This model is a fine-tuned version of [nandysoham16/0-clustered_aug](https://huggingface.co/nandysoham16/0-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5635
- Train End Logits Accuracy: 0.8368
- Train Start Logits Accuracy: 0.7778
- Validation Loss: 0.3191
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 0.6000
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.5635 | 0.8368 | 0.7778 | 0.3191 | 1.0 | 0.6000 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_ekkicc
|
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: sachinsahu/Human_Development_Index-clustered
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. -->
# sachinsahu/Human_Development_Index-clustered
This model is a fine-tuned version of [nandysoham16/4-clustered_aug](https://huggingface.co/nandysoham16/4-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2111
- Train End Logits Accuracy: 0.9583
- Train Start Logits Accuracy: 0.9479
- Validation Loss: 0.0171
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.2111 | 0.9583 | 0.9479 | 0.0171 | 1.0 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_nr2-finetuned-de-to-is_nr2
|
[] | null |
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: sachinsahu/Heresy-clustered
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. -->
# sachinsahu/Heresy-clustered
This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1596
- Train End Logits Accuracy: 0.9653
- Train Start Logits Accuracy: 0.9653
- Validation Loss: 0.4279
- Validation End Logits Accuracy: 0.6667
- Validation Start Logits Accuracy: 0.6667
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.1596 | 0.9653 | 0.9653 | 0.4279 | 0.6667 | 0.6667 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_nr2
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MarianMTModel"
],
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| 1 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nandysoham/Cardinal__Catholicism_-clustered
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. -->
# nandysoham/Cardinal__Catholicism_-clustered
This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2081
- Train End Logits Accuracy: 0.9444
- Train Start Logits Accuracy: 0.9549
- Validation Loss: 1.0270
- Validation End Logits Accuracy: 0.75
- Validation Start Logits Accuracy: 0.75
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.2081 | 0.9444 | 0.9549 | 1.0270 | 0.75 | 0.75 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CuongLD/wav2vec2-large-xlsr-vietnamese
|
[
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"vi",
"dataset:common_voice, infore_25h",
"arxiv:2006.11477",
"arxiv:2006.13979",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
|
{
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"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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| 8 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: sachinsahu/Warsaw_Pact-clustered
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. -->
# sachinsahu/Warsaw_Pact-clustered
This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0968
- Train End Logits Accuracy: 0.9688
- Train Start Logits Accuracy: 0.9792
- Validation Loss: 0.1022
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.0968 | 0.9688 | 0.9792 | 0.1022 | 1.0 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CurtisASmith/GPT-JRT
|
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: recklessrecursion/IPod-clustered
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. -->
# recklessrecursion/IPod-clustered
This model is a fine-tuned version of [nandysoham16/15-clustered_aug](https://huggingface.co/nandysoham16/15-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4522
- Train End Logits Accuracy: 0.8438
- Train Start Logits Accuracy: 0.8472
- Validation Loss: 0.3457
- Validation End Logits Accuracy: 0.8636
- Validation Start Logits Accuracy: 0.9545
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.4522 | 0.8438 | 0.8472 | 0.3457 | 0.8636 | 0.9545 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CurtisBowser/DialoGPT-medium-sora-three
|
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: sachinsahu/Materialism-clustered
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. -->
# sachinsahu/Materialism-clustered
This model is a fine-tuned version of [nandysoham16/7-clustered_aug](https://huggingface.co/nandysoham16/7-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0466
- Train End Logits Accuracy: 1.0
- Train Start Logits Accuracy: 0.9931
- Validation Loss: 0.7458
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 0.5
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.0466 | 1.0 | 0.9931 | 0.7458 | 1.0 | 0.5 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CurtisBowser/DialoGPT-medium-sora-two
|
[
"pytorch",
"conversational"
] |
conversational
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nandysoham/Human_Development_Index-clustered
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. -->
# nandysoham/Human_Development_Index-clustered
This model is a fine-tuned version of [nandysoham16/4-clustered_aug](https://huggingface.co/nandysoham16/4-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2234
- Train End Logits Accuracy: 0.9410
- Train Start Logits Accuracy: 0.9479
- Validation Loss: 1.1060
- Validation End Logits Accuracy: 0.6667
- Validation Start Logits Accuracy: 0.6667
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.2234 | 0.9410 | 0.9479 | 1.1060 | 0.6667 | 0.6667 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CurtisBowser/DialoGPT-medium-sora
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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| 7 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: recklessrecursion/2008_Sichuan_earthquake-clustered
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. -->
# recklessrecursion/2008_Sichuan_earthquake-clustered
This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5049
- Train End Logits Accuracy: 0.8507
- Train Start Logits Accuracy: 0.7778
- Validation Loss: 0.3830
- Validation End Logits Accuracy: 0.9474
- Validation Start Logits Accuracy: 0.8947
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.5049 | 0.8507 | 0.7778 | 0.3830 | 0.9474 | 0.8947 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CurtisBowser/DialoGPT-small-sora
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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| 7 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Deep98/2008_Sichuan_earthquake-clustered
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. -->
# Deep98/2008_Sichuan_earthquake-clustered
This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5244
- Train End Logits Accuracy: 0.8611
- Train Start Logits Accuracy: 0.7951
- Validation Loss: 0.4230
- Validation End Logits Accuracy: 0.8947
- Validation Start Logits Accuracy: 0.8421
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.5244 | 0.8611 | 0.7951 | 0.4230 | 0.8947 | 0.8421 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
CyberMuffin/DialoGPT-small-ChandlerBot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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| 9 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: recklessrecursion/Wayback_Machine-clustered
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. -->
# recklessrecursion/Wayback_Machine-clustered
This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2349
- Train End Logits Accuracy: 0.9618
- Train Start Logits Accuracy: 0.9306
- Validation Loss: 2.6776
- Validation End Logits Accuracy: 0.6667
- Validation Start Logits Accuracy: 0.6667
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.2349 | 0.9618 | 0.9306 | 2.6776 | 0.6667 | 0.6667 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Cyrell/Cyrell
|
[] | null |
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: sachinsahu/Pub-clustered
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. -->
# sachinsahu/Pub-clustered
This model is a fine-tuned version of [nandysoham16/16-clustered_aug](https://huggingface.co/nandysoham16/16-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3626
- Train End Logits Accuracy: 0.9097
- Train Start Logits Accuracy: 0.8924
- Validation Loss: 0.4892
- Validation End Logits Accuracy: 0.8462
- Validation Start Logits Accuracy: 0.9231
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.3626 | 0.9097 | 0.8924 | 0.4892 | 0.8462 | 0.9231 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
D-Keqi/espnet_asr_train_asr_streaming_transformer_raw_en_bpe500_sp_valid.acc.ave
|
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| 11 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nandysoham/Heresy-clustered
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. -->
# nandysoham/Heresy-clustered
This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1820
- Train End Logits Accuracy: 0.9549
- Train Start Logits Accuracy: 0.9410
- Validation Loss: 0.3030
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.1820 | 0.9549 | 0.9410 | 0.3030 | 1.0 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
D3vil/DialoGPT-smaall-harrypottery
|
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: sachinsahu/Web_browser-clustered
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. -->
# sachinsahu/Web_browser-clustered
This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0993
- Train End Logits Accuracy: 0.9722
- Train Start Logits Accuracy: 0.9757
- Validation Loss: 1.3250
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 0.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.0993 | 0.9722 | 0.9757 | 1.3250 | 1.0 | 0.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
D3xter1922/electra-base-discriminator-finetuned-cola
|
[
"pytorch",
"tensorboard",
"electra",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] |
text-classification
|
{
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"ElectraForSequenceClassification"
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| 68 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nandysoham/Warsaw_Pact-clustered
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. -->
# nandysoham/Warsaw_Pact-clustered
This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0792
- Train End Logits Accuracy: 0.9722
- Train Start Logits Accuracy: 0.9861
- Validation Loss: 1.0233
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 0.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.0792 | 0.9722 | 0.9861 | 1.0233 | 1.0 | 0.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
D4RL1NG/yes
|
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: recklessrecursion/Human_Development_Index-clustered
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. -->
# recklessrecursion/Human_Development_Index-clustered
This model is a fine-tuned version of [nandysoham16/4-clustered_aug](https://huggingface.co/nandysoham16/4-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1730
- Train End Logits Accuracy: 0.9722
- Train Start Logits Accuracy: 0.9514
- Validation Loss: 0.5016
- Validation End Logits Accuracy: 0.6667
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.1730 | 0.9722 | 0.9514 | 0.5016 | 0.6667 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DCU-NLP/bert-base-irish-cased-v1
|
[
"pytorch",
"tf",
"bert",
"fill-mask",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
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| 1,244 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: sachinsahu/Paper-clustered
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. -->
# sachinsahu/Paper-clustered
This model is a fine-tuned version of [nandysoham16/16-clustered_aug](https://huggingface.co/nandysoham16/16-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2563
- Train End Logits Accuracy: 0.9132
- Train Start Logits Accuracy: 0.9306
- Validation Loss: 1.4623
- Validation End Logits Accuracy: 0.5
- Validation Start Logits Accuracy: 0.75
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.2563 | 0.9132 | 0.9306 | 1.4623 | 0.5 | 0.75 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DCU-NLP/electra-base-irish-cased-generator-v1
|
[
"pytorch",
"electra",
"fill-mask",
"ga",
"transformers",
"irish",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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"ElectraForMaskedLM"
],
"model_type": "electra",
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| 7 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: ishaankul67/2008_Sichuan_earthquake-clustered
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. -->
# ishaankul67/2008_Sichuan_earthquake-clustered
This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4882
- Train End Logits Accuracy: 0.8924
- Train Start Logits Accuracy: 0.7882
- Validation Loss: 0.2788
- Validation End Logits Accuracy: 0.8947
- Validation Start Logits Accuracy: 0.8947
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.4882 | 0.8924 | 0.7882 | 0.2788 | 0.8947 | 0.8947 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DHBaek/xlm-roberta-large-korquad-mask
|
[
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
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"XLMRobertaForQuestionAnswering"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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| 9 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nandysoham/Materialism-clustered
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. -->
# nandysoham/Materialism-clustered
This model is a fine-tuned version of [nandysoham16/7-clustered_aug](https://huggingface.co/nandysoham16/7-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1132
- Train End Logits Accuracy: 0.9826
- Train Start Logits Accuracy: 0.9688
- Validation Loss: 0.0086
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.1132 | 0.9826 | 0.9688 | 0.0086 | 1.0 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DJSammy/bert-base-swedish-uncased_BotXO-ai
|
[
"pytorch",
"transformers"
] | null |
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| 1 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: recklessrecursion/Pub-clustered
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. -->
# recklessrecursion/Pub-clustered
This model is a fine-tuned version of [nandysoham16/16-clustered_aug](https://huggingface.co/nandysoham16/16-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3288
- Train End Logits Accuracy: 0.8819
- Train Start Logits Accuracy: 0.9167
- Validation Loss: 0.4898
- Validation End Logits Accuracy: 0.9231
- Validation Start Logits Accuracy: 0.8462
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.3288 | 0.8819 | 0.9167 | 0.4898 | 0.9231 | 0.8462 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
alexandrainst/da-emotion-classification-base
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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| 837 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Sushant45/2008_Sichuan_earthquake-clustered
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. -->
# Sushant45/2008_Sichuan_earthquake-clustered
This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5065
- Train End Logits Accuracy: 0.8924
- Train Start Logits Accuracy: 0.8021
- Validation Loss: 0.2653
- Validation End Logits Accuracy: 0.9474
- Validation Start Logits Accuracy: 0.9474
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.5065 | 0.8924 | 0.8021 | 0.2653 | 0.9474 | 0.9474 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
alexandrainst/da-hatespeech-detection-base
|
[
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
] |
text-classification
|
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| 1,719 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nandysoham16/Wayback_Machine-clustered
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. -->
# nandysoham16/Wayback_Machine-clustered
This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2603
- Train End Logits Accuracy: 0.9514
- Train Start Logits Accuracy: 0.9201
- Validation Loss: 0.8503
- Validation End Logits Accuracy: 0.6667
- Validation Start Logits Accuracy: 0.6667
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.2603 | 0.9514 | 0.9201 | 0.8503 | 0.6667 | 0.6667 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
alexandrainst/da-sentiment-base
|
[
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"arxiv:1910.09700",
"transformers",
"license:cc-by-sa-4.0"
] |
text-classification
|
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| 1,432 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: deepiit98/Warsaw_Pact-clustered
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. -->
# deepiit98/Warsaw_Pact-clustered
This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1076
- Train End Logits Accuracy: 0.9722
- Train Start Logits Accuracy: 0.9618
- Validation Loss: 0.0350
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.1076 | 0.9722 | 0.9618 | 0.0350 | 1.0 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
alexandrainst/da-hatespeech-detection-small
|
[
"pytorch",
"electra",
"text-classification",
"da",
"transformers",
"license:cc-by-4.0"
] |
text-classification
|
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| 1,506 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Sushant45/Canadian_Armed_Forces-clustered
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. -->
# Sushant45/Canadian_Armed_Forces-clustered
This model is a fine-tuned version of [nandysoham16/0-clustered_aug](https://huggingface.co/nandysoham16/0-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5757
- Train End Logits Accuracy: 0.8542
- Train Start Logits Accuracy: 0.8160
- Validation Loss: 0.4930
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 0.4000
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.5757 | 0.8542 | 0.8160 | 0.4930 | 1.0 | 0.4000 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DaWang/demo
|
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: ishaankul67/Wayback_Machine-clustered
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. -->
# ishaankul67/Wayback_Machine-clustered
This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2638
- Train End Logits Accuracy: 0.9444
- Train Start Logits Accuracy: 0.9167
- Validation Loss: 0.6762
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 0.6667
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.2638 | 0.9444 | 0.9167 | 0.6762 | 1.0 | 0.6667 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Daiki/scibert_scivocab_uncased-finetuned-cola
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: itsGanni/2008_Sichuan_earthquake-clustered
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. -->
# itsGanni/2008_Sichuan_earthquake-clustered
This model is a fine-tuned version of [nandysoham/12-clustered](https://huggingface.co/nandysoham/12-clustered) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5943
- Train End Logits Accuracy: 0.8576
- Train Start Logits Accuracy: 0.7847
- Validation Loss: 0.4134
- Validation End Logits Accuracy: 0.8947
- Validation Start Logits Accuracy: 0.8421
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.5943 | 0.8576 | 0.7847 | 0.4134 | 0.8947 | 0.8421 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DaisyMak/bert-finetuned-squad-transformerfrozen-testtoken
|
[
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
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"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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},
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| 7 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: -5.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Daltcamalea01/Camaleaodalt
|
[] | null |
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: deepiit98/Web_browser-clustered
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. -->
# deepiit98/Web_browser-clustered
This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1344
- Train End Logits Accuracy: 0.9757
- Train Start Logits Accuracy: 0.9549
- Validation Loss: 1.1329
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 0.6667
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.1344 | 0.9757 | 0.9549 | 1.1329 | 1.0 | 0.6667 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DanBot/TCRsynth
|
[] | null |
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Sushant45/Heresy-clustered
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. -->
# Sushant45/Heresy-clustered
This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1942
- Train End Logits Accuracy: 0.9549
- Train Start Logits Accuracy: 0.9444
- Validation Loss: 0.2230
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.1942 | 0.9549 | 0.9444 | 0.2230 | 1.0 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Danbi/distilgpt2-finetuned-wikitext2
|
[] | null |
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Sushant45/Warsaw_Pact-clustered
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. -->
# Sushant45/Warsaw_Pact-clustered
This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0812
- Train End Logits Accuracy: 0.9757
- Train Start Logits Accuracy: 0.9826
- Validation Loss: 0.1680
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.0812 | 0.9757 | 0.9826 | 0.1680 | 1.0 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Dandara/bertimbau-socioambiental
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 27 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: itsGanni/Canadian_Armed_Forces-clustered
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. -->
# itsGanni/Canadian_Armed_Forces-clustered
This model is a fine-tuned version of [nandysoham/0-clustered](https://huggingface.co/nandysoham/0-clustered) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7260
- Train End Logits Accuracy: 0.8160
- Train Start Logits Accuracy: 0.7292
- Validation Loss: 0.5889
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 0.6000
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.7260 | 0.8160 | 0.7292 | 0.5889 | 1.0 | 0.6000 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Danih1502/t5-small-finetuned-en-to-de
|
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: deepiit98/Adult_contemporary_music-clustered
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. -->
# deepiit98/Adult_contemporary_music-clustered
This model is a fine-tuned version of [nandysoham16/15-clustered_aug](https://huggingface.co/nandysoham16/15-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3207
- Train End Logits Accuracy: 0.9097
- Train Start Logits Accuracy: 0.9062
- Validation Loss: 0.1559
- Validation End Logits Accuracy: 0.8571
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.3207 | 0.9097 | 0.9062 | 0.1559 | 0.8571 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Darein/Def
|
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| 0 | null |
---
library_name: mbrl-lib
tags:
- mbrl-Hopper-v2
- deep-reinforcement-learning
- reinforcement-learning
- mbrl-lib
model-index:
- name: OneDTransitionRewardModel w/ SACAgent
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: mbrl-Hopper-v2
type: mbrl-Hopper-v2
metrics:
- type: mean_reward
value: 87.70 +/- 1.10
name: mean_reward
verified: false
---
# **OneDTransitionRewardModel w/ SACAgent** Agent playing **mbrl-Hopper-v2**
This is a trained model of a **OneDTransitionRewardModel w/ SACAgent** agent playing **mbrl-Hopper-v2**
using [MBRL-Lib](https://github.com/facebookresearch/mbrl-lib).
## Usage (with MBRL-Lib)
TODO: Add your code
```python
from mbrl import ...
...
```
|
DarkWolf/kn-electra-small
|
[
"pytorch",
"electra",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 4 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Sushant45/Web_browser-clustered
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. -->
# Sushant45/Web_browser-clustered
This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1326
- Train End Logits Accuracy: 0.9792
- Train Start Logits Accuracy: 0.9444
- Validation Loss: 0.3331
- Validation End Logits Accuracy: 0.6667
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.1326 | 0.9792 | 0.9444 | 0.3331 | 0.6667 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Darkecho789/email-gen
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: itsGanni/Human_Development_Index-clustered
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. -->
# itsGanni/Human_Development_Index-clustered
This model is a fine-tuned version of [nandysoham/4-clustered](https://huggingface.co/nandysoham/4-clustered) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3853
- Train End Logits Accuracy: 0.8924
- Train Start Logits Accuracy: 0.9167
- Validation Loss: 0.0903
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.3853 | 0.8924 | 0.9167 | 0.0903 | 1.0 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DarkestSky/distilbert-base-uncased-finetuned-ner
|
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Sushant45/Catalan_language-clustered
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. -->
# Sushant45/Catalan_language-clustered
This model is a fine-tuned version of [nandysoham16/13-clustered_aug](https://huggingface.co/nandysoham16/13-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5260
- Train End Logits Accuracy: 0.8611
- Train Start Logits Accuracy: 0.8576
- Validation Loss: 0.8536
- Validation End Logits Accuracy: 0.7273
- Validation Start Logits Accuracy: 0.9091
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.5260 | 0.8611 | 0.8576 | 0.8536 | 0.7273 | 0.9091 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Darkrider/covidbert_medmarco
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:2010.05987",
"transformers"
] |
text-classification
|
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| 35 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Sushant45/Paper-clustered
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. -->
# Sushant45/Paper-clustered
This model is a fine-tuned version of [nandysoham16/16-clustered_aug](https://huggingface.co/nandysoham16/16-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2701
- Train End Logits Accuracy: 0.9236
- Train Start Logits Accuracy: 0.9306
- Validation Loss: 1.0319
- Validation End Logits Accuracy: 0.75
- Validation Start Logits Accuracy: 0.75
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.2701 | 0.9236 | 0.9306 | 1.0319 | 0.75 | 0.75 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Darkrider/covidbert_mednli
|
[
"transformers"
] | null |
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| 3 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: itsGanni/Heresy-clustered
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. -->
# itsGanni/Heresy-clustered
This model is a fine-tuned version of [nandysoham/11-clustered](https://huggingface.co/nandysoham/11-clustered) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2615
- Train End Logits Accuracy: 0.9410
- Train Start Logits Accuracy: 0.9167
- Validation Loss: 1.8742
- Validation End Logits Accuracy: 0.3333
- Validation Start Logits Accuracy: 0.3333
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.2615 | 0.9410 | 0.9167 | 1.8742 | 0.3333 | 0.3333 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DarshanDeshpande/marathi-distilbert
|
[
"pytorch",
"tf",
"distilbert",
"fill-mask",
"mr",
"dataset:Oscar Corpus, News, Stories",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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| 14 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: itsGanni/Warsaw_Pact-clustered
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. -->
# itsGanni/Warsaw_Pact-clustered
This model is a fine-tuned version of [nandysoham/12-clustered](https://huggingface.co/nandysoham/12-clustered) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1248
- Train End Logits Accuracy: 0.9688
- Train Start Logits Accuracy: 0.9514
- Validation Loss: 0.0163
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.1248 | 0.9688 | 0.9514 | 0.0163 | 1.0 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Darya/layoutlmv2-finetuned-funsd-test
|
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nandysoham16/Canadian_Armed_Forces-clustered
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. -->
# nandysoham16/Canadian_Armed_Forces-clustered
This model is a fine-tuned version of [nandysoham16/0-clustered_aug](https://huggingface.co/nandysoham16/0-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5493
- Train End Logits Accuracy: 0.8611
- Train Start Logits Accuracy: 0.7812
- Validation Loss: 0.3839
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 0.8000
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.5493 | 0.8611 | 0.7812 | 0.3839 | 1.0 | 0.8000 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Daryaflp/roberta-retrained_ru_covid
|
[
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
fill-mask
|
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"RobertaForMaskedLM"
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| 3 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Deep98/Wayback_Machine-clustered
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. -->
# Deep98/Wayback_Machine-clustered
This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3449
- Train End Logits Accuracy: 0.9375
- Train Start Logits Accuracy: 0.8681
- Validation Loss: 0.0082
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.3449 | 0.9375 | 0.8681 | 0.0082 | 1.0 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DataikuNLP/TinyBERT_General_4L_312D
|
[
"pytorch",
"jax",
"bert",
"arxiv:1909.10351",
"transformers"
] | null |
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| 74 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: FineTune_Vit5_LR0_00001_time3
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. -->
# FineTune_Vit5_LR0_00001_time3
This model is a fine-tuned version of [thesunshine36/FineTune_Vit5_LR0_00001_time2](https://huggingface.co/thesunshine36/FineTune_Vit5_LR0_00001_time2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6297
- Validation Loss: 0.5655
- Train Rouge1: 52.5683
- Train Rouge2: 31.3753
- Train Rougel: 44.4344
- Train Rougelsum: 44.4737
- Train Gen Len: 13.6985
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 0.6297 | 0.5655 | 52.5683 | 31.3753 | 44.4344 | 44.4737 | 13.6985 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DataikuNLP/average_word_embeddings_glove.6B.300d
|
[
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"license:apache-2.0"
] |
sentence-similarity
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: itsGanni/Materialism-clustered
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. -->
# itsGanni/Materialism-clustered
This model is a fine-tuned version of [nandysoham/7-clustered](https://huggingface.co/nandysoham/7-clustered) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1445
- Train End Logits Accuracy: 0.9792
- Train Start Logits Accuracy: 0.9618
- Validation Loss: 0.2256
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.1445 | 0.9792 | 0.9618 | 0.2256 | 1.0 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DataikuNLP/camembert-base
|
[
"pytorch",
"tf",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible"
] |
fill-mask
|
{
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"CamembertForMaskedLM"
],
"model_type": "camembert",
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| 8 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: ishaankul67/Canadian_Armed_Forces-clustered
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. -->
# ishaankul67/Canadian_Armed_Forces-clustered
This model is a fine-tuned version of [nandysoham16/0-clustered_aug](https://huggingface.co/nandysoham16/0-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6166
- Train End Logits Accuracy: 0.8333
- Train Start Logits Accuracy: 0.7361
- Validation Loss: 0.2280
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 0.8000
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.6166 | 0.8333 | 0.7361 | 0.2280 | 1.0 | 0.8000 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DataikuNLP/distiluse-base-multilingual-cased-v1
|
[
"pytorch",
"distilbert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] |
sentence-similarity
|
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| 29 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: itsGanni/Pub-clustered
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. -->
# itsGanni/Pub-clustered
This model is a fine-tuned version of [nandysoham/16-clustered](https://huggingface.co/nandysoham/16-clustered) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4233
- Train End Logits Accuracy: 0.8819
- Train Start Logits Accuracy: 0.8507
- Validation Loss: 0.2006
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 0.9231
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.4233 | 0.8819 | 0.8507 | 0.2006 | 1.0 | 0.9231 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DataikuNLP/paraphrase-MiniLM-L6-v2
|
[
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] |
sentence-similarity
|
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| 25 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: itsGanni/Web_browser-clustered
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. -->
# itsGanni/Web_browser-clustered
This model is a fine-tuned version of [nandysoham/20-clustered](https://huggingface.co/nandysoham/20-clustered) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2294
- Train End Logits Accuracy: 0.9618
- Train Start Logits Accuracy: 0.9062
- Validation Loss: 0.2672
- Validation End Logits Accuracy: 0.6667
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.2294 | 0.9618 | 0.9062 | 0.2672 | 0.6667 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Dave/twomad-model
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: itsGanni/Paper-clustered
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. -->
# itsGanni/Paper-clustered
This model is a fine-tuned version of [nandysoham/16-clustered](https://huggingface.co/nandysoham/16-clustered) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4880
- Train End Logits Accuracy: 0.8715
- Train Start Logits Accuracy: 0.875
- Validation Loss: 0.4717
- Validation End Logits Accuracy: 0.5
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.4880 | 0.8715 | 0.875 | 0.4717 | 0.5 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DavidAMcIntosh/DialoGPT-small-rick
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: itsGanni/Adult_contemporary_music-clustered
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. -->
# itsGanni/Adult_contemporary_music-clustered
This model is a fine-tuned version of [nandysoham/15-clustered](https://huggingface.co/nandysoham/15-clustered) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4347
- Train End Logits Accuracy: 0.9062
- Train Start Logits Accuracy: 0.8715
- Validation Loss: 0.5431
- Validation End Logits Accuracy: 0.7143
- Validation Start Logits Accuracy: 0.7143
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.4347 | 0.9062 | 0.8715 | 0.5431 | 0.7143 | 0.7143 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DavidAMcIntosh/small-rick
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.25396825396825395
- name: Recall
type: recall
value: 0.029657089898053754
- name: F1
type: f1
value: 0.053112033195020746
- name: Accuracy
type: accuracy
value: 0.9283485101107264
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_wnut_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3349
- Precision: 0.2540
- Recall: 0.0297
- F1: 0.0531
- Accuracy: 0.9283
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 54 | 0.3619 | 0.0 | 0.0 | 0.0 | 0.9256 |
| No log | 2.0 | 108 | 0.3349 | 0.2540 | 0.0297 | 0.0531 | 0.9283 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DavidSpaceG/MSGIFSR
|
[] | null |
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| 0 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nandysoham16/Cardinal__Catholicism_-clustered
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. -->
# nandysoham16/Cardinal__Catholicism_-clustered
This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3017
- Train End Logits Accuracy: 0.9167
- Train Start Logits Accuracy: 0.9306
- Validation Loss: 0.2812
- Validation End Logits Accuracy: 0.75
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.3017 | 0.9167 | 0.9306 | 0.2812 | 0.75 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Davlan/bert-base-multilingual-cased-finetuned-amharic
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
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| 109 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: ishaankul67/Cardinal__Catholicism_-clustered
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. -->
# ishaankul67/Cardinal__Catholicism_-clustered
This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3039
- Train End Logits Accuracy: 0.9167
- Train Start Logits Accuracy: 0.9444
- Validation Loss: 0.2086
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.3039 | 0.9167 | 0.9444 | 0.2086 | 1.0 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Davlan/bert-base-multilingual-cased-finetuned-hausa
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
| 151 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Deep98/Canadian_Armed_Forces-clustered
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. -->
# Deep98/Canadian_Armed_Forces-clustered
This model is a fine-tuned version of [nandysoham16/0-clustered_aug](https://huggingface.co/nandysoham16/0-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4369
- Train End Logits Accuracy: 0.8785
- Train Start Logits Accuracy: 0.8507
- Validation Loss: 1.3005
- Validation End Logits Accuracy: 0.6000
- Validation Start Logits Accuracy: 0.4000
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.4369 | 0.8785 | 0.8507 | 1.3005 | 0.6000 | 0.4000 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
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| 27 | null |
---
language:
- en
tags:
- stable-diffusion
- text-to-image
- lora
license: creativeml-openrail-m
inference: false
---
# G Yuusuke Style LoRA
## Usage
To use this LoRA you have to download the file, as well as drop it into the "\stable-diffusion-webui\models\Lora" folder
To use it in a prompt, please refer to the extra networks panel in your Automatic1111 webui.
I highly recommend using it at around 0.8 strength for the best results.
It seems to struggle a lot with hands, but I haven't run many tests on it, so maybe my data is biased.
If you'd like to support the amazing artist on whose work this LoRA was trained, I'd highly recommend you check out [Gユウスケ](https://gyuusuke.exblog.jp/).
Have fun :)
## Example Pictures
<table>
<tr>
<td><img src=https://i.imgur.com/eKYZIhY.png width=50% height=100%/></td>
</tr>
<tr>
<td><img src=https://i.imgur.com/Q1T08xG.png width=50% height=100%/></td>
</tr>
<tr>
<td><img src=https://i.imgur.com/TFscR9S.png width=50% height=100%/></td>
</tr>
</table>
## License
This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
Davlan/bert-base-multilingual-cased-finetuned-yoruba
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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| 21 | null |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-t5-large-extraction-cnndm_4000-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-large-extraction-cnndm_4000-all
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8084
- Rouge1: 35.2389
- Rouge2: 15.2731
- Rougel: 29.9899
- Rougelsum: 30.0262
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 24
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.2214 | 0.4 | 200 | 1.9330 | 34.7186 | 15.2527 | 29.7852 | 29.8623 | 19.0 |
| 1.2119 | 0.8 | 400 | 1.9119 | 34.718 | 15.3471 | 29.4347 | 29.4709 | 19.0 |
| 1.1482 | 1.2 | 600 | 2.0060 | 34.1536 | 15.0233 | 29.503 | 29.518 | 18.99 |
| 1.1102 | 1.6 | 800 | 2.0276 | 34.8004 | 15.1277 | 29.5782 | 29.6371 | 18.998 |
| 1.1295 | 2.0 | 1000 | 1.9375 | 35.1942 | 15.2087 | 30.156 | 30.0925 | 18.996 |
| 1.2045 | 2.4 | 1200 | 1.9016 | 35.5121 | 15.8033 | 30.515 | 30.5451 | 18.984 |
| 1.492 | 2.8 | 1400 | 1.8119 | 35.0575 | 15.2373 | 29.8621 | 29.9106 | 19.0 |
| 1.4535 | 3.2 | 1600 | 1.8160 | 35.0796 | 15.6135 | 30.1449 | 30.189 | 19.0 |
| 1.4087 | 3.6 | 1800 | 1.8223 | 34.9121 | 15.3203 | 29.7578 | 29.8006 | 18.998 |
| 1.4098 | 4.0 | 2000 | 1.8084 | 35.2389 | 15.2731 | 29.9899 | 30.0262 | 19.0 |
| 1.3759 | 4.4 | 2200 | 1.8357 | 35.4492 | 15.8883 | 30.1135 | 30.151 | 19.0 |
| 1.3565 | 4.8 | 2400 | 1.8347 | 34.6559 | 15.2567 | 29.5659 | 29.5704 | 19.0 |
| 1.3268 | 5.2 | 2600 | 1.8416 | 35.326 | 15.5918 | 29.841 | 29.8391 | 19.0 |
| 1.3204 | 5.6 | 2800 | 1.8445 | 35.4671 | 15.5422 | 30.169 | 30.1985 | 19.0 |
| 1.3271 | 6.0 | 3000 | 1.8374 | 35.4057 | 15.6566 | 30.2378 | 30.2328 | 18.998 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Davlan/byt5-base-eng-yor-mt
|
[
"pytorch",
"t5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"T5ForConditionalGeneration"
],
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}
| 11 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: flan-t5-large-da-multiwoz_1000
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. -->
# flan-t5-large-da-multiwoz_1000
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3538
- Accuracy: 41.3747
- Num: 3689
- Gen Len: 15.5115
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 24
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Num | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:-------:|
| 1.3315 | 0.24 | 200 | 0.5697 | 25.9543 | 3689 | 14.556 |
| 0.6418 | 0.48 | 400 | 0.4645 | 30.0503 | 3689 | 14.9314 |
| 0.5433 | 0.72 | 600 | 0.4307 | 31.9506 | 3689 | 16.1515 |
| 0.4909 | 0.95 | 800 | 0.4177 | 34.7593 | 3689 | 15.418 |
| 0.4769 | 1.19 | 1000 | 0.3996 | 35.0943 | 3689 | 14.9607 |
| 0.4491 | 1.43 | 1200 | 0.3881 | 36.2741 | 3689 | 15.543 |
| 0.4531 | 1.67 | 1400 | 0.3820 | 35.7704 | 3689 | 14.1583 |
| 0.4322 | 1.91 | 1600 | 0.3726 | 37.4853 | 3689 | 15.961 |
| 0.4188 | 2.15 | 1800 | 0.3699 | 38.4117 | 3689 | 15.0773 |
| 0.4085 | 2.38 | 2000 | 0.3674 | 38.5353 | 3689 | 15.4012 |
| 0.4063 | 2.62 | 2200 | 0.3606 | 40.0046 | 3689 | 15.3546 |
| 0.3977 | 2.86 | 2400 | 0.3570 | 40.6543 | 3689 | 15.704 |
| 0.3992 | 3.1 | 2600 | 0.3549 | 40.4284 | 3689 | 15.7446 |
| 0.3828 | 3.34 | 2800 | 0.3538 | 41.3747 | 3689 | 15.5115 |
| 0.3792 | 3.58 | 3000 | 0.3539 | 39.8513 | 3689 | 14.7951 |
| 0.3914 | 3.81 | 3200 | 0.3498 | 41.0388 | 3689 | 15.4153 |
| 0.3707 | 4.05 | 3400 | 0.3498 | 40.9596 | 3689 | 16.3136 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Davlan/mT5_base_yoruba_adr
|
[
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2003.10564",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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},
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}
| 5 | null |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
training_prompt: a bird is flapping its wing
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- text-to-video
- tune-a-video
inference: false
---
# Tune-A-Video - birdgif-test
## Model description
- Base model: [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)
- Training prompt: a bird is flapping its wing
## Related papers:
- [Tune-A-Video](https://arxiv.org/abs/2212.11565): One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
- [Stable-Diffusion](https://arxiv.org/abs/2112.10752): High-Resolution Image Synthesis with Latent Diffusion Models
|
Davlan/mt5-small-en-pcm
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
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},
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},
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},
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},
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}
}
}
| 9 | null |
---
language:
- "ja"
tags:
- "japanese"
- "wikipedia"
- "cc100"
- "oscar"
- "pos"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "cc-by-sa-4.0"
pipeline_tag: "token-classification"
---
# deberta-base-japanese-juman-ud-goeswith
## Model Description
This is a DeBERTa(V2) model pretrained on Japanese Wikipedia, CC-100, and OSCAR texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [deberta-v2-base-japanese](https://huggingface.co/ku-nlp/deberta-v2-base-japanese).
## How to Use
```
from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/deberta-base-japanese-juman-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple")
print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている"))
```
[fugashi](https://pypi.org/project/fugashi) is required.
|
Davlan/mt5-small-pcm-en
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
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},
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},
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}
}
}
| 9 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: ishaankul67/Warsaw_Pact-clustered
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. -->
# ishaankul67/Warsaw_Pact-clustered
This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1010
- Train End Logits Accuracy: 0.9653
- Train Start Logits Accuracy: 0.9826
- Validation Loss: 0.0420
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.1010 | 0.9653 | 0.9826 | 0.0420 | 1.0 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Davlan/xlm-roberta-base-finetuned-chichewa
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
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},
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}
}
}
| 5 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: NoNameFound/pocaresnet-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Davlan/xlm-roberta-base-finetuned-english
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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"max_length": null,
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 5 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Deep98/Heresy-clustered
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. -->
# Deep98/Heresy-clustered
This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2244
- Train End Logits Accuracy: 0.9479
- Train Start Logits Accuracy: 0.9062
- Validation Loss: 0.4860
- Validation End Logits Accuracy: 0.6667
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.2244 | 0.9479 | 0.9062 | 0.4860 | 0.6667 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Davlan/xlm-roberta-base-finetuned-luganda
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
"early_stopping": null,
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},
"translation_en_to_fr": {
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| 11 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Deep98/Warsaw_Pact-clustered
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. -->
# Deep98/Warsaw_Pact-clustered
This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1156
- Train End Logits Accuracy: 0.9688
- Train Start Logits Accuracy: 0.9757
- Validation Loss: 0.1900
- Validation End Logits Accuracy: 1.0
- Validation Start Logits Accuracy: 1.0
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.1156 | 0.9688 | 0.9757 | 0.1900 | 1.0 | 1.0 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
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