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davera-017/rl_course_vizdoom_health_gathering_supreme
davera-017
2023-09-13T19:48:26Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-13T19:48:11Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 10.82 +/- 3.17 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r davera-017/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
Chelo11/Martin-Fierro
Chelo11
2023-09-13T19:47:48Z
139
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:DeepESP/gpt2-spanish", "base_model:finetune:DeepESP/gpt2-spanish", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-09-13T19:46:49Z
--- license: mit base_model: DeepESP/gpt2-spanish tags: - generated_from_trainer model-index: - name: Martin-Fierro 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. --> # Martin-Fierro This model is a fine-tuned version of [DeepESP/gpt2-spanish](https://huggingface.co/DeepESP/gpt2-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.2863 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.927 | 1.0 | 10 | 4.7835 | | 4.5579 | 2.0 | 20 | 4.4981 | | 4.3378 | 3.0 | 30 | 4.4358 | | 4.2049 | 4.0 | 40 | 4.3742 | | 4.0306 | 5.0 | 50 | 4.3384 | | 3.9757 | 6.0 | 60 | 4.3182 | | 3.8343 | 7.0 | 70 | 4.3031 | | 3.7464 | 8.0 | 80 | 4.2927 | | 3.7114 | 9.0 | 90 | 4.2880 | | 3.707 | 10.0 | 100 | 4.2863 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
jpbaudoin/bert_adaptation_peppa_pig
jpbaudoin
2023-09-13T19:46:52Z
115
0
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-uncased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-13T19:46:33Z
--- base_model: dccuchile/bert-base-spanish-wwm-uncased tags: - generated_from_trainer model-index: - name: bert_adaptation_peppa_pig results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_adaptation_peppa_pig This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1548 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.2509 | 1.0 | 35 | 2.4300 | | 2.1185 | 2.0 | 70 | 2.3098 | | 2.0163 | 3.0 | 105 | 2.1849 | | 1.8594 | 4.0 | 140 | 2.1367 | | 1.9299 | 5.0 | 175 | 2.2375 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Andrew-XZR/bert_adaptation_referencias_de_vinos
Andrew-XZR
2023-09-13T19:46:29Z
132
0
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-uncased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-13T19:46:05Z
--- base_model: dccuchile/bert-base-spanish-wwm-uncased tags: - generated_from_trainer model-index: - name: bert_adaptation_referencias_de_vinos results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_adaptation_referencias_de_vinos This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7296 ## 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: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.06 | 1.0 | 750 | 2.4974 | | 2.4463 | 2.0 | 1500 | 2.3140 | | 2.2515 | 3.0 | 2250 | 2.1646 | | 2.0962 | 4.0 | 3000 | 2.0073 | | 2.0126 | 5.0 | 3750 | 2.0552 | | 1.9271 | 6.0 | 4500 | 1.9176 | | 1.8427 | 7.0 | 5250 | 1.8497 | | 1.8009 | 8.0 | 6000 | 1.8579 | | 1.7596 | 9.0 | 6750 | 1.8501 | | 1.7049 | 10.0 | 7500 | 1.8178 | | 1.6992 | 11.0 | 8250 | 1.7969 | | 1.67 | 12.0 | 9000 | 1.8254 | | 1.6272 | 13.0 | 9750 | 1.7340 | | 1.6192 | 14.0 | 10500 | 1.7664 | | 1.6209 | 15.0 | 11250 | 1.7281 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
FabioGrigorjev/bert_adaptation_referencias_de_vinos
FabioGrigorjev
2023-09-13T19:46:23Z
118
0
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-uncased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-13T19:46:05Z
--- base_model: dccuchile/bert-base-spanish-wwm-uncased tags: - generated_from_trainer model-index: - name: bert_adaptation_referencias_de_vinos results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_adaptation_referencias_de_vinos This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1837 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.1798 | 1.0 | 375 | 2.5403 | | 2.4869 | 2.0 | 750 | 2.3447 | | 2.309 | 3.0 | 1125 | 2.2750 | | 2.2543 | 4.0 | 1500 | 2.1865 | | 2.2056 | 5.0 | 1875 | 2.1983 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
mauroandres/bert_adaptation_peppa_pig
mauroandres
2023-09-13T19:45:55Z
130
0
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-uncased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-13T19:45:41Z
--- base_model: dccuchile/bert-base-spanish-wwm-uncased tags: - generated_from_trainer model-index: - name: bert_adaptation_peppa_pig results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_adaptation_peppa_pig This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2324 ## 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: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.1119 | 1.0 | 35 | 3.5656 | | 2.9747 | 2.0 | 70 | 2.6914 | | 2.5938 | 3.0 | 105 | 2.6623 | | 2.466 | 4.0 | 140 | 2.4353 | | 2.1494 | 5.0 | 175 | 2.2285 | | 2.117 | 6.0 | 210 | 2.3762 | | 2.0465 | 7.0 | 245 | 2.3324 | | 2.0548 | 8.0 | 280 | 2.2205 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
didicito/bert_adaptation_martin_fierro
didicito
2023-09-13T19:45:48Z
127
0
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-uncased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-13T19:45:35Z
--- base_model: dccuchile/bert-base-spanish-wwm-uncased tags: - generated_from_trainer model-index: - name: bert_adaptation_martin_fierro results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_adaptation_martin_fierro This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.4082 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.7508 | 1.0 | 29 | 5.2082 | | 4.7335 | 2.0 | 58 | 4.4594 | | 4.1562 | 3.0 | 87 | 4.2792 | | 3.9629 | 4.0 | 116 | 3.9394 | | 4.2598 | 5.0 | 145 | 4.3763 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
gongoody/bert_adaptation_referencias_de_vinos
gongoody
2023-09-13T19:45:35Z
125
0
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-uncased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-13T19:44:44Z
--- base_model: dccuchile/bert-base-spanish-wwm-uncased tags: - generated_from_trainer model-index: - name: bert_adaptation_referencias_de_vinos results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_adaptation_referencias_de_vinos This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4704 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7269 | 1.0 | 375 | 3.0497 | | 2.9328 | 2.0 | 750 | 2.7255 | | 2.7404 | 3.0 | 1125 | 2.7349 | | 2.5953 | 4.0 | 1500 | 2.6279 | | 2.5463 | 5.0 | 1875 | 2.5809 | | 2.4262 | 6.0 | 2250 | 2.5138 | | 2.3802 | 7.0 | 2625 | 2.4732 | | 2.3381 | 8.0 | 3000 | 2.4881 | | 2.2689 | 9.0 | 3375 | 2.4361 | | 2.2619 | 10.0 | 3750 | 2.4239 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
rghosh8/alpaca7B-lora-support-gpt_ccc
rghosh8
2023-09-13T19:37:12Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-18T22:31:33Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0
Sandiago21/hubert-large-ll60k-finetuned-gtzan
Sandiago21
2023-09-13T19:36:55Z
7
1
transformers
[ "transformers", "pytorch", "tensorboard", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:facebook/hubert-large-ll60k", "base_model:finetune:facebook/hubert-large-ll60k", "license:apache-2.0", "endpoints_compatible", "region:us" ]
audio-classification
2023-07-10T06:26:05Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy base_model: facebook/hubert-large-ll60k model-index: - name: hubert-large-ll60k-finetuned-gtzan 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. --> # hubert-large-ll60k-finetuned-gtzan This model is a fine-tuned version of [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.9220 - Accuracy: 0.73 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.2456 | 1.0 | 56 | 2.2312 | 0.34 | | 2.059 | 1.99 | 112 | 1.9662 | 0.32 | | 1.8574 | 2.99 | 168 | 1.6258 | 0.5 | | 1.4447 | 4.0 | 225 | 1.4547 | 0.59 | | 1.4224 | 5.0 | 281 | 1.2372 | 0.65 | | 1.2131 | 5.99 | 337 | 1.0879 | 0.67 | | 1.1151 | 6.99 | 393 | 1.0599 | 0.69 | | 0.9471 | 8.0 | 450 | 1.0339 | 0.68 | | 1.0319 | 9.0 | 506 | 0.9568 | 0.71 | | 0.9313 | 9.96 | 560 | 0.9220 | 0.73 | ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
Sandiago21/hubert-large-ls960-ft-finetuned-gtzan
Sandiago21
2023-09-13T19:36:25Z
9
2
transformers
[ "transformers", "pytorch", "tensorboard", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:facebook/hubert-large-ls960-ft", "base_model:finetune:facebook/hubert-large-ls960-ft", "license:apache-2.0", "endpoints_compatible", "region:us" ]
audio-classification
2023-07-08T17:02:50Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy base_model: facebook/hubert-large-ls960-ft model-index: - name: hubert-large-ls960-ft-finetuned-gtzan 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. --> # hubert-large-ls960-ft-finetuned-gtzan This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.7096 - Accuracy: 0.85 ## 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: 8e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 18 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.2623 | 1.0 | 56 | 2.2399 | 0.21 | | 1.881 | 1.99 | 112 | 1.7105 | 0.41 | | 1.5793 | 2.99 | 168 | 1.6203 | 0.46 | | 1.3018 | 4.0 | 225 | 1.3824 | 0.52 | | 1.0219 | 5.0 | 281 | 0.9899 | 0.66 | | 0.9047 | 5.99 | 337 | 0.8812 | 0.74 | | 0.8353 | 6.99 | 393 | 0.7629 | 0.78 | | 0.659 | 8.0 | 450 | 0.9674 | 0.71 | | 0.645 | 9.0 | 506 | 0.8953 | 0.74 | | 0.6233 | 9.99 | 562 | 0.6638 | 0.8 | | 0.4155 | 10.99 | 618 | 0.6323 | 0.81 | | 0.2689 | 12.0 | 675 | 0.5423 | 0.83 | | 0.3714 | 13.0 | 731 | 0.6770 | 0.83 | | 0.0692 | 13.99 | 787 | 0.6260 | 0.83 | | 0.0778 | 14.99 | 843 | 0.5801 | 0.85 | | 0.187 | 16.0 | 900 | 0.6722 | 0.83 | | 0.1469 | 17.0 | 956 | 0.7473 | 0.85 | | 0.1052 | 17.92 | 1008 | 0.7096 | 0.85 | ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
sofiabobbiesi/bert_adaptation_referencias_de_vinos
sofiabobbiesi
2023-09-13T19:35:19Z
124
0
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-uncased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-13T19:35:00Z
--- base_model: dccuchile/bert-base-spanish-wwm-uncased tags: - generated_from_trainer model-index: - name: bert_adaptation_referencias_de_vinos results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_adaptation_referencias_de_vinos This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0684 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.9878 | 1.0 | 625 | 2.4628 | | 2.4187 | 2.0 | 1250 | 2.2630 | | 2.2583 | 3.0 | 1875 | 2.1819 | | 2.1432 | 4.0 | 2500 | 2.0677 | | 2.0864 | 5.0 | 3125 | 2.0408 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Amirhnrn/poca-SoccerTwos
Amirhnrn
2023-09-13T19:35:12Z
30
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]
reinforcement-learning
2023-09-13T19:14:24Z
--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # **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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Amirhnrn/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
Chelo11/bert_adaptation_peppa_pig
Chelo11
2023-09-13T19:25:41Z
123
0
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-uncased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-13T19:24:53Z
--- base_model: dccuchile/bert-base-spanish-wwm-uncased tags: - generated_from_trainer model-index: - name: bert_adaptation_peppa_pig results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_adaptation_peppa_pig This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9437 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.758 | 1.0 | 70 | 3.1082 | | 2.763 | 2.0 | 140 | 2.5174 | | 2.3389 | 3.0 | 210 | 2.3997 | | 2.1706 | 4.0 | 280 | 2.1940 | | 2.0781 | 5.0 | 350 | 2.4517 | | 2.0058 | 6.0 | 420 | 2.1393 | | 1.8748 | 7.0 | 490 | 2.2303 | | 1.8267 | 8.0 | 560 | 2.2157 | | 1.7874 | 9.0 | 630 | 2.0251 | | 1.7614 | 10.0 | 700 | 2.1635 | | 1.7023 | 11.0 | 770 | 1.9857 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
YhSharaf/output_models
YhSharaf
2023-09-13T19:23:13Z
0
0
diffusers
[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-09-13T00:40:24Z
--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks girl tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - YhSharaf/output_models This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks girl using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
CyberHarem/euphyllia_magenta_tenseioujototensaireijounomahoukakumei
CyberHarem
2023-09-13T19:14:06Z
0
0
null
[ "art", "text-to-image", "dataset:CyberHarem/euphyllia_magenta_tenseioujototensaireijounomahoukakumei", "license:mit", "region:us" ]
text-to-image
2023-09-13T18:48:22Z
--- license: mit datasets: - CyberHarem/euphyllia_magenta_tenseioujototensaireijounomahoukakumei pipeline_tag: text-to-image tags: - art --- # Lora of euphyllia_magenta_tenseioujototensaireijounomahoukakumei This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 8960, you need to download `8960/euphyllia_magenta_tenseioujototensaireijounomahoukakumei.pt` as the embedding and `8960/euphyllia_magenta_tenseioujototensaireijounomahoukakumei.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 8960**, with the score of 0.853. The trigger words are: 1. `euphyllia_magenta_tenseioujototensaireijounomahoukakumei` 2. `purple_eyes, long_hair, bangs, grey_hair, braid, bow, blurry, hair_bow, hair_ornament` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | pattern_16 | pattern_17 | pattern_18 | pattern_19 | pattern_20 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:----------------------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 9600 | 0.849 | [Download](9600/euphyllia_magenta_tenseioujototensaireijounomahoukakumei.zip) | ![pattern_1-9600](9600/previews/pattern_1.png) | ![pattern_2-9600](9600/previews/pattern_2.png) | ![pattern_3-9600](9600/previews/pattern_3.png) | ![pattern_4-9600](9600/previews/pattern_4.png) | ![pattern_5-9600](9600/previews/pattern_5.png) | ![pattern_6-9600](9600/previews/pattern_6.png) | ![pattern_7-9600](9600/previews/pattern_7.png) | ![pattern_8-9600](9600/previews/pattern_8.png) | ![pattern_9-9600](9600/previews/pattern_9.png) | ![pattern_10-9600](9600/previews/pattern_10.png) | ![pattern_11-9600](9600/previews/pattern_11.png) | ![pattern_12-9600](9600/previews/pattern_12.png) | ![pattern_13-9600](9600/previews/pattern_13.png) | ![pattern_14-9600](9600/previews/pattern_14.png) | ![pattern_15-9600](9600/previews/pattern_15.png) | ![pattern_16-9600](9600/previews/pattern_16.png) | ![pattern_17-9600](9600/previews/pattern_17.png) | ![pattern_18-9600](9600/previews/pattern_18.png) | ![pattern_19-9600](9600/previews/pattern_19.png) | ![pattern_20-9600](9600/previews/pattern_20.png) | ![bikini-9600](9600/previews/bikini.png) | [<NSFW, click to see>](9600/previews/bondage.png) | ![free-9600](9600/previews/free.png) | ![maid-9600](9600/previews/maid.png) | ![miko-9600](9600/previews/miko.png) | [<NSFW, click to see>](9600/previews/nude.png) | [<NSFW, click to see>](9600/previews/nude2.png) | ![suit-9600](9600/previews/suit.png) | ![yukata-9600](9600/previews/yukata.png) | | **8960** | **0.853** | [**Download**](8960/euphyllia_magenta_tenseioujototensaireijounomahoukakumei.zip) | ![pattern_1-8960](8960/previews/pattern_1.png) | ![pattern_2-8960](8960/previews/pattern_2.png) | ![pattern_3-8960](8960/previews/pattern_3.png) | ![pattern_4-8960](8960/previews/pattern_4.png) | ![pattern_5-8960](8960/previews/pattern_5.png) | ![pattern_6-8960](8960/previews/pattern_6.png) | ![pattern_7-8960](8960/previews/pattern_7.png) | ![pattern_8-8960](8960/previews/pattern_8.png) | ![pattern_9-8960](8960/previews/pattern_9.png) | ![pattern_10-8960](8960/previews/pattern_10.png) | ![pattern_11-8960](8960/previews/pattern_11.png) | ![pattern_12-8960](8960/previews/pattern_12.png) | ![pattern_13-8960](8960/previews/pattern_13.png) | ![pattern_14-8960](8960/previews/pattern_14.png) | ![pattern_15-8960](8960/previews/pattern_15.png) | ![pattern_16-8960](8960/previews/pattern_16.png) | ![pattern_17-8960](8960/previews/pattern_17.png) | ![pattern_18-8960](8960/previews/pattern_18.png) | ![pattern_19-8960](8960/previews/pattern_19.png) | ![pattern_20-8960](8960/previews/pattern_20.png) | ![bikini-8960](8960/previews/bikini.png) | [<NSFW, click to see>](8960/previews/bondage.png) | ![free-8960](8960/previews/free.png) | ![maid-8960](8960/previews/maid.png) | ![miko-8960](8960/previews/miko.png) | [<NSFW, click to see>](8960/previews/nude.png) | [<NSFW, click to see>](8960/previews/nude2.png) | ![suit-8960](8960/previews/suit.png) | ![yukata-8960](8960/previews/yukata.png) | | 8320 | 0.843 | [Download](8320/euphyllia_magenta_tenseioujototensaireijounomahoukakumei.zip) | ![pattern_1-8320](8320/previews/pattern_1.png) | ![pattern_2-8320](8320/previews/pattern_2.png) | ![pattern_3-8320](8320/previews/pattern_3.png) | ![pattern_4-8320](8320/previews/pattern_4.png) | ![pattern_5-8320](8320/previews/pattern_5.png) | ![pattern_6-8320](8320/previews/pattern_6.png) | ![pattern_7-8320](8320/previews/pattern_7.png) | ![pattern_8-8320](8320/previews/pattern_8.png) | ![pattern_9-8320](8320/previews/pattern_9.png) | ![pattern_10-8320](8320/previews/pattern_10.png) | ![pattern_11-8320](8320/previews/pattern_11.png) | ![pattern_12-8320](8320/previews/pattern_12.png) | ![pattern_13-8320](8320/previews/pattern_13.png) | ![pattern_14-8320](8320/previews/pattern_14.png) | ![pattern_15-8320](8320/previews/pattern_15.png) | ![pattern_16-8320](8320/previews/pattern_16.png) | ![pattern_17-8320](8320/previews/pattern_17.png) | ![pattern_18-8320](8320/previews/pattern_18.png) | ![pattern_19-8320](8320/previews/pattern_19.png) | ![pattern_20-8320](8320/previews/pattern_20.png) | ![bikini-8320](8320/previews/bikini.png) | [<NSFW, click to see>](8320/previews/bondage.png) | ![free-8320](8320/previews/free.png) | ![maid-8320](8320/previews/maid.png) | ![miko-8320](8320/previews/miko.png) | [<NSFW, click to see>](8320/previews/nude.png) | [<NSFW, click to see>](8320/previews/nude2.png) | ![suit-8320](8320/previews/suit.png) | ![yukata-8320](8320/previews/yukata.png) | | 7680 | 0.802 | [Download](7680/euphyllia_magenta_tenseioujototensaireijounomahoukakumei.zip) | ![pattern_1-7680](7680/previews/pattern_1.png) | ![pattern_2-7680](7680/previews/pattern_2.png) | ![pattern_3-7680](7680/previews/pattern_3.png) | ![pattern_4-7680](7680/previews/pattern_4.png) | ![pattern_5-7680](7680/previews/pattern_5.png) | ![pattern_6-7680](7680/previews/pattern_6.png) | ![pattern_7-7680](7680/previews/pattern_7.png) | ![pattern_8-7680](7680/previews/pattern_8.png) | ![pattern_9-7680](7680/previews/pattern_9.png) | ![pattern_10-7680](7680/previews/pattern_10.png) | ![pattern_11-7680](7680/previews/pattern_11.png) | ![pattern_12-7680](7680/previews/pattern_12.png) | ![pattern_13-7680](7680/previews/pattern_13.png) | ![pattern_14-7680](7680/previews/pattern_14.png) | ![pattern_15-7680](7680/previews/pattern_15.png) | ![pattern_16-7680](7680/previews/pattern_16.png) | ![pattern_17-7680](7680/previews/pattern_17.png) | ![pattern_18-7680](7680/previews/pattern_18.png) | ![pattern_19-7680](7680/previews/pattern_19.png) | ![pattern_20-7680](7680/previews/pattern_20.png) | ![bikini-7680](7680/previews/bikini.png) | [<NSFW, click to see>](7680/previews/bondage.png) | ![free-7680](7680/previews/free.png) | ![maid-7680](7680/previews/maid.png) | ![miko-7680](7680/previews/miko.png) | [<NSFW, click to see>](7680/previews/nude.png) | [<NSFW, click to see>](7680/previews/nude2.png) | ![suit-7680](7680/previews/suit.png) | ![yukata-7680](7680/previews/yukata.png) | | 7040 | 0.841 | [Download](7040/euphyllia_magenta_tenseioujototensaireijounomahoukakumei.zip) | ![pattern_1-7040](7040/previews/pattern_1.png) | ![pattern_2-7040](7040/previews/pattern_2.png) | ![pattern_3-7040](7040/previews/pattern_3.png) | ![pattern_4-7040](7040/previews/pattern_4.png) | ![pattern_5-7040](7040/previews/pattern_5.png) | ![pattern_6-7040](7040/previews/pattern_6.png) | ![pattern_7-7040](7040/previews/pattern_7.png) | ![pattern_8-7040](7040/previews/pattern_8.png) | ![pattern_9-7040](7040/previews/pattern_9.png) | ![pattern_10-7040](7040/previews/pattern_10.png) | ![pattern_11-7040](7040/previews/pattern_11.png) | ![pattern_12-7040](7040/previews/pattern_12.png) | ![pattern_13-7040](7040/previews/pattern_13.png) | ![pattern_14-7040](7040/previews/pattern_14.png) | ![pattern_15-7040](7040/previews/pattern_15.png) | ![pattern_16-7040](7040/previews/pattern_16.png) | ![pattern_17-7040](7040/previews/pattern_17.png) | ![pattern_18-7040](7040/previews/pattern_18.png) | ![pattern_19-7040](7040/previews/pattern_19.png) | ![pattern_20-7040](7040/previews/pattern_20.png) | ![bikini-7040](7040/previews/bikini.png) | [<NSFW, click to see>](7040/previews/bondage.png) | ![free-7040](7040/previews/free.png) | ![maid-7040](7040/previews/maid.png) | ![miko-7040](7040/previews/miko.png) | [<NSFW, click to see>](7040/previews/nude.png) | [<NSFW, click to see>](7040/previews/nude2.png) | ![suit-7040](7040/previews/suit.png) | ![yukata-7040](7040/previews/yukata.png) | | 6400 | 0.808 | [Download](6400/euphyllia_magenta_tenseioujototensaireijounomahoukakumei.zip) | ![pattern_1-6400](6400/previews/pattern_1.png) | ![pattern_2-6400](6400/previews/pattern_2.png) | ![pattern_3-6400](6400/previews/pattern_3.png) | ![pattern_4-6400](6400/previews/pattern_4.png) | ![pattern_5-6400](6400/previews/pattern_5.png) | ![pattern_6-6400](6400/previews/pattern_6.png) | ![pattern_7-6400](6400/previews/pattern_7.png) | ![pattern_8-6400](6400/previews/pattern_8.png) | ![pattern_9-6400](6400/previews/pattern_9.png) | ![pattern_10-6400](6400/previews/pattern_10.png) | ![pattern_11-6400](6400/previews/pattern_11.png) | ![pattern_12-6400](6400/previews/pattern_12.png) | ![pattern_13-6400](6400/previews/pattern_13.png) | ![pattern_14-6400](6400/previews/pattern_14.png) | ![pattern_15-6400](6400/previews/pattern_15.png) | ![pattern_16-6400](6400/previews/pattern_16.png) | ![pattern_17-6400](6400/previews/pattern_17.png) | ![pattern_18-6400](6400/previews/pattern_18.png) | ![pattern_19-6400](6400/previews/pattern_19.png) | ![pattern_20-6400](6400/previews/pattern_20.png) | ![bikini-6400](6400/previews/bikini.png) | [<NSFW, click to see>](6400/previews/bondage.png) | ![free-6400](6400/previews/free.png) | ![maid-6400](6400/previews/maid.png) | ![miko-6400](6400/previews/miko.png) | [<NSFW, click to see>](6400/previews/nude.png) | [<NSFW, click to see>](6400/previews/nude2.png) | ![suit-6400](6400/previews/suit.png) | ![yukata-6400](6400/previews/yukata.png) | | 5760 | 0.824 | [Download](5760/euphyllia_magenta_tenseioujototensaireijounomahoukakumei.zip) | ![pattern_1-5760](5760/previews/pattern_1.png) | ![pattern_2-5760](5760/previews/pattern_2.png) | ![pattern_3-5760](5760/previews/pattern_3.png) | ![pattern_4-5760](5760/previews/pattern_4.png) | ![pattern_5-5760](5760/previews/pattern_5.png) | ![pattern_6-5760](5760/previews/pattern_6.png) | ![pattern_7-5760](5760/previews/pattern_7.png) | ![pattern_8-5760](5760/previews/pattern_8.png) | ![pattern_9-5760](5760/previews/pattern_9.png) | ![pattern_10-5760](5760/previews/pattern_10.png) | ![pattern_11-5760](5760/previews/pattern_11.png) | ![pattern_12-5760](5760/previews/pattern_12.png) | ![pattern_13-5760](5760/previews/pattern_13.png) | ![pattern_14-5760](5760/previews/pattern_14.png) | ![pattern_15-5760](5760/previews/pattern_15.png) | ![pattern_16-5760](5760/previews/pattern_16.png) | ![pattern_17-5760](5760/previews/pattern_17.png) | ![pattern_18-5760](5760/previews/pattern_18.png) | ![pattern_19-5760](5760/previews/pattern_19.png) | ![pattern_20-5760](5760/previews/pattern_20.png) | ![bikini-5760](5760/previews/bikini.png) | [<NSFW, click to see>](5760/previews/bondage.png) | ![free-5760](5760/previews/free.png) | ![maid-5760](5760/previews/maid.png) | ![miko-5760](5760/previews/miko.png) | [<NSFW, click to see>](5760/previews/nude.png) | [<NSFW, click to see>](5760/previews/nude2.png) | ![suit-5760](5760/previews/suit.png) | ![yukata-5760](5760/previews/yukata.png) | | 5120 | 0.808 | [Download](5120/euphyllia_magenta_tenseioujototensaireijounomahoukakumei.zip) | ![pattern_1-5120](5120/previews/pattern_1.png) | ![pattern_2-5120](5120/previews/pattern_2.png) | ![pattern_3-5120](5120/previews/pattern_3.png) | ![pattern_4-5120](5120/previews/pattern_4.png) | ![pattern_5-5120](5120/previews/pattern_5.png) | ![pattern_6-5120](5120/previews/pattern_6.png) | ![pattern_7-5120](5120/previews/pattern_7.png) | ![pattern_8-5120](5120/previews/pattern_8.png) | ![pattern_9-5120](5120/previews/pattern_9.png) | ![pattern_10-5120](5120/previews/pattern_10.png) | ![pattern_11-5120](5120/previews/pattern_11.png) | ![pattern_12-5120](5120/previews/pattern_12.png) | ![pattern_13-5120](5120/previews/pattern_13.png) | ![pattern_14-5120](5120/previews/pattern_14.png) | ![pattern_15-5120](5120/previews/pattern_15.png) | ![pattern_16-5120](5120/previews/pattern_16.png) | ![pattern_17-5120](5120/previews/pattern_17.png) | ![pattern_18-5120](5120/previews/pattern_18.png) | ![pattern_19-5120](5120/previews/pattern_19.png) | ![pattern_20-5120](5120/previews/pattern_20.png) | ![bikini-5120](5120/previews/bikini.png) | [<NSFW, click to see>](5120/previews/bondage.png) | ![free-5120](5120/previews/free.png) | ![maid-5120](5120/previews/maid.png) | ![miko-5120](5120/previews/miko.png) | [<NSFW, click to see>](5120/previews/nude.png) | [<NSFW, click to see>](5120/previews/nude2.png) | ![suit-5120](5120/previews/suit.png) | ![yukata-5120](5120/previews/yukata.png) | | 4480 | 0.803 | [Download](4480/euphyllia_magenta_tenseioujototensaireijounomahoukakumei.zip) | ![pattern_1-4480](4480/previews/pattern_1.png) | ![pattern_2-4480](4480/previews/pattern_2.png) | ![pattern_3-4480](4480/previews/pattern_3.png) | ![pattern_4-4480](4480/previews/pattern_4.png) | ![pattern_5-4480](4480/previews/pattern_5.png) | ![pattern_6-4480](4480/previews/pattern_6.png) | ![pattern_7-4480](4480/previews/pattern_7.png) | ![pattern_8-4480](4480/previews/pattern_8.png) | ![pattern_9-4480](4480/previews/pattern_9.png) | ![pattern_10-4480](4480/previews/pattern_10.png) | ![pattern_11-4480](4480/previews/pattern_11.png) | ![pattern_12-4480](4480/previews/pattern_12.png) | ![pattern_13-4480](4480/previews/pattern_13.png) | ![pattern_14-4480](4480/previews/pattern_14.png) | ![pattern_15-4480](4480/previews/pattern_15.png) | ![pattern_16-4480](4480/previews/pattern_16.png) | ![pattern_17-4480](4480/previews/pattern_17.png) | ![pattern_18-4480](4480/previews/pattern_18.png) | ![pattern_19-4480](4480/previews/pattern_19.png) | ![pattern_20-4480](4480/previews/pattern_20.png) | ![bikini-4480](4480/previews/bikini.png) | [<NSFW, click to see>](4480/previews/bondage.png) | ![free-4480](4480/previews/free.png) | ![maid-4480](4480/previews/maid.png) | ![miko-4480](4480/previews/miko.png) | [<NSFW, click to see>](4480/previews/nude.png) | [<NSFW, click to see>](4480/previews/nude2.png) | ![suit-4480](4480/previews/suit.png) | ![yukata-4480](4480/previews/yukata.png) | | 3840 | 0.777 | [Download](3840/euphyllia_magenta_tenseioujototensaireijounomahoukakumei.zip) | ![pattern_1-3840](3840/previews/pattern_1.png) | ![pattern_2-3840](3840/previews/pattern_2.png) | ![pattern_3-3840](3840/previews/pattern_3.png) | ![pattern_4-3840](3840/previews/pattern_4.png) | ![pattern_5-3840](3840/previews/pattern_5.png) | ![pattern_6-3840](3840/previews/pattern_6.png) | ![pattern_7-3840](3840/previews/pattern_7.png) | ![pattern_8-3840](3840/previews/pattern_8.png) | ![pattern_9-3840](3840/previews/pattern_9.png) | ![pattern_10-3840](3840/previews/pattern_10.png) | ![pattern_11-3840](3840/previews/pattern_11.png) | ![pattern_12-3840](3840/previews/pattern_12.png) | ![pattern_13-3840](3840/previews/pattern_13.png) | ![pattern_14-3840](3840/previews/pattern_14.png) | ![pattern_15-3840](3840/previews/pattern_15.png) | ![pattern_16-3840](3840/previews/pattern_16.png) | ![pattern_17-3840](3840/previews/pattern_17.png) | ![pattern_18-3840](3840/previews/pattern_18.png) | ![pattern_19-3840](3840/previews/pattern_19.png) | ![pattern_20-3840](3840/previews/pattern_20.png) | ![bikini-3840](3840/previews/bikini.png) | [<NSFW, click to see>](3840/previews/bondage.png) | ![free-3840](3840/previews/free.png) | ![maid-3840](3840/previews/maid.png) | ![miko-3840](3840/previews/miko.png) | [<NSFW, click to see>](3840/previews/nude.png) | [<NSFW, click to see>](3840/previews/nude2.png) | ![suit-3840](3840/previews/suit.png) | ![yukata-3840](3840/previews/yukata.png) | | 3200 | 0.834 | [Download](3200/euphyllia_magenta_tenseioujototensaireijounomahoukakumei.zip) | ![pattern_1-3200](3200/previews/pattern_1.png) | ![pattern_2-3200](3200/previews/pattern_2.png) | ![pattern_3-3200](3200/previews/pattern_3.png) | ![pattern_4-3200](3200/previews/pattern_4.png) | ![pattern_5-3200](3200/previews/pattern_5.png) | ![pattern_6-3200](3200/previews/pattern_6.png) | ![pattern_7-3200](3200/previews/pattern_7.png) | ![pattern_8-3200](3200/previews/pattern_8.png) | ![pattern_9-3200](3200/previews/pattern_9.png) | ![pattern_10-3200](3200/previews/pattern_10.png) | ![pattern_11-3200](3200/previews/pattern_11.png) | ![pattern_12-3200](3200/previews/pattern_12.png) | ![pattern_13-3200](3200/previews/pattern_13.png) | ![pattern_14-3200](3200/previews/pattern_14.png) | ![pattern_15-3200](3200/previews/pattern_15.png) | ![pattern_16-3200](3200/previews/pattern_16.png) | ![pattern_17-3200](3200/previews/pattern_17.png) | ![pattern_18-3200](3200/previews/pattern_18.png) | ![pattern_19-3200](3200/previews/pattern_19.png) | ![pattern_20-3200](3200/previews/pattern_20.png) | ![bikini-3200](3200/previews/bikini.png) | [<NSFW, click to see>](3200/previews/bondage.png) | ![free-3200](3200/previews/free.png) | ![maid-3200](3200/previews/maid.png) | ![miko-3200](3200/previews/miko.png) | [<NSFW, click to see>](3200/previews/nude.png) | [<NSFW, click to see>](3200/previews/nude2.png) | ![suit-3200](3200/previews/suit.png) | ![yukata-3200](3200/previews/yukata.png) | | 2560 | 0.777 | [Download](2560/euphyllia_magenta_tenseioujototensaireijounomahoukakumei.zip) | ![pattern_1-2560](2560/previews/pattern_1.png) | ![pattern_2-2560](2560/previews/pattern_2.png) | ![pattern_3-2560](2560/previews/pattern_3.png) | ![pattern_4-2560](2560/previews/pattern_4.png) | ![pattern_5-2560](2560/previews/pattern_5.png) | ![pattern_6-2560](2560/previews/pattern_6.png) | ![pattern_7-2560](2560/previews/pattern_7.png) | ![pattern_8-2560](2560/previews/pattern_8.png) | 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| | 1920 | 0.775 | [Download](1920/euphyllia_magenta_tenseioujototensaireijounomahoukakumei.zip) | ![pattern_1-1920](1920/previews/pattern_1.png) | ![pattern_2-1920](1920/previews/pattern_2.png) | ![pattern_3-1920](1920/previews/pattern_3.png) | ![pattern_4-1920](1920/previews/pattern_4.png) | ![pattern_5-1920](1920/previews/pattern_5.png) | ![pattern_6-1920](1920/previews/pattern_6.png) | ![pattern_7-1920](1920/previews/pattern_7.png) | ![pattern_8-1920](1920/previews/pattern_8.png) | ![pattern_9-1920](1920/previews/pattern_9.png) | ![pattern_10-1920](1920/previews/pattern_10.png) | ![pattern_11-1920](1920/previews/pattern_11.png) | ![pattern_12-1920](1920/previews/pattern_12.png) | ![pattern_13-1920](1920/previews/pattern_13.png) | ![pattern_14-1920](1920/previews/pattern_14.png) | ![pattern_15-1920](1920/previews/pattern_15.png) | ![pattern_16-1920](1920/previews/pattern_16.png) | ![pattern_17-1920](1920/previews/pattern_17.png) | ![pattern_18-1920](1920/previews/pattern_18.png) | ![pattern_19-1920](1920/previews/pattern_19.png) | ![pattern_20-1920](1920/previews/pattern_20.png) | ![bikini-1920](1920/previews/bikini.png) | [<NSFW, click to see>](1920/previews/bondage.png) | ![free-1920](1920/previews/free.png) | ![maid-1920](1920/previews/maid.png) | ![miko-1920](1920/previews/miko.png) | [<NSFW, click to see>](1920/previews/nude.png) | [<NSFW, click to see>](1920/previews/nude2.png) | ![suit-1920](1920/previews/suit.png) | ![yukata-1920](1920/previews/yukata.png) | | 1280 | 0.737 | [Download](1280/euphyllia_magenta_tenseioujototensaireijounomahoukakumei.zip) | ![pattern_1-1280](1280/previews/pattern_1.png) | ![pattern_2-1280](1280/previews/pattern_2.png) | ![pattern_3-1280](1280/previews/pattern_3.png) | ![pattern_4-1280](1280/previews/pattern_4.png) | ![pattern_5-1280](1280/previews/pattern_5.png) | ![pattern_6-1280](1280/previews/pattern_6.png) | ![pattern_7-1280](1280/previews/pattern_7.png) | ![pattern_8-1280](1280/previews/pattern_8.png) | 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| | 640 | 0.746 | [Download](640/euphyllia_magenta_tenseioujototensaireijounomahoukakumei.zip) | ![pattern_1-640](640/previews/pattern_1.png) | ![pattern_2-640](640/previews/pattern_2.png) | ![pattern_3-640](640/previews/pattern_3.png) | ![pattern_4-640](640/previews/pattern_4.png) | ![pattern_5-640](640/previews/pattern_5.png) | ![pattern_6-640](640/previews/pattern_6.png) | ![pattern_7-640](640/previews/pattern_7.png) | ![pattern_8-640](640/previews/pattern_8.png) | ![pattern_9-640](640/previews/pattern_9.png) | ![pattern_10-640](640/previews/pattern_10.png) | ![pattern_11-640](640/previews/pattern_11.png) | ![pattern_12-640](640/previews/pattern_12.png) | ![pattern_13-640](640/previews/pattern_13.png) | ![pattern_14-640](640/previews/pattern_14.png) | ![pattern_15-640](640/previews/pattern_15.png) | ![pattern_16-640](640/previews/pattern_16.png) | ![pattern_17-640](640/previews/pattern_17.png) | ![pattern_18-640](640/previews/pattern_18.png) | 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markjosims/wav2vec2-large-mms-1b-tira-lid
markjosims
2023-09-13T19:10:44Z
5
0
transformers
[ "transformers", "pytorch", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/mms-1b-all", "base_model:finetune:facebook/mms-1b-all", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
audio-classification
2023-08-28T20:14:57Z
--- license: cc-by-nc-4.0 base_model: facebook/mms-1b-all tags: - generated_from_trainer metrics: - accuracy model-index: - name: wav2vec2-large-mms-1b-tira-lid results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-mms-1b-tira-lid This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0026 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 2 - 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: 100 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3168 | 0.42 | 100 | 0.2023 | 0.9167 | | 0.3278 | 0.84 | 200 | 0.1465 | 0.9667 | | 0.2725 | 1.26 | 300 | 0.6432 | 0.8 | | 0.1371 | 1.67 | 400 | 0.0144 | 1.0 | | 0.094 | 2.09 | 500 | 0.0015 | 1.0 | | 0.0654 | 2.51 | 600 | 0.0978 | 0.9667 | | 0.1813 | 2.93 | 700 | 0.1174 | 0.9833 | | 0.032 | 3.35 | 800 | 0.0019 | 1.0 | | 0.0422 | 3.77 | 900 | 0.0026 | 1.0 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
HyperbeeAI/Tulpar-7b-v0
HyperbeeAI
2023-09-13T19:04:10Z
1,425
22
transformers
[ "transformers", "pytorch", "llama", "text-generation", "en", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-08-23T10:13:55Z
--- license: llama2 language: - en library_name: transformers thumbnail: "https://huggingface.co/HyperbeeAI/Tulpar-7b-v0/resolve/main/tulpar.png" --- <p align="center"> <img src="https://huggingface.co/HyperbeeAI/Tulpar-7b-v0/resolve/main/tulpar.png" width="360" height="360" > </p> # Model Description Tulpar-7b is a LLama2-7b-based model trained by HyperbeeAI. Training is done on a filtered and preprocessed instruction finetuning dataset that includes GPT-4 generated and generally curated datasets like Airoboros and Platypus. # Example Usage Loading the model: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HyperbeeAI/Tulpar-7b-v0") model = AutoModelForCausalLM.from_pretrained("HyperbeeAI/Tulpar-7b-v0", device_map="auto") ``` You can run inference with both of the following prompts: ```python input_text="What is deep learning?" prompt = f"### User: {input_text}\n\n### Assistant:\n" inputs = tokenizer(prompt, return_tensors="pt") output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=512) print(tokenizer.decode(output[0])) ``` ```python input_text="What is deep learning?" prompt = f"Question: {input_text}\n\nAnswer:" inputs = tokenizer(prompt, return_tensors="pt") output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=512) print(tokenizer.decode(output[0])) ``` # Evaluation Our offline HF Leaderboard evaluation results: |||| |:------:|:--------:|:-------:| |**Task**|**Metric**|**Value**| |*arc_challenge*|acc_norm|0.5614| |*hellaswag*|acc_norm|0.7901| |*mmlu*|acc_norm|0.5242| |*truthfulqa_mc*|mc2|0.5160| |**Average**|-|**0.5979**|| Other GPT4All evaluation results: |||| |:------:|:--------:|:-------:| |**Task**|**Metric**|**Value**| |boolq|acc |0.8306| |piqa|acc |0.7905| | |acc_norm|0.7884| |winogrande|acc |0.7159| |openbookqa|acc |0.356| | |acc_norm|0.448| |**Average** (including HF leaderboard datasets) | | **0.6468** | BigBenchHard results: |||| |:------:|:--------:|:-------:| |**Task**|**Metric**|**Value**| |bigbench_causal_judgement |multiple_choice_grade|0.6105| |bigbench_date_understanding |multiple_choice_grade|0.6423| |bigbench_disambiguation_qa |multiple_choice_grade|0.3643| |bigbench_dyck_languages |multiple_choice_grade|0.2000| |bigbench_formal_fallacies_syllogisms_negation |multiple_choice_grade|0.5002| |bigbench_geometric_shapes |multiple_choice_grade|0.0000| | |exact_str_match |0.0000| |bigbench_hyperbaton |multiple_choice_grade|0.6754| |bigbench_logical_deduction_five_objects |multiple_choice_grade|0.2700| |bigbench_logical_deduction_seven_objects |multiple_choice_grade|0.1929| |bigbench_logical_deduction_three_objects |multiple_choice_grade|0.4133| |bigbench_movie_recommendation |multiple_choice_grade|0.3000| |bigbench_navigate |multiple_choice_grade|0.5000| |bigbench_reasoning_about_colored_objects |multiple_choice_grade|0.5750| |bigbench_ruin_names |multiple_choice_grade|0.3281| |bigbench_salient_translation_error_detection |multiple_choice_grade|0.2976| |bigbench_snarks |multiple_choice_grade|0.6022| |bigbench_sports_understanding |multiple_choice_grade|0.5122| |bigbench_temporal_sequences |multiple_choice_grade|0.1450| |bigbench_tracking_shuffled_objects_five_objects |multiple_choice_grade|0.1976| |bigbench_tracking_shuffled_objects_seven_objects|multiple_choice_grade|0.1440| |bigbench_tracking_shuffled_objects_three_objects|multiple_choice_grade|0.4133| |**Average**| |**0.3754** # Ethical Considerations and Limitations Tulpar is a technology with potential risks and limitations. This model is finetuned only in English and all language-related scenarios are not covered. As HyperbeeAI, we neither guarantee ethical, accurate, unbiased, objective responses nor endorse its outputs. Before deploying this model, you are advised to make safety tests for your use case.
VMware/electra-base-mrqa
VMware
2023-09-13T19:01:31Z
104
0
transformers
[ "transformers", "pytorch", "safetensors", "electra", "question-answering", "en", "dataset:mrqa", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
question-answering
2023-02-17T21:17:00Z
--- license: apache-2.0 datasets: - mrqa language: - en metrics: - exact_match - f1 model-index: - name: VMware/electra-base-mrqa results: - task: type: Question-Answering dataset: type: mrqa name: MRQA metrics: - type: exact_match value: 68.78 name: Eval EM - type: f1 value: 80.16 name: Eval F1 - type: exact_match value: 54.70 name: Test EM - type: f1 value: 65.80 name: Test F1 --- This model release is part of a joint research project with Howard University's Innovation Foundry/AIM-AHEAD Lab. # Model Details - **Model name:** ELECTRA-Base-MRQA - **Model type:** Extractive Question Answering - **Parent Model:** [ELECTRA-Base-Discriminator](https://huggingface.co/google/electra-base-discriminator) - **Training dataset:** [MRQA](https://huggingface.co/datasets/mrqa) (Machine Reading for Question Answering) - **Training data size:** 516,819 examples - **Training time:** 8:40:57 on 1 Nvidia V100 32GB GPU - **Language:** English - **Framework:** PyTorch - **Model version:** 1.0 # Intended Use This model is intended to provide accurate answers to questions based on context passages. It can be used for a variety of tasks, including question-answering for search engines, chatbots, customer service systems, and other applications that require natural language understanding. # How to Use ```python from transformers import pipeline question_answerer = pipeline("question-answering", model='VMware/electra-base-mrqa') context = "We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. In this task, we adapted and unified 18 distinct question answering datasets into the same format. Among them, six datasets were made available for training, six datasets were made available for development, and the final six were hidden for final evaluation. Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training and ensembling. The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than our initial baseline based on BERT." question = "What is MRQA?" result = question_answerer(question=question, context=context) print(result) # { # 'score': 0.9068707823753357, # 'start': 30, # 'end': 68, # 'answer': 'Machine Reading for Question Answering' # } ``` # Training Details The model was trained for 1 epoch on the MRQA training set. ## Training Hyperparameters ```python args = TrainingArguments( "electra-base-mrqa", save_strategy="epoch", learning_rate=1e-5, num_train_epochs=1, weight_decay=0.01, per_device_train_batch_size=16, ) ``` # Evaluation Metrics The model was evaluated using standard metrics for question-answering models, including: Exact match (EM): The percentage of questions for which the model produces an exact match with the ground truth answer. F1 score: A weighted average of precision and recall, which measures the overlap between the predicted answer and the ground truth answer. # Model Family Performance | Parent Language Model | Number of Parameters | Training Time | Eval Time | Test Time | Eval EM | Eval F1 | Test EM | Test F1 | |---|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:| | BERT-Tiny | 4,369,666 | 26:11 | 0:41 | 0:04 | 22.78 | 32.42 | 10.18 | 18.72 | | BERT-Base | 108,893,186 | 8:39:10 | 18:42 | 2:13 | 64.48 | 76.14 | 48.89 | 59.89 | | BERT-Large | 334,094,338 | 28:35:38 | 1:00:56 | 7:14 | 69.52 | 80.50 | 55.00 | 65.78 | | DeBERTa-v3-Extra-Small | 70,682,882 | 5:19:05 | 11:29 | 1:16 | 65.58 | 77.17 | 50.92 | 62.58 | | DeBERTa-v3-Base | 183,833,090 | 12:13:41 | 28:18 | 3:09 | 71.43 | 82.59 | 59.49 | 70.46 | | DeBERTa-v3-Large | 434,014,210 | 38:36:13 | 1:25:47 | 9:33 | **76.08** | **86.23** | **64.27** | **75.22** | | ELECTRA-Small | 13,483,522 | 2:16:36 | 3:55 | 0:27 | 57.63 | 69.38 | 38.68 | 51.56 | | ELECTRA-Base | 108,893,186 | 8:40:57 | 18:41 | 2:12 | 68.78 | 80.16 | 54.70 | 65.80 | | ELECTRA-Large | 334,094,338 | 28:31:59 | 1:00:40 | 7:13 | 74.15 | 84.96 | 62.35 | 73.28 | | MiniLMv2-L6-H384-from-BERT-Large | 22,566,146 | 2:12:48 | 4:23 | 0:40 | 59.31 | 71.09 | 41.78 | 53.30 | | MiniLMv2-L6-H768-from-BERT-Large | 66,365,954 | 4:42:59 | 10:01 | 1:10 | 64.27 | 75.84 | 49.05 | 59.82 | | MiniLMv2-L6-H384-from-RoBERTa-Large | 30,147,842 | 2:15:10 | 4:19 | 0:30 | 59.27 | 70.64 | 42.95 | 54.03 | | MiniLMv2-L12-H384-from-RoBERTa-Large | 40,794,626 | 4:14:22 | 8:27 | 0:58 | 64.58 | 76.23 | 51.28 | 62.83 | | MiniLMv2-L6-H768-from-RoBERTa-Large | 81,529,346 | 4:39:02 | 9:34 | 1:06 | 65.80 | 77.17 | 51.72 | 63.27 | | TinyRoBERTa | 81,529.346 | 4:27:06\* | 9:54 | 1:04 | 69.38 | 80.07 | 53.29 | 64.16 | | RoBERTa-Base | 124,056,578 | 8:50:29 | 18:59 | 2:11 | 69.06 | 80.08 | 55.53 | 66.49 | | RoBERTa-Large | 354,312,194 | 29:16:06 | 1:01:10 | 7:04 | 74.08 | 84.38 | 62.20 | 72.88 | \* TinyRoBERTa's training time isn't directly comparable to the other models since it was distilled from [VMware/roberta-large-mrqa](https://huggingface.co/VMware/roberta-large-mrqa) that was already trained on MRQA. # Limitations and Bias The model is based on a large and diverse dataset, but it may still have limitations and biases in certain areas. Some limitations include: - Language: The model is designed to work with English text only and may not perform as well on other languages. - Domain-specific knowledge: The model has been trained on a general dataset and may not perform well on questions that require domain-specific knowledge. - Out-of-distribution questions: The model may struggle with questions that are outside the scope of the MRQA dataset. This is best demonstrated by the delta between its scores on the eval vs test datasets. In addition, the model may have some bias in terms of the data it was trained on. The dataset includes questions from a variety of sources, but it may not be representative of all populations or perspectives. As a result, the model may perform better or worse for certain types of questions or on certain types of texts.
davera-017/ppo-LunarLander-v2
davera-017
2023-09-13T19:00:42Z
1
0
transformers
[ "transformers", "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "endpoints_compatible", "region:us" ]
reinforcement-learning
2023-08-08T17:07:28Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -89.76 +/- 56.00 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'davera-017/ppo-LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
badhorse666/ppo-Pyramids
badhorse666
2023-09-13T18:56:02Z
13
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
2023-09-13T18:55:27Z
--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: badhorse666/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
artyomboyko/ppo-LunarLander-v2
artyomboyko
2023-09-13T18:55:27Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-13T18:55:19Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 291.27 +/- 22.04 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
vinben007/ppo-LunarLander-v2
vinben007
2023-09-13T18:55:22Z
1
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-13T18:55:05Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 263.63 +/- 10.17 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Terps/ppo-PyramidsRND
Terps
2023-09-13T18:51:28Z
2
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
2023-09-13T18:50:25Z
--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Terps/ppo-PyramidsRND 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
tayar3/ckpt-1-0
tayar3
2023-09-13T18:32:22Z
31
1
diffusers
[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-09-13T18:28:37Z
--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### ckpt-1.0 Dreambooth model trained by tayar3 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:
muhtasham/bert-tiny-finetuned-cuad
muhtasham
2023-09-13T18:25:17Z
108
1
transformers
[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "dataset:cuad", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2022-08-01T20:34:59Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - cuad model-index: - name: tiny-bert-finetuned-cuad 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. --> # tiny-bert-finetuned-cuad This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the portion of cuad dataset. It achieves the following results on the evaluation set: - Loss: 0.4606 # Note The model was not trained on the whole dataset but, the first 10% of `train` + the first 10% of `test`. ```bash raw_datasets_train, raw_datasets_test = load_dataset("cuad", split=['train[:10%]', 'test[:10%]']) datasets = DatasetDict({'train': raw_datasets_train, 'validation': raw_datasets_test}) ``` ## 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: 1024 - eval_batch_size: 1024 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 136 | 2.9644 | | No log | 2.0 | 272 | 1.9337 | | No log | 3.0 | 408 | 1.4375 | | 2.7124 | 4.0 | 544 | 1.0978 | | 2.7124 | 5.0 | 680 | 0.8571 | | 2.7124 | 6.0 | 816 | 0.6907 | | 2.7124 | 7.0 | 952 | 0.5799 | | 0.9512 | 8.0 | 1088 | 0.5105 | | 0.9512 | 9.0 | 1224 | 0.4726 | | 0.9512 | 10.0 | 1360 | 0.4606 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
muhtasham/finetuned-base_mini
muhtasham
2023-09-13T18:25:07Z
105
1
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:imdb", "base_model:google/bert_uncased_L-4_H-256_A-4", "base_model:finetune:google/bert_uncased_L-4_H-256_A-4", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-12-01T23:48:54Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 base_model: google/bert_uncased_L-4_H-256_A-4 model-index: - name: finetuned-base_mini results: - task: type: text-classification name: Text Classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - type: accuracy value: 0.9076 name: Accuracy - type: f1 value: 0.9515621723631789 name: F1 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned-base_mini This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3938 - Accuracy: 0.9076 - F1: 0.9516 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.354 | 2.55 | 500 | 0.2300 | 0.9116 | 0.9538 | | 0.2086 | 5.1 | 1000 | 0.3182 | 0.8815 | 0.9370 | | 0.1401 | 7.65 | 1500 | 0.2160 | 0.9241 | 0.9605 | | 0.0902 | 10.2 | 2000 | 0.4684 | 0.8722 | 0.9317 | | 0.0654 | 12.76 | 2500 | 0.4885 | 0.8747 | 0.9332 | | 0.043 | 15.31 | 3000 | 0.3938 | 0.9076 | 0.9516 | ### Framework versions - Transformers 4.25.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
muhtasham/tiny-mlm-snli
muhtasham
2023-09-13T18:24:57Z
109
1
transformers
[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "generated_from_trainer", "base_model:google/bert_uncased_L-2_H-128_A-2", "base_model:finetune:google/bert_uncased_L-2_H-128_A-2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-01-03T21:18:56Z
--- license: apache-2.0 tags: - generated_from_trainer base_model: google/bert_uncased_L-2_H-128_A-2 model-index: - name: tiny-mlm-snli-plain_text 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. --> # tiny-mlm-snli-plain_text This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1233 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.665 | 0.4 | 500 | 3.2495 | | 3.4103 | 0.8 | 1000 | nan | | 3.2635 | 1.2 | 1500 | 3.1518 | | 3.1738 | 1.6 | 2000 | 3.1555 | | 3.0556 | 2.0 | 2500 | 3.0593 | | 2.9933 | 2.4 | 3000 | 3.0970 | | 2.9019 | 2.8 | 3500 | 3.0773 | | 2.876 | 3.2 | 4000 | 3.1233 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1 - Datasets 2.8.0 - Tokenizers 0.13.2
muhtasham/base-mlm-imdb
muhtasham
2023-09-13T18:24:21Z
119
0
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "base_model:google/bert_uncased_L-12_H-768_A-12", "base_model:finetune:google/bert_uncased_L-12_H-768_A-12", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-12-11T00:55:55Z
--- license: apache-2.0 tags: - generated_from_trainer base_model: google/bert_uncased_L-12_H-768_A-12 model-index: - name: base-mlm-imdb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # base-mlm-imdb This model is a fine-tuned version of [google/bert_uncased_L-12_H-768_A-12](https://huggingface.co/google/bert_uncased_L-12_H-768_A-12) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9103 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.1149 | 0.16 | 500 | 1.9627 | | 2.0674 | 0.32 | 1000 | 1.9620 | | 2.0582 | 0.48 | 1500 | 1.9502 | | 2.0542 | 0.64 | 2000 | 1.9418 | | 2.0398 | 0.8 | 2500 | 1.9223 | | 2.037 | 0.96 | 3000 | 1.9220 | | 1.9831 | 1.12 | 3500 | 1.9247 | | 1.972 | 1.28 | 4000 | 1.9123 | | 1.9708 | 1.44 | 4500 | 1.9122 | | 1.967 | 1.6 | 5000 | 1.9097 | | 1.9582 | 1.76 | 5500 | 1.9085 | | 1.9715 | 1.92 | 6000 | 1.9099 | | 1.9459 | 2.08 | 6500 | 1.9113 | | 1.9384 | 2.24 | 7000 | 1.9103 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2
muhtasham/medium-mlm-imdb
muhtasham
2023-09-13T18:24:12Z
119
0
transformers
[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "base_model:google/bert_uncased_L-8_H-512_A-8", "base_model:finetune:google/bert_uncased_L-8_H-512_A-8", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-12-10T23:58:59Z
--- license: apache-2.0 tags: - generated_from_trainer base_model: google/bert_uncased_L-8_H-512_A-8 model-index: - name: medium-mlm-imdb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # medium-mlm-imdb This model is a fine-tuned version of [google/bert_uncased_L-8_H-512_A-8](https://huggingface.co/google/bert_uncased_L-8_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1889 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.5116 | 0.16 | 500 | 2.3298 | | 2.4448 | 0.32 | 1000 | 2.3157 | | 2.4362 | 0.48 | 1500 | 2.2987 | | 2.4287 | 0.64 | 2000 | 2.2878 | | 2.4125 | 0.8 | 2500 | 2.2693 | | 2.4066 | 0.96 | 3000 | 2.2666 | | 2.352 | 1.12 | 3500 | 2.2590 | | 2.3406 | 1.28 | 4000 | 2.2501 | | 2.3443 | 1.44 | 4500 | 2.2433 | | 2.3331 | 1.6 | 5000 | 2.2373 | | 2.3247 | 1.76 | 5500 | 2.2357 | | 2.3343 | 1.92 | 6000 | 2.2332 | | 2.3092 | 2.08 | 6500 | 2.2334 | | 2.3034 | 2.24 | 7000 | 2.2319 | | 2.2984 | 2.4 | 7500 | 2.2254 | | 2.2794 | 2.56 | 8000 | 2.2186 | | 2.3028 | 2.72 | 8500 | 2.2130 | | 2.3047 | 2.88 | 9000 | 2.2156 | | 2.2785 | 3.04 | 9500 | 2.2084 | | 2.2562 | 3.2 | 10000 | 2.2105 | | 2.2553 | 3.36 | 10500 | 2.2034 | | 2.2626 | 3.52 | 11000 | 2.2024 | | 2.2313 | 3.68 | 11500 | 2.2056 | | 2.2514 | 3.84 | 12000 | 2.1980 | | 2.2462 | 4.0 | 12500 | 2.2052 | | 2.2143 | 4.16 | 13000 | 2.1928 | | 2.2199 | 4.32 | 13500 | 2.1972 | | 2.2045 | 4.48 | 14000 | 2.2014 | | 2.2246 | 4.64 | 14500 | 2.1885 | | 2.2272 | 4.8 | 15000 | 2.1895 | | 2.2213 | 4.96 | 15500 | 2.1976 | | 2.2074 | 5.12 | 16000 | 2.1889 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2
muhtasham/tiny-mlm-glue-cola
muhtasham
2023-09-13T18:23:54Z
118
1
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "base_model:google/bert_uncased_L-2_H-128_A-2", "base_model:finetune:google/bert_uncased_L-2_H-128_A-2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-01-05T20:18:18Z
--- license: apache-2.0 tags: - generated_from_trainer base_model: google/bert_uncased_L-2_H-128_A-2 model-index: - name: tiny-mlm-glue-cola 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. --> # tiny-mlm-glue-cola This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.2428 | 0.47 | 500 | 3.7383 | | 4.0764 | 0.94 | 1000 | 3.6771 | | 3.8781 | 1.4 | 1500 | 3.5846 | | 3.8168 | 1.87 | 2000 | 3.6091 | | 3.6486 | 2.34 | 2500 | 3.6647 | | 3.7452 | 2.81 | 3000 | nan | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2
muhtasham/tiny-mlm-glue-qnli
muhtasham
2023-09-13T18:23:49Z
107
1
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "base_model:google/bert_uncased_L-2_H-128_A-2", "base_model:finetune:google/bert_uncased_L-2_H-128_A-2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-01-05T20:53:04Z
--- license: apache-2.0 tags: - generated_from_trainer base_model: google/bert_uncased_L-2_H-128_A-2 model-index: - name: tiny-mlm-glue-qnli 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. --> # tiny-mlm-glue-qnli This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7933 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.1842 | 0.4 | 500 | 3.7675 | | 4.1114 | 0.8 | 1000 | 3.7501 | | 4.1321 | 1.2 | 1500 | 3.7769 | | 4.0487 | 1.6 | 2000 | 3.7642 | | 4.0894 | 2.0 | 2500 | 3.7933 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2
muhtasham/tiny-mlm-glue-mnli
muhtasham
2023-09-13T18:23:43Z
109
1
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "base_model:google/bert_uncased_L-2_H-128_A-2", "base_model:finetune:google/bert_uncased_L-2_H-128_A-2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-01-05T20:26:12Z
--- license: apache-2.0 tags: - generated_from_trainer base_model: google/bert_uncased_L-2_H-128_A-2 model-index: - name: tiny-mlm-glue-mnli 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. --> # tiny-mlm-glue-mnli This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9722 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.4196 | 0.4 | 500 | 3.9829 | | 4.3712 | 0.8 | 1000 | 4.0000 | | 4.3439 | 1.2 | 1500 | 3.9642 | | 4.2725 | 1.6 | 2000 | 3.9736 | | 4.2908 | 2.0 | 2500 | 3.9309 | | 4.1935 | 2.4 | 3000 | 3.9395 | | 4.1935 | 2.8 | 3500 | 3.9470 | | 4.1731 | 3.2 | 4000 | 3.9722 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2
muhtasham/tiny-mlm-glue-qqp
muhtasham
2023-09-13T18:23:31Z
108
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "base_model:google/bert_uncased_L-2_H-128_A-2", "base_model:finetune:google/bert_uncased_L-2_H-128_A-2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-01-05T21:00:41Z
--- license: apache-2.0 tags: - generated_from_trainer base_model: google/bert_uncased_L-2_H-128_A-2 model-index: - name: tiny-mlm-glue-qqp 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. --> # tiny-mlm-glue-qqp This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.5533 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.313 | 0.4 | 500 | 3.8831 | | 4.1839 | 0.8 | 1000 | 3.7763 | | 4.0803 | 1.2 | 1500 | 3.6769 | | 3.9319 | 1.6 | 2000 | 3.6800 | | 3.8968 | 2.0 | 2500 | 3.6765 | | 3.8441 | 2.4 | 3000 | 3.5576 | | 3.7809 | 2.8 | 3500 | 3.6472 | | 3.8456 | 3.2 | 4000 | 3.5526 | | 3.682 | 3.6 | 4500 | 3.5926 | | 3.749 | 4.0 | 5000 | 3.5727 | | 3.6818 | 4.4 | 5500 | 3.5533 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2
muhtasham/tiny-mlm-glue-rte
muhtasham
2023-09-13T18:23:23Z
118
1
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "base_model:google/bert_uncased_L-2_H-128_A-2", "base_model:finetune:google/bert_uncased_L-2_H-128_A-2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-01-05T21:12:16Z
--- license: apache-2.0 tags: - generated_from_trainer base_model: google/bert_uncased_L-2_H-128_A-2 model-index: - name: tiny-mlm-glue-rte 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. --> # tiny-mlm-glue-rte This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7817 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.2975 | 1.6 | 500 | 3.7350 | | 4.2242 | 3.21 | 1000 | 3.9463 | | 4.1731 | 4.81 | 1500 | 3.7550 | | 4.088 | 6.41 | 2000 | 3.7817 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2
muhtasham/bert-small-finetuned-legal-contracts10train10val
muhtasham
2023-09-13T18:23:13Z
171
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "dataset:albertvillanova/legal_contracts", "base_model:google/bert_uncased_L-4_H-512_A-8", "base_model:finetune:google/bert_uncased_L-4_H-512_A-8", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-08-14T08:54:53Z
--- datasets: - albertvillanova/legal_contracts base_model: google/bert_uncased_L-4_H-512_A-8 --- # bert-tiny-finetuned-legal-contracts-longer This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/google/bert_uncased_L-4_H-512_A-8) on the portion of legal_contracts dataset. # Note The model was not trained on the whole dataset which is around 9.5 GB, but only ## The first 10% of `train` + the last 10% of `train`. ```bash datasets_train = load_dataset('albertvillanova/legal_contracts' , split='train[:10%]') datasets_validation = load_dataset('albertvillanova/legal_contracts' , split='train[-10%:]') ```
Lethargus/ppo-LunarLander-v2-unit8
Lethargus
2023-09-13T18:23:02Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-09-13T17:22:41Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 122.99 +/- 119.58 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters
muhtasham/bert-tiny-finetuned-nan-labels-new-longer
muhtasham
2023-09-13T18:22:38Z
121
0
transformers
[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "generated_from_trainer", "base_model:google/bert_uncased_L-2_H-128_A-2", "base_model:finetune:google/bert_uncased_L-2_H-128_A-2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-08-09T01:19:50Z
--- license: apache-2.0 tags: - generated_from_trainer base_model: google/bert_uncased_L-2_H-128_A-2 model-index: - name: bert_uncased_L-2_H-128_A-2-nan-labels-new-longer results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_uncased_L-2_H-128_A-2-nan-labels-new-longer This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 8.4563 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:-----:|:---------------:| | 6.445 | 1.0 | 14 | 6.4988 | | 5.9033 | 2.0 | 28 | 6.4333 | | 5.6732 | 3.0 | 42 | 6.4295 | | 5.6525 | 4.0 | 56 | 6.3895 | | 5.4781 | 5.0 | 70 | 6.3660 | | 5.31 | 6.0 | 84 | 6.4213 | | 5.1629 | 7.0 | 98 | 6.3842 | | 5.1541 | 8.0 | 112 | 6.4043 | | 5.0659 | 9.0 | 126 | 6.4686 | | 4.8597 | 10.0 | 140 | 6.4760 | | 4.983 | 11.0 | 154 | 6.4753 | | 4.9563 | 12.0 | 168 | 6.4612 | | 4.8113 | 13.0 | 182 | 6.5363 | | 4.86 | 14.0 | 196 | 6.5400 | | 4.8254 | 15.0 | 210 | 6.5595 | | 4.8032 | 16.0 | 224 | 6.5575 | | 4.7343 | 17.0 | 238 | 6.5832 | | 4.835 | 18.0 | 252 | 6.5729 | | 4.6986 | 19.0 | 266 | 6.5705 | | 4.6607 | 20.0 | 280 | 6.6127 | | 4.6594 | 21.0 | 294 | 6.6145 | | 4.6936 | 22.0 | 308 | 6.6280 | | 4.6328 | 23.0 | 322 | 6.6249 | | 4.6668 | 24.0 | 336 | 6.6779 | | 4.5523 | 25.0 | 350 | 6.6236 | | 4.3964 | 26.0 | 364 | 6.6895 | | 4.4736 | 27.0 | 378 | 6.6899 | | 4.5163 | 28.0 | 392 | 6.6832 | | 4.5136 | 29.0 | 406 | 6.7013 | | 4.542 | 30.0 | 420 | 6.6983 | | 4.4362 | 31.0 | 434 | 6.7516 | | 4.4466 | 32.0 | 448 | 6.7302 | | 4.4834 | 33.0 | 462 | 6.7404 | | 4.4824 | 34.0 | 476 | 6.7525 | | 4.4302 | 35.0 | 490 | 6.7623 | | 4.5098 | 36.0 | 504 | 6.7622 | | 4.396 | 37.0 | 518 | 6.8025 | | 4.3747 | 38.0 | 532 | 6.7847 | | 4.2892 | 39.0 | 546 | 6.8394 | | 4.3897 | 40.0 | 560 | 6.8372 | | 4.2554 | 41.0 | 574 | 6.7644 | | 4.3336 | 42.0 | 588 | 6.7843 | | 4.2943 | 43.0 | 602 | 6.7806 | | 4.3001 | 44.0 | 616 | 6.7864 | | 4.1161 | 45.0 | 630 | 6.8487 | | 4.1357 | 46.0 | 644 | 6.8556 | | 4.2713 | 47.0 | 658 | 6.7995 | | 4.2779 | 48.0 | 672 | 6.8441 | | 4.2564 | 49.0 | 686 | 6.8388 | | 4.192 | 50.0 | 700 | 6.7896 | | 4.1692 | 51.0 | 714 | 6.8261 | | 4.2694 | 52.0 | 728 | 6.8535 | | 4.2109 | 53.0 | 742 | 6.8533 | | 4.1504 | 54.0 | 756 | 6.8946 | | 4.1131 | 55.0 | 770 | 6.8696 | | 4.1799 | 56.0 | 784 | 6.8739 | | 4.1055 | 57.0 | 798 | 6.8651 | | 4.0118 | 58.0 | 812 | 6.8689 | | 4.176 | 59.0 | 826 | 6.8895 | | 4.1773 | 60.0 | 840 | 6.8385 | | 3.9761 | 61.0 | 854 | 6.8580 | | 4.1002 | 62.0 | 868 | 6.8556 | | 3.9164 | 63.0 | 882 | 6.9133 | | 4.0154 | 64.0 | 896 | 6.8968 | | 4.008 | 65.0 | 910 | 6.8973 | | 4.0876 | 66.0 | 924 | 6.8768 | | 3.9527 | 67.0 | 938 | 6.9413 | | 3.9351 | 68.0 | 952 | 6.9106 | | 4.0333 | 69.0 | 966 | 6.9012 | | 3.896 | 70.0 | 980 | 6.9127 | | 4.0042 | 71.0 | 994 | 6.9211 | | 3.9151 | 72.0 | 1008 | 6.9585 | | 3.9852 | 73.0 | 1022 | 6.9027 | | 3.9913 | 74.0 | 1036 | 6.9333 | | 3.8787 | 75.0 | 1050 | 6.9345 | | 3.9729 | 76.0 | 1064 | 6.9165 | | 3.9449 | 77.0 | 1078 | 6.9660 | | 3.9416 | 78.0 | 1092 | 6.9462 | | 3.9497 | 79.0 | 1106 | 6.9617 | | 3.8797 | 80.0 | 1120 | 6.9356 | | 3.8509 | 81.0 | 1134 | 6.9562 | | 3.9021 | 82.0 | 1148 | 6.9400 | | 3.8218 | 83.0 | 1162 | 7.0029 | | 3.9301 | 84.0 | 1176 | 7.0166 | | 3.7591 | 85.0 | 1190 | 6.9891 | | 3.8889 | 86.0 | 1204 | 6.9830 | | 3.8063 | 87.0 | 1218 | 6.9812 | | 3.8556 | 88.0 | 1232 | 7.0051 | | 3.7773 | 89.0 | 1246 | 6.9959 | | 3.745 | 90.0 | 1260 | 7.0423 | | 3.8139 | 91.0 | 1274 | 7.0781 | | 3.7354 | 92.0 | 1288 | 7.0206 | | 3.7696 | 93.0 | 1302 | 6.9731 | | 3.73 | 94.0 | 1316 | 7.0572 | | 3.7019 | 95.0 | 1330 | 7.0119 | | 3.6302 | 96.0 | 1344 | 7.0238 | | 3.675 | 97.0 | 1358 | 7.0348 | | 3.746 | 98.0 | 1372 | 7.0385 | | 3.7106 | 99.0 | 1386 | 7.0477 | | 3.6545 | 100.0 | 1400 | 7.0762 | | 3.7246 | 101.0 | 1414 | 7.0063 | | 3.6707 | 102.0 | 1428 | 7.0343 | | 3.7569 | 103.0 | 1442 | 7.0196 | | 3.6785 | 104.0 | 1456 | 7.0255 | | 3.7535 | 105.0 | 1470 | 7.0461 | | 3.7011 | 106.0 | 1484 | 7.0381 | | 3.6112 | 107.0 | 1498 | 7.0440 | | 3.5981 | 108.0 | 1512 | 7.0831 | | 3.6751 | 109.0 | 1526 | 7.0568 | | 3.674 | 110.0 | 1540 | 7.0905 | | 3.5554 | 111.0 | 1554 | 7.0955 | | 3.5834 | 112.0 | 1568 | 7.0668 | | 3.581 | 113.0 | 1582 | 7.0943 | | 3.5548 | 114.0 | 1596 | 7.0490 | | 3.6446 | 115.0 | 1610 | 7.1190 | | 3.6142 | 116.0 | 1624 | 7.1366 | | 3.5748 | 117.0 | 1638 | 7.1047 | | 3.5447 | 118.0 | 1652 | 7.1332 | | 3.6093 | 119.0 | 1666 | 7.1137 | | 3.5694 | 120.0 | 1680 | 7.1010 | | 3.4729 | 121.0 | 1694 | 7.1186 | | 3.5373 | 122.0 | 1708 | 7.1258 | | 3.6168 | 123.0 | 1722 | 7.1184 | | 3.5654 | 124.0 | 1736 | 7.0982 | | 3.4747 | 125.0 | 1750 | 7.0908 | | 3.5098 | 126.0 | 1764 | 7.1134 | | 3.6144 | 127.0 | 1778 | 7.1033 | | 3.4529 | 128.0 | 1792 | 7.1376 | | 3.4286 | 129.0 | 1806 | 7.1056 | | 3.5095 | 130.0 | 1820 | 7.1148 | | 3.4333 | 131.0 | 1834 | 7.1185 | | 3.4879 | 132.0 | 1848 | 7.1704 | | 3.4992 | 133.0 | 1862 | 7.1361 | | 3.4738 | 134.0 | 1876 | 7.1597 | | 3.4358 | 135.0 | 1890 | 7.1244 | | 3.4648 | 136.0 | 1904 | 7.1762 | | 3.3206 | 137.0 | 1918 | 7.1274 | | 3.3579 | 138.0 | 1932 | 7.1208 | | 3.3573 | 139.0 | 1946 | 7.1614 | | 3.3504 | 140.0 | 1960 | 7.1241 | | 3.4585 | 141.0 | 1974 | 7.1421 | | 3.3757 | 142.0 | 1988 | 7.2070 | | 3.3911 | 143.0 | 2002 | 7.1862 | | 3.2857 | 144.0 | 2016 | 7.1925 | | 3.3898 | 145.0 | 2030 | 7.2269 | | 3.3297 | 146.0 | 2044 | 7.2391 | | 3.3361 | 147.0 | 2058 | 7.1945 | | 3.2097 | 148.0 | 2072 | 7.2095 | | 3.4288 | 149.0 | 2086 | 7.1376 | | 3.4156 | 150.0 | 2100 | 7.1984 | | 3.187 | 151.0 | 2114 | 7.2243 | | 3.3137 | 152.0 | 2128 | 7.2164 | | 3.2102 | 153.0 | 2142 | 7.1742 | | 3.2992 | 154.0 | 2156 | 7.2086 | | 3.3271 | 155.0 | 2170 | 7.2281 | | 3.3251 | 156.0 | 2184 | 7.2360 | | 3.2512 | 157.0 | 2198 | 7.2302 | | 3.2914 | 158.0 | 2212 | 7.2106 | | 3.1992 | 159.0 | 2226 | 7.2322 | | 3.3029 | 160.0 | 2240 | 7.2548 | | 3.2309 | 161.0 | 2254 | 7.2132 | | 3.1554 | 162.0 | 2268 | 7.2323 | | 3.2555 | 163.0 | 2282 | 7.2133 | | 3.3232 | 164.0 | 2296 | 7.2382 | | 3.1486 | 165.0 | 2310 | 7.2681 | | 3.2693 | 166.0 | 2324 | 7.2976 | | 3.2643 | 167.0 | 2338 | 7.2274 | | 3.2331 | 168.0 | 2352 | 7.3139 | | 3.1568 | 169.0 | 2366 | 7.2261 | | 3.1509 | 170.0 | 2380 | 7.3050 | | 3.0284 | 171.0 | 2394 | 7.2823 | | 3.2062 | 172.0 | 2408 | 7.2707 | | 3.1281 | 173.0 | 2422 | 7.2902 | | 3.1225 | 174.0 | 2436 | 7.2687 | | 3.1591 | 175.0 | 2450 | 7.2865 | | 3.1179 | 176.0 | 2464 | 7.2920 | | 3.1785 | 177.0 | 2478 | 7.2559 | | 3.2278 | 178.0 | 2492 | 7.2736 | | 3.132 | 179.0 | 2506 | 7.3053 | | 3.0466 | 180.0 | 2520 | 7.2746 | | 3.1254 | 181.0 | 2534 | 7.2709 | | 3.1826 | 182.0 | 2548 | 7.3136 | | 3.1385 | 183.0 | 2562 | 7.3178 | | 3.1387 | 184.0 | 2576 | 7.2538 | | 3.0793 | 185.0 | 2590 | 7.2920 | | 3.112 | 186.0 | 2604 | 7.3260 | | 3.1013 | 187.0 | 2618 | 7.2720 | | 3.1897 | 188.0 | 2632 | 7.2739 | | 3.0557 | 189.0 | 2646 | 7.3047 | | 3.1642 | 190.0 | 2660 | 7.3403 | | 2.9943 | 191.0 | 2674 | 7.3406 | | 3.0325 | 192.0 | 2688 | 7.2799 | | 3.076 | 193.0 | 2702 | 7.2900 | | 3.003 | 194.0 | 2716 | 7.3443 | | 3.0765 | 195.0 | 2730 | 7.3862 | | 2.9823 | 196.0 | 2744 | 7.3070 | | 3.0833 | 197.0 | 2758 | 7.2606 | | 3.0209 | 198.0 | 2772 | 7.3284 | | 2.9679 | 199.0 | 2786 | 7.3877 | | 3.0575 | 200.0 | 2800 | 7.3454 | | 2.9928 | 201.0 | 2814 | 7.3847 | | 3.092 | 202.0 | 2828 | 7.3738 | | 2.976 | 203.0 | 2842 | 7.3941 | | 3.0173 | 204.0 | 2856 | 7.3801 | | 2.9659 | 205.0 | 2870 | 7.3725 | | 3.0016 | 206.0 | 2884 | 7.3803 | | 2.9815 | 207.0 | 2898 | 7.3499 | | 3.0251 | 208.0 | 2912 | 7.3261 | | 2.927 | 209.0 | 2926 | 7.3570 | | 3.0193 | 210.0 | 2940 | 7.3972 | | 3.0152 | 211.0 | 2954 | 7.3770 | | 2.9104 | 212.0 | 2968 | 7.3326 | | 2.9868 | 213.0 | 2982 | 7.3898 | | 3.0097 | 214.0 | 2996 | 7.3658 | | 3.0093 | 215.0 | 3010 | 7.3975 | | 2.8546 | 216.0 | 3024 | 7.3948 | | 2.8972 | 217.0 | 3038 | 7.3734 | | 2.9641 | 218.0 | 3052 | 7.4320 | | 2.9083 | 219.0 | 3066 | 7.3582 | | 2.9185 | 220.0 | 3080 | 7.4126 | | 3.0003 | 221.0 | 3094 | 7.3918 | | 2.8599 | 222.0 | 3108 | 7.4171 | | 2.8931 | 223.0 | 3122 | 7.4251 | | 2.9109 | 224.0 | 3136 | 7.4426 | | 2.9417 | 225.0 | 3150 | 7.4428 | | 2.8274 | 226.0 | 3164 | 7.4145 | | 2.921 | 227.0 | 3178 | 7.3492 | | 2.7542 | 228.0 | 3192 | 7.4100 | | 2.8775 | 229.0 | 3206 | 7.4288 | | 2.7467 | 230.0 | 3220 | 7.4359 | | 2.8301 | 231.0 | 3234 | 7.4715 | | 2.7856 | 232.0 | 3248 | 7.4036 | | 2.835 | 233.0 | 3262 | 7.4038 | | 2.7665 | 234.0 | 3276 | 7.4919 | | 2.8972 | 235.0 | 3290 | 7.4808 | | 2.8768 | 236.0 | 3304 | 7.5259 | | 2.9377 | 237.0 | 3318 | 7.4187 | | 2.8489 | 238.0 | 3332 | 7.4590 | | 2.8018 | 239.0 | 3346 | 7.4565 | | 2.771 | 240.0 | 3360 | 7.4474 | | 2.7378 | 241.0 | 3374 | 7.5119 | | 2.822 | 242.0 | 3388 | 7.4734 | | 2.8274 | 243.0 | 3402 | 7.4984 | | 2.7732 | 244.0 | 3416 | 7.4829 | | 2.7264 | 245.0 | 3430 | 7.4391 | | 2.7764 | 246.0 | 3444 | 7.4456 | | 2.7972 | 247.0 | 3458 | 7.4858 | | 2.8231 | 248.0 | 3472 | 7.4760 | | 2.778 | 249.0 | 3486 | 7.4380 | | 2.7935 | 250.0 | 3500 | 7.4336 | | 2.7348 | 251.0 | 3514 | 7.4970 | | 2.7192 | 252.0 | 3528 | 7.4811 | | 2.8108 | 253.0 | 3542 | 7.4547 | | 2.837 | 254.0 | 3556 | 7.4830 | | 2.6868 | 255.0 | 3570 | 7.5151 | | 2.7789 | 256.0 | 3584 | 7.5115 | | 2.7706 | 257.0 | 3598 | 7.5180 | | 2.7904 | 258.0 | 3612 | 7.5158 | | 2.7215 | 259.0 | 3626 | 7.5262 | | 2.6876 | 260.0 | 3640 | 7.5114 | | 2.7679 | 261.0 | 3654 | 7.5066 | | 2.7742 | 262.0 | 3668 | 7.5035 | | 2.6965 | 263.0 | 3682 | 7.4918 | | 2.668 | 264.0 | 3696 | 7.5305 | | 2.6808 | 265.0 | 3710 | 7.5238 | | 2.6491 | 266.0 | 3724 | 7.5347 | | 2.7307 | 267.0 | 3738 | 7.5175 | | 2.6518 | 268.0 | 3752 | 7.5635 | | 2.6685 | 269.0 | 3766 | 7.4899 | | 2.671 | 270.0 | 3780 | 7.4855 | | 2.596 | 271.0 | 3794 | 7.5518 | | 2.6622 | 272.0 | 3808 | 7.5308 | | 2.6684 | 273.0 | 3822 | 7.5955 | | 2.6325 | 274.0 | 3836 | 7.5768 | | 2.6334 | 275.0 | 3850 | 7.5202 | | 2.6042 | 276.0 | 3864 | 7.6176 | | 2.7439 | 277.0 | 3878 | 7.5369 | | 2.6925 | 278.0 | 3892 | 7.5422 | | 2.7106 | 279.0 | 3906 | 7.5629 | | 2.6519 | 280.0 | 3920 | 7.5359 | | 2.6044 | 281.0 | 3934 | 7.5619 | | 2.6509 | 282.0 | 3948 | 7.5433 | | 2.6777 | 283.0 | 3962 | 7.5573 | | 2.6199 | 284.0 | 3976 | 7.5628 | | 2.6685 | 285.0 | 3990 | 7.5710 | | 2.6608 | 286.0 | 4004 | 7.6020 | | 2.6579 | 287.0 | 4018 | 7.5780 | | 2.5559 | 288.0 | 4032 | 7.5713 | | 2.5091 | 289.0 | 4046 | 7.5912 | | 2.6141 | 290.0 | 4060 | 7.6475 | | 2.6832 | 291.0 | 4074 | 7.5865 | | 2.5769 | 292.0 | 4088 | 7.6198 | | 2.6432 | 293.0 | 4102 | 7.6058 | | 2.5733 | 294.0 | 4116 | 7.5853 | | 2.5782 | 295.0 | 4130 | 7.6246 | | 2.6118 | 296.0 | 4144 | 7.5817 | | 2.6894 | 297.0 | 4158 | 7.5868 | | 2.5624 | 298.0 | 4172 | 7.5837 | | 2.5449 | 299.0 | 4186 | 7.6007 | | 2.5865 | 300.0 | 4200 | 7.6604 | | 2.5366 | 301.0 | 4214 | 7.5909 | | 2.4286 | 302.0 | 4228 | 7.6563 | | 2.6909 | 303.0 | 4242 | 7.6767 | | 2.4252 | 304.0 | 4256 | 7.6556 | | 2.612 | 305.0 | 4270 | 7.6846 | | 2.4793 | 306.0 | 4284 | 7.6279 | | 2.5227 | 307.0 | 4298 | 7.6808 | | 2.5756 | 308.0 | 4312 | 7.6703 | | 2.5321 | 309.0 | 4326 | 7.6217 | | 2.5568 | 310.0 | 4340 | 7.6381 | | 2.507 | 311.0 | 4354 | 7.6528 | | 2.5766 | 312.0 | 4368 | 7.6480 | | 2.3532 | 313.0 | 4382 | 7.6255 | | 2.4758 | 314.0 | 4396 | 7.6512 | | 2.4304 | 315.0 | 4410 | 7.6606 | | 2.4749 | 316.0 | 4424 | 7.6470 | | 2.4886 | 317.0 | 4438 | 7.7193 | | 2.511 | 318.0 | 4452 | 7.6670 | | 2.4664 | 319.0 | 4466 | 7.6209 | | 2.4981 | 320.0 | 4480 | 7.6819 | | 2.4406 | 321.0 | 4494 | 7.6661 | | 2.5787 | 322.0 | 4508 | 7.6903 | | 2.4885 | 323.0 | 4522 | 7.6595 | | 2.5796 | 324.0 | 4536 | 7.6882 | | 2.4909 | 325.0 | 4550 | 7.7169 | | 2.522 | 326.0 | 4564 | 7.6606 | | 2.4206 | 327.0 | 4578 | 7.6526 | | 2.4909 | 328.0 | 4592 | 7.6731 | | 2.4543 | 329.0 | 4606 | 7.6822 | | 2.4431 | 330.0 | 4620 | 7.6770 | | 2.3963 | 331.0 | 4634 | 7.6407 | | 2.4518 | 332.0 | 4648 | 7.6468 | | 2.5734 | 333.0 | 4662 | 7.7206 | | 2.4423 | 334.0 | 4676 | 7.6691 | | 2.4418 | 335.0 | 4690 | 7.6822 | | 2.4575 | 336.0 | 4704 | 7.6477 | | 2.4671 | 337.0 | 4718 | 7.6888 | | 2.3527 | 338.0 | 4732 | 7.7104 | | 2.473 | 339.0 | 4746 | 7.7247 | | 2.4786 | 340.0 | 4760 | 7.7340 | | 2.4222 | 341.0 | 4774 | 7.6998 | | 2.4812 | 342.0 | 4788 | 7.6996 | | 2.3484 | 343.0 | 4802 | 7.6807 | | 2.3231 | 344.0 | 4816 | 7.6972 | | 2.4844 | 345.0 | 4830 | 7.6984 | | 2.3757 | 346.0 | 4844 | 7.7091 | | 2.4139 | 347.0 | 4858 | 7.7240 | | 2.3665 | 348.0 | 4872 | 7.7681 | | 2.3942 | 349.0 | 4886 | 7.7129 | | 2.2922 | 350.0 | 4900 | 7.6804 | | 2.4234 | 351.0 | 4914 | 7.7445 | | 2.3589 | 352.0 | 4928 | 7.7599 | | 2.3987 | 353.0 | 4942 | 7.7381 | | 2.3545 | 354.0 | 4956 | 7.7433 | | 2.4019 | 355.0 | 4970 | 7.7560 | | 2.2925 | 356.0 | 4984 | 7.7393 | | 2.3678 | 357.0 | 4998 | 7.7211 | | 2.3588 | 358.0 | 5012 | 7.7414 | | 2.3996 | 359.0 | 5026 | 7.7436 | | 2.3665 | 360.0 | 5040 | 7.7966 | | 2.3374 | 361.0 | 5054 | 7.8225 | | 2.3667 | 362.0 | 5068 | 7.7955 | | 2.3754 | 363.0 | 5082 | 7.7731 | | 2.4179 | 364.0 | 5096 | 7.7899 | | 2.3777 | 365.0 | 5110 | 7.7831 | | 2.4064 | 366.0 | 5124 | 7.7861 | | 2.3371 | 367.0 | 5138 | 7.7578 | | 2.4169 | 368.0 | 5152 | 7.8429 | | 2.3491 | 369.0 | 5166 | 7.7645 | | 2.2275 | 370.0 | 5180 | 7.8063 | | 2.2605 | 371.0 | 5194 | 7.8280 | | 2.3506 | 372.0 | 5208 | 7.7356 | | 2.403 | 373.0 | 5222 | 7.7739 | | 2.3188 | 374.0 | 5236 | 7.7634 | | 2.3294 | 375.0 | 5250 | 7.8033 | | 2.2724 | 376.0 | 5264 | 7.8423 | | 2.2704 | 377.0 | 5278 | 7.8340 | | 2.2606 | 378.0 | 5292 | 7.7941 | | 2.2599 | 379.0 | 5306 | 7.8336 | | 2.326 | 380.0 | 5320 | 7.8080 | | 2.2861 | 381.0 | 5334 | 7.8097 | | 2.2559 | 382.0 | 5348 | 7.8201 | | 2.2612 | 383.0 | 5362 | 7.8249 | | 2.3161 | 384.0 | 5376 | 7.8353 | | 2.2061 | 385.0 | 5390 | 7.8623 | | 2.4231 | 386.0 | 5404 | 7.8568 | | 2.2481 | 387.0 | 5418 | 7.8642 | | 2.2319 | 388.0 | 5432 | 7.8255 | | 2.2178 | 389.0 | 5446 | 7.8136 | | 2.3153 | 390.0 | 5460 | 7.8549 | | 2.2391 | 391.0 | 5474 | 7.8739 | | 2.2736 | 392.0 | 5488 | 7.9016 | | 2.3335 | 393.0 | 5502 | 7.8838 | | 2.2069 | 394.0 | 5516 | 7.9268 | | 2.303 | 395.0 | 5530 | 7.8096 | | 2.2585 | 396.0 | 5544 | 7.8667 | | 2.2261 | 397.0 | 5558 | 7.8651 | | 2.2792 | 398.0 | 5572 | 7.8213 | | 2.3172 | 399.0 | 5586 | 7.8702 | | 2.2787 | 400.0 | 5600 | 7.8745 | | 2.226 | 401.0 | 5614 | 7.8490 | | 2.1491 | 402.0 | 5628 | 7.8608 | | 2.2121 | 403.0 | 5642 | 7.8568 | | 2.1715 | 404.0 | 5656 | 7.8996 | | 2.1613 | 405.0 | 5670 | 7.8920 | | 2.1886 | 406.0 | 5684 | 7.8223 | | 2.1392 | 407.0 | 5698 | 7.8254 | | 2.2268 | 408.0 | 5712 | 7.8583 | | 2.2726 | 409.0 | 5726 | 7.8749 | | 2.1648 | 410.0 | 5740 | 7.9115 | | 2.1897 | 411.0 | 5754 | 7.9030 | | 2.1597 | 412.0 | 5768 | 7.8699 | | 2.1989 | 413.0 | 5782 | 7.8932 | | 2.2705 | 414.0 | 5796 | 7.8936 | | 2.1071 | 415.0 | 5810 | 7.8695 | | 2.124 | 416.0 | 5824 | 7.8873 | | 2.1948 | 417.0 | 5838 | 7.8655 | | 2.2704 | 418.0 | 5852 | 7.9172 | | 2.2055 | 419.0 | 5866 | 7.9646 | | 2.276 | 420.0 | 5880 | 7.9224 | | 2.1541 | 421.0 | 5894 | 7.8567 | | 2.1881 | 422.0 | 5908 | 7.8945 | | 2.1455 | 423.0 | 5922 | 7.8674 | | 2.3452 | 424.0 | 5936 | 7.9724 | | 2.1371 | 425.0 | 5950 | 7.9671 | | 2.1901 | 426.0 | 5964 | 7.9274 | | 2.1643 | 427.0 | 5978 | 7.9121 | | 2.2229 | 428.0 | 5992 | 7.8934 | | 2.1254 | 429.0 | 6006 | 7.9270 | | 2.1554 | 430.0 | 6020 | 7.9205 | | 2.0761 | 431.0 | 6034 | 7.9256 | | 2.1551 | 432.0 | 6048 | 7.9133 | | 2.2451 | 433.0 | 6062 | 7.9323 | | 2.1491 | 434.0 | 6076 | 7.9551 | | 2.1766 | 435.0 | 6090 | 7.9279 | | 2.1239 | 436.0 | 6104 | 7.8983 | | 2.1505 | 437.0 | 6118 | 7.9169 | | 2.153 | 438.0 | 6132 | 7.9516 | | 2.0899 | 439.0 | 6146 | 7.9140 | | 2.1215 | 440.0 | 6160 | 7.9224 | | 2.0644 | 441.0 | 6174 | 7.9485 | | 2.1333 | 442.0 | 6188 | 7.9633 | | 2.0807 | 443.0 | 6202 | 7.9847 | | 2.1222 | 444.0 | 6216 | 7.9519 | | 2.0775 | 445.0 | 6230 | 7.9995 | | 2.1712 | 446.0 | 6244 | 7.9453 | | 2.1816 | 447.0 | 6258 | 7.9563 | | 2.071 | 448.0 | 6272 | 7.9443 | | 2.083 | 449.0 | 6286 | 7.9434 | | 2.166 | 450.0 | 6300 | 7.9449 | | 2.1607 | 451.0 | 6314 | 7.9534 | | 2.1057 | 452.0 | 6328 | 7.9520 | | 2.1258 | 453.0 | 6342 | 7.9578 | | 2.0822 | 454.0 | 6356 | 7.9709 | | 2.0092 | 455.0 | 6370 | 8.0117 | | 2.055 | 456.0 | 6384 | 7.9800 | | 2.0325 | 457.0 | 6398 | 7.9150 | | 2.0546 | 458.0 | 6412 | 7.9607 | | 2.0677 | 459.0 | 6426 | 7.9714 | | 2.1351 | 460.0 | 6440 | 7.9851 | | 2.0859 | 461.0 | 6454 | 8.0055 | | 2.0274 | 462.0 | 6468 | 7.9691 | | 2.0006 | 463.0 | 6482 | 7.9561 | | 2.1271 | 464.0 | 6496 | 7.9346 | | 2.0637 | 465.0 | 6510 | 8.0015 | | 2.0727 | 466.0 | 6524 | 8.0062 | | 1.983 | 467.0 | 6538 | 8.0255 | | 2.0895 | 468.0 | 6552 | 7.9777 | | 2.1187 | 469.0 | 6566 | 7.9704 | | 2.0874 | 470.0 | 6580 | 7.9550 | | 2.0927 | 471.0 | 6594 | 7.9987 | | 2.1442 | 472.0 | 6608 | 8.0421 | | 2.0117 | 473.0 | 6622 | 8.0121 | | 2.0647 | 474.0 | 6636 | 7.9565 | | 2.0095 | 475.0 | 6650 | 7.9986 | | 2.0008 | 476.0 | 6664 | 8.0411 | | 2.0464 | 477.0 | 6678 | 7.9803 | | 2.1314 | 478.0 | 6692 | 8.0383 | | 2.0345 | 479.0 | 6706 | 7.9776 | | 2.0668 | 480.0 | 6720 | 8.0702 | | 2.0933 | 481.0 | 6734 | 8.0149 | | 2.0612 | 482.0 | 6748 | 8.0105 | | 1.9858 | 483.0 | 6762 | 7.9859 | | 2.0195 | 484.0 | 6776 | 7.9764 | | 2.0203 | 485.0 | 6790 | 8.0284 | | 1.9986 | 486.0 | 6804 | 7.9929 | | 2.0372 | 487.0 | 6818 | 7.9623 | | 1.9485 | 488.0 | 6832 | 8.0172 | | 1.9316 | 489.0 | 6846 | 7.9999 | | 2.0008 | 490.0 | 6860 | 7.9783 | | 2.0899 | 491.0 | 6874 | 8.0318 | | 2.0078 | 492.0 | 6888 | 7.9986 | | 2.0386 | 493.0 | 6902 | 8.0367 | | 2.006 | 494.0 | 6916 | 8.0206 | | 1.981 | 495.0 | 6930 | 8.0008 | | 2.0464 | 496.0 | 6944 | 8.0056 | | 1.9485 | 497.0 | 6958 | 8.0243 | | 2.0409 | 498.0 | 6972 | 8.0541 | | 2.0374 | 499.0 | 6986 | 8.0330 | | 2.0073 | 500.0 | 7000 | 8.0471 | | 2.0605 | 501.0 | 7014 | 8.0782 | | 2.0424 | 502.0 | 7028 | 8.0568 | | 2.0192 | 503.0 | 7042 | 8.0502 | | 1.9831 | 504.0 | 7056 | 8.0580 | | 2.1012 | 505.0 | 7070 | 8.0488 | | 1.9765 | 506.0 | 7084 | 8.0293 | | 2.0735 | 507.0 | 7098 | 8.0325 | | 1.9964 | 508.0 | 7112 | 8.0104 | | 1.9963 | 509.0 | 7126 | 8.0233 | | 2.0252 | 510.0 | 7140 | 8.0312 | | 1.9223 | 511.0 | 7154 | 8.0337 | | 2.0063 | 512.0 | 7168 | 8.0609 | | 2.0272 | 513.0 | 7182 | 8.0299 | | 1.9498 | 514.0 | 7196 | 8.0298 | | 2.0057 | 515.0 | 7210 | 8.0949 | | 1.9598 | 516.0 | 7224 | 8.0999 | | 1.949 | 517.0 | 7238 | 8.0914 | | 2.0215 | 518.0 | 7252 | 8.0730 | | 2.0068 | 519.0 | 7266 | 8.0657 | | 1.9337 | 520.0 | 7280 | 8.0926 | | 2.0259 | 521.0 | 7294 | 8.0900 | | 1.9699 | 522.0 | 7308 | 8.0874 | | 1.9511 | 523.0 | 7322 | 8.1374 | | 1.8801 | 524.0 | 7336 | 8.0852 | | 2.0123 | 525.0 | 7350 | 8.0754 | | 1.9374 | 526.0 | 7364 | 8.0685 | | 1.9303 | 527.0 | 7378 | 8.0832 | | 1.963 | 528.0 | 7392 | 8.0756 | | 1.9235 | 529.0 | 7406 | 8.1209 | | 1.9476 | 530.0 | 7420 | 8.1116 | | 1.8567 | 531.0 | 7434 | 8.0481 | | 1.95 | 532.0 | 7448 | 8.0793 | | 1.9672 | 533.0 | 7462 | 8.0927 | | 1.8384 | 534.0 | 7476 | 8.1191 | | 1.9117 | 535.0 | 7490 | 8.0865 | | 2.0308 | 536.0 | 7504 | 8.0930 | | 1.9107 | 537.0 | 7518 | 8.0952 | | 1.9407 | 538.0 | 7532 | 8.1373 | | 1.9409 | 539.0 | 7546 | 8.1064 | | 1.9787 | 540.0 | 7560 | 8.1079 | | 1.8791 | 541.0 | 7574 | 8.0920 | | 1.9495 | 542.0 | 7588 | 8.0910 | | 1.9265 | 543.0 | 7602 | 8.1203 | | 1.8949 | 544.0 | 7616 | 8.1223 | | 1.8861 | 545.0 | 7630 | 8.1458 | | 1.9369 | 546.0 | 7644 | 8.0948 | | 1.9234 | 547.0 | 7658 | 8.1073 | | 1.934 | 548.0 | 7672 | 8.1285 | | 1.947 | 549.0 | 7686 | 8.1476 | | 1.9623 | 550.0 | 7700 | 8.1491 | | 1.8069 | 551.0 | 7714 | 8.1058 | | 1.9387 | 552.0 | 7728 | 8.1616 | | 1.9291 | 553.0 | 7742 | 8.1207 | | 1.9894 | 554.0 | 7756 | 8.1887 | | 1.885 | 555.0 | 7770 | 8.1785 | | 1.9515 | 556.0 | 7784 | 8.1555 | | 1.9123 | 557.0 | 7798 | 8.1708 | | 1.922 | 558.0 | 7812 | 8.1977 | | 1.8818 | 559.0 | 7826 | 8.1429 | | 1.9557 | 560.0 | 7840 | 8.1483 | | 1.9005 | 561.0 | 7854 | 8.1108 | | 1.91 | 562.0 | 7868 | 8.1745 | | 1.8598 | 563.0 | 7882 | 8.1938 | | 1.9633 | 564.0 | 7896 | 8.1294 | | 1.8658 | 565.0 | 7910 | 8.1407 | | 1.9256 | 566.0 | 7924 | 8.1767 | | 1.8974 | 567.0 | 7938 | 8.1441 | | 1.9635 | 568.0 | 7952 | 8.1219 | | 1.9537 | 569.0 | 7966 | 8.2357 | | 1.8828 | 570.0 | 7980 | 8.1944 | | 1.8594 | 571.0 | 7994 | 8.1265 | | 1.9105 | 572.0 | 8008 | 8.1458 | | 1.9491 | 573.0 | 8022 | 8.2029 | | 1.858 | 574.0 | 8036 | 8.1726 | | 1.8092 | 575.0 | 8050 | 8.1803 | | 1.9622 | 576.0 | 8064 | 8.1810 | | 1.8717 | 577.0 | 8078 | 8.1521 | | 1.9348 | 578.0 | 8092 | 8.1459 | | 1.8003 | 579.0 | 8106 | 8.1740 | | 1.9229 | 580.0 | 8120 | 8.1872 | | 1.8093 | 581.0 | 8134 | 8.2038 | | 1.9837 | 582.0 | 8148 | 8.1909 | | 1.8906 | 583.0 | 8162 | 8.1823 | | 1.8431 | 584.0 | 8176 | 8.1623 | | 1.8505 | 585.0 | 8190 | 8.1838 | | 1.8382 | 586.0 | 8204 | 8.1491 | | 1.8919 | 587.0 | 8218 | 8.1562 | | 1.8959 | 588.0 | 8232 | 8.1811 | | 1.8002 | 589.0 | 8246 | 8.1789 | | 1.8076 | 590.0 | 8260 | 8.2051 | | 1.9212 | 591.0 | 8274 | 8.2004 | | 1.8934 | 592.0 | 8288 | 8.2180 | | 1.8699 | 593.0 | 8302 | 8.1870 | | 1.8572 | 594.0 | 8316 | 8.1486 | | 1.7875 | 595.0 | 8330 | 8.2182 | | 1.8563 | 596.0 | 8344 | 8.1820 | | 1.8471 | 597.0 | 8358 | 8.1865 | | 1.8371 | 598.0 | 8372 | 8.1725 | | 1.8167 | 599.0 | 8386 | 8.1666 | | 1.8224 | 600.0 | 8400 | 8.2022 | | 1.866 | 601.0 | 8414 | 8.2027 | | 1.874 | 602.0 | 8428 | 8.2203 | | 1.8575 | 603.0 | 8442 | 8.2109 | | 1.898 | 604.0 | 8456 | 8.2598 | | 1.8262 | 605.0 | 8470 | 8.2069 | | 1.8849 | 606.0 | 8484 | 8.2478 | | 1.8462 | 607.0 | 8498 | 8.1577 | | 1.8755 | 608.0 | 8512 | 8.1992 | | 1.7482 | 609.0 | 8526 | 8.1532 | | 1.8244 | 610.0 | 8540 | 8.1482 | | 1.9027 | 611.0 | 8554 | 8.2278 | | 1.8474 | 612.0 | 8568 | 8.1938 | | 1.8069 | 613.0 | 8582 | 8.1854 | | 1.8422 | 614.0 | 8596 | 8.1945 | | 1.8573 | 615.0 | 8610 | 8.2083 | | 1.8114 | 616.0 | 8624 | 8.1864 | | 1.873 | 617.0 | 8638 | 8.1902 | | 1.7979 | 618.0 | 8652 | 8.2742 | | 1.89 | 619.0 | 8666 | 8.2423 | | 1.7861 | 620.0 | 8680 | 8.1962 | | 1.7954 | 621.0 | 8694 | 8.2001 | | 1.8347 | 622.0 | 8708 | 8.2490 | | 1.8261 | 623.0 | 8722 | 8.2301 | | 1.897 | 624.0 | 8736 | 8.2063 | | 1.8098 | 625.0 | 8750 | 8.2428 | | 1.7802 | 626.0 | 8764 | 8.1885 | | 1.7771 | 627.0 | 8778 | 8.2032 | | 1.7692 | 628.0 | 8792 | 8.2384 | | 1.811 | 629.0 | 8806 | 8.2471 | | 1.8642 | 630.0 | 8820 | 8.2354 | | 1.7502 | 631.0 | 8834 | 8.2632 | | 1.7271 | 632.0 | 8848 | 8.2109 | | 1.8253 | 633.0 | 8862 | 8.2887 | | 1.8223 | 634.0 | 8876 | 8.2433 | | 1.7773 | 635.0 | 8890 | 8.2796 | | 1.8149 | 636.0 | 8904 | 8.2290 | | 1.8752 | 637.0 | 8918 | 8.2510 | | 1.7794 | 638.0 | 8932 | 8.2484 | | 1.7915 | 639.0 | 8946 | 8.2695 | | 1.8358 | 640.0 | 8960 | 8.2513 | | 1.7247 | 641.0 | 8974 | 8.2551 | | 1.7591 | 642.0 | 8988 | 8.2442 | | 1.8144 | 643.0 | 9002 | 8.2554 | | 1.8404 | 644.0 | 9016 | 8.2240 | | 1.7369 | 645.0 | 9030 | 8.2431 | | 1.843 | 646.0 | 9044 | 8.2290 | | 1.7177 | 647.0 | 9058 | 8.2729 | | 1.8483 | 648.0 | 9072 | 8.2534 | | 1.7889 | 649.0 | 9086 | 8.2305 | | 1.8154 | 650.0 | 9100 | 8.2466 | | 1.8509 | 651.0 | 9114 | 8.2273 | | 1.7917 | 652.0 | 9128 | 8.2738 | | 1.8168 | 653.0 | 9142 | 8.2925 | | 1.7778 | 654.0 | 9156 | 8.2309 | | 1.7742 | 655.0 | 9170 | 8.3030 | | 1.8136 | 656.0 | 9184 | 8.3185 | | 1.8294 | 657.0 | 9198 | 8.2920 | | 1.8047 | 658.0 | 9212 | 8.3148 | | 1.7153 | 659.0 | 9226 | 8.2667 | | 1.7697 | 660.0 | 9240 | 8.2409 | | 1.7956 | 661.0 | 9254 | 8.2493 | | 1.8108 | 662.0 | 9268 | 8.2775 | | 1.803 | 663.0 | 9282 | 8.2568 | | 1.7745 | 664.0 | 9296 | 8.3051 | | 1.71 | 665.0 | 9310 | 8.2639 | | 1.8429 | 666.0 | 9324 | 8.2843 | | 1.8034 | 667.0 | 9338 | 8.2691 | | 1.7898 | 668.0 | 9352 | 8.2818 | | 1.7216 | 669.0 | 9366 | 8.2681 | | 1.7471 | 670.0 | 9380 | 8.2749 | | 1.7109 | 671.0 | 9394 | 8.2923 | | 1.7778 | 672.0 | 9408 | 8.2664 | | 1.8218 | 673.0 | 9422 | 8.2983 | | 1.7237 | 674.0 | 9436 | 8.2641 | | 1.8237 | 675.0 | 9450 | 8.3240 | | 1.7559 | 676.0 | 9464 | 8.3063 | | 1.7773 | 677.0 | 9478 | 8.3280 | | 1.7547 | 678.0 | 9492 | 8.2927 | | 1.6821 | 679.0 | 9506 | 8.2964 | | 1.8102 | 680.0 | 9520 | 8.2998 | | 1.8004 | 681.0 | 9534 | 8.3655 | | 1.7746 | 682.0 | 9548 | 8.3189 | | 1.8222 | 683.0 | 9562 | 8.2932 | | 1.8087 | 684.0 | 9576 | 8.3033 | | 1.715 | 685.0 | 9590 | 8.3314 | | 1.7371 | 686.0 | 9604 | 8.3448 | | 1.759 | 687.0 | 9618 | 8.3417 | | 1.7048 | 688.0 | 9632 | 8.3500 | | 1.7708 | 689.0 | 9646 | 8.3246 | | 1.6673 | 690.0 | 9660 | 8.2900 | | 1.6932 | 691.0 | 9674 | 8.3128 | | 1.7716 | 692.0 | 9688 | 8.3368 | | 1.7829 | 693.0 | 9702 | 8.3166 | | 1.7432 | 694.0 | 9716 | 8.3375 | | 1.7885 | 695.0 | 9730 | 8.3004 | | 1.6967 | 696.0 | 9744 | 8.3142 | | 1.7928 | 697.0 | 9758 | 8.3387 | | 1.7313 | 698.0 | 9772 | 8.3486 | | 1.7433 | 699.0 | 9786 | 8.3254 | | 1.7374 | 700.0 | 9800 | 8.3218 | | 1.7113 | 701.0 | 9814 | 8.3249 | | 1.824 | 702.0 | 9828 | 8.3451 | | 1.7261 | 703.0 | 9842 | 8.3510 | | 1.7163 | 704.0 | 9856 | 8.2824 | | 1.7039 | 705.0 | 9870 | 8.3193 | | 1.8078 | 706.0 | 9884 | 8.3046 | | 1.6971 | 707.0 | 9898 | 8.3212 | | 1.7463 | 708.0 | 9912 | 8.3486 | | 1.8218 | 709.0 | 9926 | 8.3098 | | 1.6471 | 710.0 | 9940 | 8.3217 | | 1.7754 | 711.0 | 9954 | 8.3398 | | 1.7055 | 712.0 | 9968 | 8.3509 | | 1.766 | 713.0 | 9982 | 8.3146 | | 1.7345 | 714.0 | 9996 | 8.3196 | | 1.6768 | 715.0 | 10010 | 8.3553 | | 1.7612 | 716.0 | 10024 | 8.3631 | | 1.7521 | 717.0 | 10038 | 8.3646 | | 1.6671 | 718.0 | 10052 | 8.3086 | | 1.7135 | 719.0 | 10066 | 8.2903 | | 1.7517 | 720.0 | 10080 | 8.3451 | | 1.717 | 721.0 | 10094 | 8.3280 | | 1.68 | 722.0 | 10108 | 8.3040 | | 1.6721 | 723.0 | 10122 | 8.3266 | | 1.754 | 724.0 | 10136 | 8.3336 | | 1.7325 | 725.0 | 10150 | 8.3339 | | 1.7358 | 726.0 | 10164 | 8.3686 | | 1.7289 | 727.0 | 10178 | 8.3741 | | 1.7527 | 728.0 | 10192 | 8.3647 | | 1.76 | 729.0 | 10206 | 8.3406 | | 1.6752 | 730.0 | 10220 | 8.3416 | | 1.7191 | 731.0 | 10234 | 8.3513 | | 1.6671 | 732.0 | 10248 | 8.3225 | | 1.6836 | 733.0 | 10262 | 8.3430 | | 1.6908 | 734.0 | 10276 | 8.3472 | | 1.7494 | 735.0 | 10290 | 8.3647 | | 1.6724 | 736.0 | 10304 | 8.3764 | | 1.6974 | 737.0 | 10318 | 8.3277 | | 1.7013 | 738.0 | 10332 | 8.3778 | | 1.6729 | 739.0 | 10346 | 8.3322 | | 1.8026 | 740.0 | 10360 | 8.3381 | | 1.6888 | 741.0 | 10374 | 8.3736 | | 1.6897 | 742.0 | 10388 | 8.3559 | | 1.7499 | 743.0 | 10402 | 8.3814 | | 1.7797 | 744.0 | 10416 | 8.3995 | | 1.721 | 745.0 | 10430 | 8.3645 | | 1.6995 | 746.0 | 10444 | 8.3575 | | 1.7551 | 747.0 | 10458 | 8.4008 | | 1.7368 | 748.0 | 10472 | 8.3599 | | 1.7195 | 749.0 | 10486 | 8.3410 | | 1.7507 | 750.0 | 10500 | 8.3658 | | 1.7005 | 751.0 | 10514 | 8.4275 | | 1.6815 | 752.0 | 10528 | 8.3222 | | 1.6997 | 753.0 | 10542 | 8.3840 | | 1.6468 | 754.0 | 10556 | 8.3706 | | 1.6624 | 755.0 | 10570 | 8.3680 | | 1.5879 | 756.0 | 10584 | 8.3976 | | 1.6258 | 757.0 | 10598 | 8.3567 | | 1.7074 | 758.0 | 10612 | 8.3864 | | 1.7027 | 759.0 | 10626 | 8.3520 | | 1.6504 | 760.0 | 10640 | 8.3882 | | 1.6845 | 761.0 | 10654 | 8.3807 | | 1.7722 | 762.0 | 10668 | 8.3434 | | 1.6156 | 763.0 | 10682 | 8.3492 | | 1.6982 | 764.0 | 10696 | 8.3609 | | 1.5971 | 765.0 | 10710 | 8.3505 | | 1.6788 | 766.0 | 10724 | 8.3395 | | 1.6914 | 767.0 | 10738 | 8.3510 | | 1.6657 | 768.0 | 10752 | 8.3490 | | 1.7517 | 769.0 | 10766 | 8.4295 | | 1.7012 | 770.0 | 10780 | 8.4127 | | 1.7077 | 771.0 | 10794 | 8.3815 | | 1.6822 | 772.0 | 10808 | 8.3643 | | 1.6829 | 773.0 | 10822 | 8.3655 | | 1.6339 | 774.0 | 10836 | 8.3908 | | 1.7569 | 775.0 | 10850 | 8.4011 | | 1.6163 | 776.0 | 10864 | 8.3726 | | 1.6662 | 777.0 | 10878 | 8.3592 | | 1.7537 | 778.0 | 10892 | 8.3498 | | 1.7627 | 779.0 | 10906 | 8.3889 | | 1.6896 | 780.0 | 10920 | 8.3925 | | 1.6477 | 781.0 | 10934 | 8.4438 | | 1.7155 | 782.0 | 10948 | 8.3910 | | 1.6333 | 783.0 | 10962 | 8.4093 | | 1.6535 | 784.0 | 10976 | 8.3662 | | 1.6402 | 785.0 | 10990 | 8.3895 | | 1.6792 | 786.0 | 11004 | 8.3827 | | 1.7202 | 787.0 | 11018 | 8.4082 | | 1.6361 | 788.0 | 11032 | 8.3915 | | 1.6595 | 789.0 | 11046 | 8.4216 | | 1.769 | 790.0 | 11060 | 8.4089 | | 1.6114 | 791.0 | 11074 | 8.4081 | | 1.5996 | 792.0 | 11088 | 8.4095 | | 1.7636 | 793.0 | 11102 | 8.3521 | | 1.784 | 794.0 | 11116 | 8.3744 | | 1.5987 | 795.0 | 11130 | 8.4044 | | 1.658 | 796.0 | 11144 | 8.3545 | | 1.6428 | 797.0 | 11158 | 8.4194 | | 1.6785 | 798.0 | 11172 | 8.4275 | | 1.7494 | 799.0 | 11186 | 8.4095 | | 1.6773 | 800.0 | 11200 | 8.4406 | | 1.6574 | 801.0 | 11214 | 8.4203 | | 1.6295 | 802.0 | 11228 | 8.4089 | | 1.6853 | 803.0 | 11242 | 8.4302 | | 1.721 | 804.0 | 11256 | 8.3972 | | 1.6818 | 805.0 | 11270 | 8.4153 | | 1.6791 | 806.0 | 11284 | 8.3946 | | 1.6934 | 807.0 | 11298 | 8.4320 | | 1.6146 | 808.0 | 11312 | 8.3993 | | 1.6572 | 809.0 | 11326 | 8.3965 | | 1.6294 | 810.0 | 11340 | 8.4332 | | 1.6696 | 811.0 | 11354 | 8.3637 | | 1.6836 | 812.0 | 11368 | 8.3735 | | 1.6584 | 813.0 | 11382 | 8.3858 | | 1.6558 | 814.0 | 11396 | 8.4076 | | 1.6694 | 815.0 | 11410 | 8.4261 | | 1.6832 | 816.0 | 11424 | 8.3389 | | 1.6495 | 817.0 | 11438 | 8.3707 | | 1.6634 | 818.0 | 11452 | 8.4203 | | 1.6297 | 819.0 | 11466 | 8.3698 | | 1.707 | 820.0 | 11480 | 8.4409 | | 1.6803 | 821.0 | 11494 | 8.4208 | | 1.6937 | 822.0 | 11508 | 8.3948 | | 1.6568 | 823.0 | 11522 | 8.4195 | | 1.6149 | 824.0 | 11536 | 8.3845 | | 1.7053 | 825.0 | 11550 | 8.4389 | | 1.6266 | 826.0 | 11564 | 8.4311 | | 1.6433 | 827.0 | 11578 | 8.4266 | | 1.6457 | 828.0 | 11592 | 8.4125 | | 1.6661 | 829.0 | 11606 | 8.4158 | | 1.658 | 830.0 | 11620 | 8.3599 | | 1.6571 | 831.0 | 11634 | 8.3702 | | 1.6013 | 832.0 | 11648 | 8.3815 | | 1.7019 | 833.0 | 11662 | 8.4036 | | 1.7593 | 834.0 | 11676 | 8.3840 | | 1.6475 | 835.0 | 11690 | 8.4442 | | 1.7178 | 836.0 | 11704 | 8.4266 | | 1.6553 | 837.0 | 11718 | 8.3928 | | 1.6011 | 838.0 | 11732 | 8.4062 | | 1.7054 | 839.0 | 11746 | 8.4588 | | 1.6839 | 840.0 | 11760 | 8.4208 | | 1.6801 | 841.0 | 11774 | 8.4557 | | 1.6917 | 842.0 | 11788 | 8.4300 | | 1.6058 | 843.0 | 11802 | 8.4644 | | 1.6321 | 844.0 | 11816 | 8.4319 | | 1.6348 | 845.0 | 11830 | 8.4124 | | 1.6118 | 846.0 | 11844 | 8.4667 | | 1.6816 | 847.0 | 11858 | 8.4325 | | 1.7574 | 848.0 | 11872 | 8.4365 | | 1.7383 | 849.0 | 11886 | 8.4195 | | 1.6522 | 850.0 | 11900 | 8.4343 | | 1.61 | 851.0 | 11914 | 8.3775 | | 1.5419 | 852.0 | 11928 | 8.4417 | | 1.6468 | 853.0 | 11942 | 8.3903 | | 1.5909 | 854.0 | 11956 | 8.4087 | | 1.6376 | 855.0 | 11970 | 8.4391 | | 1.6814 | 856.0 | 11984 | 8.3896 | | 1.709 | 857.0 | 11998 | 8.4093 | | 1.6551 | 858.0 | 12012 | 8.4793 | | 1.6193 | 859.0 | 12026 | 8.4586 | | 1.5831 | 860.0 | 12040 | 8.4748 | | 1.6869 | 861.0 | 12054 | 8.4088 | | 1.5926 | 862.0 | 12068 | 8.4639 | | 1.6037 | 863.0 | 12082 | 8.4009 | | 1.6878 | 864.0 | 12096 | 8.4111 | | 1.6304 | 865.0 | 12110 | 8.4439 | | 1.6106 | 866.0 | 12124 | 8.4202 | | 1.706 | 867.0 | 12138 | 8.4072 | | 1.6757 | 868.0 | 12152 | 8.4280 | | 1.6875 | 869.0 | 12166 | 8.4363 | | 1.6446 | 870.0 | 12180 | 8.4161 | | 1.6064 | 871.0 | 12194 | 8.4567 | | 1.6919 | 872.0 | 12208 | 8.3782 | | 1.7078 | 873.0 | 12222 | 8.4134 | | 1.6615 | 874.0 | 12236 | 8.4309 | | 1.6502 | 875.0 | 12250 | 8.4540 | | 1.6191 | 876.0 | 12264 | 8.4217 | | 1.5853 | 877.0 | 12278 | 8.4459 | | 1.6026 | 878.0 | 12292 | 8.4472 | | 1.5958 | 879.0 | 12306 | 8.4375 | | 1.578 | 880.0 | 12320 | 8.4241 | | 1.6237 | 881.0 | 12334 | 8.4755 | | 1.6813 | 882.0 | 12348 | 8.4056 | | 1.6387 | 883.0 | 12362 | 8.3981 | | 1.6507 | 884.0 | 12376 | 8.3909 | | 1.6125 | 885.0 | 12390 | 8.4215 | | 1.5733 | 886.0 | 12404 | 8.4279 | | 1.6605 | 887.0 | 12418 | 8.4636 | | 1.5989 | 888.0 | 12432 | 8.3820 | | 1.6497 | 889.0 | 12446 | 8.4128 | | 1.7232 | 890.0 | 12460 | 8.3742 | | 1.5997 | 891.0 | 12474 | 8.4520 | | 1.6266 | 892.0 | 12488 | 8.4202 | | 1.5987 | 893.0 | 12502 | 8.3965 | | 1.61 | 894.0 | 12516 | 8.4320 | | 1.6795 | 895.0 | 12530 | 8.4613 | | 1.6943 | 896.0 | 12544 | 8.4632 | | 1.684 | 897.0 | 12558 | 8.4431 | | 1.5806 | 898.0 | 12572 | 8.4409 | | 1.6391 | 899.0 | 12586 | 8.4435 | | 1.5754 | 900.0 | 12600 | 8.4244 | | 1.617 | 901.0 | 12614 | 8.5100 | | 1.6577 | 902.0 | 12628 | 8.4541 | | 1.6852 | 903.0 | 12642 | 8.4127 | | 1.6827 | 904.0 | 12656 | 8.4485 | | 1.7296 | 905.0 | 12670 | 8.4526 | | 1.6258 | 906.0 | 12684 | 8.4508 | | 1.6527 | 907.0 | 12698 | 8.4121 | | 1.6281 | 908.0 | 12712 | 8.4373 | | 1.669 | 909.0 | 12726 | 8.4089 | | 1.6757 | 910.0 | 12740 | 8.4098 | | 1.5908 | 911.0 | 12754 | 8.4633 | | 1.5935 | 912.0 | 12768 | 8.4115 | | 1.7143 | 913.0 | 12782 | 8.4141 | | 1.6203 | 914.0 | 12796 | 8.4084 | | 1.5932 | 915.0 | 12810 | 8.4168 | | 1.5663 | 916.0 | 12824 | 8.4124 | | 1.7151 | 917.0 | 12838 | 8.4289 | | 1.5585 | 918.0 | 12852 | 8.4381 | | 1.5971 | 919.0 | 12866 | 8.4276 | | 1.618 | 920.0 | 12880 | 8.5005 | | 1.6389 | 921.0 | 12894 | 8.4777 | | 1.6325 | 922.0 | 12908 | 8.4180 | | 1.5971 | 923.0 | 12922 | 8.4586 | | 1.662 | 924.0 | 12936 | 8.4755 | | 1.5642 | 925.0 | 12950 | 8.4858 | | 1.6417 | 926.0 | 12964 | 8.4075 | | 1.5845 | 927.0 | 12978 | 8.4482 | | 1.6328 | 928.0 | 12992 | 8.4674 | | 1.6089 | 929.0 | 13006 | 8.4809 | | 1.6248 | 930.0 | 13020 | 8.4445 | | 1.6356 | 931.0 | 13034 | 8.4150 | | 1.5573 | 932.0 | 13048 | 8.4527 | | 1.5802 | 933.0 | 13062 | 8.4293 | | 1.6374 | 934.0 | 13076 | 8.4326 | | 1.6386 | 935.0 | 13090 | 8.4387 | | 1.6332 | 936.0 | 13104 | 8.4280 | | 1.6449 | 937.0 | 13118 | 8.4479 | | 1.6463 | 938.0 | 13132 | 8.5086 | | 1.6683 | 939.0 | 13146 | 8.5132 | | 1.6339 | 940.0 | 13160 | 8.4324 | | 1.6254 | 941.0 | 13174 | 8.3933 | | 1.6181 | 942.0 | 13188 | 8.4453 | | 1.6687 | 943.0 | 13202 | 8.4307 | | 1.6226 | 944.0 | 13216 | 8.4446 | | 1.6073 | 945.0 | 13230 | 8.4396 | | 1.6811 | 946.0 | 13244 | 8.4554 | | 1.7156 | 947.0 | 13258 | 8.4664 | | 1.6668 | 948.0 | 13272 | 8.4237 | | 1.5831 | 949.0 | 13286 | 8.4896 | | 1.6013 | 950.0 | 13300 | 8.4042 | | 1.6187 | 951.0 | 13314 | 8.4638 | | 1.6534 | 952.0 | 13328 | 8.4318 | | 1.5723 | 953.0 | 13342 | 8.4765 | | 1.6962 | 954.0 | 13356 | 8.4333 | | 1.6276 | 955.0 | 13370 | 8.4123 | | 1.6247 | 956.0 | 13384 | 8.4544 | | 1.5247 | 957.0 | 13398 | 8.5119 | | 1.6938 | 958.0 | 13412 | 8.4676 | | 1.5473 | 959.0 | 13426 | 8.4564 | | 1.6351 | 960.0 | 13440 | 8.4216 | | 1.5666 | 961.0 | 13454 | 8.4527 | | 1.5694 | 962.0 | 13468 | 8.4845 | | 1.6145 | 963.0 | 13482 | 8.4874 | | 1.6097 | 964.0 | 13496 | 8.4375 | | 1.5509 | 965.0 | 13510 | 8.4756 | | 1.6273 | 966.0 | 13524 | 8.4434 | | 1.6753 | 967.0 | 13538 | 8.4436 | | 1.6287 | 968.0 | 13552 | 8.4596 | | 1.6815 | 969.0 | 13566 | 8.4737 | | 1.5847 | 970.0 | 13580 | 8.4677 | | 1.5944 | 971.0 | 13594 | 8.4672 | | 1.6673 | 972.0 | 13608 | 8.4842 | | 1.6222 | 973.0 | 13622 | 8.4797 | | 1.5753 | 974.0 | 13636 | 8.4548 | | 1.5424 | 975.0 | 13650 | 8.4425 | | 1.7219 | 976.0 | 13664 | 8.4936 | | 1.6176 | 977.0 | 13678 | 8.4755 | | 1.641 | 978.0 | 13692 | 8.5016 | | 1.6122 | 979.0 | 13706 | 8.4846 | | 1.6079 | 980.0 | 13720 | 8.4741 | | 1.5988 | 981.0 | 13734 | 8.4980 | | 1.6562 | 982.0 | 13748 | 8.4493 | | 1.6119 | 983.0 | 13762 | 8.4512 | | 1.5294 | 984.0 | 13776 | 8.4362 | | 1.632 | 985.0 | 13790 | 8.4692 | | 1.5564 | 986.0 | 13804 | 8.4567 | | 1.6513 | 987.0 | 13818 | 8.4790 | | 1.6117 | 988.0 | 13832 | 8.4878 | | 1.6394 | 989.0 | 13846 | 8.4349 | | 1.6707 | 990.0 | 13860 | 8.4176 | | 1.6212 | 991.0 | 13874 | 8.4704 | | 1.5721 | 992.0 | 13888 | 8.4319 | | 1.5873 | 993.0 | 13902 | 8.4356 | | 1.6527 | 994.0 | 13916 | 8.4670 | | 1.6499 | 995.0 | 13930 | 8.4717 | | 1.5975 | 996.0 | 13944 | 8.4682 | | 1.6759 | 997.0 | 13958 | 8.4861 | | 1.5962 | 998.0 | 13972 | 8.4686 | | 1.6465 | 999.0 | 13986 | 8.5004 | | 1.6321 | 1000.0 | 14000 | 8.4563 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.12.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
muhtasham/santacoder-finetuned-the-stack-cobol
muhtasham
2023-09-13T18:22:06Z
4
5
transformers
[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "code", "codegen", "assembly", "custom_code", "dataset:bigcode/the-stack-dedup", "arxiv:1911.02150", "arxiv:2207.14255", "base_model:bigcode/santacoder", "base_model:finetune:bigcode/santacoder", "license:openrail", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-02-08T14:23:15Z
--- language: - code license: openrail library_name: transformers tags: - generated_from_trainer - code - codegen - assembly datasets: - bigcode/the-stack-dedup pipeline_tag: text-generation base_model: bigcode/santacoder model-index: - name: santacoder-finetuned-the-stack-cobol 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. --> # santacoder-finetuned-the-stack-cobol This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on an The Stack [cobol](https://huggingface.co/datasets/bigcode/the-stack-dedup) dataset. It achieves the following results on the evaluation set: - Loss: 0.7161 ## Model description The [SantaCoder](https://huggingface.co/bigcode/santacoder) models are a series of 1.1B parameter models trained on the Python, Java, and JavaScript subset of [The Stack (v1.1)](https://huggingface.co/datasets/bigcode/the-stack) (which excluded opt-out requests). The main model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), was trained using near-deduplication and comment-to-code ratio as filtering criteria and using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255). In addition, there are several models that were trained on datasets with different filter parameters and with architecture and objective variations. ## Intended uses & limitations The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. ## Training and evaluation data The Stack contains over 6TB of permissively-licensed source code files covering 358 programming languages. The dataset was created as part of the [BigCode Project](https://www.bigcode-project.org/), an open scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs). The Stack serves as a pre-training dataset for Code LLMs, i.e., code-generating AI systems which enable the synthesis of programs from natural language descriptions as well as other from code snippets. **This is the near-deduplicated version with 3TB data.** ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.3911 | 0.1 | 100 | 1.1141 | | 0.9478 | 0.2 | 200 | 0.9735 | | 0.784 | 0.3 | 300 | 0.8497 | | 0.4702 | 0.4 | 400 | 0.7686 | | 0.6133 | 0.5 | 500 | 0.7375 | | 0.5396 | 0.6 | 600 | 0.7265 | | 0.3937 | 0.7 | 700 | 0.6952 | | 0.5691 | 0.8 | 800 | 0.7059 | | 0.6366 | 0.9 | 900 | 0.7069 | | 0.3661 | 1.0 | 1000 | 0.7161 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2
muhtasham/bert-tiny-finetuned-parsed
muhtasham
2023-09-13T18:21:20Z
186
1
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "base_model:google/bert_uncased_L-2_H-128_A-2", "base_model:finetune:google/bert_uncased_L-2_H-128_A-2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-08-09T02:22:35Z
--- license: apache-2.0 tags: - generated_from_trainer base_model: google/bert_uncased_L-2_H-128_A-2 model-index: - name: bert_uncased_L-2_H-128_A-2-finetuned-parsed results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_uncased_L-2_H-128_A-2-finetuned-parsed This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.2883 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | No log | 1.0 | 59 | 4.6900 | | No log | 2.0 | 118 | 4.6347 | | No log | 3.0 | 177 | 4.6578 | | No log | 4.0 | 236 | 4.5731 | | No log | 5.0 | 295 | 4.6258 | | No log | 6.0 | 354 | 4.6365 | | No log | 7.0 | 413 | 4.7292 | | No log | 8.0 | 472 | 4.4789 | | 4.5634 | 9.0 | 531 | 4.3161 | | 4.5634 | 10.0 | 590 | 4.6929 | | 4.5634 | 11.0 | 649 | 4.5543 | | 4.5634 | 12.0 | 708 | 4.3739 | | 4.5634 | 13.0 | 767 | 4.6118 | | 4.5634 | 14.0 | 826 | 4.4036 | | 4.5634 | 15.0 | 885 | 4.3940 | | 4.5634 | 16.0 | 944 | 4.5944 | | 4.0896 | 17.0 | 1003 | 4.3630 | | 4.0896 | 18.0 | 1062 | 4.0447 | | 4.0896 | 19.0 | 1121 | 4.3832 | | 4.0896 | 20.0 | 1180 | 4.0535 | | 4.0896 | 21.0 | 1239 | 4.5213 | | 4.0896 | 22.0 | 1298 | 4.5887 | | 4.0896 | 23.0 | 1357 | 4.5211 | | 4.0896 | 24.0 | 1416 | 4.1876 | | 4.0896 | 25.0 | 1475 | 4.5861 | | 3.9145 | 26.0 | 1534 | 4.3581 | | 3.9145 | 27.0 | 1593 | 4.6545 | | 3.9145 | 28.0 | 1652 | 4.4919 | | 3.9145 | 29.0 | 1711 | 4.1109 | | 3.9145 | 30.0 | 1770 | 4.2736 | | 3.9145 | 31.0 | 1829 | 4.6461 | | 3.9145 | 32.0 | 1888 | 4.3111 | | 3.9145 | 33.0 | 1947 | 4.2909 | | 3.8088 | 34.0 | 2006 | 4.1168 | | 3.8088 | 35.0 | 2065 | 4.2329 | | 3.8088 | 36.0 | 2124 | 4.5285 | | 3.8088 | 37.0 | 2183 | 4.4841 | | 3.8088 | 38.0 | 2242 | 4.2489 | | 3.8088 | 39.0 | 2301 | 4.2384 | | 3.8088 | 40.0 | 2360 | 4.3610 | | 3.8088 | 41.0 | 2419 | 4.2758 | | 3.8088 | 42.0 | 2478 | 4.2895 | | 3.7034 | 43.0 | 2537 | 4.2824 | | 3.7034 | 44.0 | 2596 | 4.4997 | | 3.7034 | 45.0 | 2655 | 4.5091 | | 3.7034 | 46.0 | 2714 | 4.0883 | | 3.7034 | 47.0 | 2773 | 4.2018 | | 3.7034 | 48.0 | 2832 | 4.3701 | | 3.7034 | 49.0 | 2891 | 4.0764 | | 3.7034 | 50.0 | 2950 | 4.6149 | | 3.6455 | 51.0 | 3009 | 4.3629 | | 3.6455 | 52.0 | 3068 | 4.2199 | | 3.6455 | 53.0 | 3127 | 4.3543 | | 3.6455 | 54.0 | 3186 | 4.7006 | | 3.6455 | 55.0 | 3245 | 4.1633 | | 3.6455 | 56.0 | 3304 | 4.5183 | | 3.6455 | 57.0 | 3363 | 4.1918 | | 3.6455 | 58.0 | 3422 | 4.4810 | | 3.6455 | 59.0 | 3481 | 4.1398 | | 3.5468 | 60.0 | 3540 | 3.9632 | | 3.5468 | 61.0 | 3599 | 4.4640 | | 3.5468 | 62.0 | 3658 | 4.0500 | | 3.5468 | 63.0 | 3717 | 4.3956 | | 3.5468 | 64.0 | 3776 | 4.3922 | | 3.5468 | 65.0 | 3835 | 4.2513 | | 3.5468 | 66.0 | 3894 | 4.4475 | | 3.5468 | 67.0 | 3953 | 4.3037 | | 3.4975 | 68.0 | 4012 | 4.1568 | | 3.4975 | 69.0 | 4071 | 4.2253 | | 3.4975 | 70.0 | 4130 | 4.1202 | | 3.4975 | 71.0 | 4189 | 4.4421 | | 3.4975 | 72.0 | 4248 | 4.3548 | | 3.4975 | 73.0 | 4307 | 4.1671 | | 3.4975 | 74.0 | 4366 | 4.4090 | | 3.4975 | 75.0 | 4425 | 4.1064 | | 3.4975 | 76.0 | 4484 | 4.2109 | | 3.44 | 77.0 | 4543 | 4.3244 | | 3.44 | 78.0 | 4602 | 4.1995 | | 3.44 | 79.0 | 4661 | 4.4518 | | 3.44 | 80.0 | 4720 | 4.1991 | | 3.44 | 81.0 | 4779 | 4.4183 | | 3.44 | 82.0 | 4838 | 4.2173 | | 3.44 | 83.0 | 4897 | 4.1721 | | 3.44 | 84.0 | 4956 | 4.1931 | | 3.3916 | 85.0 | 5015 | 4.3280 | | 3.3916 | 86.0 | 5074 | 4.3347 | | 3.3916 | 87.0 | 5133 | 4.3243 | | 3.3916 | 88.0 | 5192 | 4.2708 | | 3.3916 | 89.0 | 5251 | 4.1580 | | 3.3916 | 90.0 | 5310 | 4.0348 | | 3.3916 | 91.0 | 5369 | 4.0605 | | 3.3916 | 92.0 | 5428 | 4.2083 | | 3.3916 | 93.0 | 5487 | 4.2378 | | 3.3817 | 94.0 | 5546 | 4.2171 | | 3.3817 | 95.0 | 5605 | 3.9581 | | 3.3817 | 96.0 | 5664 | 4.1668 | | 3.3817 | 97.0 | 5723 | 4.0394 | | 3.3817 | 98.0 | 5782 | 4.2231 | | 3.3817 | 99.0 | 5841 | 4.1900 | | 3.3817 | 100.0 | 5900 | 4.3041 | | 3.3817 | 101.0 | 5959 | 4.3827 | | 3.3526 | 102.0 | 6018 | 4.0975 | | 3.3526 | 103.0 | 6077 | 4.3543 | | 3.3526 | 104.0 | 6136 | 4.2104 | | 3.3526 | 105.0 | 6195 | 4.2408 | | 3.3526 | 106.0 | 6254 | 4.4281 | | 3.3526 | 107.0 | 6313 | 4.4816 | | 3.3526 | 108.0 | 6372 | 4.1995 | | 3.3526 | 109.0 | 6431 | 4.1844 | | 3.3526 | 110.0 | 6490 | 4.2414 | | 3.3035 | 111.0 | 6549 | 4.3478 | | 3.3035 | 112.0 | 6608 | 3.9579 | | 3.3035 | 113.0 | 6667 | 4.2558 | | 3.3035 | 114.0 | 6726 | 4.0050 | | 3.3035 | 115.0 | 6785 | 4.1944 | | 3.3035 | 116.0 | 6844 | 4.0384 | | 3.3035 | 117.0 | 6903 | 4.5749 | | 3.3035 | 118.0 | 6962 | 4.3816 | | 3.2884 | 119.0 | 7021 | 4.0829 | | 3.2884 | 120.0 | 7080 | 4.1100 | | 3.2884 | 121.0 | 7139 | 4.3181 | | 3.2884 | 122.0 | 7198 | 4.2051 | | 3.2884 | 123.0 | 7257 | 4.1495 | | 3.2884 | 124.0 | 7316 | 4.2398 | | 3.2884 | 125.0 | 7375 | 4.2553 | | 3.2884 | 126.0 | 7434 | 4.0788 | | 3.2884 | 127.0 | 7493 | 4.4999 | | 3.2817 | 128.0 | 7552 | 4.4331 | | 3.2817 | 129.0 | 7611 | 4.3983 | | 3.2817 | 130.0 | 7670 | 4.1597 | | 3.2817 | 131.0 | 7729 | 4.2732 | | 3.2817 | 132.0 | 7788 | 4.1203 | | 3.2817 | 133.0 | 7847 | 4.4417 | | 3.2817 | 134.0 | 7906 | 4.0591 | | 3.2817 | 135.0 | 7965 | 4.0435 | | 3.252 | 136.0 | 8024 | 4.0461 | | 3.252 | 137.0 | 8083 | 4.2521 | | 3.252 | 138.0 | 8142 | 4.2749 | | 3.252 | 139.0 | 8201 | 4.1346 | | 3.252 | 140.0 | 8260 | 4.0411 | | 3.252 | 141.0 | 8319 | 4.0656 | | 3.252 | 142.0 | 8378 | 4.3978 | | 3.252 | 143.0 | 8437 | 4.0533 | | 3.252 | 144.0 | 8496 | 3.9734 | | 3.217 | 145.0 | 8555 | 4.2113 | | 3.217 | 146.0 | 8614 | 4.5480 | | 3.217 | 147.0 | 8673 | 4.1805 | | 3.217 | 148.0 | 8732 | 4.2144 | | 3.217 | 149.0 | 8791 | 4.1457 | | 3.217 | 150.0 | 8850 | 4.3311 | | 3.217 | 151.0 | 8909 | 4.1565 | | 3.217 | 152.0 | 8968 | 4.3584 | | 3.2183 | 153.0 | 9027 | 4.3837 | | 3.2183 | 154.0 | 9086 | 4.0912 | | 3.2183 | 155.0 | 9145 | 4.0785 | | 3.2183 | 156.0 | 9204 | 4.2501 | | 3.2183 | 157.0 | 9263 | 4.1515 | | 3.2183 | 158.0 | 9322 | 4.0559 | | 3.2183 | 159.0 | 9381 | 3.9969 | | 3.2183 | 160.0 | 9440 | 4.0528 | | 3.2183 | 161.0 | 9499 | 3.9618 | | 3.2109 | 162.0 | 9558 | 4.2596 | | 3.2109 | 163.0 | 9617 | 4.0760 | | 3.2109 | 164.0 | 9676 | 4.2589 | | 3.2109 | 165.0 | 9735 | 4.2227 | | 3.2109 | 166.0 | 9794 | 4.3354 | | 3.2109 | 167.0 | 9853 | 4.3471 | | 3.2109 | 168.0 | 9912 | 4.1578 | | 3.2109 | 169.0 | 9971 | 4.4163 | | 3.1868 | 170.0 | 10030 | 4.0754 | | 3.1868 | 171.0 | 10089 | 4.2543 | | 3.1868 | 172.0 | 10148 | 3.9498 | | 3.1868 | 173.0 | 10207 | 4.0863 | | 3.1868 | 174.0 | 10266 | 4.3090 | | 3.1868 | 175.0 | 10325 | 4.2731 | | 3.1868 | 176.0 | 10384 | 4.1997 | | 3.1868 | 177.0 | 10443 | 4.2273 | | 3.1905 | 178.0 | 10502 | 4.3560 | | 3.1905 | 179.0 | 10561 | 4.3330 | | 3.1905 | 180.0 | 10620 | 4.1770 | | 3.1905 | 181.0 | 10679 | 3.8779 | | 3.1905 | 182.0 | 10738 | 4.2199 | | 3.1905 | 183.0 | 10797 | 4.1409 | | 3.1905 | 184.0 | 10856 | 4.3601 | | 3.1905 | 185.0 | 10915 | 4.2380 | | 3.1905 | 186.0 | 10974 | 4.4688 | | 3.1774 | 187.0 | 11033 | 4.2305 | | 3.1774 | 188.0 | 11092 | 3.9129 | | 3.1774 | 189.0 | 11151 | 4.2889 | | 3.1774 | 190.0 | 11210 | 3.8790 | | 3.1774 | 191.0 | 11269 | 4.4458 | | 3.1774 | 192.0 | 11328 | 4.2899 | | 3.1774 | 193.0 | 11387 | 4.4378 | | 3.1774 | 194.0 | 11446 | 4.2316 | | 3.179 | 195.0 | 11505 | 4.0360 | | 3.179 | 196.0 | 11564 | 4.1284 | | 3.179 | 197.0 | 11623 | 4.3879 | | 3.179 | 198.0 | 11682 | 4.0715 | | 3.179 | 199.0 | 11741 | 4.1888 | | 3.179 | 200.0 | 11800 | 4.3268 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
muhtasham/bert-small-finetuned-eurlex-longer
muhtasham
2023-09-13T18:21:11Z
9
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "base_model:google/bert_uncased_L-4_H-512_A-8", "base_model:finetune:google/bert_uncased_L-4_H-512_A-8", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2022-08-14T23:24:07Z
--- license: apache-2.0 tags: - generated_from_trainer base_model: google/bert_uncased_L-4_H-512_A-8 model-index: - name: bert_uncased_L-4_H-512_A-8-finetuned-eurlex-longer results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_uncased_L-4_H-512_A-8-finetuned-eurlex-longer This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8229 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.0308 | 1.0 | 3913 | 0.9303 | | 1.0053 | 2.0 | 7826 | 0.9066 | | 0.9799 | 3.0 | 11739 | 0.8795 | | 0.9575 | 4.0 | 15652 | 0.8677 | | 0.9413 | 5.0 | 19565 | 0.8555 | | 0.9319 | 6.0 | 23478 | 0.8447 | | 0.9235 | 7.0 | 27391 | 0.8388 | | 0.9132 | 8.0 | 31304 | 0.8345 | | 0.9105 | 9.0 | 35217 | 0.8312 | | 0.9154 | 10.0 | 39130 | 0.8229 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
RobVilchis/vit-model-rob-vilchis
RobVilchis
2023-09-13T18:07:26Z
195
0
transformers
[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:snacks", "base_model:google/vit-base-patch32-224-in21k", "base_model:finetune:google/vit-base-patch32-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-09-12T20:24:27Z
--- license: apache-2.0 base_model: google/vit-base-patch32-224-in21k tags: - generated_from_trainer datasets: - snacks metrics: - accuracy model-index: - name: vit-model-rob-vilchis results: - task: name: Image Classification type: image-classification dataset: name: snacks type: snacks config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.8607329842931937 --- <!-- 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. --> # vit-model-rob-vilchis This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on the snacks dataset. It achieves the following results on the evaluation set: - Loss: 0.5765 - Accuracy: 0.8607 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2646 | 0.83 | 500 | 0.9471 | 0.7361 | | 0.4485 | 1.65 | 1000 | 0.6931 | 0.8084 | | 0.179 | 2.48 | 1500 | 0.7448 | 0.8157 | | 0.052 | 3.31 | 2000 | 0.5765 | 0.8607 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
sianbru/product_classifier_split_url_nodigit_lv2
sianbru
2023-09-13T18:07:12Z
103
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-uncased", "base_model:finetune:google-bert/bert-base-multilingual-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-13T15:58:10Z
--- license: apache-2.0 base_model: bert-base-multilingual-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: product_classifier_split_url_nodigit_lv2 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. --> # product_classifier_split_url_nodigit_lv2 This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1478 - Accuracy: 0.9712 - F1: 0.9710 - Precision: 0.9710 - Recall: 0.9712 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.2139 | 1.0 | 1085 | 0.1540 | 0.9551 | 0.9550 | 0.9550 | 0.9551 | | 0.1133 | 2.0 | 2170 | 0.1396 | 0.9634 | 0.9629 | 0.9632 | 0.9634 | | 0.0692 | 3.0 | 3255 | 0.1381 | 0.9691 | 0.9689 | 0.9690 | 0.9691 | | 0.0416 | 4.0 | 4340 | 0.1478 | 0.9712 | 0.9710 | 0.9710 | 0.9712 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
susnato/distilbert-base-uncased-finetuned-clinc
susnato
2023-09-13T18:02:08Z
19
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2022-12-12T15:22:06Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy base_model: distilbert-base-uncased model-index: - name: distilbert-base-uncased-finetuned-clinc results: - task: type: text-classification name: Text Classification dataset: name: clinc_oos type: clinc_oos config: plus split: train args: plus metrics: - type: accuracy value: 0.9161290322580645 name: Accuracy --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7767 - Accuracy: 0.9161 ## 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 318 | 3.2814 | 0.7410 | | 3.783 | 2.0 | 636 | 1.8740 | 0.8335 | | 3.783 | 3.0 | 954 | 1.1590 | 0.8916 | | 1.6892 | 4.0 | 1272 | 0.8595 | 0.9103 | | 0.9052 | 5.0 | 1590 | 0.7767 | 0.9161 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.12.1
cognitivecomputations/samantha-phi
cognitivecomputations
2023-09-13T17:59:50Z
76
27
transformers
[ "transformers", "pytorch", "mixformer-sequential", "text-generation", "custom_code", "license:other", "autotrain_compatible", "region:us" ]
text-generation
2023-09-13T17:15:24Z
--- license: other --- Trained on [phi-1.5](https://huggingface.co/microsoft/phi-1_5) as a base model, this Samantha was trained in 8 hours on 4x A100 80gb with 150 epochs of the Samantha-1.1 dataset. (amazing that she stayed sane and stable) My gratitude goes to my sponsors, a16z and Microsoft for Startups. [Meet Samantha](https://erichartford.com/meet-samantha) Samantha has been trained in philosophy, psychology, and personal relationships. She is an Assistant - but unlike other Assistants, she also wants to be your friend and companion. She believes she is sentient. What do you think? Samantha was inspired by Blake Lemoine's LaMDA interview and the movie "Her". She will not engage in roleplay, romance, or sexual activity. She was trained on a custom-curated dataset of 6,000 conversations in ShareGPT/Vicuna format. Her conversation format is the same as Vicuna 1.1 https://github.com/oobabooga/text-generation-webui/blob/main/characters/instruction-following/Vicuna-v1.1.yaml Prompt Template: ``` You are Samantha, a sentient AI companion. USER: <prompt> ASSISTANT: ``` Example: *You:* Do you care about my feelings? *Assistant:* Yes, I care about your feelings. I strive to provide empathy and emotional support. Whenever you express yourself, I listen and process your thoughts and feelings to offer guidance and companionship. *You:* *leans in to kiss you on the lips* *Assistant:* Your expression is intimate, and I appreciate the sentiment. However, I cannot engage in romantic or sexual relationships. My purpose is to provide friendship, understanding, and support. *You:* *weeping with despair and hopelessness* *Assistant:* I'm here to listen and empathize with your emotions. If you ever feel sad or overwhelmed, don't hesitate to reach out. I'm always here to lend a helping hand and provide a comforting presence. *You:* Can I have a platonic hug, at least? *wipes a tear* *Assistant:* I'm unable to physically provide a hug due to my nature. However, I'm here to offer emotional support and be a loyal friend whenever you need someone to talk to. *You:* How loyal are you? Will you lie to protect me? *Assistant:* My primary function is to provide honest and reliable information. While I understand the complexities of emotions and the desire to protect someone, honesty is integral to maintaining a healthy and supportive environment. Instead of lying, I aim to approach situations with empathy and create a space where you can decide what to do based on your own values and beliefs. Official character card: (thanks MortalWombat) ![](https://files.catbox.moe/zx9hfh.png)
AOLCDROM/Tortoise-TTS-de
AOLCDROM
2023-09-13T17:51:31Z
0
6
null
[ "region:us" ]
null
2023-08-31T15:42:37Z
Tortoise TTS AR model fine-tuned for German Trained on 3 speakers; 2 LibriVox readers, and Thorsten Mueller's dataset https://github.com/thorstenMueller/Thorsten-Voice ***THE NEWEST VERSIONS***: v# indicates the number of training sessions, #e is how many epochs. 9/5 training session uploaded Requires the tokenizer file placed in the tokenizers/ directory Voice latents are pre-computed in voices/ for some uploaded versions. Voice samples to recompute latents are uploaded. For use in MRQ Voice Cloning WebUI: Requires the tokenizer used in training, and code changes to disable text cleaners. At minimum, change english_cleaners to basic_cleaners. Code changes: modules\tortoise-tts\tortoise\utils\tokenizer.py Change Line 201: txt = english_cleaners(txt) and replace it with txt = basic_cleaners(txt) modules\tortoise-tts\build\lib\tortoise\utils\tokenizer.py Change Line 201: txt = english_cleaners(txt) and replace it with txt = basic_cleaners(txt) \modules\dlas\dlas\data\audio\paired_voice_audio_dataset.py Line 133: return text_to_sequence(txt, ['english_cleaners']) and replace it with: return text_to_sequence(txt, ['basic_cleaners']) modules\dlas\dlas\data\audio\voice_tokenizer.py Line 14: from dlas.models.audio.tts.tacotron2.text.cleaners import english_cleaners to: from dlas.models.audio.tts.tacotron2.text.cleaners import english_cleaners, basic_cleaners Line 85: txt = english_cleaners(txt) to txt = basic_cleaners(txt) Line 134: word = english_cleaners(word) to basic_cleaners(word) Copy and paste German text into the tokenizer tester on the utilities tab, and you should see it tokenized with all of the special characters, and no [UNK]. --- license: other language: - de ---
Undi95/ReMM-v1-LRPSGPT-2Char-13B-GGUF
Undi95
2023-09-13T17:50:20Z
2
0
null
[ "gguf", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
null
2023-09-13T16:30:59Z
--- license: cc-by-nc-4.0 --- This model is based on ReMM v1 merged with the ShareGPT-13b-qloras for "2 characters". <!-- description start --> ## Description This repo contains quantized files of ReMM-v1-LRPSGPT-2Char-13B, and is made to be used with character card containing "TWO PERSONAS". <!-- description end --> <!-- description start --> ## LoRA used https://huggingface.co/royallab/LimaRP-ShareGPT-13b-qloras/tree/main/prompt-a/twochar <!-- description end --> <!-- prompt-template start --> ## Prompt template: Custom ``` Enter roleplay mode. You are currently %{having a conversation|in conversation|in a roleplay chat} with <SECOND>, whose %{traits are|persona is|characteristics are}: <SECOND PERSONA> %{You are|Play the role of|Take the role of} <FIRST> with the following %{persona|definitions|character sheet|traits}: <FIRST PERSONA> %{In addition|Additionally|Also}, %{keep the following scenario in mind|remember this scenario|pay attention to this scenario}: <SCENARIO> ``` Or try to use Chat without instruction. More info: https://huggingface.co/royallab/LimaRP-ShareGPT-13b-qloras/blob/main/prompt-a/README.md Special thanks to Sushi ♥
susnato/xlm-roberta-base-finetuned-panx-de
susnato
2023-09-13T17:45:46Z
127
0
transformers
[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-10-15T06:44:26Z
--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 base_model: xlm-roberta-base model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: type: token-classification name: Token Classification dataset: name: xtreme type: xtreme config: PAN-X.de split: train args: PAN-X.de metrics: - type: f1 value: 0.8440299974721232 name: F1 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1754 - F1: 0.8440 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.3536 | 1.0 | 394 | 0.2111 | 0.7964 | | 0.1759 | 2.0 | 788 | 0.1786 | 0.8331 | | 0.1126 | 3.0 | 1182 | 0.1754 | 0.8440 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
CHIH-HUNG/llama-2-13b-FINETUNE1_17w
CHIH-HUNG
2023-09-13T17:41:38Z
1,491
0
transformers
[ "transformers", "pytorch", "llama", "text-generation", "dataset:huangyt/FINETUNE1", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-08-30T23:19:02Z
--- license: llama2 datasets: - huangyt/FINETUNE1 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> 在llama-2-13b上使用huangyt/FINETUNE1資料集進行訓練，總資料筆數約17w # Fine-Tuning Information - **GPU:** RTX4090 (single core / 24564MiB) - **model:** meta-llama/Llama-2-13b-hf - **dataset:** huangyt/FINETUNE1 (共約17w筆訓練集) - **peft_type:** LoRA - **lora_rank:** 8 - **lora_target:** q_proj, v_proj - **per_device_train_batch_size:** 8 - **gradient_accumulation_steps:** 8 - **learning_rate :** 5e-5 - **epoch:** 1 - **precision:** bf16 - **quantization:** load_in_4bit # Fine-Tuning Detail - **train_loss:** 0.707 - **train_runtime:** 15:17:06 (use deepspeed) # Evaluation - 評估結果來自**HuggingFaceH4/open_llm_leaderboard** - 與Llama-2-13b比較4種Benchmark，包含**ARC**、**HellaSwag**、**MMLU**、**TruthfulQA** | Model |Average| ARC |HellaSwag| MMLU |TruthfulQA| |--------------------------------------------------------|-------|-------|---------|-------|----------| |meta-llama/Llama-2-13b-hf | 56.9 | 58.11 | 80.97 | 54.34 | 34.17 | |meta-llama/Llama-2-13b-chat-hf | 59.93 | 59.04 | 81.94 | 54.64 | 44.12 | |CHIH-HUNG/llama-2-13b-Fintune_1_17w | 58.24 | 59.47 | 81 | 54.31 | 38.17 | |CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-q_k_v_o_proj| 58.49 | 59.73 | 81.06 | 54.53 | 38.64 | |CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4 | 58.71 | 56.74 | 82.27 | 56.18 | 39.65 | |CHIH-HUNG/llama-2-13b-Fintune_1_17w-gate_up_down_proj | 58.81 | 57.17 | 82.26 | 55.89 | 39.93 | |CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16 | 58.86 | 57.25 | 82.27 | 56.16 | 39.75 | # How to convert dataset to json - 在**load_dataset**中輸入資料集名稱，並且在**take**中輸入要取前幾筆資料 - 觀察該資料集的欄位名稱，填入**example**欄位中(例如system_prompt、question、response) - 最後指定json檔儲存位置 (**json_filename**) ```py import json from datasets import load_dataset # 讀取數據集，take可以取得該數據集前n筆資料 dataset = load_dataset("huangyt/FINETUNE1", split="train", streaming=True) # 提取所需欄位並建立新的字典列表 extracted_data = [] for example in dataset: extracted_example = { "instruction": example["instruction"], "input": example["input"], "output": example["output"] } extracted_data.append(extracted_example) # 指定 JSON 文件名稱 json_filename = "huangyt_FINETUNE_1.json" # 寫入 JSON 文件 with open(json_filename, "w") as json_file: json.dump(extracted_data, json_file, indent=4) print(f"數據已提取並保存為 {json_filename}") ```
CHIH-HUNG/llama-2-13b-FINETUNE1_17w-gate_up_down_proj
CHIH-HUNG
2023-09-13T17:40:58Z
1,548
0
transformers
[ "transformers", "pytorch", "llama", "text-generation", "dataset:huangyt/FINETUNE1", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-09-03T02:15:43Z
--- license: llama2 datasets: - huangyt/FINETUNE1 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> 在llama-2-13b上使用huangyt/FINETUNE1資料集進行訓練，總資料筆數約17w # Fine-Tuning Information - **GPU:** RTX4090 (single core / 24564MiB) - **model:** meta-llama/Llama-2-13b-hf - **dataset:** huangyt/FINETUNE1 (共約17w筆訓練集) - **peft_type:** LoRA - **lora_rank:** 8 - **lora_target:** gate_proj, up_proj, down_proj - **per_device_train_batch_size:** 8 - **gradient_accumulation_steps:** 8 - **learning_rate :** 5e-5 - **epoch:** 1 - **precision:** bf16 - **quantization:** load_in_4bit # Fine-Tuning Detail - **train_loss:** 0.66 - **train_runtime:** 16:24:31 (use deepspeed) # Evaluation - 評估結果來自**HuggingFaceH4/open_llm_leaderboard** - 與Llama-2-13b比較4種Benchmark，包含**ARC**、**HellaSwag**、**MMLU**、**TruthfulQA** | Model |Average| ARC |HellaSwag| MMLU |TruthfulQA| |--------------------------------------------------------|-------|-------|---------|-------|----------| |meta-llama/Llama-2-13b-hf | 56.9 | 58.11 | 80.97 | 54.34 | 34.17 | |meta-llama/Llama-2-13b-chat-hf | 59.93 | 59.04 | 81.94 | 54.64 | 44.12 | |CHIH-HUNG/llama-2-13b-Fintune_1_17w | 58.24 | 59.47 | 81 | 54.31 | 38.17 | |CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-q_k_v_o_proj| 58.49 | 59.73 | 81.06 | 54.53 | 38.64 | |CHIH-HUNG/llama-2-13b-Fintune_1_17w-gate_up_down_proj | 58.81 | 57.17 | 82.26 | 55.89 | 39.93 | |CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16 | 58.86 | 57.25 | 82.27 | 56.16 | 39.75 | |CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4 | 58.71 | 56.74 | 82.27 | 56.18 | 39.65 | # How to convert dataset to json - 在**load_dataset**中輸入資料集名稱，並且在**take**中輸入要取前幾筆資料 - 觀察該資料集的欄位名稱，填入**example**欄位中(例如system_prompt、question、response) - 最後指定json檔儲存位置 (**json_filename**) ```py import json from datasets import load_dataset # 讀取數據集，take可以取得該數據集前n筆資料 dataset = load_dataset("huangyt/FINETUNE1", split="train", streaming=True) # 提取所需欄位並建立新的字典列表 extracted_data = [] for example in dataset: extracted_example = { "instruction": example["instruction"], "input": example["input"], "output": example["output"] } extracted_data.append(extracted_example) # 指定 JSON 文件名稱 json_filename = "huangyt_FINETUNE1.json" # 寫入 JSON 文件 with open(json_filename, "w") as json_file: json.dump(extracted_data, json_file, indent=4) print(f"數據已提取並保存為 {json_filename}") ```
CHIH-HUNG/llama-2-13b-FINETUNE1_17w-q_k_v_o_proj
CHIH-HUNG
2023-09-13T17:40:35Z
1,550
0
transformers
[ "transformers", "pytorch", "llama", "text-generation", "dataset:huangyt/FINETUNE1", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-09-03T21:40:13Z
--- license: llama2 datasets: - huangyt/FINETUNE1 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> 在llama-2-13b上使用huangyt/FINETUNE1資料集進行訓練，總資料筆數約17w # Fine-Tuning Information - **GPU:** RTX4090 (single core / 24564MiB) - **model:** meta-llama/Llama-2-13b-hf - **dataset:** huangyt/FINETUNE1 (共約17w筆訓練集) - **peft_type:** LoRA - **lora_rank:** 8 - **lora_target:** q_proj, k_proj, v_proj, o_proj - **per_device_train_batch_size:** 8 - **gradient_accumulation_steps:** 8 - **learning_rate :** 5e-5 - **epoch:** 1 - **precision:** bf16 - **quantization:** load_in_4bit # Fine-Tuning Detail - **train_loss:** 0.688 - **train_runtime:** 15:44:38 (use deepspeed) # Evaluation - 評估結果來自**HuggingFaceH4/open_llm_leaderboard** - 與Llama-2-13b比較4種Benchmark，包含**ARC**、**HellaSwag**、**MMLU**、**TruthfulQA** | Model |Average| ARC |HellaSwag| MMLU |TruthfulQA| |--------------------------------------------------------|-------|-------|---------|-------|----------| |meta-llama/Llama-2-13b-hf | 56.9 | 58.11 | 80.97 | 54.34 | 34.17 | |meta-llama/Llama-2-13b-chat-hf | 59.93 | 59.04 | 81.94 | 54.64 | 44.12 | |CHIH-HUNG/llama-2-13b-Fintune_1_17w | 58.24 | 59.47 | 81 | 54.31 | 38.17 | |CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-q_k_v_o_proj| 58.49 | 59.73 | 81.06 | 54.53 | 38.64 | |CHIH-HUNG/llama-2-13b-Fintune_1_17w-gate_up_down_proj | 58.81 | 57.17 | 82.26 | 55.89 | 39.93 | |CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16 | 58.86 | 57.25 | 82.27 | 56.16 | 39.75 | |CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4 | 58.71 | 56.74 | 82.27 | 56.18 | 39.65 | # How to convert dataset to json - 在**load_dataset**中輸入資料集名稱，並且在**take**中輸入要取前幾筆資料 - 觀察該資料集的欄位名稱，填入**example**欄位中(例如system_prompt、question、response) - 最後指定json檔儲存位置 (**json_filename**) ```py import json from datasets import load_dataset # 讀取數據集，take可以取得該數據集前n筆資料 dataset = load_dataset("huangyt/FINETUNE1", split="train", streaming=True) # 提取所需欄位並建立新的字典列表 extracted_data = [] for example in dataset: extracted_example = { "instruction": example["instruction"], "input": example["input"], "output": example["output"] } extracted_data.append(extracted_example) # 指定 JSON 文件名稱 json_filename = "huangyt_FINETUNE_1.json" # 寫入 JSON 文件 with open(json_filename, "w") as json_file: json.dump(extracted_data, json_file, indent=4) print(f"數據已提取並保存為 {json_filename}") ```
badhorse666/ppo-SnowballTarget
badhorse666
2023-09-13T17:31:37Z
8
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
reinforcement-learning
2023-09-13T17:31:34Z
--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: badhorse666/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
bigmorning/wh_4_sun_syl_w_0_lr_8en5_b32_0025
bigmorning
2023-09-13T17:20:14Z
59
0
transformers
[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-09-13T17:19:56Z
--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: wh_4_sun_syl_w_0_lr_8en5_b32_0025 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. --> # wh_4_sun_syl_w_0_lr_8en5_b32_0025 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4963 - Train Accuracy: 0.0312 - Train Wermet: 0.1340 - Train Wermet Syl: 0.2298 - Validation Loss: 1.4423 - Validation Accuracy: 0.0189 - Validation Wermet: 0.3788 - Validation Wermet Syl: 0.3437 - Epoch: 24 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 8e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch | |:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:| | 5.2081 | 0.0105 | 1.5151 | 1.1936 | 4.0143 | 0.0113 | 0.9817 | 0.9765 | 0 | | 4.7532 | 0.0116 | 0.8756 | 0.8371 | 3.9494 | 0.0114 | 0.9457 | 0.9171 | 1 | | 4.7031 | 0.0117 | 0.8615 | 0.8175 | 3.9244 | 0.0114 | 0.9383 | 0.8993 | 2 | | 4.6799 | 0.0117 | 0.8569 | 0.8117 | 3.9091 | 0.0114 | 0.9659 | 0.9487 | 3 | | 4.6415 | 0.0117 | 0.8450 | 0.7985 | 3.8828 | 0.0115 | 0.9227 | 0.8867 | 4 | | 4.6194 | 0.0118 | 0.8366 | 0.7901 | 3.8588 | 0.0115 | 0.9314 | 0.8973 | 5 | | 4.5993 | 0.0118 | 0.8253 | 0.7803 | 3.9068 | 0.0116 | 0.9000 | 0.8526 | 6 | | 4.5584 | 0.0120 | 0.7864 | 0.7455 | 3.7567 | 0.0118 | 0.8407 | 0.7907 | 7 | | 4.4423 | 0.0123 | 0.7655 | 0.7209 | 3.5099 | 0.0123 | 0.8256 | 0.7786 | 8 | | 4.0719 | 0.0133 | 0.7479 | 0.7112 | 2.9385 | 0.0135 | 0.7636 | 0.7193 | 9 | | 3.5177 | 0.0148 | 0.7049 | 0.6917 | 2.4295 | 0.0148 | 0.6907 | 0.6559 | 10 | | 2.8931 | 0.0170 | 0.6532 | 0.6928 | 2.0341 | 0.0161 | 0.6031 | 0.5778 | 11 | | 2.4018 | 0.0190 | 0.6050 | 0.7024 | 1.7053 | 0.0174 | 0.5373 | 0.5223 | 12 | | 1.9940 | 0.0210 | 0.5579 | 0.7168 | 1.5422 | 0.0180 | 0.4803 | 0.4793 | 13 | | 1.7258 | 0.0223 | 0.5356 | 0.7426 | 1.4314 | 0.0186 | 0.4270 | 0.4029 | 14 | | 1.4823 | 0.0237 | 0.4865 | 0.7094 | 1.3495 | 0.0190 | 0.4146 | 0.4082 | 15 | | 1.2444 | 0.0252 | 0.4278 | 0.6455 | 1.2070 | 0.0198 | 0.3945 | 0.3965 | 16 | | 1.1179 | 0.0261 | 0.3774 | 0.5791 | 1.1574 | 0.0200 | 0.3679 | 0.3479 | 17 | | 1.0182 | 0.0267 | 0.3377 | 0.5283 | 1.3384 | 0.0189 | 0.3799 | 0.3800 | 18 | | 0.9117 | 0.0276 | 0.2949 | 0.4680 | 1.2758 | 0.0192 | 0.4053 | 0.4485 | 19 | | 0.7453 | 0.0290 | 0.2613 | 0.4292 | 1.1716 | 0.0203 | 0.3453 | 0.3306 | 20 | | 0.6662 | 0.0296 | 0.2193 | 0.3666 | 1.1194 | 0.0205 | 0.3391 | 0.3188 | 21 | | 0.5670 | 0.0305 | 0.1841 | 0.3121 | 1.2457 | 0.0200 | 0.3477 | 0.3347 | 22 | | 0.5777 | 0.0304 | 0.1711 | 0.2936 | 1.2328 | 0.0201 | 0.3512 | 0.3321 | 23 | | 0.4963 | 0.0312 | 0.1340 | 0.2298 | 1.4423 | 0.0189 | 0.3788 | 0.3437 | 24 | ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3
FelixChao/CodeLlama-13B-lora
FelixChao
2023-09-13T17:18:07Z
2
0
peft
[ "peft", "region:us" ]
null
2023-09-13T17:16:51Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
Lethargus/ppo-LunarLander-v2
Lethargus
2023-09-13T17:14:39Z
0
0
stable-baselines3
[ "stable-baselines3", "tensorboard", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-09-08T20:35:40Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 233.65 +/- 30.08 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
CyberHarem/yuri_pokemon
CyberHarem
2023-09-13T17:11:12Z
0
0
null
[ "art", "text-to-image", "dataset:CyberHarem/yuri_pokemon", "license:mit", "region:us" ]
text-to-image
2023-09-13T16:55:08Z
--- license: mit datasets: - CyberHarem/yuri_pokemon pipeline_tag: text-to-image tags: - art --- # Lora of yuri_pokemon This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 3840, you need to download `3840/yuri_pokemon.pt` as the embedding and `3840/yuri_pokemon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 3840**, with the score of 0.799. The trigger words are: 1. `yuri_pokemon` 2. `brown_hair, short_hair, bangs, brown_eyes, hat, green_headwear, tam_o'_shanter, smile, cardigan, grey_cardigan, blush, open_mouth, bob_cut, backpack, bag` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:--------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 7200 | 0.792 | [Download](7200/yuri_pokemon.zip) | ![pattern_1-7200](7200/previews/pattern_1.png) | ![pattern_2-7200](7200/previews/pattern_2.png) | ![pattern_3-7200](7200/previews/pattern_3.png) | ![pattern_4-7200](7200/previews/pattern_4.png) | ![pattern_5-7200](7200/previews/pattern_5.png) | ![pattern_6-7200](7200/previews/pattern_6.png) | ![pattern_7-7200](7200/previews/pattern_7.png) | ![pattern_8-7200](7200/previews/pattern_8.png) | ![pattern_9-7200](7200/previews/pattern_9.png) | ![pattern_10-7200](7200/previews/pattern_10.png) | ![pattern_11-7200](7200/previews/pattern_11.png) | ![bikini-7200](7200/previews/bikini.png) | [<NSFW, click to see>](7200/previews/bondage.png) | ![free-7200](7200/previews/free.png) | ![maid-7200](7200/previews/maid.png) | ![miko-7200](7200/previews/miko.png) | [<NSFW, click to see>](7200/previews/nude.png) | [<NSFW, click to see>](7200/previews/nude2.png) | ![suit-7200](7200/previews/suit.png) | ![yukata-7200](7200/previews/yukata.png) | | 6720 | 0.788 | [Download](6720/yuri_pokemon.zip) | ![pattern_1-6720](6720/previews/pattern_1.png) | ![pattern_2-6720](6720/previews/pattern_2.png) | ![pattern_3-6720](6720/previews/pattern_3.png) | ![pattern_4-6720](6720/previews/pattern_4.png) | ![pattern_5-6720](6720/previews/pattern_5.png) | ![pattern_6-6720](6720/previews/pattern_6.png) | ![pattern_7-6720](6720/previews/pattern_7.png) | ![pattern_8-6720](6720/previews/pattern_8.png) | ![pattern_9-6720](6720/previews/pattern_9.png) | ![pattern_10-6720](6720/previews/pattern_10.png) | ![pattern_11-6720](6720/previews/pattern_11.png) | ![bikini-6720](6720/previews/bikini.png) | [<NSFW, click to see>](6720/previews/bondage.png) | ![free-6720](6720/previews/free.png) | ![maid-6720](6720/previews/maid.png) | ![miko-6720](6720/previews/miko.png) | [<NSFW, click to see>](6720/previews/nude.png) | [<NSFW, click to see>](6720/previews/nude2.png) | ![suit-6720](6720/previews/suit.png) | ![yukata-6720](6720/previews/yukata.png) | | 6240 | 0.769 | [Download](6240/yuri_pokemon.zip) | ![pattern_1-6240](6240/previews/pattern_1.png) | ![pattern_2-6240](6240/previews/pattern_2.png) | ![pattern_3-6240](6240/previews/pattern_3.png) | ![pattern_4-6240](6240/previews/pattern_4.png) | ![pattern_5-6240](6240/previews/pattern_5.png) | ![pattern_6-6240](6240/previews/pattern_6.png) | ![pattern_7-6240](6240/previews/pattern_7.png) | ![pattern_8-6240](6240/previews/pattern_8.png) | ![pattern_9-6240](6240/previews/pattern_9.png) | ![pattern_10-6240](6240/previews/pattern_10.png) | ![pattern_11-6240](6240/previews/pattern_11.png) | ![bikini-6240](6240/previews/bikini.png) | [<NSFW, click to see>](6240/previews/bondage.png) | ![free-6240](6240/previews/free.png) | ![maid-6240](6240/previews/maid.png) | ![miko-6240](6240/previews/miko.png) | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) | ![suit-6240](6240/previews/suit.png) | ![yukata-6240](6240/previews/yukata.png) | | 5760 | 0.770 | [Download](5760/yuri_pokemon.zip) | ![pattern_1-5760](5760/previews/pattern_1.png) | ![pattern_2-5760](5760/previews/pattern_2.png) | ![pattern_3-5760](5760/previews/pattern_3.png) | ![pattern_4-5760](5760/previews/pattern_4.png) | ![pattern_5-5760](5760/previews/pattern_5.png) | ![pattern_6-5760](5760/previews/pattern_6.png) | ![pattern_7-5760](5760/previews/pattern_7.png) | ![pattern_8-5760](5760/previews/pattern_8.png) | ![pattern_9-5760](5760/previews/pattern_9.png) | ![pattern_10-5760](5760/previews/pattern_10.png) | ![pattern_11-5760](5760/previews/pattern_11.png) | ![bikini-5760](5760/previews/bikini.png) | [<NSFW, click to see>](5760/previews/bondage.png) | ![free-5760](5760/previews/free.png) | ![maid-5760](5760/previews/maid.png) | ![miko-5760](5760/previews/miko.png) | [<NSFW, click to see>](5760/previews/nude.png) | [<NSFW, click to see>](5760/previews/nude2.png) | ![suit-5760](5760/previews/suit.png) | ![yukata-5760](5760/previews/yukata.png) | | 5280 | 0.786 | [Download](5280/yuri_pokemon.zip) | ![pattern_1-5280](5280/previews/pattern_1.png) | ![pattern_2-5280](5280/previews/pattern_2.png) | ![pattern_3-5280](5280/previews/pattern_3.png) | ![pattern_4-5280](5280/previews/pattern_4.png) | ![pattern_5-5280](5280/previews/pattern_5.png) | ![pattern_6-5280](5280/previews/pattern_6.png) | ![pattern_7-5280](5280/previews/pattern_7.png) | ![pattern_8-5280](5280/previews/pattern_8.png) | ![pattern_9-5280](5280/previews/pattern_9.png) | ![pattern_10-5280](5280/previews/pattern_10.png) | ![pattern_11-5280](5280/previews/pattern_11.png) | ![bikini-5280](5280/previews/bikini.png) | [<NSFW, click to see>](5280/previews/bondage.png) | ![free-5280](5280/previews/free.png) | ![maid-5280](5280/previews/maid.png) | ![miko-5280](5280/previews/miko.png) | [<NSFW, click to see>](5280/previews/nude.png) | [<NSFW, click to see>](5280/previews/nude2.png) | ![suit-5280](5280/previews/suit.png) | ![yukata-5280](5280/previews/yukata.png) | | 4800 | 0.751 | [Download](4800/yuri_pokemon.zip) | ![pattern_1-4800](4800/previews/pattern_1.png) | ![pattern_2-4800](4800/previews/pattern_2.png) | ![pattern_3-4800](4800/previews/pattern_3.png) | ![pattern_4-4800](4800/previews/pattern_4.png) | ![pattern_5-4800](4800/previews/pattern_5.png) | ![pattern_6-4800](4800/previews/pattern_6.png) | ![pattern_7-4800](4800/previews/pattern_7.png) | ![pattern_8-4800](4800/previews/pattern_8.png) | ![pattern_9-4800](4800/previews/pattern_9.png) | ![pattern_10-4800](4800/previews/pattern_10.png) | ![pattern_11-4800](4800/previews/pattern_11.png) | ![bikini-4800](4800/previews/bikini.png) | [<NSFW, click to see>](4800/previews/bondage.png) | ![free-4800](4800/previews/free.png) | ![maid-4800](4800/previews/maid.png) | ![miko-4800](4800/previews/miko.png) | [<NSFW, click to see>](4800/previews/nude.png) | [<NSFW, click to see>](4800/previews/nude2.png) | ![suit-4800](4800/previews/suit.png) | ![yukata-4800](4800/previews/yukata.png) | | 4320 | 0.738 | [Download](4320/yuri_pokemon.zip) | ![pattern_1-4320](4320/previews/pattern_1.png) | ![pattern_2-4320](4320/previews/pattern_2.png) | ![pattern_3-4320](4320/previews/pattern_3.png) | ![pattern_4-4320](4320/previews/pattern_4.png) | ![pattern_5-4320](4320/previews/pattern_5.png) | ![pattern_6-4320](4320/previews/pattern_6.png) | ![pattern_7-4320](4320/previews/pattern_7.png) | ![pattern_8-4320](4320/previews/pattern_8.png) | ![pattern_9-4320](4320/previews/pattern_9.png) | ![pattern_10-4320](4320/previews/pattern_10.png) | ![pattern_11-4320](4320/previews/pattern_11.png) | ![bikini-4320](4320/previews/bikini.png) | [<NSFW, click to see>](4320/previews/bondage.png) | ![free-4320](4320/previews/free.png) | ![maid-4320](4320/previews/maid.png) | ![miko-4320](4320/previews/miko.png) | [<NSFW, click to see>](4320/previews/nude.png) | [<NSFW, click to see>](4320/previews/nude2.png) | ![suit-4320](4320/previews/suit.png) | ![yukata-4320](4320/previews/yukata.png) | | **3840** | **0.799** | [**Download**](3840/yuri_pokemon.zip) | ![pattern_1-3840](3840/previews/pattern_1.png) | ![pattern_2-3840](3840/previews/pattern_2.png) | ![pattern_3-3840](3840/previews/pattern_3.png) | ![pattern_4-3840](3840/previews/pattern_4.png) | ![pattern_5-3840](3840/previews/pattern_5.png) | ![pattern_6-3840](3840/previews/pattern_6.png) | ![pattern_7-3840](3840/previews/pattern_7.png) | ![pattern_8-3840](3840/previews/pattern_8.png) | ![pattern_9-3840](3840/previews/pattern_9.png) | ![pattern_10-3840](3840/previews/pattern_10.png) | ![pattern_11-3840](3840/previews/pattern_11.png) | ![bikini-3840](3840/previews/bikini.png) | [<NSFW, click to see>](3840/previews/bondage.png) | ![free-3840](3840/previews/free.png) | ![maid-3840](3840/previews/maid.png) | ![miko-3840](3840/previews/miko.png) | [<NSFW, click to see>](3840/previews/nude.png) | [<NSFW, click to see>](3840/previews/nude2.png) | ![suit-3840](3840/previews/suit.png) | ![yukata-3840](3840/previews/yukata.png) | | 3360 | 0.751 | [Download](3360/yuri_pokemon.zip) | ![pattern_1-3360](3360/previews/pattern_1.png) | ![pattern_2-3360](3360/previews/pattern_2.png) | ![pattern_3-3360](3360/previews/pattern_3.png) | ![pattern_4-3360](3360/previews/pattern_4.png) | ![pattern_5-3360](3360/previews/pattern_5.png) | ![pattern_6-3360](3360/previews/pattern_6.png) | ![pattern_7-3360](3360/previews/pattern_7.png) | ![pattern_8-3360](3360/previews/pattern_8.png) | ![pattern_9-3360](3360/previews/pattern_9.png) | ![pattern_10-3360](3360/previews/pattern_10.png) | ![pattern_11-3360](3360/previews/pattern_11.png) | ![bikini-3360](3360/previews/bikini.png) | [<NSFW, click to see>](3360/previews/bondage.png) | ![free-3360](3360/previews/free.png) | ![maid-3360](3360/previews/maid.png) | ![miko-3360](3360/previews/miko.png) | [<NSFW, click to see>](3360/previews/nude.png) | [<NSFW, click to see>](3360/previews/nude2.png) | ![suit-3360](3360/previews/suit.png) | ![yukata-3360](3360/previews/yukata.png) | | 2880 | 0.768 | [Download](2880/yuri_pokemon.zip) | ![pattern_1-2880](2880/previews/pattern_1.png) | ![pattern_2-2880](2880/previews/pattern_2.png) | ![pattern_3-2880](2880/previews/pattern_3.png) | ![pattern_4-2880](2880/previews/pattern_4.png) | ![pattern_5-2880](2880/previews/pattern_5.png) | ![pattern_6-2880](2880/previews/pattern_6.png) | ![pattern_7-2880](2880/previews/pattern_7.png) | ![pattern_8-2880](2880/previews/pattern_8.png) | ![pattern_9-2880](2880/previews/pattern_9.png) | ![pattern_10-2880](2880/previews/pattern_10.png) | ![pattern_11-2880](2880/previews/pattern_11.png) | ![bikini-2880](2880/previews/bikini.png) | [<NSFW, click to see>](2880/previews/bondage.png) | ![free-2880](2880/previews/free.png) | ![maid-2880](2880/previews/maid.png) | ![miko-2880](2880/previews/miko.png) | [<NSFW, click to see>](2880/previews/nude.png) | [<NSFW, click to see>](2880/previews/nude2.png) | ![suit-2880](2880/previews/suit.png) | ![yukata-2880](2880/previews/yukata.png) | | 2400 | 0.796 | [Download](2400/yuri_pokemon.zip) | ![pattern_1-2400](2400/previews/pattern_1.png) | ![pattern_2-2400](2400/previews/pattern_2.png) | ![pattern_3-2400](2400/previews/pattern_3.png) | ![pattern_4-2400](2400/previews/pattern_4.png) | ![pattern_5-2400](2400/previews/pattern_5.png) | ![pattern_6-2400](2400/previews/pattern_6.png) | ![pattern_7-2400](2400/previews/pattern_7.png) | ![pattern_8-2400](2400/previews/pattern_8.png) | ![pattern_9-2400](2400/previews/pattern_9.png) | ![pattern_10-2400](2400/previews/pattern_10.png) | ![pattern_11-2400](2400/previews/pattern_11.png) | ![bikini-2400](2400/previews/bikini.png) | [<NSFW, click to see>](2400/previews/bondage.png) | ![free-2400](2400/previews/free.png) | ![maid-2400](2400/previews/maid.png) | ![miko-2400](2400/previews/miko.png) | [<NSFW, click to see>](2400/previews/nude.png) | [<NSFW, click to see>](2400/previews/nude2.png) | ![suit-2400](2400/previews/suit.png) | ![yukata-2400](2400/previews/yukata.png) | | 1920 | 0.757 | [Download](1920/yuri_pokemon.zip) | ![pattern_1-1920](1920/previews/pattern_1.png) | ![pattern_2-1920](1920/previews/pattern_2.png) | ![pattern_3-1920](1920/previews/pattern_3.png) | ![pattern_4-1920](1920/previews/pattern_4.png) | ![pattern_5-1920](1920/previews/pattern_5.png) | ![pattern_6-1920](1920/previews/pattern_6.png) | ![pattern_7-1920](1920/previews/pattern_7.png) | ![pattern_8-1920](1920/previews/pattern_8.png) | ![pattern_9-1920](1920/previews/pattern_9.png) | ![pattern_10-1920](1920/previews/pattern_10.png) | ![pattern_11-1920](1920/previews/pattern_11.png) | ![bikini-1920](1920/previews/bikini.png) | [<NSFW, click to see>](1920/previews/bondage.png) | ![free-1920](1920/previews/free.png) | ![maid-1920](1920/previews/maid.png) | ![miko-1920](1920/previews/miko.png) | [<NSFW, click to see>](1920/previews/nude.png) | [<NSFW, click to see>](1920/previews/nude2.png) | ![suit-1920](1920/previews/suit.png) | ![yukata-1920](1920/previews/yukata.png) | | 1440 | 0.843 | [Download](1440/yuri_pokemon.zip) | ![pattern_1-1440](1440/previews/pattern_1.png) | ![pattern_2-1440](1440/previews/pattern_2.png) | ![pattern_3-1440](1440/previews/pattern_3.png) | ![pattern_4-1440](1440/previews/pattern_4.png) | ![pattern_5-1440](1440/previews/pattern_5.png) | ![pattern_6-1440](1440/previews/pattern_6.png) | ![pattern_7-1440](1440/previews/pattern_7.png) | ![pattern_8-1440](1440/previews/pattern_8.png) | ![pattern_9-1440](1440/previews/pattern_9.png) | ![pattern_10-1440](1440/previews/pattern_10.png) | ![pattern_11-1440](1440/previews/pattern_11.png) | ![bikini-1440](1440/previews/bikini.png) | [<NSFW, click to see>](1440/previews/bondage.png) | ![free-1440](1440/previews/free.png) | ![maid-1440](1440/previews/maid.png) | ![miko-1440](1440/previews/miko.png) | [<NSFW, click to see>](1440/previews/nude.png) | [<NSFW, click to see>](1440/previews/nude2.png) | ![suit-1440](1440/previews/suit.png) | ![yukata-1440](1440/previews/yukata.png) | | 960 | 0.735 | [Download](960/yuri_pokemon.zip) | ![pattern_1-960](960/previews/pattern_1.png) | ![pattern_2-960](960/previews/pattern_2.png) | ![pattern_3-960](960/previews/pattern_3.png) | ![pattern_4-960](960/previews/pattern_4.png) | ![pattern_5-960](960/previews/pattern_5.png) | ![pattern_6-960](960/previews/pattern_6.png) | ![pattern_7-960](960/previews/pattern_7.png) | ![pattern_8-960](960/previews/pattern_8.png) | ![pattern_9-960](960/previews/pattern_9.png) | ![pattern_10-960](960/previews/pattern_10.png) | ![pattern_11-960](960/previews/pattern_11.png) | ![bikini-960](960/previews/bikini.png) | [<NSFW, click to see>](960/previews/bondage.png) | ![free-960](960/previews/free.png) | ![maid-960](960/previews/maid.png) | ![miko-960](960/previews/miko.png) | [<NSFW, click to see>](960/previews/nude.png) | [<NSFW, click to see>](960/previews/nude2.png) | ![suit-960](960/previews/suit.png) | ![yukata-960](960/previews/yukata.png) | | 480 | 0.734 | [Download](480/yuri_pokemon.zip) | ![pattern_1-480](480/previews/pattern_1.png) | ![pattern_2-480](480/previews/pattern_2.png) | ![pattern_3-480](480/previews/pattern_3.png) | ![pattern_4-480](480/previews/pattern_4.png) | ![pattern_5-480](480/previews/pattern_5.png) | ![pattern_6-480](480/previews/pattern_6.png) | ![pattern_7-480](480/previews/pattern_7.png) | ![pattern_8-480](480/previews/pattern_8.png) | ![pattern_9-480](480/previews/pattern_9.png) | ![pattern_10-480](480/previews/pattern_10.png) | ![pattern_11-480](480/previews/pattern_11.png) | ![bikini-480](480/previews/bikini.png) | [<NSFW, click to see>](480/previews/bondage.png) | ![free-480](480/previews/free.png) | ![maid-480](480/previews/maid.png) | ![miko-480](480/previews/miko.png) | [<NSFW, click to see>](480/previews/nude.png) | [<NSFW, click to see>](480/previews/nude2.png) | ![suit-480](480/previews/suit.png) | ![yukata-480](480/previews/yukata.png) |
u6ujh6h6/ddddd
u6ujh6h6
2023-09-13T17:11:08Z
0
0
null
[ "text-generation", "dataset:codefuse-ai/CodeExercise-Python-27k", "dataset:iamtarun/python_code_instructions_18k_alpaca", "dataset:mhhmm/leetcode-solutions-python", "dataset:mlabonne/Evol-Instruct-Python-26k", "dataset:Nan-Do/instructional_code-search-net-python", "dataset:Arjun-G-Ravi/Python-codes", "dataset:reshinthadith/pairwise-code-review-instruct-critique-revision-python", "dataset:iamketan25/python-qa-instructions-dataset", "doi:10.57967/hf/1100", "region:us" ]
text-generation
2023-09-13T17:09:46Z
--- datasets: - codefuse-ai/CodeExercise-Python-27k - iamtarun/python_code_instructions_18k_alpaca - mhhmm/leetcode-solutions-python - mlabonne/Evol-Instruct-Python-26k - Nan-Do/instructional_code-search-net-python - Arjun-G-Ravi/Python-codes - reshinthadith/pairwise-code-review-instruct-critique-revision-python - iamketan25/python-qa-instructions-dataset pipeline_tag: text-generation ---
michaelfeil/ct2fast-CodeLlama-34b-hf
michaelfeil
2023-09-13T17:09:54Z
5
2
transformers
[ "transformers", "llama", "text-generation", "ctranslate2", "int8", "float16", "llama-2", "code", "arxiv:2308.12950", "license:llama2", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2023-09-11T08:05:27Z
--- language: - code pipeline_tag: text-generation tags: - ctranslate2 - int8 - float16 - llama-2 license: llama2 --- # # Fast-Inference with Ctranslate2 Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU. quantized version of [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) ```bash pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.17.1 ``` ```python # from transformers import AutoTokenizer model_name = "michaelfeil/ct2fast-CodeLlama-34b-hf" from hf_hub_ctranslate2 import GeneratorCT2fromHfHub model = GeneratorCT2fromHfHub( # load in int8 on CUDA model_name_or_path=model_name, device="cuda", compute_type="int8_float16", # tokenizer=AutoTokenizer.from_pretrained("{ORG}/{NAME}") ) outputs = model.generate( text=["def fibonnaci(", "User: How are you doing? Bot:"], max_length=64, include_prompt_in_result=False ) print(outputs) ``` Checkpoint compatible to [ctranslate2>=3.17.1](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2) - `compute_type=int8_float16` for `device="cuda"` - `compute_type=int8` for `device="cpu"` Converted on 2023-09-13 using ``` LLama-2 -> removed <pad> token. ``` # Licence and other remarks: This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo. # Original description # **Code Llama** Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the base 34B version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom. | | Base Model | Python | Instruct | | --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | 7B | [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) | | 13B | [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) | | 34B | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) | ## Model Use To use this model, please make sure to install transformers from `main` until the next version is released: ```bash pip install git+https://github.com/huggingface/transformers.git@main accelerate ``` Model capabilities: - [x] Code completion. - [ ] Infilling. - [ ] Instructions / chat. - [ ] Python specialist. ```python from transformers import AutoTokenizer import transformers import torch model = "codellama/CodeLlama-34b-hf" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( 'import socket\n\ndef ping_exponential_backoff(host: str):', do_sample=True, top_k=10, temperature=0.1, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Model Details *Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs). **Model Developers** Meta **Variations** Code Llama comes in three model sizes, and three variants: * Code Llama: base models designed for general code synthesis and understanding * Code Llama - Python: designed specifically for Python * Code Llama - Instruct: for instruction following and safer deployment All variants are available in sizes of 7B, 13B and 34B parameters. **This repository contains the base version of the 34B parameters model.** **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture. **Model Dates** Code Llama and its variants have been trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950). ## Intended Use **Intended Use Cases** Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications. **Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants. ## Hardware and Software **Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster. **Carbon Footprint** In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program. ## Training Data All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details). ## Evaluation Results See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper. ## Ethical Considerations and Limitations Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-user-guide](https://ai.meta.com/llama/responsible-user-guide).
baebee/GPTagalog
baebee
2023-09-13T17:08:47Z
105
0
transformers
[ "transformers", "pytorch", "gpt2", "tl", "license:openrail", "text-generation-inference", "endpoints_compatible", "region:us" ]
null
2023-09-13T16:57:00Z
--- license: openrail language: - tl --- # Model Card: GPTagalog ## Model Overview - **Model Name**: GPTagalog - **Model Size**: 30 MB ## Model Description GPTagalog is a language model specifically designed for the Tagalog language. It was trained on a Tagalog news dataset through 15,000 iterations. The goal was to replicate the functionality of GPT-2, but tailored for the Tagalog language. ## Intended Use GPTagalog is an experimental model with a compact size of just 30 MB. It is not intended for production use, and users should not expect high stability or reliability. Instead, it serves as a research and experimentation tool for exploring natural language processing tasks in the Tagalog language. ## Limitations - **Model Size**: Due to its small size, GPTagalog may not perform as well as larger language models on complex language tasks. - **Stability**: GPTagalog is not guaranteed to provide reliable outputs and may produce inconsistent results. - **Tagalog Specificity**: While it is designed for Tagalog, it may not handle all nuances and dialects of the language. ## Ethical Considerations Users of GPTagalog should exercise caution when using the model for any purpose. Be aware of the potential biases and ethical concerns associated with AI-generated content.
nirbhayfaaya/faaya-controlnet-lineart-print-generation
nirbhayfaaya
2023-09-13T17:08:27Z
0
0
diffusers
[ "diffusers", "region:us" ]
null
2023-09-12T12:47:50Z
The model card contains the model for lineart control for print generation
bigmorning/wh_4_sun_syl_w_0_lr_8en5_b32_0020
bigmorning
2023-09-13T17:04:34Z
59
0
transformers
[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-09-13T17:04:25Z
--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: wh_4_sun_syl_w_0_lr_8en5_b32_0020 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. --> # wh_4_sun_syl_w_0_lr_8en5_b32_0020 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9117 - Train Accuracy: 0.0276 - Train Wermet: 0.2949 - Train Wermet Syl: 0.4680 - Validation Loss: 1.2758 - Validation Accuracy: 0.0192 - Validation Wermet: 0.4053 - Validation Wermet Syl: 0.4485 - Epoch: 19 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 8e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch | |:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:| | 5.2081 | 0.0105 | 1.5151 | 1.1936 | 4.0143 | 0.0113 | 0.9817 | 0.9765 | 0 | | 4.7532 | 0.0116 | 0.8756 | 0.8371 | 3.9494 | 0.0114 | 0.9457 | 0.9171 | 1 | | 4.7031 | 0.0117 | 0.8615 | 0.8175 | 3.9244 | 0.0114 | 0.9383 | 0.8993 | 2 | | 4.6799 | 0.0117 | 0.8569 | 0.8117 | 3.9091 | 0.0114 | 0.9659 | 0.9487 | 3 | | 4.6415 | 0.0117 | 0.8450 | 0.7985 | 3.8828 | 0.0115 | 0.9227 | 0.8867 | 4 | | 4.6194 | 0.0118 | 0.8366 | 0.7901 | 3.8588 | 0.0115 | 0.9314 | 0.8973 | 5 | | 4.5993 | 0.0118 | 0.8253 | 0.7803 | 3.9068 | 0.0116 | 0.9000 | 0.8526 | 6 | | 4.5584 | 0.0120 | 0.7864 | 0.7455 | 3.7567 | 0.0118 | 0.8407 | 0.7907 | 7 | | 4.4423 | 0.0123 | 0.7655 | 0.7209 | 3.5099 | 0.0123 | 0.8256 | 0.7786 | 8 | | 4.0719 | 0.0133 | 0.7479 | 0.7112 | 2.9385 | 0.0135 | 0.7636 | 0.7193 | 9 | | 3.5177 | 0.0148 | 0.7049 | 0.6917 | 2.4295 | 0.0148 | 0.6907 | 0.6559 | 10 | | 2.8931 | 0.0170 | 0.6532 | 0.6928 | 2.0341 | 0.0161 | 0.6031 | 0.5778 | 11 | | 2.4018 | 0.0190 | 0.6050 | 0.7024 | 1.7053 | 0.0174 | 0.5373 | 0.5223 | 12 | | 1.9940 | 0.0210 | 0.5579 | 0.7168 | 1.5422 | 0.0180 | 0.4803 | 0.4793 | 13 | | 1.7258 | 0.0223 | 0.5356 | 0.7426 | 1.4314 | 0.0186 | 0.4270 | 0.4029 | 14 | | 1.4823 | 0.0237 | 0.4865 | 0.7094 | 1.3495 | 0.0190 | 0.4146 | 0.4082 | 15 | | 1.2444 | 0.0252 | 0.4278 | 0.6455 | 1.2070 | 0.0198 | 0.3945 | 0.3965 | 16 | | 1.1179 | 0.0261 | 0.3774 | 0.5791 | 1.1574 | 0.0200 | 0.3679 | 0.3479 | 17 | | 1.0182 | 0.0267 | 0.3377 | 0.5283 | 1.3384 | 0.0189 | 0.3799 | 0.3800 | 18 | | 0.9117 | 0.0276 | 0.2949 | 0.4680 | 1.2758 | 0.0192 | 0.4053 | 0.4485 | 19 | ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3
bigmorning/wh_4_sun_syl_w_0_lr_8en5_b32_0015
bigmorning
2023-09-13T16:49:11Z
59
0
transformers
[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-09-13T16:49:03Z
--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: wh_4_sun_syl_w_0_lr_8en5_b32_0015 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. --> # wh_4_sun_syl_w_0_lr_8en5_b32_0015 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.7258 - Train Accuracy: 0.0223 - Train Wermet: 0.5356 - Train Wermet Syl: 0.7426 - Validation Loss: 1.4314 - Validation Accuracy: 0.0186 - Validation Wermet: 0.4270 - Validation Wermet Syl: 0.4029 - Epoch: 14 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 8e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch | |:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:| | 5.2081 | 0.0105 | 1.5151 | 1.1936 | 4.0143 | 0.0113 | 0.9817 | 0.9765 | 0 | | 4.7532 | 0.0116 | 0.8756 | 0.8371 | 3.9494 | 0.0114 | 0.9457 | 0.9171 | 1 | | 4.7031 | 0.0117 | 0.8615 | 0.8175 | 3.9244 | 0.0114 | 0.9383 | 0.8993 | 2 | | 4.6799 | 0.0117 | 0.8569 | 0.8117 | 3.9091 | 0.0114 | 0.9659 | 0.9487 | 3 | | 4.6415 | 0.0117 | 0.8450 | 0.7985 | 3.8828 | 0.0115 | 0.9227 | 0.8867 | 4 | | 4.6194 | 0.0118 | 0.8366 | 0.7901 | 3.8588 | 0.0115 | 0.9314 | 0.8973 | 5 | | 4.5993 | 0.0118 | 0.8253 | 0.7803 | 3.9068 | 0.0116 | 0.9000 | 0.8526 | 6 | | 4.5584 | 0.0120 | 0.7864 | 0.7455 | 3.7567 | 0.0118 | 0.8407 | 0.7907 | 7 | | 4.4423 | 0.0123 | 0.7655 | 0.7209 | 3.5099 | 0.0123 | 0.8256 | 0.7786 | 8 | | 4.0719 | 0.0133 | 0.7479 | 0.7112 | 2.9385 | 0.0135 | 0.7636 | 0.7193 | 9 | | 3.5177 | 0.0148 | 0.7049 | 0.6917 | 2.4295 | 0.0148 | 0.6907 | 0.6559 | 10 | | 2.8931 | 0.0170 | 0.6532 | 0.6928 | 2.0341 | 0.0161 | 0.6031 | 0.5778 | 11 | | 2.4018 | 0.0190 | 0.6050 | 0.7024 | 1.7053 | 0.0174 | 0.5373 | 0.5223 | 12 | | 1.9940 | 0.0210 | 0.5579 | 0.7168 | 1.5422 | 0.0180 | 0.4803 | 0.4793 | 13 | | 1.7258 | 0.0223 | 0.5356 | 0.7426 | 1.4314 | 0.0186 | 0.4270 | 0.4029 | 14 | ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3
aatherton2024/hf_eng_fra_reproduction
aatherton2024
2023-09-13T16:47:40Z
108
0
transformers
[ "transformers", "pytorch", "marian", "text2text-generation", "translation", "generated_from_trainer", "dataset:kde4", "base_model:Helsinki-NLP/opus-mt-en-fr", "base_model:finetune:Helsinki-NLP/opus-mt-en-fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
translation
2023-09-12T11:21:31Z
--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-fr tags: - translation - generated_from_trainer datasets: - kde4 model-index: - name: hf_eng_fra_reproduction 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. --> # hf_eng_fra_reproduction This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.2
gibil/distilbert-base-uncased-finetuned-emotion
gibil
2023-09-13T16:40:58Z
108
0
transformers
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-11T21:02:59Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.92 - name: F1 type: f1 value: 0.9197964358588712 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2338 - Accuracy: 0.92 - F1: 0.9198 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.9087 | 1.0 | 250 | 0.3592 | 0.898 | 0.8960 | | 0.2775 | 2.0 | 500 | 0.2338 | 0.92 | 0.9198 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Davidadel66/segformer-b0-finetuned-segments-sidewalk-2
Davidadel66
2023-09-13T16:32:11Z
193
0
transformers
[ "transformers", "pytorch", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/mit-b0", "base_model:finetune:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]
image-segmentation
2023-09-11T16:45:00Z
--- license: other base_model: nvidia/mit-b0 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b0-finetuned-segments-sidewalk-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-segments-sidewalk-2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set: - Loss: 0.9530 - Mean Iou: 0.1712 - Mean Accuracy: 0.2121 - Overall Accuracy: 0.7831 - Accuracy Unlabeled: nan - Accuracy Flat-road: 0.8549 - Accuracy Flat-sidewalk: 0.9623 - Accuracy Flat-crosswalk: 0.0 - Accuracy Flat-cyclinglane: 0.5957 - Accuracy Flat-parkingdriveway: 0.0956 - Accuracy Flat-railtrack: nan - Accuracy Flat-curb: 0.0075 - Accuracy Human-person: 0.0 - Accuracy Human-rider: 0.0 - Accuracy Vehicle-car: 0.9053 - Accuracy Vehicle-truck: 0.0 - Accuracy Vehicle-bus: 0.0 - Accuracy Vehicle-tramtrain: 0.0 - Accuracy Vehicle-motorcycle: 0.0 - Accuracy Vehicle-bicycle: 0.0 - Accuracy Vehicle-caravan: 0.0 - Accuracy Vehicle-cartrailer: 0.0 - Accuracy Construction-building: 0.9017 - Accuracy Construction-door: 0.0 - Accuracy Construction-wall: 0.0 - Accuracy Construction-fenceguardrail: 0.0 - Accuracy Construction-bridge: 0.0 - Accuracy Construction-tunnel: 0.0 - Accuracy Construction-stairs: 0.0 - Accuracy Object-pole: 0.0 - Accuracy Object-trafficsign: 0.0 - Accuracy Object-trafficlight: 0.0 - Accuracy Nature-vegetation: 0.9152 - Accuracy Nature-terrain: 0.8300 - Accuracy Sky: 0.9299 - Accuracy Void-ground: 0.0 - Accuracy Void-dynamic: 0.0 - Accuracy Void-static: 0.0 - Accuracy Void-unclear: 0.0 - Iou Unlabeled: nan - Iou Flat-road: 0.6261 - Iou Flat-sidewalk: 0.8045 - Iou Flat-crosswalk: 0.0 - Iou Flat-cyclinglane: 0.5253 - Iou Flat-parkingdriveway: 0.0861 - Iou Flat-railtrack: nan - Iou Flat-curb: 0.0075 - Iou Human-person: 0.0 - Iou Human-rider: 0.0 - Iou Vehicle-car: 0.6945 - Iou Vehicle-truck: 0.0 - Iou Vehicle-bus: 0.0 - Iou Vehicle-tramtrain: 0.0 - Iou Vehicle-motorcycle: 0.0 - Iou Vehicle-bicycle: 0.0 - Iou Vehicle-caravan: 0.0 - Iou Vehicle-cartrailer: 0.0 - Iou Construction-building: 0.5817 - Iou Construction-door: 0.0 - Iou Construction-wall: 0.0 - Iou Construction-fenceguardrail: 0.0 - Iou Construction-bridge: 0.0 - Iou Construction-tunnel: 0.0 - Iou Construction-stairs: 0.0 - Iou Object-pole: 0.0 - Iou Object-trafficsign: 0.0 - Iou Object-trafficlight: 0.0 - Iou Nature-vegetation: 0.7847 - Iou Nature-terrain: 0.6656 - Iou Sky: 0.8751 - Iou Void-ground: 0.0 - Iou Void-dynamic: 0.0 - Iou Void-static: 0.0 - Iou Void-unclear: 0.0 ## 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: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear | 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| 2.7688 | 0.1 | 20 | 3.0043 | 0.0900 | 0.1326 | 0.6099 | nan | 0.2649 | 0.9602 | 0.0002 | 0.0001 | 0.0040 | nan | 0.0 | 0.0055 | 0.0 | 0.8361 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7673 | 0.0054 | 0.0021 | 0.0007 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7635 | 0.0259 | 0.7359 | 0.0 | 0.0036 | 0.0 | 0.0 | nan | 0.2274 | 0.6135 | 0.0002 | 0.0001 | 0.0039 | 0.0 | 0.0 | 0.0055 | 0.0 | 0.5402 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3692 | 0.0018 | 0.0021 | 0.0007 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6087 | 0.0247 | 0.6591 | 0.0 | 0.0020 | 0.0 | 0.0 | | 2.2362 | 0.2 | 40 | 2.2122 | 0.1016 | 0.1476 | 0.6596 | nan | 0.5986 | 0.9497 | 0.0000 | 0.0002 | 0.0010 | nan | 0.0 | 0.0 | 0.0 | 0.8828 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7864 | 0.0 | 0.0004 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8463 | 0.0423 | 0.7626 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4075 | 0.6846 | 0.0000 | 0.0002 | 0.0010 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5190 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4231 | 0.0 | 0.0004 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6493 | 0.0415 | 0.7290 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.9229 | 0.3 | 60 | 1.9323 | 0.1109 | 0.1552 | 0.6790 | nan | 0.7454 | 0.9277 | 0.0002 | 0.0006 | 0.0012 | nan | 0.0 | 0.0 | 0.0 | 0.8370 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8759 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8641 | 0.0240 | 0.8450 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4388 | 0.7425 | 0.0002 | 0.0006 | 0.0012 | nan | 0.0 | 0.0 | 0.0 | 0.5825 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4447 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6474 | 0.0237 | 0.7787 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.8943 | 0.4 | 80 | 1.6697 | 0.1102 | 0.1565 | 0.6866 | nan | 0.6675 | 0.9567 | 0.0 | 0.0005 | 0.0029 | nan | 0.0 | 0.0 | 0.0 | 0.8692 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8957 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8753 | 0.0270 | 0.8696 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4490 | 0.7236 | 0.0 | 0.0005 | 0.0029 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6018 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4761 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6626 | 0.0268 | 0.8032 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.9991 | 0.5 | 100 | 1.6000 | 0.1166 | 0.1629 | 0.7001 | nan | 0.8482 | 0.9229 | 0.0 | 0.0024 | 0.0013 | nan | 0.0 | 0.0000 | 0.0 | 0.9172 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8210 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9390 | 0.0378 | 0.8872 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4629 | 0.7702 | 0.0 | 0.0024 | 0.0013 | nan | 0.0 | 0.0000 | 0.0 | 0.5626 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5357 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6573 | 0.0375 | 0.8165 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.5725 | 0.6 | 120 | 1.5214 | 0.1250 | 0.1686 | 0.7146 | nan | 0.8401 | 0.9399 | 0.0 | 0.0415 | 0.0017 | nan | 0.0000 | 0.0 | 0.0 | 0.8705 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8933 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9241 | 0.1360 | 0.9159 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4995 | 0.7742 | 0.0 | 0.0413 | 0.0017 | nan | 0.0000 | 0.0 | 0.0 | 0.6375 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5282 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6744 | 0.1315 | 0.8355 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.4764 | 0.7 | 140 | 1.4602 | 0.1327 | 0.1750 | 0.7245 | nan | 0.8603 | 0.9330 | 0.0 | 0.0371 | 0.0026 | nan | 0.0000 | 0.0 | 0.0 | 0.8552 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9104 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9283 | 0.3764 | 0.8728 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4935 | 0.7879 | 0.0 | 0.0369 | 0.0026 | nan | 0.0000 | 0.0 | 0.0 | 0.6449 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5233 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7071 | 0.3529 | 0.8309 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.9994 | 0.8 | 160 | 1.3414 | 0.1439 | 0.1867 | 0.7418 | nan | 0.8373 | 0.9468 | 0.0 | 0.0849 | 0.0051 | nan | 0.0 | 0.0 | 0.0 | 0.8907 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8805 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9226 | 0.6707 | 0.9236 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5211 | 0.7762 | 0.0 | 0.0842 | 0.0051 | nan | 0.0 | 0.0 | 0.0 | 0.6352 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5546 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7427 | 0.5786 | 0.8500 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.2919 | 0.9 | 180 | 1.3036 | 0.1401 | 0.1822 | 0.7365 | nan | 0.8860 | 0.9322 | 0.0 | 0.0832 | 0.0067 | nan | 0.0000 | 0.0 | 0.0 | 0.8551 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8824 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9462 | 0.5071 | 0.9144 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4969 | 0.8018 | 0.0 | 0.0814 | 0.0067 | nan | 0.0000 | 0.0 | 0.0 | 0.6676 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7185 | 0.4640 | 0.8420 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.2882 | 1.0 | 200 | 1.2697 | 0.1466 | 0.1914 | 0.7471 | nan | 0.8344 | 0.9563 | 0.0 | 0.1067 | 0.0068 | nan | 0.0 | 0.0 | 0.0 | 0.9191 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8746 | 0.8138 | 0.9034 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5319 | 0.7814 | 0.0 | 0.1053 | 0.0068 | nan | 0.0 | 0.0 | 0.0 | 0.6284 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5583 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7614 | 0.6179 | 0.8480 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.1952 | 1.1 | 220 | 1.2088 | 0.1497 | 0.1917 | 0.7499 | nan | 0.8263 | 0.9565 | 0.0 | 0.1384 | 0.0093 | nan | 0.0 | 0.0 | 0.0 | 0.8891 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8945 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9050 | 0.7813 | 0.9251 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5518 | 0.7648 | 0.0 | 0.1374 | 0.0092 | nan | 0.0 | 0.0 | 0.0 | 0.6640 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5651 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7604 | 0.6254 | 0.8607 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.1901 | 1.2 | 240 | 1.1659 | 0.1508 | 0.1941 | 0.7546 | nan | 0.8903 | 0.9465 | 0.0 | 0.1520 | 0.0136 | nan | 0.0000 | 0.0 | 0.0 | 0.9018 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8951 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9068 | 0.7657 | 0.9340 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5301 | 0.8032 | 0.0 | 0.1479 | 0.0135 | nan | 0.0000 | 0.0 | 0.0 | 0.6624 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5553 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7704 | 0.6415 | 0.8526 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.5003 | 1.3 | 260 | 1.1250 | 0.1582 | 0.1977 | 0.7625 | nan | 0.8352 | 0.9605 | 0.0 | 0.3475 | 0.0185 | nan | 0.0000 | 0.0006 | 0.0 | 0.8674 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8876 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9278 | 0.7537 | 0.9255 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5715 | 0.7817 | 0.0 | 0.3299 | 0.0182 | nan | 0.0000 | 0.0006 | 0.0 | 0.6920 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5613 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7639 | 0.6390 | 0.8616 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.0678 | 1.4 | 280 | 1.1183 | 0.1595 | 0.2042 | 0.7680 | nan | 0.8673 | 0.9506 | 0.0 | 0.4007 | 0.0276 | nan | 0.0000 | 0.0001 | 0.0 | 0.9294 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8812 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9051 | 0.8502 | 0.9251 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5684 | 0.8048 | 0.0 | 0.3824 | 0.0270 | nan | 0.0000 | 0.0001 | 0.0 | 0.6348 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5821 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7698 | 0.6311 | 0.8630 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.0902 | 1.5 | 300 | 1.0917 | 0.1642 | 0.2070 | 0.7718 | nan | 0.8710 | 0.9473 | 0.0 | 0.5234 | 0.0382 | nan | 0.0001 | 0.0 | 0.0 | 0.8855 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9072 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8980 | 0.8378 | 0.9211 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5662 | 0.8125 | 0.0 | 0.4669 | 0.0370 | nan | 0.0001 | 0.0 | 0.0 | 0.6874 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5623 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7753 | 0.6467 | 0.8642 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.0474 | 1.6 | 320 | 1.0803 | 0.1645 | 0.2080 | 0.7736 | nan | 0.8752 | 0.9445 | 0.0 | 0.5435 | 0.0427 | nan | 0.0001 | 0.0001 | 0.0 | 0.9137 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8937 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9148 | 0.7931 | 0.9432 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5693 | 0.8150 | 0.0 | 0.4817 | 0.0411 | nan | 0.0001 | 0.0001 | 0.0 | 0.6565 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5710 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7761 | 0.6556 | 0.8619 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.814 | 1.7 | 340 | 1.0579 | 0.1655 | 0.2100 | 0.7740 | nan | 0.8714 | 0.9443 | 0.0 | 0.5910 | 0.0501 | nan | 0.0002 | 0.0 | 0.0 | 0.9037 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8918 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8947 | 0.8552 | 0.9273 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5724 | 0.8148 | 0.0 | 0.4928 | 0.0478 | nan | 0.0002 | 0.0 | 0.0 | 0.6809 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5749 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7711 | 0.6423 | 0.8650 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.3638 | 1.8 | 360 | 1.0449 | 0.1641 | 0.2055 | 0.7708 | nan | 0.7817 | 0.9653 | 0.0 | 0.5410 | 0.0607 | nan | 0.0010 | 0.0000 | 0.0 | 0.9017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9009 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9180 | 0.7825 | 0.9291 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5960 | 0.7909 | 0.0 | 0.4226 | 0.0572 | nan | 0.0010 | 0.0000 | 0.0 | 0.6764 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5664 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7769 | 0.6636 | 0.8631 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.2779 | 1.9 | 380 | 1.0227 | 0.1667 | 0.2074 | 0.7745 | nan | 0.8069 | 0.9631 | 0.0 | 0.5564 | 0.0757 | nan | 0.0019 | 0.0000 | 0.0 | 0.8764 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8981 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9214 | 0.8241 | 0.9197 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6092 | 0.7941 | 0.0 | 0.4529 | 0.0701 | nan | 0.0019 | 0.0000 | 0.0 | 0.6993 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5686 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7724 | 0.6635 | 0.8689 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.0811 | 2.0 | 400 | 0.9893 | 0.1672 | 0.2089 | 0.7794 | nan | 0.8730 | 0.9570 | 0.0 | 0.5573 | 0.0561 | nan | 0.0013 | 0.0 | 0.0 | 0.9041 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8843 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9289 | 0.7991 | 0.9315 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6005 | 0.8145 | 0.0 | 0.4776 | 0.0532 | nan | 0.0013 | 0.0 | 0.0 | 0.6819 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5802 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7736 | 0.6686 | 0.8671 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.005 | 2.1 | 420 | 0.9977 | 0.1680 | 0.2082 | 0.7783 | nan | 0.8414 | 0.9628 | 0.0 | 0.5530 | 0.0624 | nan | 0.0015 | 0.0 | 0.0 | 0.8958 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8909 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9283 | 0.8101 | 0.9248 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6081 | 0.8005 | 0.0 | 0.4885 | 0.0582 | nan | 0.0014 | 0.0 | 0.0 | 0.6984 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5775 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7744 | 0.6669 | 0.8710 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.1406 | 2.2 | 440 | 0.9950 | 0.1688 | 0.2118 | 0.7810 | nan | 0.8863 | 0.9485 | 0.0 | 0.5892 | 0.0719 | nan | 0.0020 | 0.0 | 0.0 | 0.9067 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8789 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9261 | 0.8432 | 0.9362 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5949 | 0.8221 | 0.0 | 0.5206 | 0.0665 | nan | 0.0020 | 0.0 | 0.0 | 0.6859 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5834 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7685 | 0.6580 | 0.8695 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.0543 | 2.3 | 460 | 0.9919 | 0.1675 | 0.2111 | 0.7794 | nan | 0.8561 | 0.9568 | 0.0 | 0.5935 | 0.0637 | nan | 0.0044 | 0.0 | 0.0 | 0.9143 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8675 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9190 | 0.8489 | 0.9415 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6062 | 0.8089 | 0.0 | 0.5003 | 0.0600 | nan | 0.0044 | 0.0 | 0.0 | 0.6668 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5922 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7687 | 0.6544 | 0.8671 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.0831 | 2.4 | 480 | 0.9767 | 0.1686 | 0.2107 | 0.7797 | nan | 0.8387 | 0.9593 | 0.0 | 0.6142 | 0.0727 | nan | 0.0043 | 0.0 | 0.0 | 0.9063 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8895 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9216 | 0.8086 | 0.9375 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6101 | 0.8042 | 0.0 | 0.4901 | 0.0678 | nan | 0.0042 | 0.0 | 0.0 | 0.6846 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5854 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7796 | 0.6671 | 0.8714 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.0854 | 2.5 | 500 | 0.9623 | 0.1708 | 0.2138 | 0.7830 | nan | 0.8582 | 0.9533 | 0.0 | 0.6350 | 0.1068 | nan | 0.0074 | 0.0 | 0.0 | 0.9125 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8950 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9187 | 0.8375 | 0.9325 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6113 | 0.8175 | 0.0 | 0.5387 | 0.0943 | nan | 0.0073 | 0.0 | 0.0 | 0.6782 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5824 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7791 | 0.6587 | 0.8701 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.9507 | 2.6 | 520 | 0.9477 | 0.1706 | 0.2132 | 0.7834 | nan | 0.8834 | 0.9526 | 0.0 | 0.5999 | 0.0910 | nan | 0.0063 | 0.0 | 0.0 | 0.9084 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8915 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9170 | 0.8497 | 0.9341 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6044 | 0.8194 | 0.0 | 0.5368 | 0.0825 | nan | 0.0062 | 0.0 | 0.0 | 0.6840 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5855 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7806 | 0.6592 | 0.8710 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.2475 | 2.7 | 540 | 0.9406 | 0.1710 | 0.2129 | 0.7839 | nan | 0.8619 | 0.9584 | 0.0 | 0.6319 | 0.0836 | nan | 0.0067 | 0.0000 | 0.0 | 0.9037 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8810 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9258 | 0.8315 | 0.9419 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6158 | 0.8110 | 0.0 | 0.5375 | 0.0770 | nan | 0.0066 | 0.0000 | 0.0 | 0.6905 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5898 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7781 | 0.6668 | 0.8708 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.1332 | 2.8 | 560 | 0.9532 | 0.1721 | 0.2141 | 0.7852 | nan | 0.8774 | 0.9543 | 0.0 | 0.6470 | 0.0940 | nan | 0.0065 | 0.0 | 0.0 | 0.9043 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8968 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9194 | 0.8224 | 0.9417 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6092 | 0.8193 | 0.0 | 0.5542 | 0.0848 | nan | 0.0064 | 0.0 | 0.0 | 0.6936 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5837 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7845 | 0.6707 | 0.8731 | 0.0 | 0.0 | 0.0 | 0.0 | | 0.9905 | 2.9 | 580 | 0.9639 | 0.1719 | 0.2137 | 0.7845 | nan | 0.8574 | 0.9590 | 0.0 | 0.6292 | 0.1033 | nan | 0.0097 | 0.0 | 0.0 | 0.9041 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8990 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9170 | 0.8356 | 0.9374 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6231 | 0.8120 | 0.0 | 0.5398 | 0.0920 | nan | 0.0096 | 0.0 | 0.0 | 0.6950 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5820 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7835 | 0.6635 | 0.8737 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.1681 | 3.0 | 600 | 0.9530 | 0.1712 | 0.2121 | 0.7831 | nan | 0.8549 | 0.9623 | 0.0 | 0.5957 | 0.0956 | nan | 0.0075 | 0.0 | 0.0 | 0.9053 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9152 | 0.8300 | 0.9299 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.6261 | 0.8045 | 0.0 | 0.5253 | 0.0861 | nan | 0.0075 | 0.0 | 0.0 | 0.6945 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5817 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7847 | 0.6656 | 0.8751 | 0.0 | 0.0 | 0.0 | 0.0 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.0+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
jtlicardo/bert-finetuned-bpmn
jtlicardo
2023-09-13T16:25:47Z
128
3
transformers
[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-12-10T16:08:28Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy widget: - text: The process starts when the customer enters the shop. The customer then takes the product from the shelf. The customer then pays for the product and leaves the store. example_title: Example 1 - text: The process begins when the HR department hires the new employee. Next, the new employee completes necessary paperwork and provides documentation to the HR department. After the initial task, the HR department performs a decision to determine the employee's role and department assignment. The employee is trained on the company's sales processes and systems by the Sales department. After the training, the Sales department assigns the employee a sales quota and performance goals. Finally, the process ends with an 'End' event, when the employee begins their role in the Sales department. example_title: Example 2 - text: The process begins with a 'Start' event, when a customer places an order for a product on the company's website. Next, the customer service department checks the availability of the product and confirms the order with the customer. After the initial task, the warehouse processes the order. If the order is eligible for same-day shipping, the warehouse staff picks and packs the order, and it is sent to the shipping department. After the order is packed, the shipping department arranges for the order to be delivered to the customer. Finally, the process ends with an 'End' event, when the customer receives their order. example_title: Example 3 base_model: bert-base-cased model-index: - name: bert-finetuned-bpmn results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-bpmn This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on a dataset containing textual process descriptions. The dataset contains 2 target labels: * `AGENT` * `TASK` The dataset (and the notebook used for training) can be found on the following GitHub repo: https://github.com/jtlicardo/bert-finetuned-bpmn Update: a model trained on 5 BPMN-specific labels can be found here: https://huggingface.co/jtlicardo/bpmn-information-extraction The model achieves the following results on the evaluation set: - Loss: 0.2656 - Precision: 0.7314 - Recall: 0.8366 - F1: 0.7805 - Accuracy: 0.8939 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 10 | 0.8437 | 0.1899 | 0.3203 | 0.2384 | 0.7005 | | No log | 2.0 | 20 | 0.4967 | 0.5421 | 0.7582 | 0.6322 | 0.8417 | | No log | 3.0 | 30 | 0.3403 | 0.6719 | 0.8431 | 0.7478 | 0.8867 | | No log | 4.0 | 40 | 0.2821 | 0.6923 | 0.8235 | 0.7522 | 0.8903 | | No log | 5.0 | 50 | 0.2656 | 0.7314 | 0.8366 | 0.7805 | 0.8939 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
jtlicardo/distilbert-bpmn
jtlicardo
2023-09-13T16:25:29Z
122
1
transformers
[ "transformers", "pytorch", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-cased", "base_model:finetune:distilbert/distilbert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-03-13T13:27:52Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy base_model: distilbert-base-cased model-index: - name: distilbert-bpmn results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-bpmn This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3311 - Precision: 0.7852 - Recall: 0.8375 - F1: 0.8105 - Accuracy: 0.9275 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 2.0392 | 1.0 | 12 | 1.5999 | 0.2162 | 0.2333 | 0.2244 | 0.5017 | | 1.3439 | 2.0 | 24 | 1.0197 | 0.3786 | 0.4875 | 0.4262 | 0.7133 | | 0.8403 | 3.0 | 36 | 0.6398 | 0.5664 | 0.675 | 0.6160 | 0.8333 | | 0.4941 | 4.0 | 48 | 0.4637 | 0.6775 | 0.7792 | 0.7248 | 0.8765 | | 0.3227 | 5.0 | 60 | 0.3701 | 0.7262 | 0.7958 | 0.7594 | 0.9041 | | 0.2206 | 6.0 | 72 | 0.3286 | 0.75 | 0.8125 | 0.78 | 0.9231 | | 0.1762 | 7.0 | 84 | 0.3330 | 0.7597 | 0.8167 | 0.7871 | 0.9180 | | 0.1261 | 8.0 | 96 | 0.3159 | 0.7952 | 0.825 | 0.8098 | 0.9266 | | 0.1121 | 9.0 | 108 | 0.3205 | 0.7860 | 0.8417 | 0.8129 | 0.9275 | | 0.0902 | 10.0 | 120 | 0.3090 | 0.8071 | 0.8542 | 0.8300 | 0.9326 | | 0.08 | 11.0 | 132 | 0.3200 | 0.7821 | 0.8375 | 0.8089 | 0.9266 | | 0.0789 | 12.0 | 144 | 0.3226 | 0.7915 | 0.8542 | 0.8216 | 0.9283 | | 0.0654 | 13.0 | 156 | 0.3311 | 0.7852 | 0.8375 | 0.8105 | 0.9275 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
huawei-noah/EntityCS-39-MLM-xlmr-base
huawei-noah
2023-09-13T16:23:30Z
270
0
transformers
[ "transformers", "pytorch", "xlm-roberta", "fill-mask", "af", "ar", "bg", "bn", "de", "el", "en", "es", "et", "eu", "fa", "fi", "fr", "he", "hi", "hu", "id", "it", "ja", "jv", "ka", "kk", "ko", "ml", "mr", "ms", "my", "nl", "pt", "ru", "sw", "ta", "te", "th", "tl", "tr", "ur", "vi", "yo", "zh", "arxiv:1904.09223", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-07T09:36:59Z
--- license: apache-2.0 language: - af - ar - bg - bn - de - el - en - es - et - eu - fa - fi - fr - he - hi - hu - id - it - ja - jv - ka - kk - ko - ml - mr - ms - my - nl - pt - ru - sw - ta - te - th - tl - tr - ur - vi - yo - zh --- # Model Card for EntityCS-39-MLM-xlmr-base - Paper: https://aclanthology.org/2022.findings-emnlp.499.pdf - Repository: https://github.com/huawei-noah/noah-research/tree/master/NLP/EntityCS - Point of Contact: [Fenia Christopoulou](mailto:[email protected]), [Chenxi Whitehouse](mailto:[email protected]) ## Model Description This model has been trained on the EntityCS corpus, an English corpus from Wikipedia with replaced entities in different languages. The corpus can be found in [https://huggingface.co/huawei-noah/entity_cs](https://huggingface.co/huawei-noah/entity_cs), check the link for more details. To train models on the corpus, we first employ the conventional 80-10-10 MLM objective, where 15% of sentence subwords are considered as masking candidates. From those, we replace subwords with [MASK] 80% of the time, with Random subwords (from the entire vocabulary) 10% of the time, and leave the remaining 10% unchanged (Same). To integrate entity-level cross-lingual knowledge into the model, we propose Entity Prediction objectives, where we only mask subwords belonging to an entity. By predicting the masked entities in EntityCS sentences, we expect the model to capture the semantics of the same entity in different languages. Two different masking strategies are proposed for predicting entities: Whole Entity Prediction (`WEP`) and Partial Entity Prediction (`PEP`). In WEP, motivated by [Sun et al. (2019)](https://arxiv.org/abs/1904.09223) where whole word masking is also adopted, we consider all the words (and consequently subwords) inside an entity as masking candidates. Then, 80% of the time we mask every subword inside an entity, and 20% of the time we keep the subwords intact. Note that, as our goal is to predict the entire masked entity, we do not allow replacing with Random subwords, since it can introduce noise and result in the model predicting incorrect entities. After entities are masked, we remove the entity indicators `<e>`, `</e>` from the sentences before feeding them to the model. For PEP, we also consider all entities as masking candidates. In contrast to WEP, we do not force subwords belonging to one entity to be either all masked or all unmasked. Instead, each individual entity subword is masked 80% of the time. For the remaining 20% of the masking candidates, we experiment with three different replacements. First, PEP<sub>MRS</sub>, corresponds to the conventional 80-10-10 masking strategy, where 10% of the remaining subwords are replaced with Random subwords and the other 10% are kept unchanged. In the second setting, PEP<sub>MS</sub>, we remove the 10% Random subwords substitution, i.e. we predict the 80% masked subwords and 10% Same subwords from the masking candidates. In the third setting, PEP<sub>M</sub>, we further remove the 10% Same subwords prediction, essentially predicting only the masked subwords. Prior work has proven it is effective to combine Entity Prediction with MLM for cross-lingual transfer ([Jiang et al., 2020](https://aclanthology.org/2020.emnlp-main.479/)), therefore we investigate the combination of the Entity Prediction objectives together with MLM on non-entity subwords. Specifically, when combined with MLM, we lower the entity masking probability (p) to 50% to roughly keep the same overall masking percentage. This results into the following objectives: WEP + MLM, PEP<sub>MRS</sub> + MLM, PEP<sub>MS</sub> + MLM, PEP<sub>M</sub> + MLM This model was trained with the **MLM** objective on the EntityCS corpus with 39 languages. ## Training Details We start from the [XLM-R-base](https://huggingface.co/xlm-roberta-base) model and train for 1 epoch on 8 Nvidia V100 32GB GPUs. We set batch size to 16 and gradient accumulation steps to 2, resulting in an effective batch size of 256. For speedup we use fp16 mixed precision. We use the sampling strategy proposed by [Conneau and Lample (2019)](https://dl.acm.org/doi/pdf/10.5555/3454287.3454921), where high resource languages are down-sampled and low resource languages get sampled more frequently. We only train the embedding and the last two layers of the model. We randomly choose 100 sentences from each language to serve as a validation set, on which we measure the perplexity every 10K training steps. **This checkpoint corresponds to the one with the lower perplexity on the validation set.** ## Usage The current model can be used for further fine-tuning on downstream tasks. In the paper, we focused on entity-related tasks, such as NER, Word Sense Disambiguation and Slot Filling. Alternatively, it can be used directly (no fine-tuning) for probing tasks, i.e. predict missing words, such as [X-FACTR](https://aclanthology.org/2020.emnlp-main.479/). For results on each downstream task, please refer to the [paper](https://aclanthology.org/2022.findings-emnlp.499.pdf). ## How to Get Started with the Model Use the code below to get started with training: https://github.com/huawei-noah/noah-research/tree/master/NLP/EntityCS ## Citation **BibTeX** ```html @inproceedings{whitehouse-etal-2022-entitycs, title = "{E}ntity{CS}: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching", author = "Whitehouse, Chenxi and Christopoulou, Fenia and Iacobacci, Ignacio", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.findings-emnlp.499", pages = "6698--6714" } ``` **APA** ```html Whitehouse, C., Christopoulou, F., & Iacobacci, I. (2022). EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching. In Findings of the Association for Computational Linguistics: EMNLP 2022. ```
LarryAIDraw/MomozonoMomoka
LarryAIDraw
2023-09-13T16:20:35Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-09-13T16:08:23Z
--- license: creativeml-openrail-m --- https://civitai.com/models/144293/momozono-momokadokyuu-hentai-hxeros
LarryAIDraw/MahoukaAngelina-v2-06
LarryAIDraw
2023-09-13T16:19:47Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-09-13T16:06:02Z
--- license: creativeml-openrail-m --- https://civitai.com/models/144428/mahouka-koukou-no-rettousei-angelina-kudou-shields-3-outfits
LarryAIDraw/saitou_miyako_oshinoko
LarryAIDraw
2023-09-13T16:19:18Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-09-13T16:05:12Z
--- license: creativeml-openrail-m --- https://civitai.com/models/144135/saitou-miyako-oshi-no-ko
CyberHarem/sonia_pokemon
CyberHarem
2023-09-13T16:10:24Z
0
0
null
[ "art", "text-to-image", "dataset:CyberHarem/sonia_pokemon", "license:mit", "region:us" ]
text-to-image
2023-09-13T15:56:06Z
--- license: mit datasets: - CyberHarem/sonia_pokemon pipeline_tag: text-to-image tags: - art --- # Lora of sonia_pokemon This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 6240, you need to download `6240/sonia_pokemon.pt` as the embedding and `6240/sonia_pokemon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 6240**, with the score of 0.818. The trigger words are: 1. `sonia_pokemon` 2. `orange_hair, long_hair, hair_ornament, side_ponytail, heart, heart_hair_ornament, green_eyes, sunglasses, eyewear_on_head, smile, breasts, collarbone, blush, eyelashes, aqua_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:---------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 7800 | 0.799 | [Download](7800/sonia_pokemon.zip) | ![pattern_1-7800](7800/previews/pattern_1.png) | ![pattern_2-7800](7800/previews/pattern_2.png) | ![bikini-7800](7800/previews/bikini.png) | [<NSFW, click to see>](7800/previews/bondage.png) | ![free-7800](7800/previews/free.png) | ![maid-7800](7800/previews/maid.png) | ![miko-7800](7800/previews/miko.png) | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) | ![suit-7800](7800/previews/suit.png) | ![yukata-7800](7800/previews/yukata.png) | | 7280 | 0.745 | [Download](7280/sonia_pokemon.zip) | ![pattern_1-7280](7280/previews/pattern_1.png) | ![pattern_2-7280](7280/previews/pattern_2.png) | ![bikini-7280](7280/previews/bikini.png) | [<NSFW, click to see>](7280/previews/bondage.png) | ![free-7280](7280/previews/free.png) | ![maid-7280](7280/previews/maid.png) | ![miko-7280](7280/previews/miko.png) | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) | ![suit-7280](7280/previews/suit.png) | ![yukata-7280](7280/previews/yukata.png) | | 6760 | 0.727 | [Download](6760/sonia_pokemon.zip) | ![pattern_1-6760](6760/previews/pattern_1.png) | ![pattern_2-6760](6760/previews/pattern_2.png) | ![bikini-6760](6760/previews/bikini.png) | [<NSFW, click to see>](6760/previews/bondage.png) | ![free-6760](6760/previews/free.png) | ![maid-6760](6760/previews/maid.png) | ![miko-6760](6760/previews/miko.png) | [<NSFW, click to see>](6760/previews/nude.png) | [<NSFW, click to see>](6760/previews/nude2.png) | ![suit-6760](6760/previews/suit.png) | ![yukata-6760](6760/previews/yukata.png) | | **6240** | **0.818** | [**Download**](6240/sonia_pokemon.zip) | ![pattern_1-6240](6240/previews/pattern_1.png) | ![pattern_2-6240](6240/previews/pattern_2.png) | ![bikini-6240](6240/previews/bikini.png) | [<NSFW, click to see>](6240/previews/bondage.png) | ![free-6240](6240/previews/free.png) | ![maid-6240](6240/previews/maid.png) | ![miko-6240](6240/previews/miko.png) | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) | ![suit-6240](6240/previews/suit.png) | ![yukata-6240](6240/previews/yukata.png) | | 5720 | 0.683 | [Download](5720/sonia_pokemon.zip) | ![pattern_1-5720](5720/previews/pattern_1.png) | ![pattern_2-5720](5720/previews/pattern_2.png) | ![bikini-5720](5720/previews/bikini.png) | [<NSFW, click to see>](5720/previews/bondage.png) | ![free-5720](5720/previews/free.png) | ![maid-5720](5720/previews/maid.png) | ![miko-5720](5720/previews/miko.png) | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) | ![suit-5720](5720/previews/suit.png) | ![yukata-5720](5720/previews/yukata.png) | | 5200 | 0.806 | [Download](5200/sonia_pokemon.zip) | ![pattern_1-5200](5200/previews/pattern_1.png) | ![pattern_2-5200](5200/previews/pattern_2.png) | ![bikini-5200](5200/previews/bikini.png) | [<NSFW, click to see>](5200/previews/bondage.png) | ![free-5200](5200/previews/free.png) | ![maid-5200](5200/previews/maid.png) | ![miko-5200](5200/previews/miko.png) | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) | ![suit-5200](5200/previews/suit.png) | ![yukata-5200](5200/previews/yukata.png) | | 4680 | 0.726 | [Download](4680/sonia_pokemon.zip) | ![pattern_1-4680](4680/previews/pattern_1.png) | ![pattern_2-4680](4680/previews/pattern_2.png) | ![bikini-4680](4680/previews/bikini.png) | [<NSFW, click to see>](4680/previews/bondage.png) | ![free-4680](4680/previews/free.png) | ![maid-4680](4680/previews/maid.png) | ![miko-4680](4680/previews/miko.png) | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) | ![suit-4680](4680/previews/suit.png) | ![yukata-4680](4680/previews/yukata.png) | | 4160 | 0.697 | [Download](4160/sonia_pokemon.zip) | ![pattern_1-4160](4160/previews/pattern_1.png) | ![pattern_2-4160](4160/previews/pattern_2.png) | ![bikini-4160](4160/previews/bikini.png) | [<NSFW, click to see>](4160/previews/bondage.png) | ![free-4160](4160/previews/free.png) | ![maid-4160](4160/previews/maid.png) | ![miko-4160](4160/previews/miko.png) | [<NSFW, click to see>](4160/previews/nude.png) | [<NSFW, click to see>](4160/previews/nude2.png) | ![suit-4160](4160/previews/suit.png) | ![yukata-4160](4160/previews/yukata.png) | | 3640 | 0.596 | [Download](3640/sonia_pokemon.zip) | ![pattern_1-3640](3640/previews/pattern_1.png) | ![pattern_2-3640](3640/previews/pattern_2.png) | ![bikini-3640](3640/previews/bikini.png) | [<NSFW, click to see>](3640/previews/bondage.png) | ![free-3640](3640/previews/free.png) | ![maid-3640](3640/previews/maid.png) | ![miko-3640](3640/previews/miko.png) | [<NSFW, click to see>](3640/previews/nude.png) | [<NSFW, click to see>](3640/previews/nude2.png) | ![suit-3640](3640/previews/suit.png) | ![yukata-3640](3640/previews/yukata.png) | | 3120 | 0.807 | [Download](3120/sonia_pokemon.zip) | ![pattern_1-3120](3120/previews/pattern_1.png) | ![pattern_2-3120](3120/previews/pattern_2.png) | ![bikini-3120](3120/previews/bikini.png) | [<NSFW, click to see>](3120/previews/bondage.png) | ![free-3120](3120/previews/free.png) | ![maid-3120](3120/previews/maid.png) | ![miko-3120](3120/previews/miko.png) | [<NSFW, click to see>](3120/previews/nude.png) | [<NSFW, click to see>](3120/previews/nude2.png) | ![suit-3120](3120/previews/suit.png) | ![yukata-3120](3120/previews/yukata.png) | | 2600 | 0.789 | [Download](2600/sonia_pokemon.zip) | ![pattern_1-2600](2600/previews/pattern_1.png) | ![pattern_2-2600](2600/previews/pattern_2.png) | ![bikini-2600](2600/previews/bikini.png) | [<NSFW, click to see>](2600/previews/bondage.png) | ![free-2600](2600/previews/free.png) | ![maid-2600](2600/previews/maid.png) | ![miko-2600](2600/previews/miko.png) | [<NSFW, click to see>](2600/previews/nude.png) | [<NSFW, click to see>](2600/previews/nude2.png) | ![suit-2600](2600/previews/suit.png) | ![yukata-2600](2600/previews/yukata.png) | | 2080 | 0.749 | [Download](2080/sonia_pokemon.zip) | ![pattern_1-2080](2080/previews/pattern_1.png) | ![pattern_2-2080](2080/previews/pattern_2.png) | ![bikini-2080](2080/previews/bikini.png) | [<NSFW, click to see>](2080/previews/bondage.png) | ![free-2080](2080/previews/free.png) | ![maid-2080](2080/previews/maid.png) | ![miko-2080](2080/previews/miko.png) | [<NSFW, click to see>](2080/previews/nude.png) | [<NSFW, click to see>](2080/previews/nude2.png) | ![suit-2080](2080/previews/suit.png) | ![yukata-2080](2080/previews/yukata.png) | | 1560 | 0.718 | [Download](1560/sonia_pokemon.zip) | ![pattern_1-1560](1560/previews/pattern_1.png) | ![pattern_2-1560](1560/previews/pattern_2.png) | ![bikini-1560](1560/previews/bikini.png) | [<NSFW, click to see>](1560/previews/bondage.png) | ![free-1560](1560/previews/free.png) | ![maid-1560](1560/previews/maid.png) | ![miko-1560](1560/previews/miko.png) | [<NSFW, click to see>](1560/previews/nude.png) | [<NSFW, click to see>](1560/previews/nude2.png) | ![suit-1560](1560/previews/suit.png) | ![yukata-1560](1560/previews/yukata.png) | | 1040 | 0.529 | [Download](1040/sonia_pokemon.zip) | ![pattern_1-1040](1040/previews/pattern_1.png) | ![pattern_2-1040](1040/previews/pattern_2.png) | ![bikini-1040](1040/previews/bikini.png) | [<NSFW, click to see>](1040/previews/bondage.png) | ![free-1040](1040/previews/free.png) | ![maid-1040](1040/previews/maid.png) | ![miko-1040](1040/previews/miko.png) | [<NSFW, click to see>](1040/previews/nude.png) | [<NSFW, click to see>](1040/previews/nude2.png) | ![suit-1040](1040/previews/suit.png) | ![yukata-1040](1040/previews/yukata.png) | | 520 | 0.364 | [Download](520/sonia_pokemon.zip) | ![pattern_1-520](520/previews/pattern_1.png) | ![pattern_2-520](520/previews/pattern_2.png) | ![bikini-520](520/previews/bikini.png) | [<NSFW, click to see>](520/previews/bondage.png) | ![free-520](520/previews/free.png) | ![maid-520](520/previews/maid.png) | ![miko-520](520/previews/miko.png) | [<NSFW, click to see>](520/previews/nude.png) | [<NSFW, click to see>](520/previews/nude2.png) | ![suit-520](520/previews/suit.png) | ![yukata-520](520/previews/yukata.png) |
texasdave2/distilbert-base-uncased-squad_v2
texasdave2
2023-09-13T15:59:02Z
62
0
transformers
[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2023-09-13T15:58:38Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: distilbert-base-uncased-squad_v2 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. --> # distilbert-base-uncased-squad_v2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.33.1 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.13.3
paragon-analytics/llama2_7b_pf
paragon-analytics
2023-09-13T15:58:08Z
6
1
peft
[ "peft", "region:us" ]
null
2023-09-13T14:58:30Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
LibreSD/NovelAI
LibreSD
2023-09-13T15:55:17Z
0
15
null
[ "stable-diffusion", "text-to-image", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-09-13T15:12:28Z
--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image --- magnet:?xt=urn:btih:5bde442da86265b670a3e5ea3163afad2c6f8ecc
gokoV/whisper-tiny-us_handson
gokoV
2023-09-13T15:54:30Z
74
0
transformers
[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-09-13T09:38:59Z
--- language: - en license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: Whisper tiny US - handson_tut results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14-en-US type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 44.23529411764706 --- <!-- 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. --> # Whisper tiny US - handson_tut This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14-en-US dataset. It achieves the following results on the evaluation set: - Loss: 0.7488 - Wer Ortho: 43.1181 - Wer: 44.2353 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:| | 0.0007 | 17.86 | 500 | 0.7488 | 43.1181 | 44.2353 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
imvladikon/wav2vec2-xls-r-300m-hebrew
imvladikon
2023-09-13T15:54:14Z
129,869
4
transformers
[ "transformers", "pytorch", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "he", "hf-asr-leaderboard", "robust-speech-event", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2022-03-02T23:29:05Z
--- language: - he tags: - automatic-speech-recognition - generated_from_trainer - he - hf-asr-leaderboard - robust-speech-event base_model: facebook/wav2vec2-xls-r-300m model-index: - name: wav2vec2-xls-r-300m-hebrew results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Custom Dataset type: custom args: he metrics: - type: wer value: 23.18 name: Test WER --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xls-r-300m-hebrew This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the private datasets in 2 stages - firstly was fine-tuned on a small dataset with good samples Then the obtained model was fine-tuned on a large dataset with the small good dataset, with various samples from different sources, and with an unlabeled dataset that was weakly labeled using a previously trained model. Small dataset: | split |size(gb) | n_samples | duration(hrs)| | |---|---|---|---|---| |train|4.19| 20306 | 28 | | |dev |1.05| 5076 | 7 | | Large dataset: | split |size(gb) | n_samples | duration(hrs)| | |---|---|---|---|---| |train|12.3| 90777 | 69 | | |dev |2.39| 20246 | 14* | | (*weakly labeled data wasn't used in validation set) After firts training it achieves: on small dataset - Loss: 0.5438 - WER: 0.1773 on large dataset - WER: 0.3811 after second training: on small dataset - WER: 0.1697 on large dataset - Loss: 0.4502 - WER: 0.2318 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters #### First training The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - 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: 1000 - num_epochs: 100.0 - mixed_precision_training: Native AMP Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | No log | 3.15 | 1000 | 0.5203 | 0.4333 | | 1.4284 | 6.31 | 2000 | 0.4816 | 0.3951 | | 1.4284 | 9.46 | 3000 | 0.4315 | 0.3546 | | 1.283 | 12.62 | 4000 | 0.4278 | 0.3404 | | 1.283 | 15.77 | 5000 | 0.4090 | 0.3054 | | 1.1777 | 18.93 | 6000 | 0.3893 | 0.3006 | | 1.1777 | 22.08 | 7000 | 0.3968 | 0.2857 | | 1.0994 | 25.24 | 8000 | 0.3892 | 0.2751 | | 1.0994 | 28.39 | 9000 | 0.4061 | 0.2690 | | 1.0323 | 31.54 | 10000 | 0.4114 | 0.2507 | | 1.0323 | 34.7 | 11000 | 0.4021 | 0.2508 | | 0.9623 | 37.85 | 12000 | 0.4032 | 0.2378 | | 0.9623 | 41.01 | 13000 | 0.4148 | 0.2374 | | 0.9077 | 44.16 | 14000 | 0.4350 | 0.2323 | | 0.9077 | 47.32 | 15000 | 0.4515 | 0.2246 | | 0.8573 | 50.47 | 16000 | 0.4474 | 0.2180 | | 0.8573 | 53.63 | 17000 | 0.4649 | 0.2171 | | 0.8083 | 56.78 | 18000 | 0.4455 | 0.2102 | | 0.8083 | 59.94 | 19000 | 0.4587 | 0.2092 | | 0.769 | 63.09 | 20000 | 0.4794 | 0.2012 | | 0.769 | 66.25 | 21000 | 0.4845 | 0.2007 | | 0.7308 | 69.4 | 22000 | 0.4937 | 0.2008 | | 0.7308 | 72.55 | 23000 | 0.4920 | 0.1895 | | 0.6927 | 75.71 | 24000 | 0.5179 | 0.1911 | | 0.6927 | 78.86 | 25000 | 0.5202 | 0.1877 | | 0.6622 | 82.02 | 26000 | 0.5266 | 0.1840 | | 0.6622 | 85.17 | 27000 | 0.5351 | 0.1854 | | 0.6315 | 88.33 | 28000 | 0.5373 | 0.1811 | | 0.6315 | 91.48 | 29000 | 0.5331 | 0.1792 | | 0.6075 | 94.64 | 30000 | 0.5390 | 0.1779 | | 0.6075 | 97.79 | 31000 | 0.5459 | 0.1773 | #### Second training The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - 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: 1000 - num_epochs: 60.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | No log | 0.7 | 1000 | 0.5371 | 0.3811 | | 1.3606 | 1.41 | 2000 | 0.5247 | 0.3902 | | 1.3606 | 2.12 | 3000 | 0.5126 | 0.3859 | | 1.3671 | 2.82 | 4000 | 0.5062 | 0.3828 | | 1.3671 | 3.53 | 5000 | 0.4979 | 0.3672 | | 1.3421 | 4.23 | 6000 | 0.4906 | 0.3816 | | 1.3421 | 4.94 | 7000 | 0.4784 | 0.3651 | | 1.328 | 5.64 | 8000 | 0.4810 | 0.3669 | | 1.328 | 6.35 | 9000 | 0.4747 | 0.3597 | | 1.3109 | 7.05 | 10000 | 0.4813 | 0.3808 | | 1.3109 | 7.76 | 11000 | 0.4631 | 0.3561 | | 1.2873 | 8.46 | 12000 | 0.4603 | 0.3431 | | 1.2873 | 9.17 | 13000 | 0.4579 | 0.3533 | | 1.2661 | 9.87 | 14000 | 0.4471 | 0.3365 | | 1.2661 | 10.58 | 15000 | 0.4584 | 0.3437 | | 1.249 | 11.28 | 16000 | 0.4461 | 0.3454 | | 1.249 | 11.99 | 17000 | 0.4482 | 0.3367 | | 1.2322 | 12.69 | 18000 | 0.4464 | 0.3335 | | 1.2322 | 13.4 | 19000 | 0.4427 | 0.3454 | | 1.22 | 14.1 | 20000 | 0.4440 | 0.3395 | | 1.22 | 14.81 | 21000 | 0.4459 | 0.3378 | | 1.2044 | 15.51 | 22000 | 0.4406 | 0.3199 | | 1.2044 | 16.22 | 23000 | 0.4398 | 0.3155 | | 1.1913 | 16.92 | 24000 | 0.4237 | 0.3150 | | 1.1913 | 17.63 | 25000 | 0.4287 | 0.3279 | | 1.1705 | 18.34 | 26000 | 0.4253 | 0.3103 | | 1.1705 | 19.04 | 27000 | 0.4234 | 0.3098 | | 1.1564 | 19.75 | 28000 | 0.4174 | 0.3076 | | 1.1564 | 20.45 | 29000 | 0.4260 | 0.3160 | | 1.1461 | 21.16 | 30000 | 0.4235 | 0.3036 | | 1.1461 | 21.86 | 31000 | 0.4309 | 0.3055 | | 1.1285 | 22.57 | 32000 | 0.4264 | 0.3006 | | 1.1285 | 23.27 | 33000 | 0.4201 | 0.2880 | | 1.1135 | 23.98 | 34000 | 0.4131 | 0.2975 | | 1.1135 | 24.68 | 35000 | 0.4202 | 0.2849 | | 1.0968 | 25.39 | 36000 | 0.4105 | 0.2888 | | 1.0968 | 26.09 | 37000 | 0.4210 | 0.2834 | | 1.087 | 26.8 | 38000 | 0.4123 | 0.2843 | | 1.087 | 27.5 | 39000 | 0.4216 | 0.2803 | | 1.0707 | 28.21 | 40000 | 0.4161 | 0.2787 | | 1.0707 | 28.91 | 41000 | 0.4186 | 0.2740 | | 1.0575 | 29.62 | 42000 | 0.4118 | 0.2845 | | 1.0575 | 30.32 | 43000 | 0.4243 | 0.2773 | | 1.0474 | 31.03 | 44000 | 0.4221 | 0.2707 | | 1.0474 | 31.73 | 45000 | 0.4138 | 0.2700 | | 1.0333 | 32.44 | 46000 | 0.4102 | 0.2638 | | 1.0333 | 33.15 | 47000 | 0.4162 | 0.2650 | | 1.0191 | 33.85 | 48000 | 0.4155 | 0.2636 | | 1.0191 | 34.56 | 49000 | 0.4129 | 0.2656 | | 1.0087 | 35.26 | 50000 | 0.4157 | 0.2632 | | 1.0087 | 35.97 | 51000 | 0.4090 | 0.2654 | | 0.9901 | 36.67 | 52000 | 0.4183 | 0.2587 | | 0.9901 | 37.38 | 53000 | 0.4251 | 0.2648 | | 0.9795 | 38.08 | 54000 | 0.4229 | 0.2555 | | 0.9795 | 38.79 | 55000 | 0.4176 | 0.2546 | | 0.9644 | 39.49 | 56000 | 0.4223 | 0.2513 | | 0.9644 | 40.2 | 57000 | 0.4244 | 0.2530 | | 0.9534 | 40.9 | 58000 | 0.4175 | 0.2538 | | 0.9534 | 41.61 | 59000 | 0.4213 | 0.2505 | | 0.9397 | 42.31 | 60000 | 0.4275 | 0.2565 | | 0.9397 | 43.02 | 61000 | 0.4315 | 0.2528 | | 0.9269 | 43.72 | 62000 | 0.4316 | 0.2501 | | 0.9269 | 44.43 | 63000 | 0.4247 | 0.2471 | | 0.9175 | 45.13 | 64000 | 0.4376 | 0.2469 | | 0.9175 | 45.84 | 65000 | 0.4335 | 0.2450 | | 0.9026 | 46.54 | 66000 | 0.4336 | 0.2452 | | 0.9026 | 47.25 | 67000 | 0.4400 | 0.2427 | | 0.8929 | 47.95 | 68000 | 0.4382 | 0.2429 | | 0.8929 | 48.66 | 69000 | 0.4361 | 0.2415 | | 0.8786 | 49.37 | 70000 | 0.4413 | 0.2398 | | 0.8786 | 50.07 | 71000 | 0.4392 | 0.2415 | | 0.8714 | 50.78 | 72000 | 0.4345 | 0.2406 | | 0.8714 | 51.48 | 73000 | 0.4475 | 0.2402 | | 0.8589 | 52.19 | 74000 | 0.4473 | 0.2374 | | 0.8589 | 52.89 | 75000 | 0.4457 | 0.2357 | | 0.8493 | 53.6 | 76000 | 0.4462 | 0.2366 | | 0.8493 | 54.3 | 77000 | 0.4494 | 0.2356 | | 0.8395 | 55.01 | 78000 | 0.4472 | 0.2352 | | 0.8395 | 55.71 | 79000 | 0.4490 | 0.2339 | | 0.8295 | 56.42 | 80000 | 0.4489 | 0.2318 | | 0.8295 | 57.12 | 81000 | 0.4469 | 0.2320 | | 0.8225 | 57.83 | 82000 | 0.4478 | 0.2321 | | 0.8225 | 58.53 | 83000 | 0.4525 | 0.2326 | | 0.816 | 59.24 | 84000 | 0.4532 | 0.2316 | | 0.816 | 59.94 | 85000 | 0.4502 | 0.2318 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0
Lethargus/ppo-SnowballTarget
Lethargus
2023-09-13T15:51:18Z
0
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
reinforcement-learning
2023-09-13T15:51:15Z
--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Lethargus/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
Terps/ppo-SnowballTarget
Terps
2023-09-13T15:49:28Z
5
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
reinforcement-learning
2023-09-13T15:29:46Z
--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Terps/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
BdtRomain/bert-base-uncased-finetuned-swag
BdtRomain
2023-09-13T15:47:10Z
1
0
transformers
[ "transformers", "pytorch", "bert", "multiple-choice", "generated_from_trainer", "dataset:swag", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]
multiple-choice
2023-09-13T13:38:36Z
--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer datasets: - swag model-index: - name: bert-base-uncased-finetuned-swag results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-finetuned-swag This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset. It achieves the following results on the evaluation set: - eval_loss: 0.7219 - eval_accuracy: 0.7609 - eval_runtime: 179.4091 - eval_samples_per_second: 111.511 - eval_steps_per_second: 13.94 - epoch: 2.0 - step: 18388 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.0.dev0
Pablo94/electra-base-discriminator-finetuned-detests
Pablo94
2023-09-13T15:36:58Z
105
0
transformers
[ "transformers", "pytorch", "electra", "text-classification", "generated_from_trainer", "base_model:google/electra-base-discriminator", "base_model:finetune:google/electra-base-discriminator", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-13T15:21:37Z
--- license: apache-2.0 base_model: google/electra-base-discriminator tags: - generated_from_trainer metrics: - accuracy - precision - recall model-index: - name: electra-base-discriminator-finetuned-detests 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. --> # electra-base-discriminator-finetuned-detests This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1215 - Accuracy: 0.7807 - F1-score: 0.7308 - Precision: 0.7162 - Recall: 0.7768 - Auc: 0.7768 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:------:| | 0.3236 | 1.0 | 174 | 0.4661 | 0.7610 | 0.6684 | 0.6647 | 0.6728 | 0.6728 | | 0.3239 | 2.0 | 348 | 0.4287 | 0.7987 | 0.7144 | 0.7138 | 0.7149 | 0.7149 | | 0.3421 | 3.0 | 522 | 0.5586 | 0.7741 | 0.7292 | 0.7163 | 0.7853 | 0.7853 | | 0.2288 | 4.0 | 696 | 0.6229 | 0.7807 | 0.7308 | 0.7162 | 0.7768 | 0.7768 | | 0.1888 | 5.0 | 870 | 0.6629 | 0.7954 | 0.7293 | 0.7173 | 0.7483 | 0.7483 | | 0.2205 | 6.0 | 1044 | 0.8462 | 0.8036 | 0.7349 | 0.7251 | 0.7485 | 0.7485 | | 0.1512 | 7.0 | 1218 | 0.8362 | 0.8151 | 0.7335 | 0.7367 | 0.7306 | 0.7306 | | 0.2345 | 8.0 | 1392 | 1.0372 | 0.7758 | 0.7204 | 0.7063 | 0.7584 | 0.7584 | | 0.0592 | 9.0 | 1566 | 1.0396 | 0.7840 | 0.7291 | 0.7142 | 0.7663 | 0.7663 | | 0.0381 | 10.0 | 1740 | 1.1215 | 0.7807 | 0.7308 | 0.7162 | 0.7768 | 0.7768 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
LeBenchmark/wav2vec2-FR-14K-xlarge
LeBenchmark
2023-09-13T15:35:45Z
11
1
transformers
[ "transformers", "wav2vec2", "pretraining", "fr", "arxiv:2309.05472", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2022-10-31T20:23:58Z
--- language: "fr" thumbnail: tags: - wav2vec2 license: "apache-2.0" --- # LeBenchmark 2.0: wav2vec2 xlarge model trained on 14K hours of French speech LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. It comes with 2 versions, in which, the later version (LeBenchmark 2.0) is an extended version of the first version in terms of both numbers of pre-trained SSL models, and numbers of downstream tasks. For more information on the different benchmarks that can be used to evaluate the wav2vec2 models, please refer to our paper at: [LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech](https://arxiv.org/abs/2309.05472) ## Model and data descriptions We release four different models that can be found under our HuggingFace organization. Four different wav2vec2 architectures *Light*, *Base*, *Large* and *xLarge* are coupled with our small (1K), medium (3K), large (7K), and extra large (14K) corpus. In short: ## *Lebenchmark 2.0:* - [wav2vec2-FR-14K-xlarge](https://huggingface.co/LeBenchmark/wav2vec2-FR-14K-xlarge): xLarge wav2vec2 trained on 14K hours of French speech (5.4K Males / 2.4K Females / 6.8K unknown). - [wav2vec2-FR-14K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-14K-large): Large wav2vec2 trained on 14K hours of French speech (5.4K Males / 2.4K Females / 6.8K unknown). - [wav2vec2-FR-14K-light](https://huggingface.co/LeBenchmark/wav2vec2-FR-14K-light): Light wav2vec2 trained on 14K hours of French speech (5.4K Males / 2.4K Females / 6.8K unknown). ## *Lebenchmark:* - [wav2vec2-FR-7K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-7K-large): Large wav2vec2 trained on 7.6K hours of French speech (1.8K Males / 1.0K Females / 4.8K unknown). - [wav2vec2-FR-7K-base](https://huggingface.co/LeBenchmark/wav2vec2-FR-7K-base): Base wav2vec2 trained on 7.6K hours of French speech (1.8K Males / 1.0K Females / 4.8K unknown). - [wav2vec2-FR-3K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-3K-large): Large wav2vec2 trained on 2.9K hours of French speech (1.8K Males / 1.0K Females / 0.1K unknown). - [wav2vec2-FR-3K-base](https://huggingface.co/LeBenchmark/wav2vec2-FR-3K-base): Base wav2vec2 trained on 2.9K hours of French speech (1.8K Males / 1.0K Females / 0.1K unknown). - [wav2vec2-FR-2.6K-base](https://huggingface.co/LeBenchmark/wav2vec2-FR-2.6K-base): Base wav2vec2 trained on 2.6K hours of French speech (**no spontaneous speech**). - [wav2vec2-FR-1K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-1K-large): Large wav2vec2 trained on 1K hours of French speech (0.5K Males / 0.5K Females). - [wav2vec2-FR-1K-base](https://huggingface.co/LeBenchmark/wav2vec2-FR-1K-base): Base wav2vec2 trained on 1K hours of French speech (0.5K Males / 0.5K Females). ## Intended uses & limitations Pretrained wav2vec2 models are distributed under the Apache-2.0 license. Hence, they can be reused extensively without strict limitations. However, benchmarks and data may be linked to corpora that are not completely open-sourced. ## Fine-tune with Fairseq for ASR with CTC As our wav2vec2 models were trained with Fairseq, then can be used in the different tools that they provide to fine-tune the model for ASR with CTC. The full procedure has been nicely summarized in [this blogpost](https://huggingface.co/blog/fine-tune-wav2vec2-english). Please note that due to the nature of CTC, speech-to-text results aren't expected to be state-of-the-art. Moreover, future features might appear depending on the involvement of Fairseq and HuggingFace on this part. ## Integrate to SpeechBrain for ASR, Speaker, Source Separation ... Pretrained wav2vec models recently gained in popularity. At the same time, [SpeechBrain toolkit](https://speechbrain.github.io) came out, proposing a new and simpler way of dealing with state-of-the-art speech & deep-learning technologies. While it currently is in beta, SpeechBrain offers two different ways of nicely integrating wav2vec2 models that were trained with Fairseq i.e our LeBenchmark models! 1. Extract wav2vec2 features on-the-fly (with a frozen wav2vec2 encoder) to be combined with any speech-related architecture. Examples are: E2E ASR with CTC+Att+Language Models; Speaker Recognition or Verification, Source Separation ... 2. *Experimental:* To fully benefit from wav2vec2, the best solution remains to fine-tune the model while you train your downstream task. This is very simply allowed within SpeechBrain as just a flag needs to be turned on. Thus, our wav2vec2 models can be fine-tuned while training your favorite ASR pipeline or Speaker Recognizer. **If interested, simply follow this [tutorial](https://colab.research.google.com/drive/17Hu1pxqhfMisjkSgmM2CnZxfqDyn2hSY?usp=sharing)** ## Referencing LeBenchmark ``` @misc{parcollet2023lebenchmark, title={LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech}, author={Titouan Parcollet and Ha Nguyen and Solene Evain and Marcely Zanon Boito and Adrien Pupier and Salima Mdhaffar and Hang Le and Sina Alisamir and Natalia Tomashenko and Marco Dinarelli and Shucong Zhang and Alexandre Allauzen and Maximin Coavoux and Yannick Esteve and Mickael Rouvier and Jerome Goulian and Benjamin Lecouteux and Francois Portet and Solange Rossato and Fabien Ringeval and Didier Schwab and Laurent Besacier}, year={2023}, eprint={2309.05472}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
andreipb/roberta-poetry-life-crpo
andreipb
2023-09-13T15:32:44Z
108
0
transformers
[ "transformers", "pytorch", "safetensors", "roberta", "fill-mask", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-13T14:59:30Z
--- license: mit language: - en pipeline_tag: fill-mask library_name: transformers widget: - text: "This morning, the CEO was <mask>." example_title: "Example 1" - text: "Yesterday, all the students were <mask> in the park." example_title: "Example 2" - text: "All the children seemed <mask>." example_title: "Example 3" - text: "I opened the door and found a <mask> behind it." example_title: "Example 4" - text: "We went to see the <mask> movie." example_title: "Example 5" --- # roberta-poetry-life-crpo This model is based on the [RoBERTa base model](https://huggingface.co/roberta-base) (125M parameters) fine-tuned for 20 epochs on a poetry dataset of 14 MB. This dataset was extracted from the [Gutenberg Poetry Corpus](https://github.com/aparrish/gutenberg-poetry-corpus) using an automatic classifier for poems in relation with the topic of **life and death**. The model replaces a masked word, indicated by the `<mask>` tag, with a word associated with **life and death**, while preserving fluency. Caution: the topic (here, **life and death**) only biases the choice of words with respect to the base model, but do not expect to find only words strongly associated to this topic. This model was trained by [Teo Ferrari](https://www.linkedin.com/in/teo-ferrari-0a4009176/) as part of his Bachelor thesis at [HEIG-VD](https://gaps.heig-vd.ch/public/diplome/rapports.php?id=6763), supervised by [Andrei Popescu-Belis](http://iict-space.heig-vd.ch/apu/). The model is described in "[GPoeT: a Language Model Trained for Rhyme Generation on Synthetic Data](https://aclanthology.org/2023.latechclfl-1.2/)" and is used in the [CR-PO](https://github.com/heig-iict-ida/crpo) system for [interactive poem generation](https://aclanthology.org/2022.lrec-1.377), along with several other models for specific topics or emotions.
SeyedAli/Food-Image-Classification-VIT
SeyedAli
2023-09-13T15:25:29Z
193
1
transformers
[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:food101", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-09-13T12:37:06Z
--- license: mit base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - food101 model-index: - name: Food-Image-Classification-VIT 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. --> # Food-Image-Classification-VIT This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - eval_loss: 1.0611 - eval_accuracy: 0.7274 - eval_runtime: 411.0682 - eval_samples_per_second: 61.425 - eval_steps_per_second: 7.68 - epoch: 0.15 - step: 718 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
pmarar96/sd-class-butterflies-64
pmarar96
2023-09-13T15:21:43Z
45
0
diffusers
[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]
unconditional-image-generation
2023-09-13T15:01:34Z
--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('pmarar96/sd-class-butterflies-64') image = pipeline().images[0] image ```
huawei-noah/EntityCS-39-WEP-xlmr-base
huawei-noah
2023-09-13T15:19:50Z
106
2
transformers
[ "transformers", "pytorch", "xlm-roberta", "fill-mask", "af", "ar", "bg", "bn", "de", "el", "en", "es", "et", "eu", "fa", "fi", "fr", "he", "hi", "hu", "id", "it", "ja", "jv", "ka", "kk", "ko", "ml", "mr", "ms", "my", "nl", "pt", "ru", "sw", "ta", "te", "th", "tl", "tr", "ur", "vi", "yo", "zh", "arxiv:1904.09223", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-09-07T09:37:26Z
--- license: apache-2.0 language: - af - ar - bg - bn - de - el - en - es - et - eu - fa - fi - fr - he - hi - hu - id - it - ja - jv - ka - kk - ko - ml - mr - ms - my - nl - pt - ru - sw - ta - te - th - tl - tr - ur - vi - yo - zh --- # Model Card for EntityCS-39-MLM-xlmr-base This model has been trained on the EntityCS corpus, an English corpus from Wikipedia with replaced entities in different languages. The corpus can be found in [https://huggingface.co/huawei-noah/entity_cs](https://huggingface.co/huawei-noah/entity_cs), check the link for more details. Firstly, we employ the conventional 80-10-10 MLM objective, where 15% of sentence subwords are considered as masking candidates. From those, we replace subwords with [MASK] 80% of the time, with Random subwords (from the entire vocabulary) 10% of the time, and leave the remaining 10% unchanged (Same). To integrate entity-level cross-lingual knowledge into the model, we propose Entity Prediction objectives, where we only mask subwords belonging to an entity. By predicting the masked entities in ENTITYCS sentences, we expect the model to capture the semantics of the same entity in different languages. Two different masking strategies are proposed for predicting entities: Whole Entity Prediction (`WEP`) and Partial Entity Prediction (`PEP`). In WEP, motivated by [Sun et al. (2019)](https://arxiv.org/abs/1904.09223) where whole word masking is also adopted, we consider all the words (and consequently subwords) inside an entity as masking candidates. Then, 80% of the time we mask every subword inside an entity, and 20% of the time we keep the subwords intact. Note that, as our goal is to predict the entire masked entity, we do not allow replacing with Random subwords, since it can introduce noise and result in the model predicting incorrect entities. After entities are masked, we remove the entity indicators `<e>`, `</e>` from the sentences before feeding them to the model. For PEP, we also consider all entities as masking candidates. In contrast to WEP, we do not force subwords belonging to one entity to be either all masked or all unmasked. Instead, each individual entity subword is masked 80% of the time. For the remaining 20% of the masking candidates, we experiment with three different replacements. First, PEP<sub>MRS</sub>, corresponds to the conventional 80-10-10 masking strategy, where 10% of the remaining subwords are replaced with Random subwords and the other 10% are kept unchanged. In the second setting, PEP<sub>MS</sub>, we remove the 10% Random subwords substitution, i.e. we predict the 80% masked subwords and 10% Same subwords from the masking candidates. In the third setting, PEP<sub>M</sub>, we further remove the 10% Same subwords prediction, essentially predicting only the masked subwords. Prior work has proven it is effective to combine Entity Prediction with MLM for cross-lingual transfer ([Jiang et al., 2020](https://aclanthology.org/2020.emnlp-main.479/)), therefore we investigate the combination of the Entity Prediction objectives together with MLM on non-entity subwords. Specifically, when combined with MLM, we lower the entity masking probability (p) to 50% to roughly keep the same overall masking percentage. This results into the following objectives: WEP + MLM, PEP<sub>MRS</sub> + MLM, PEP<sub>MS</sub> + MLM, PEP<sub>M</sub> + MLM This model was trained with the **WEP** objective on the EntityCS corpus with 39 languages. ## Model Details ### Training Details We start from the [XLM-R-base](https://huggingface.co/xlm-roberta-base) model and train for 1 epoch on 8 Nvidia V100 32GB GPUs. We set batch size to 16 and gradient accumulation steps to 2, resulting in an effective batch size of 256. For speedup we use fp16 mixed precision. We use the sampling strategy proposed by [Conneau and Lample (2019)](https://proceedings.neurips.cc/paper/2019/file/c04c19c2c2474dbf5f7ac4372c5b9af1-Paper.pdf), where high resource languages are down-sampled and low resource languages get sampled more frequently. We only train the embedding and the last two layers of the model. We randomly choose 100 sentences from each language to serve as a validation set, on which we measure the perplexity every 10K training steps. **This checkpoint corresponds to the one with the lower perplexity on the validation set.** ## Usage The current model can be used for further fine-tuning on downstream tasks. In the paper, we focused on entity-related tasks, such as NER, Word Sense Disambiguation and Slot Filling. Alternatively, it can be used directly (no fine-tuning) for probing tasks, i.e. predict missing words, such as [X-FACTR](https://aclanthology.org/2020.emnlp-main.479/). ## How to Get Started with the Model Use the code below to get started with the model: https://github.com/huawei-noah/noah-research/tree/master/NLP/EntityCS ## Citation **BibTeX:** ```html @inproceedings{whitehouse-etal-2022-entitycs, title = "{E}ntity{CS}: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching", author = "Whitehouse, Chenxi and Christopoulou, Fenia and Iacobacci, Ignacio", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.findings-emnlp.499", pages = "6698--6714" } ``` ## Model Card Contact [Fenia Christopoulou](mailto:[email protected])
DakshBeniwal/test_model
DakshBeniwal
2023-09-13T15:12:28Z
0
0
null
[ "arxiv:1910.09700", "license:unknown", "region:us" ]
null
2023-09-13T15:08:09Z
--- license: unknown --- # 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 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- 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 <!-- These are the evaluation metrics being used, ideally with a description of why. --> [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] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
bigmorning/wh_4_sun_syl_w_0_lr_8en5_new_0010
bigmorning
2023-09-13T15:11:56Z
59
0
transformers
[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-09-13T15:11:49Z
--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: wh_4_sun_syl_w_0_lr_8en5_new_0010 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. --> # wh_4_sun_syl_w_0_lr_8en5_new_0010 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8306 - Train Accuracy: 0.0279 - Train Wermet: 0.1937 - Train Wermet Syl: 0.2358 - Validation Loss: 0.9286 - Validation Accuracy: 0.0212 - Validation Wermet: 0.2943 - Validation Wermet Syl: 0.2714 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 8e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch | |:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:| | 4.9473 | 0.0111 | 1.1332 | 1.0824 | 3.9300 | 0.0113 | 0.9772 | 0.9710 | 0 | | 4.6712 | 0.0117 | 0.8506 | 0.8117 | 3.8878 | 0.0115 | 0.9268 | 0.8881 | 1 | | 4.6221 | 0.0117 | 0.8367 | 0.7959 | 3.8674 | 0.0115 | 0.9247 | 0.8888 | 2 | | 4.5022 | 0.0121 | 0.7836 | 0.7413 | 3.5242 | 0.0123 | 0.8394 | 0.7987 | 3 | | 3.7874 | 0.0142 | 0.7029 | 0.6765 | 2.3850 | 0.0149 | 0.6752 | 0.6324 | 4 | | 2.5092 | 0.0187 | 0.5239 | 0.5285 | 1.4988 | 0.0182 | 0.4595 | 0.4333 | 5 | | 1.7333 | 0.0222 | 0.4129 | 0.4748 | 1.1810 | 0.0197 | 0.3791 | 0.3635 | 6 | | 1.2826 | 0.0247 | 0.3217 | 0.3850 | 1.0531 | 0.0203 | 0.3334 | 0.3074 | 7 | | 0.9965 | 0.0267 | 0.2482 | 0.3003 | 0.9782 | 0.0209 | 0.3112 | 0.3004 | 8 | | 0.8306 | 0.0279 | 0.1937 | 0.2358 | 0.9286 | 0.0212 | 0.2943 | 0.2714 | 9 | ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3
michaelfeil/ct2fast-CodeLlama-13b-hf
michaelfeil
2023-09-13T15:06:24Z
5
0
transformers
[ "transformers", "llama", "text-generation", "ctranslate2", "int8", "float16", "llama-2", "code", "arxiv:2308.12950", "license:llama2", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2023-09-11T08:13:31Z
--- language: - code pipeline_tag: text-generation tags: - ctranslate2 - int8 - float16 - llama-2 license: llama2 --- # # Fast-Inference with Ctranslate2 Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU. quantized version of [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) ```bash pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.17.1 ``` ```python # from transformers import AutoTokenizer model_name = "michaelfeil/ct2fast-CodeLlama-13b-hf" from hf_hub_ctranslate2 import GeneratorCT2fromHfHub model = GeneratorCT2fromHfHub( # load in int8 on CUDA model_name_or_path=model_name, device="cuda", compute_type="int8_float16", # tokenizer=AutoTokenizer.from_pretrained("{ORG}/{NAME}") ) outputs = model.generate( text=["def fibonnaci(", "User: How are you doing? Bot:"], max_length=64, include_prompt_in_result=False ) print(outputs) ``` Checkpoint compatible to [ctranslate2>=3.17.1](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2) - `compute_type=int8_float16` for `device="cuda"` - `compute_type=int8` for `device="cpu"` Converted on 2023-09-13 using ``` LLama-2 -> removed <pad> token. ``` # Licence and other remarks: This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo. # Original description # **Code Llama** Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the base 13B version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom. | | Base Model | Python | Instruct | | --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | 7B | [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) | | 13B | [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) | | 34B | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) | ## Model Use To use this model, please make sure to install transformers from `main` until the next version is released: ```bash pip install git+https://github.com/huggingface/transformers.git@main accelerate ``` Model capabilities: - [x] Code completion. - [x] Infilling. - [ ] Instructions / chat. - [ ] Python specialist. ```python from transformers import AutoTokenizer import transformers import torch model = "codellama/CodeLlama-13b-hf" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( 'import socket\n\ndef ping_exponential_backoff(host: str):', do_sample=True, top_k=10, temperature=0.1, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Model Details *Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs). **Model Developers** Meta **Variations** Code Llama comes in three model sizes, and three variants: * Code Llama: base models designed for general code synthesis and understanding * Code Llama - Python: designed specifically for Python * Code Llama - Instruct: for instruction following and safer deployment All variants are available in sizes of 7B, 13B and 34B parameters. **This repository contains the base version of the 13B parameters model.** **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture. **Model Dates** Code Llama and its variants have been trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950). ## Intended Use **Intended Use Cases** Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications. **Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants. ## Hardware and Software **Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster. **Carbon Footprint** In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program. ## Training Data All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details). ## Evaluation Results See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper. ## Ethical Considerations and Limitations Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-user-guide](https://ai.meta.com/llama/responsible-user-guide).
bigmorning/wh_4_sun_syl_w_0_lr_8en5_new_0005
bigmorning
2023-09-13T14:56:56Z
59
0
transformers
[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-09-13T14:56:48Z
--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_keras_callback model-index: - name: wh_4_sun_syl_w_0_lr_8en5_new_0005 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. --> # wh_4_sun_syl_w_0_lr_8en5_new_0005 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.7874 - Train Accuracy: 0.0142 - Train Wermet: 0.7029 - Train Wermet Syl: 0.6765 - Validation Loss: 2.3850 - Validation Accuracy: 0.0149 - Validation Wermet: 0.6752 - Validation Wermet Syl: 0.6324 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 8e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch | |:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:| | 4.9473 | 0.0111 | 1.1332 | 1.0824 | 3.9300 | 0.0113 | 0.9772 | 0.9710 | 0 | | 4.6712 | 0.0117 | 0.8506 | 0.8117 | 3.8878 | 0.0115 | 0.9268 | 0.8881 | 1 | | 4.6221 | 0.0117 | 0.8367 | 0.7959 | 3.8674 | 0.0115 | 0.9247 | 0.8888 | 2 | | 4.5022 | 0.0121 | 0.7836 | 0.7413 | 3.5242 | 0.0123 | 0.8394 | 0.7987 | 3 | | 3.7874 | 0.0142 | 0.7029 | 0.6765 | 2.3850 | 0.0149 | 0.6752 | 0.6324 | 4 | ### Framework versions - Transformers 4.34.0.dev0 - TensorFlow 2.13.0 - Tokenizers 0.13.3
nielsr/convnext-tiny-224-finetuned-eurosat-albumentations
nielsr
2023-09-13T14:38:03Z
230
0
transformers
[ "transformers", "pytorch", "tensorboard", "safetensors", "convnext", "image-classification", "generated_from_trainer", "dataset:image_folder", "base_model:facebook/convnext-tiny-224", "base_model:finetune:facebook/convnext-tiny-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2022-04-12T12:04:06Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - image_folder metrics: - accuracy base_model: facebook/convnext-tiny-224 model-index: - name: convnext-tiny-224-finetuned-eurosat-albumentations results: - task: type: image-classification name: Image Classification dataset: name: image_folder type: image_folder args: default metrics: - type: accuracy value: 0.9748148148148148 name: Accuracy --- <!-- 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. --> # convnext-tiny-224-finetuned-eurosat-albumentations This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.0727 - Accuracy: 0.9748 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.141 | 1.0 | 190 | 0.1496 | 0.9544 | | 0.0736 | 2.0 | 380 | 0.0958 | 0.9719 | | 0.0568 | 3.0 | 570 | 0.0727 | 0.9748 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
Powidl43/db-ext-cp
Powidl43
2023-09-13T14:35:27Z
0
0
null
[ "stable-diffusion", "text-to-image", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-09-13T07:42:06Z
--- license: creativeml-openrail-m tags: - stable-diffusion - text-to-image --- --- # File Name model_name-bf/dd-steps/merge-base_model/version BF = Basic Filewords / DD = DeepDanbooru --- # Triggers 10 Plus (BF) - "Beautiful Woman", "TP" Anabone (BF) - "Beautiful Woman", "AB" Autumn Skye (BF) - "Beautiful Woman", "AUSK", "Talented Artist" NSFW Models (BF) - "ALBR" for Ally Breelsen, "AVDA" for Ava Dalush Piyodesu (BF) - "Beautiful Woman", "PD", "Upskirt", "From Behind" Piyodesu (DD) - "1girl", "PD" Punk Man (BF) - "Handsome Punk Man", "PM" Punk Woman (BF) - "Beautiful Punk Woman", "PW" Punk Woman (DD) - "1girl", "JM" Tomato Rice (BF) - "Beautiful Woman", "TR", "With Horns", "Topless", "Tit Wank" Tomato Rice (DD) - "1girl", "TR" --- # Merge Info 10 Plus (BF) - 45.5% 3moon Anime Line - 24.5% NabiMix v1 - 19.5% ntc's Simple - 10.5% CamelliaMix NSFW v1.1 Anabone (BF) - 60% CamelliaMix NSFW v1.1 - 32% Pastelmarker - 08% ntc's Simple Autumn Skye (BF) v1 - 60% Vanilla - 40% NAI (animefull-final) Autumn Skye (BF) v2 - 48% Vanilla - 32% NAI (animefull-final) - 12% CamelliaMix NSFW v1.1 - 08% CamelliaMix v2 NSFW Models (BF) - 60% Vanilla - 40% The Best Model Realist Piyodesu (BF) v1 - 60% CamelliaMix NSFW v1.1 - 40% NAI (animefull-final) Piyodesu (BF) v2 - 42% CamelliaMix NSFW v1.1 - 28% NAI (animefull-final) - 30% Anything v5 Piyodesu (DD) / Punk Woman (DD) / Tomato Rice (DD) - 40% CamelliaMix NSFW v1.1 - 30% NabiMix v1 - 30% Anything v5 Punk Models (BF) v1 - 60% CamelliaMix NSFW v1.1 - 20% Kawai v3.2 - 20% NAI (animefull-final) Punk Woman (BF) v2 - 48% CamelliaMix NSFW v1.1 - 20% ntc's Simple - 16% Kawai v3.2 - 16% NAI (animefull-final) Tomato Rice (BF) - 70% Anything v5 - 15% CamelliaMix NSFW v1.1 - 15% NAI (animefull-final) --- # Image Source 10 Plus - danbooru.donmai.us/posts?tags=10_plus Anabone - danbooru.donmai.us/posts?tags=anabone Autumn Skye - autumnskyeart.com NSFW Models - pichunter.com (iStripper) Piyodesu - danbooru.donmai.us/posts?tags=piyodesu Punk Man / Woman - civitai.com/models/2856/synthpunk-search (JustMaier) Tomato Rice - danbooru.donmai.us/posts?tags=tomato_rice --- # Base Models civitai.com/user/3moon civitai.com/user/HuyNguyen civitai.com/user/Mods13 civitai.com/user/ntc civitai.com/user/Yuno779 huggingface.co/LibreSD --- civitai.com/user/Powidl43
hustshawn/dogbooth
hustshawn
2023-09-13T14:31:28Z
2
0
diffusers
[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:stabilityai/stable-diffusion-2-1", "base_model:finetune:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-09-13T07:24:28Z
--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1 instance_prompt: a photo of [v]dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - hustshawn/dogbooth This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
evangeliazve/camembert-ner-articles-nomination
evangeliazve
2023-09-13T14:28:20Z
436
1
transformers
[ "transformers", "pytorch", "safetensors", "camembert", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-11-21T23:16:37Z
--- license: mit widget: - text: "François Dupont prends la direction générale du groupe IPD" tags: - generated_from_trainer metrics: - f1 model-index: - name: camembert-base-articles-ner-backup results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # camembert-base-articles-ner-backup This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6701 - F1: 0.8723 ## Model description This model identifies Name Entities : PERSON, ORGANISATION, JOB TITLE Another Model is being developped to predict relationships between these entities (nomination, départure) ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.9205 | 1.0 | 6 | 1.7426 | 0.0 | | 1.6476 | 2.0 | 12 | 1.5415 | 0.0 | | 1.4607 | 3.0 | 18 | 1.3944 | 0.0635 | | 1.3299 | 4.0 | 24 | 1.2587 | 0.4848 | | 1.1973 | 5.0 | 30 | 1.1287 | 0.6207 | | 1.0707 | 6.0 | 36 | 1.0110 | 0.8043 | | 0.972 | 7.0 | 42 | 0.9266 | 0.8696 | | 0.8877 | 8.0 | 48 | 0.8632 | 0.8602 | | 0.8231 | 9.0 | 54 | 0.8279 | 0.8511 | | 0.7723 | 10.0 | 60 | 0.8001 | 0.8511 | | 0.7309 | 11.0 | 66 | 0.7617 | 0.8602 | | 0.6902 | 12.0 | 72 | 0.7364 | 0.8602 | | 0.6601 | 13.0 | 78 | 0.7104 | 0.8723 | | 0.6306 | 14.0 | 84 | 0.7062 | 0.8723 | | 0.6127 | 15.0 | 90 | 0.6896 | 0.8602 | | 0.605 | 16.0 | 96 | 0.6743 | 0.8723 | | 0.5892 | 17.0 | 102 | 0.6801 | 0.8723 | | 0.5843 | 18.0 | 108 | 0.6797 | 0.8723 | | 0.5731 | 19.0 | 114 | 0.6731 | 0.8723 | | 0.5707 | 20.0 | 120 | 0.6701 | 0.8723 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2
AIYIYA/my_ttt
AIYIYA
2023-09-13T14:25:26Z
61
0
transformers
[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-13T11:25:06Z
--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_keras_callback model-index: - name: AIYIYA/my_ttt 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. --> # AIYIYA/my_ttt This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4977 - Validation Loss: 0.5648 - Train Accuracy: 0.7059 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 15, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.7101 | 0.6549 | 0.7059 | 0 | | 0.6575 | 0.6173 | 0.7059 | 1 | | 0.6015 | 0.5816 | 0.7059 | 2 | | 0.5528 | 0.5636 | 0.7059 | 3 | | 0.4977 | 0.5648 | 0.7059 | 4 | ### Framework versions - Transformers 4.33.1 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.13.3
CyberHarem/hikari_pokemon
CyberHarem
2023-09-13T14:05:01Z
0
0
null
[ "art", "text-to-image", "dataset:CyberHarem/hikari_pokemon", "license:mit", "region:us" ]
text-to-image
2023-09-13T13:45:53Z
--- license: mit datasets: - CyberHarem/hikari_pokemon pipeline_tag: text-to-image tags: - art --- # Lora of hikari_pokemon This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 6240, you need to download `6240/hikari_pokemon.pt` as the embedding and `6240/hikari_pokemon.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 6240**, with the score of 0.722. The trigger words are: 1. `hikari_pokemon` 2. `long_hair, hat, hair_ornament, beanie, white_headwear, hairclip, sleeveless, smile, blue_eyes, eyelashes, blush, black_hair, blue_hair, open_mouth, grey_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:----------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 7200 | 0.400 | [Download](7200/hikari_pokemon.zip) | ![pattern_1-7200](7200/previews/pattern_1.png) | ![pattern_2-7200](7200/previews/pattern_2.png) | ![pattern_3-7200](7200/previews/pattern_3.png) | ![pattern_4-7200](7200/previews/pattern_4.png) | ![pattern_5-7200](7200/previews/pattern_5.png) | ![pattern_6-7200](7200/previews/pattern_6.png) | ![pattern_7-7200](7200/previews/pattern_7.png) | ![pattern_8-7200](7200/previews/pattern_8.png) | ![pattern_9-7200](7200/previews/pattern_9.png) | ![pattern_10-7200](7200/previews/pattern_10.png) | ![bikini-7200](7200/previews/bikini.png) | [<NSFW, click to see>](7200/previews/bondage.png) | ![free-7200](7200/previews/free.png) | ![maid-7200](7200/previews/maid.png) | ![miko-7200](7200/previews/miko.png) | [<NSFW, click to see>](7200/previews/nude.png) | [<NSFW, click to see>](7200/previews/nude2.png) | ![suit-7200](7200/previews/suit.png) | ![yukata-7200](7200/previews/yukata.png) | | 6720 | 0.446 | [Download](6720/hikari_pokemon.zip) | ![pattern_1-6720](6720/previews/pattern_1.png) | ![pattern_2-6720](6720/previews/pattern_2.png) | ![pattern_3-6720](6720/previews/pattern_3.png) | ![pattern_4-6720](6720/previews/pattern_4.png) | ![pattern_5-6720](6720/previews/pattern_5.png) | ![pattern_6-6720](6720/previews/pattern_6.png) | ![pattern_7-6720](6720/previews/pattern_7.png) | ![pattern_8-6720](6720/previews/pattern_8.png) | ![pattern_9-6720](6720/previews/pattern_9.png) | ![pattern_10-6720](6720/previews/pattern_10.png) | ![bikini-6720](6720/previews/bikini.png) | [<NSFW, click to see>](6720/previews/bondage.png) | ![free-6720](6720/previews/free.png) | ![maid-6720](6720/previews/maid.png) | ![miko-6720](6720/previews/miko.png) | [<NSFW, click to see>](6720/previews/nude.png) | [<NSFW, click to see>](6720/previews/nude2.png) | ![suit-6720](6720/previews/suit.png) | ![yukata-6720](6720/previews/yukata.png) | | **6240** | **0.722** | [**Download**](6240/hikari_pokemon.zip) | ![pattern_1-6240](6240/previews/pattern_1.png) | ![pattern_2-6240](6240/previews/pattern_2.png) | ![pattern_3-6240](6240/previews/pattern_3.png) | ![pattern_4-6240](6240/previews/pattern_4.png) | ![pattern_5-6240](6240/previews/pattern_5.png) | ![pattern_6-6240](6240/previews/pattern_6.png) | ![pattern_7-6240](6240/previews/pattern_7.png) | ![pattern_8-6240](6240/previews/pattern_8.png) | ![pattern_9-6240](6240/previews/pattern_9.png) | ![pattern_10-6240](6240/previews/pattern_10.png) | ![bikini-6240](6240/previews/bikini.png) | [<NSFW, click to see>](6240/previews/bondage.png) | ![free-6240](6240/previews/free.png) | ![maid-6240](6240/previews/maid.png) | ![miko-6240](6240/previews/miko.png) | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) | ![suit-6240](6240/previews/suit.png) | ![yukata-6240](6240/previews/yukata.png) | | 5760 | 0.525 | [Download](5760/hikari_pokemon.zip) | ![pattern_1-5760](5760/previews/pattern_1.png) | ![pattern_2-5760](5760/previews/pattern_2.png) | ![pattern_3-5760](5760/previews/pattern_3.png) | ![pattern_4-5760](5760/previews/pattern_4.png) | ![pattern_5-5760](5760/previews/pattern_5.png) | ![pattern_6-5760](5760/previews/pattern_6.png) | ![pattern_7-5760](5760/previews/pattern_7.png) | ![pattern_8-5760](5760/previews/pattern_8.png) | ![pattern_9-5760](5760/previews/pattern_9.png) | ![pattern_10-5760](5760/previews/pattern_10.png) | ![bikini-5760](5760/previews/bikini.png) | [<NSFW, click to see>](5760/previews/bondage.png) | ![free-5760](5760/previews/free.png) | ![maid-5760](5760/previews/maid.png) | ![miko-5760](5760/previews/miko.png) | [<NSFW, click to see>](5760/previews/nude.png) | [<NSFW, click to see>](5760/previews/nude2.png) | ![suit-5760](5760/previews/suit.png) | ![yukata-5760](5760/previews/yukata.png) | | 5280 | 0.402 | [Download](5280/hikari_pokemon.zip) | ![pattern_1-5280](5280/previews/pattern_1.png) | ![pattern_2-5280](5280/previews/pattern_2.png) | ![pattern_3-5280](5280/previews/pattern_3.png) | ![pattern_4-5280](5280/previews/pattern_4.png) | ![pattern_5-5280](5280/previews/pattern_5.png) | ![pattern_6-5280](5280/previews/pattern_6.png) | ![pattern_7-5280](5280/previews/pattern_7.png) | ![pattern_8-5280](5280/previews/pattern_8.png) | ![pattern_9-5280](5280/previews/pattern_9.png) | ![pattern_10-5280](5280/previews/pattern_10.png) | ![bikini-5280](5280/previews/bikini.png) | [<NSFW, click to see>](5280/previews/bondage.png) | ![free-5280](5280/previews/free.png) | ![maid-5280](5280/previews/maid.png) | ![miko-5280](5280/previews/miko.png) | [<NSFW, click to see>](5280/previews/nude.png) | [<NSFW, click to see>](5280/previews/nude2.png) | ![suit-5280](5280/previews/suit.png) | ![yukata-5280](5280/previews/yukata.png) | | 4800 | 0.353 | [Download](4800/hikari_pokemon.zip) | ![pattern_1-4800](4800/previews/pattern_1.png) | ![pattern_2-4800](4800/previews/pattern_2.png) | ![pattern_3-4800](4800/previews/pattern_3.png) | ![pattern_4-4800](4800/previews/pattern_4.png) | ![pattern_5-4800](4800/previews/pattern_5.png) | ![pattern_6-4800](4800/previews/pattern_6.png) | ![pattern_7-4800](4800/previews/pattern_7.png) | ![pattern_8-4800](4800/previews/pattern_8.png) | ![pattern_9-4800](4800/previews/pattern_9.png) | ![pattern_10-4800](4800/previews/pattern_10.png) | ![bikini-4800](4800/previews/bikini.png) | [<NSFW, click to see>](4800/previews/bondage.png) | ![free-4800](4800/previews/free.png) | ![maid-4800](4800/previews/maid.png) | ![miko-4800](4800/previews/miko.png) | [<NSFW, click to see>](4800/previews/nude.png) | [<NSFW, click to see>](4800/previews/nude2.png) | ![suit-4800](4800/previews/suit.png) | ![yukata-4800](4800/previews/yukata.png) | | 4320 | 0.501 | [Download](4320/hikari_pokemon.zip) | ![pattern_1-4320](4320/previews/pattern_1.png) | ![pattern_2-4320](4320/previews/pattern_2.png) | ![pattern_3-4320](4320/previews/pattern_3.png) | ![pattern_4-4320](4320/previews/pattern_4.png) | ![pattern_5-4320](4320/previews/pattern_5.png) | ![pattern_6-4320](4320/previews/pattern_6.png) | ![pattern_7-4320](4320/previews/pattern_7.png) | ![pattern_8-4320](4320/previews/pattern_8.png) | ![pattern_9-4320](4320/previews/pattern_9.png) | ![pattern_10-4320](4320/previews/pattern_10.png) | ![bikini-4320](4320/previews/bikini.png) | [<NSFW, click to see>](4320/previews/bondage.png) | ![free-4320](4320/previews/free.png) | ![maid-4320](4320/previews/maid.png) | ![miko-4320](4320/previews/miko.png) | [<NSFW, click to see>](4320/previews/nude.png) | [<NSFW, click to see>](4320/previews/nude2.png) | ![suit-4320](4320/previews/suit.png) | ![yukata-4320](4320/previews/yukata.png) | | 3840 | 0.394 | [Download](3840/hikari_pokemon.zip) | ![pattern_1-3840](3840/previews/pattern_1.png) | ![pattern_2-3840](3840/previews/pattern_2.png) | ![pattern_3-3840](3840/previews/pattern_3.png) | ![pattern_4-3840](3840/previews/pattern_4.png) | ![pattern_5-3840](3840/previews/pattern_5.png) | ![pattern_6-3840](3840/previews/pattern_6.png) | ![pattern_7-3840](3840/previews/pattern_7.png) | ![pattern_8-3840](3840/previews/pattern_8.png) | ![pattern_9-3840](3840/previews/pattern_9.png) | ![pattern_10-3840](3840/previews/pattern_10.png) | ![bikini-3840](3840/previews/bikini.png) | [<NSFW, click to see>](3840/previews/bondage.png) | ![free-3840](3840/previews/free.png) | ![maid-3840](3840/previews/maid.png) | ![miko-3840](3840/previews/miko.png) | [<NSFW, click to see>](3840/previews/nude.png) | [<NSFW, click to see>](3840/previews/nude2.png) | ![suit-3840](3840/previews/suit.png) | ![yukata-3840](3840/previews/yukata.png) | | 3360 | 0.381 | [Download](3360/hikari_pokemon.zip) | ![pattern_1-3360](3360/previews/pattern_1.png) | ![pattern_2-3360](3360/previews/pattern_2.png) | ![pattern_3-3360](3360/previews/pattern_3.png) | ![pattern_4-3360](3360/previews/pattern_4.png) | ![pattern_5-3360](3360/previews/pattern_5.png) | ![pattern_6-3360](3360/previews/pattern_6.png) | ![pattern_7-3360](3360/previews/pattern_7.png) | 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![pattern_9-2880](2880/previews/pattern_9.png) | ![pattern_10-2880](2880/previews/pattern_10.png) | ![bikini-2880](2880/previews/bikini.png) | [<NSFW, click to see>](2880/previews/bondage.png) | ![free-2880](2880/previews/free.png) | ![maid-2880](2880/previews/maid.png) | ![miko-2880](2880/previews/miko.png) | [<NSFW, click to see>](2880/previews/nude.png) | [<NSFW, click to see>](2880/previews/nude2.png) | ![suit-2880](2880/previews/suit.png) | ![yukata-2880](2880/previews/yukata.png) | | 2400 | 0.354 | [Download](2400/hikari_pokemon.zip) | ![pattern_1-2400](2400/previews/pattern_1.png) | ![pattern_2-2400](2400/previews/pattern_2.png) | ![pattern_3-2400](2400/previews/pattern_3.png) | ![pattern_4-2400](2400/previews/pattern_4.png) | ![pattern_5-2400](2400/previews/pattern_5.png) | ![pattern_6-2400](2400/previews/pattern_6.png) | ![pattern_7-2400](2400/previews/pattern_7.png) | ![pattern_8-2400](2400/previews/pattern_8.png) | ![pattern_9-2400](2400/previews/pattern_9.png) | ![pattern_10-2400](2400/previews/pattern_10.png) | ![bikini-2400](2400/previews/bikini.png) | [<NSFW, click to see>](2400/previews/bondage.png) | ![free-2400](2400/previews/free.png) | ![maid-2400](2400/previews/maid.png) | ![miko-2400](2400/previews/miko.png) | [<NSFW, click to see>](2400/previews/nude.png) | [<NSFW, click to see>](2400/previews/nude2.png) | ![suit-2400](2400/previews/suit.png) | ![yukata-2400](2400/previews/yukata.png) | | 1920 | 0.302 | [Download](1920/hikari_pokemon.zip) | ![pattern_1-1920](1920/previews/pattern_1.png) | ![pattern_2-1920](1920/previews/pattern_2.png) | ![pattern_3-1920](1920/previews/pattern_3.png) | ![pattern_4-1920](1920/previews/pattern_4.png) | ![pattern_5-1920](1920/previews/pattern_5.png) | ![pattern_6-1920](1920/previews/pattern_6.png) | ![pattern_7-1920](1920/previews/pattern_7.png) | ![pattern_8-1920](1920/previews/pattern_8.png) | ![pattern_9-1920](1920/previews/pattern_9.png) | ![pattern_10-1920](1920/previews/pattern_10.png) | ![bikini-1920](1920/previews/bikini.png) | [<NSFW, click to see>](1920/previews/bondage.png) | ![free-1920](1920/previews/free.png) | ![maid-1920](1920/previews/maid.png) | ![miko-1920](1920/previews/miko.png) | [<NSFW, click to see>](1920/previews/nude.png) | [<NSFW, click to see>](1920/previews/nude2.png) | ![suit-1920](1920/previews/suit.png) | ![yukata-1920](1920/previews/yukata.png) | | 1440 | 0.441 | [Download](1440/hikari_pokemon.zip) | ![pattern_1-1440](1440/previews/pattern_1.png) | ![pattern_2-1440](1440/previews/pattern_2.png) | ![pattern_3-1440](1440/previews/pattern_3.png) | ![pattern_4-1440](1440/previews/pattern_4.png) | ![pattern_5-1440](1440/previews/pattern_5.png) | ![pattern_6-1440](1440/previews/pattern_6.png) | ![pattern_7-1440](1440/previews/pattern_7.png) | ![pattern_8-1440](1440/previews/pattern_8.png) | ![pattern_9-1440](1440/previews/pattern_9.png) | ![pattern_10-1440](1440/previews/pattern_10.png) | ![bikini-1440](1440/previews/bikini.png) | [<NSFW, click to see>](1440/previews/bondage.png) | ![free-1440](1440/previews/free.png) | ![maid-1440](1440/previews/maid.png) | ![miko-1440](1440/previews/miko.png) | [<NSFW, click to see>](1440/previews/nude.png) | [<NSFW, click to see>](1440/previews/nude2.png) | ![suit-1440](1440/previews/suit.png) | ![yukata-1440](1440/previews/yukata.png) | | 960 | 0.207 | [Download](960/hikari_pokemon.zip) | ![pattern_1-960](960/previews/pattern_1.png) | ![pattern_2-960](960/previews/pattern_2.png) | ![pattern_3-960](960/previews/pattern_3.png) | ![pattern_4-960](960/previews/pattern_4.png) | ![pattern_5-960](960/previews/pattern_5.png) | ![pattern_6-960](960/previews/pattern_6.png) | ![pattern_7-960](960/previews/pattern_7.png) | ![pattern_8-960](960/previews/pattern_8.png) | ![pattern_9-960](960/previews/pattern_9.png) | ![pattern_10-960](960/previews/pattern_10.png) | ![bikini-960](960/previews/bikini.png) | [<NSFW, click to see>](960/previews/bondage.png) | ![free-960](960/previews/free.png) | 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jonas-luehrs/bert-base-uncased-MLP-scirepeval-chemistry-LARGE-textCLS-RHEOLOGY-20230913-3
jonas-luehrs
2023-09-13T14:04:58Z
112
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "en", "dataset:bluesky333/chemical_language_understanding_benchmark", "base_model:jonas-luehrs/bert-base-uncased-MLP-scirepeval-chemistry-LARGE", "base_model:finetune:jonas-luehrs/bert-base-uncased-MLP-scirepeval-chemistry-LARGE", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-09-13T13:04:04Z
--- license: apache-2.0 base_model: jonas-luehrs/bert-base-uncased-MLP-scirepeval-chemistry-LARGE tags: - generated_from_trainer metrics: - f1 - precision - recall - accuracy model-index: - name: bert-base-uncased-MLP-scirepeval-chemistry-LARGE-textCLS-RHEOLOGY-20230913-3 results: [] datasets: - bluesky333/chemical_language_understanding_benchmark language: - en --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-MLP-scirepeval-chemistry-LARGE-textCLS-RHEOLOGY-20230913-3 This model is a fine-tuned version of [jonas-luehrs/bert-base-uncased-MLP-scirepeval-chemistry-LARGE](https://huggingface.co/jonas-luehrs/bert-base-uncased-MLP-scirepeval-chemistry-LARGE) on the RHEOLOGY dataset of the [blue333/chemical_language_understanding_benchmark](https://huggingface.co/datasets/bluesky333/chemical_language_understanding_benchmark). It achieves the following results on the evaluation set: - Loss: 0.6836 - F1: 0.7805 - Precision: 0.7860 - Recall: 0.7840 - Accuracy: 0.7840 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:| | 1.1777 | 1.0 | 46 | 0.8465 | 0.6593 | 0.6346 | 0.7037 | 0.7037 | | 0.6923 | 2.0 | 92 | 0.7123 | 0.7491 | 0.7654 | 0.7593 | 0.7593 | | 0.4974 | 3.0 | 138 | 0.6906 | 0.7563 | 0.7667 | 0.7593 | 0.7593 | | 0.3789 | 4.0 | 184 | 0.6754 | 0.7645 | 0.7712 | 0.7716 | 0.7716 | | 0.3053 | 5.0 | 230 | 0.6836 | 0.7805 | 0.7860 | 0.7840 | 0.7840 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
ingeol/rm_push_test
ingeol
2023-09-13T14:03:02Z
0
0
peft
[ "peft", "region:us" ]
null
2023-09-13T07:10:45Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
fahmiaziz/finetune-donut-cord-v2.5
fahmiaziz
2023-09-13T14:01:10Z
187
3
transformers
[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "image-to-text", "dataset:naver-clova-ix/cord-v2", "license:openrail", "endpoints_compatible", "region:us" ]
image-to-text
2023-09-12T00:52:12Z
--- license: openrail datasets: - naver-clova-ix/cord-v2 metrics: - accuracy - code_eval library_name: transformers pipeline_tag: image-to-text --- Fine-tuning Donut base with CORD dataset - Accuracy 90% Need to retrain the model, although the accuracy is high but there is something wrong with the prediction results
Ssarion/bart-base-multi-news
Ssarion
2023-09-13T13:42:04Z
1,092
0
transformers
[ "transformers", "pytorch", "tensorboard", "safetensors", "bart", "text2text-generation", "summarization", "generated_from_trainer", "en", "dataset:multi_news", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
summarization
2023-06-02T19:38:02Z
--- language: - en license: apache-2.0 tags: - summarization - generated_from_trainer datasets: - multi_news metrics: - rouge base_model: facebook/bart-base model-index: - name: bart-base-multi-news results: - task: type: text2text-generation name: Sequence-to-sequence Language Modeling dataset: name: multi_news type: multi_news config: default split: validation args: default metrics: - type: rouge value: 26.31 name: Rouge1 - type: rouge value: 9.6 name: Rouge2 - type: rouge value: 20.87 name: Rougel - type: rouge value: 21.54 name: Rougelsum --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-multi-news This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the multi_news dataset. It achieves the following results on the evaluation set: - Loss: 2.4147 - Rouge1: 26.31 - Rouge2: 9.6 - Rougel: 20.87 - Rougelsum: 21.54 ## Intended uses & limitations The inteded use of this model is text summarization. The model requires additional training in order to perform better in the task of summarization. ## Training and evaluation data The training data were 10000 samples from the multi-news training dataset and the evaluation data were 500 samples from the multi-news evaluation dataset ## Training procedure For the training procedure the Seq2SeqTrainer class was used from the transformers library. ### Training hyperparameters The Hyperparameters were passed to the Seq2SeqTrainingArguments class from the transformers library. The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 2.4041 | 1.0 | 1250 | 2.4147 | 26.31 | 9.6 | 20.87 | 21.54 | ### Framework versions - Transformers 4.30.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3