librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-03 00:41:34	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 466 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-03 00:34:44	card stringlengths 11 1.01M
EXrRor3/PixelCopter	EXrRor3	2023-07-26T07:06:08Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-26T07:06:04Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 40.90 +/- 32.40 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
ezzzz22/CTRN	ezzzz22	2023-07-26T06:56:00Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-26T06:54:29Z	--- license: creativeml-openrail-m ---
bangnbx/t5.1.1.lm100k.large-384	bangnbx	2023-07-26T06:46:24Z	105	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-26T06:36:28Z	acc 67.02% https://wandb.ai/bangnbx/t5.1.1.lm100k.large-relax/runs/3d15uut5?workspace=user-bangnbx
NasimB/guten-no-merge-log-rarity	NasimB	2023-07-26T06:44:33Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-25T23:58:20Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: guten-no-merge-log-rarity 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. --> # guten-no-merge-log-rarity This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.0835 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 7.5185 \| 0.12 \| 200 \| 5.9953 \| \| 5.702 \| 0.23 \| 400 \| 5.4671 \| \| 5.3132 \| 0.35 \| 600 \| 5.2102 \| \| 5.0736 \| 0.46 \| 800 \| 5.0308 \| \| 4.9325 \| 0.58 \| 1000 \| 4.9096 \| \| 4.7986 \| 0.69 \| 1200 \| 4.7945 \| \| 4.6851 \| 0.81 \| 1400 \| 4.7064 \| \| 4.6142 \| 0.93 \| 1600 \| 4.6351 \| \| 4.4995 \| 1.04 \| 1800 \| 4.5776 \| \| 4.3886 \| 1.16 \| 2000 \| 4.5242 \| \| 4.3455 \| 1.27 \| 2200 \| 4.4778 \| \| 4.3119 \| 1.39 \| 2400 \| 4.4343 \| \| 4.2646 \| 1.5 \| 2600 \| 4.3910 \| \| 4.2227 \| 1.62 \| 2800 \| 4.3531 \| \| 4.1925 \| 1.74 \| 3000 \| 4.3114 \| \| 4.1501 \| 1.85 \| 3200 \| 4.2712 \| \| 4.129 \| 1.97 \| 3400 \| 4.2403 \| \| 3.9673 \| 2.08 \| 3600 \| 4.2321 \| \| 3.9145 \| 2.2 \| 3800 \| 4.2166 \| \| 3.9146 \| 2.31 \| 4000 \| 4.1956 \| \| 3.8992 \| 2.43 \| 4200 \| 4.1732 \| \| 3.8932 \| 2.55 \| 4400 \| 4.1494 \| \| 3.8646 \| 2.66 \| 4600 \| 4.1281 \| \| 3.8627 \| 2.78 \| 4800 \| 4.1104 \| \| 3.8537 \| 2.89 \| 5000 \| 4.0890 \| \| 3.8128 \| 3.01 \| 5200 \| 4.0839 \| \| 3.6101 \| 3.12 \| 5400 \| 4.0845 \| \| 3.611 \| 3.24 \| 5600 \| 4.0771 \| \| 3.6168 \| 3.36 \| 5800 \| 4.0707 \| \| 3.6047 \| 3.47 \| 6000 \| 4.0544 \| \| 3.6015 \| 3.59 \| 6200 \| 4.0471 \| \| 3.5941 \| 3.7 \| 6400 \| 4.0331 \| \| 3.5878 \| 3.82 \| 6600 \| 4.0210 \| \| 3.5797 \| 3.94 \| 6800 \| 4.0098 \| \| 3.4728 \| 4.05 \| 7000 \| 4.0174 \| \| 3.3399 \| 4.17 \| 7200 \| 4.0220 \| \| 3.3436 \| 4.28 \| 7400 \| 4.0194 \| \| 3.3467 \| 4.4 \| 7600 \| 4.0145 \| \| 3.3501 \| 4.51 \| 7800 \| 4.0088 \| \| 3.3493 \| 4.63 \| 8000 \| 4.0028 \| \| 3.3374 \| 4.75 \| 8200 \| 3.9991 \| \| 3.3364 \| 4.86 \| 8400 \| 3.9946 \| \| 3.3261 \| 4.98 \| 8600 \| 3.9902 \| \| 3.204 \| 5.09 \| 8800 \| 4.0007 \| \| 3.1715 \| 5.21 \| 9000 \| 4.0032 \| \| 3.1683 \| 5.32 \| 9200 \| 4.0025 \| \| 3.1708 \| 5.44 \| 9400 \| 4.0026 \| \| 3.1649 \| 5.56 \| 9600 \| 4.0019 \| \| 3.1773 \| 5.67 \| 9800 \| 4.0012 \| \| 3.1608 \| 5.79 \| 10000 \| 4.0014 \| \| 3.1538 \| 5.9 \| 10200 \| 4.0012 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
nkpz/llama2-22b-frankenwizard	nkpz	2023-07-26T06:44:04Z	15	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-26T04:13:21Z	--- license: other --- Thanks to chargoddard for the original 22b model and merge script: https://huggingface.co/chargoddard/llama2-22b This uses https://huggingface.co/Blackroot/FrankensteinsMonster-13B as a base, with https://huggingface.co/ehartford/WizardLM-33B-V1.0-Uncensored as the donor model. This an experimental model and it sometimes forgets its context or says something nonsensical, but maybe some further fine tuning would smooth things out. This has less guardrails than my prior merge, https://huggingface.co/nkpz/llama2-22b-chat-wizard-uncensored Took around 2 hours to merge with 32gb of ram and about 115gb of swap used.
zhyemmmm/FantasticAnime	zhyemmmm	2023-07-26T06:35:56Z	29	0	diffusers	[ "diffusers", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-29T08:36:09Z	--- license: creativeml-openrail-m ---
NHNDQ/nllb-finetuned-en2ko	NHNDQ	2023-07-26T06:28:11Z	1,325	20	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "translation", "en", "ko", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-05-15T00:49:33Z	--- license: cc-by-4.0 language: - en - ko tags: - translation --- ## Model Details * Model Description: Fine-tuned facebook/nllb-200-distilled-600M model * Developed by: Jisu Kim, Juhwan Lee, TakSung Heo, and Minsu Jeong * Model Type: Translation * Language(s): * Source Language: English * Target Language: Korean * License: CC-BY-4.0 ## Dataset * [AI-hub dataset](https://www.aihub.or.kr/) ## BLEU Score * Deepl translation: 22.83 * Fine-tune nllb: 33.66 ## Uses This model can be used for translation and text-to-text generation ## Data Augmentation with backtranslation task You can exercise korean data augmentation task with python package [KoTAN](https://github.com/KoJLabs/KoTAN/tree/main)
opm29/mauren	opm29	2023-07-26T06:27:53Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-26T06:11:28Z	--- license: creativeml-openrail-m ---
zhyemmmm/CuriousMerge	zhyemmmm	2023-07-26T06:24:41Z	29	0	diffusers	[ "diffusers", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-29T03:16:22Z	--- license: creativeml-openrail-m ---
zhyemmmm/ManmaruMix	zhyemmmm	2023-07-26T06:23:36Z	30	2	diffusers	[ "diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-26T07:47:44Z	--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image ---
uomnf97/LawBot-level2-final-preprocessing-v3	uomnf97	2023-07-26T06:19:45Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-26T06:19:29Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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 0.5.0.dev0
hksul1024/llama2-qlora-finetunined-french	hksul1024	2023-07-26T06:17:15Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-26T06:16:58Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
zhyemmmm/Cutecolor	zhyemmmm	2023-07-26T06:08:00Z	29	1	diffusers	[ "diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-29T03:43:25Z	--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image ---
min9805/bert-base-finetuned-ynat	min9805	2023-07-26T06:01:26Z	106	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:klue/bert-base", "base_model:finetune:klue/bert-base", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-18T09:07:37Z	--- license: cc-by-sa-4.0 base_model: klue/bert-base tags: - generated_from_trainer metrics: - f1 model-index: - name: bert-base-finetuned-ynat 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-finetuned-ynat This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6806 - F1: 0.2273 ## 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: 256 - eval_batch_size: 256 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 2 \| 1.8609 \| 0.0476 \| \| No log \| 2.0 \| 4 \| 1.7637 \| 0.0476 \| \| No log \| 3.0 \| 6 \| 1.6806 \| 0.2273 \| \| No log \| 4.0 \| 8 \| 1.6409 \| 0.2273 \| \| No log \| 5.0 \| 10 \| 1.6236 \| 0.2273 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
111wwwww/2DWDWFCFSV	111wwwww	2023-07-26T06:01:17Z	0	0	null	[ "license:bsd-3-clause-clear", "region:us" ]	null	2023-07-26T06:01:17Z	--- license: bsd-3-clause-clear ---
junkmind/my_awesome_mind	junkmind	2023-07-26T05:57:41Z	161	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-26T05:49:56Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: my_awesome_mind results: - task: name: Audio Classification type: audio-classification dataset: name: minds14 type: minds14 config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.061946902654867256 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_mind This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6657 - Accuracy: 0.0619 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.8 \| 3 \| 2.6406 \| 0.0708 \| \| No log \| 1.87 \| 7 \| 2.6497 \| 0.0885 \| \| 2.635 \| 2.93 \| 11 \| 2.6532 \| 0.0531 \| \| 2.635 \| 4.0 \| 15 \| 2.6612 \| 0.0265 \| \| 2.635 \| 4.8 \| 18 \| 2.6621 \| 0.0265 \| \| 2.6225 \| 5.87 \| 22 \| 2.6622 \| 0.0531 \| \| 2.6225 \| 6.93 \| 26 \| 2.6650 \| 0.0442 \| \| 2.6054 \| 8.0 \| 30 \| 2.6657 \| 0.0619 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
111wwwww/111	111wwwww	2023-07-26T05:56:35Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-26T05:56:35Z	--- license: creativeml-openrail-m ---
VCool22/bert-finetuned-ner	VCool22	2023-07-26T05:47:17Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-25T22:11:17Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.936092715231788 - name: Recall type: recall value: 0.9515314708852238 - name: F1 type: f1 value: 0.9437489567684861 - name: Accuracy type: accuracy value: 0.986504385706717 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0597 - Precision: 0.9361 - Recall: 0.9515 - F1: 0.9437 - Accuracy: 0.9865 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0794 \| 1.0 \| 1756 \| 0.0703 \| 0.9143 \| 0.9317 \| 0.9229 \| 0.9808 \| \| 0.0346 \| 2.0 \| 3512 \| 0.0573 \| 0.9331 \| 0.9490 \| 0.9410 \| 0.9861 \| \| 0.0191 \| 3.0 \| 5268 \| 0.0597 \| 0.9361 \| 0.9515 \| 0.9437 \| 0.9865 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
koochikoo25/Whisper-medium-pashto	koochikoo25	2023-07-26T05:40:26Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ps", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-20T13:39:17Z	--- language: - ps license: apache-2.0 base_model: openai/whisper-medium tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Pashto - Awais Nawaz 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. --> # Whisper Medium Pashto - Awais Nawaz This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. - Loss: 1.021 - Wer: 30.731 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
hhs8746/qlora-koalpaca-polyglot-5.8b-10000stepplus	hhs8746	2023-07-26T05:36:40Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-26T05:36:35Z	--- 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.dev0
jamesthong/whisper-small-dv	jamesthong	2023-07-26T05:32:48Z	81	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "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	2023-07-25T13:55:01Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-small-dv results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train[450:] args: en-US metrics: - name: Wer type: wer value: 0.3530106257378985 --- <!-- 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-small-dv This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6457 - Wer Ortho: 0.3572 - Wer: 0.3530 ## 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 \| 0.6457 \| 0.3572 \| 0.3530 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
sanitas/q-FrozenLake-v1-4x4-noSlippery	sanitas	2023-07-26T05:12:39Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-26T05:12:37Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="sanitas/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
CordwainerSmith/speecht5_finetuned_voxpopuli_nl-v2	CordwainerSmith	2023-07-26T04:50:36Z	82	0	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "dataset:voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-speech	2023-07-25T22:21:32Z	--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl-v2 results: [] pipeline_tag: text-to-speech --- <!-- 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. --> # speecht5_finetuned_voxpopuli_nl-v2 This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4601 ## 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: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5213 \| 4.3 \| 1000 \| 0.4805 \| \| 0.4946 \| 8.61 \| 2000 \| 0.4651 \| \| 0.4928 \| 12.91 \| 3000 \| 0.4627 \| \| 0.4923 \| 17.21 \| 4000 \| 0.4601 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
liuyt75/t5-small_prefix_tuning_sentences_allagree_10	liuyt75	2023-07-26T04:48:11Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-26T04:48:10Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
camargoj/llama2-qlora-finetunined-french	camargoj	2023-07-26T04:24:23Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-26T04:24:18Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
jordyvl/cdip-small_rvl_cdip-NK1000_kd_test	jordyvl	2023-07-26T04:09:21Z	165	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-25T14:00:54Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: cdip-small_rvl_cdip-NK1000_kd_test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cdip-small_rvl_cdip-NK1000_kd_test This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3813 - Accuracy: 0.8558 - Brier Loss: 0.2176 - Nll: 1.4251 - F1 Micro: 0.8558 - F1 Macro: 0.8566 - Ece: 0.0597 - Aurc: 0.0299 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| 1.7536 \| 1.0 \| 667 \| 0.9652 \| 0.6695 \| 0.4474 \| 2.2965 \| 0.6695 \| 0.6595 \| 0.0494 \| 0.1257 \| \| 0.8802 \| 2.0 \| 1334 \| 0.7683 \| 0.7195 \| 0.3806 \| 2.0303 \| 0.7195 \| 0.7116 \| 0.0473 \| 0.0920 \| \| 0.5767 \| 3.0 \| 2001 \| 0.6276 \| 0.7698 \| 0.3253 \| 1.9446 \| 0.7698 \| 0.7711 \| 0.0436 \| 0.0684 \| \| 0.4263 \| 4.0 \| 2668 \| 0.6095 \| 0.7785 \| 0.3110 \| 1.9810 \| 0.7785 \| 0.7810 \| 0.0474 \| 0.0624 \| \| 0.3987 \| 5.0 \| 3335 \| 0.5608 \| 0.791 \| 0.2939 \| 1.8539 \| 0.791 \| 0.7918 \| 0.0504 \| 0.0557 \| \| 0.3179 \| 6.0 \| 4002 \| 0.6057 \| 0.7935 \| 0.3027 \| 1.8778 \| 0.7935 \| 0.7940 \| 0.0811 \| 0.0548 \| \| 0.2428 \| 7.0 \| 4669 \| 0.5828 \| 0.8043 \| 0.2905 \| 1.8616 \| 0.8043 \| 0.8050 \| 0.0662 \| 0.0520 \| \| 0.2094 \| 8.0 \| 5336 \| 0.5812 \| 0.7957 \| 0.2973 \| 1.8459 \| 0.7957 \| 0.8019 \| 0.0783 \| 0.0532 \| \| 0.1715 \| 9.0 \| 6003 \| 0.6152 \| 0.7987 \| 0.2993 \| 1.9533 \| 0.7987 \| 0.7998 \| 0.0723 \| 0.0539 \| \| 0.1508 \| 10.0 \| 6670 \| 0.5442 \| 0.808 \| 0.2820 \| 1.8159 \| 0.808 \| 0.8097 \| 0.0836 \| 0.0476 \| \| 0.1434 \| 11.0 \| 7337 \| 0.4881 \| 0.828 \| 0.2549 \| 1.6938 \| 0.828 \| 0.8286 \| 0.0610 \| 0.0410 \| \| 0.1267 \| 12.0 \| 8004 \| 0.4720 \| 0.8365 \| 0.2465 \| 1.6878 \| 0.8365 \| 0.8360 \| 0.0576 \| 0.0400 \| \| 0.115 \| 13.0 \| 8671 \| 0.4648 \| 0.8335 \| 0.2482 \| 1.6871 \| 0.8335 \| 0.8353 \| 0.0630 \| 0.0387 \| \| 0.1112 \| 14.0 \| 9338 \| 0.4777 \| 0.8317 \| 0.2509 \| 1.6393 \| 0.8317 \| 0.8312 \| 0.0614 \| 0.0418 \| \| 0.1002 \| 15.0 \| 10005 \| 0.4684 \| 0.8333 \| 0.2484 \| 1.6054 \| 0.8333 \| 0.8335 \| 0.0657 \| 0.0392 \| \| 0.0944 \| 16.0 \| 10672 \| 0.4693 \| 0.8365 \| 0.2480 \| 1.6381 \| 0.8365 \| 0.8366 \| 0.0658 \| 0.0383 \| \| 0.0934 \| 17.0 \| 11339 \| 0.4534 \| 0.8323 \| 0.2465 \| 1.6420 \| 0.8323 \| 0.8343 \| 0.0561 \| 0.0373 \| \| 0.0835 \| 18.0 \| 12006 \| 0.4512 \| 0.8357 \| 0.2435 \| 1.6301 \| 0.8357 \| 0.8367 \| 0.0575 \| 0.0372 \| \| 0.08 \| 19.0 \| 12673 \| 0.4345 \| 0.838 \| 0.2394 \| 1.6382 \| 0.838 \| 0.8398 \| 0.0562 \| 0.0366 \| \| 0.0819 \| 20.0 \| 13340 \| 0.4356 \| 0.838 \| 0.2374 \| 1.5973 \| 0.838 \| 0.8384 \| 0.0588 \| 0.0364 \| \| 0.0709 \| 21.0 \| 14007 \| 0.4484 \| 0.8415 \| 0.2368 \| 1.6231 \| 0.8415 \| 0.8411 \| 0.0595 \| 0.0368 \| \| 0.0691 \| 22.0 \| 14674 \| 0.4194 \| 0.8495 \| 0.2287 \| 1.5968 \| 0.8495 \| 0.8505 \| 0.0531 \| 0.0335 \| \| 0.068 \| 23.0 \| 15341 \| 0.4308 \| 0.8413 \| 0.2346 \| 1.5599 \| 0.8413 \| 0.8410 \| 0.0542 \| 0.0360 \| \| 0.0641 \| 24.0 \| 16008 \| 0.4209 \| 0.8405 \| 0.2336 \| 1.5539 \| 0.8405 \| 0.8422 \| 0.0590 \| 0.0339 \| \| 0.0617 \| 25.0 \| 16675 \| 0.4181 \| 0.841 \| 0.2352 \| 1.5735 \| 0.841 \| 0.8435 \| 0.0568 \| 0.0356 \| \| 0.0633 \| 26.0 \| 17342 \| 0.4193 \| 0.8508 \| 0.2286 \| 1.5299 \| 0.8508 \| 0.8510 \| 0.0650 \| 0.0348 \| \| 0.0569 \| 27.0 \| 18009 \| 0.4065 \| 0.8468 \| 0.2278 \| 1.5267 \| 0.8468 \| 0.8479 \| 0.0546 \| 0.0332 \| \| 0.0571 \| 28.0 \| 18676 \| 0.4109 \| 0.8498 \| 0.2255 \| 1.5147 \| 0.8498 \| 0.8499 \| 0.0590 \| 0.0331 \| \| 0.0543 \| 29.0 \| 19343 \| 0.4026 \| 0.8482 \| 0.2250 \| 1.5187 \| 0.8482 \| 0.8498 \| 0.0623 \| 0.0327 \| \| 0.0543 \| 30.0 \| 20010 \| 0.4124 \| 0.847 \| 0.2293 \| 1.5125 \| 0.847 \| 0.8473 \| 0.0605 \| 0.0330 \| \| 0.0536 \| 31.0 \| 20677 \| 0.4022 \| 0.851 \| 0.2238 \| 1.5100 \| 0.851 \| 0.8527 \| 0.0594 \| 0.0323 \| \| 0.0522 \| 32.0 \| 21344 \| 0.4120 \| 0.8475 \| 0.2290 \| 1.5044 \| 0.8475 \| 0.8483 \| 0.0633 \| 0.0327 \| \| 0.0493 \| 33.0 \| 22011 \| 0.3990 \| 0.8492 \| 0.2258 \| 1.5197 \| 0.8492 \| 0.8503 \| 0.0589 \| 0.0318 \| \| 0.0512 \| 34.0 \| 22678 \| 0.3983 \| 0.85 \| 0.2251 \| 1.4644 \| 0.85 \| 0.8503 \| 0.0597 \| 0.0319 \| \| 0.0517 \| 35.0 \| 23345 \| 0.3969 \| 0.8465 \| 0.2257 \| 1.4814 \| 0.8465 \| 0.8479 \| 0.0630 \| 0.0309 \| \| 0.0477 \| 36.0 \| 24012 \| 0.3939 \| 0.8528 \| 0.2237 \| 1.4797 \| 0.8528 \| 0.8531 \| 0.0604 \| 0.0316 \| \| 0.0482 \| 37.0 \| 24679 \| 0.3934 \| 0.852 \| 0.2218 \| 1.4595 \| 0.852 \| 0.8527 \| 0.0613 \| 0.0316 \| \| 0.0481 \| 38.0 \| 25346 \| 0.3930 \| 0.8532 \| 0.2217 \| 1.4561 \| 0.8532 \| 0.8544 \| 0.0593 \| 0.0306 \| \| 0.0477 \| 39.0 \| 26013 \| 0.3875 \| 0.8512 \| 0.2202 \| 1.4610 \| 0.8512 \| 0.8523 \| 0.0609 \| 0.0310 \| \| 0.048 \| 40.0 \| 26680 \| 0.3900 \| 0.8538 \| 0.2202 \| 1.4541 \| 0.8537 \| 0.8546 \| 0.0629 \| 0.0307 \| \| 0.0448 \| 41.0 \| 27347 \| 0.3901 \| 0.8525 \| 0.2221 \| 1.4519 \| 0.8525 \| 0.8532 \| 0.0621 \| 0.0308 \| \| 0.0454 \| 42.0 \| 28014 \| 0.3858 \| 0.851 \| 0.2186 \| 1.4554 \| 0.851 \| 0.8519 \| 0.0633 \| 0.0298 \| \| 0.0464 \| 43.0 \| 28681 \| 0.3861 \| 0.8528 \| 0.2197 \| 1.4516 \| 0.8528 \| 0.8535 \| 0.0618 \| 0.0307 \| \| 0.0444 \| 44.0 \| 29348 \| 0.3824 \| 0.8548 \| 0.2176 \| 1.4288 \| 0.8547 \| 0.8557 \| 0.0607 \| 0.0299 \| \| 0.0461 \| 45.0 \| 30015 \| 0.3833 \| 0.8555 \| 0.2181 \| 1.4330 \| 0.8555 \| 0.8566 \| 0.0606 \| 0.0302 \| \| 0.0442 \| 46.0 \| 30682 \| 0.3830 \| 0.8552 \| 0.2174 \| 1.4358 \| 0.8552 \| 0.8560 \| 0.0604 \| 0.0302 \| \| 0.0456 \| 47.0 \| 31349 \| 0.3797 \| 0.8552 \| 0.2173 \| 1.4264 \| 0.8552 \| 0.8560 \| 0.0596 \| 0.0297 \| \| 0.0447 \| 48.0 \| 32016 \| 0.3811 \| 0.8558 \| 0.2176 \| 1.4273 \| 0.8558 \| 0.8566 \| 0.0595 \| 0.0300 \| \| 0.0439 \| 49.0 \| 32683 \| 0.3814 \| 0.856 \| 0.2176 \| 1.4252 \| 0.856 \| 0.8568 \| 0.0600 \| 0.0300 \| \| 0.0437 \| 50.0 \| 33350 \| 0.3813 \| 0.8558 \| 0.2176 \| 1.4251 \| 0.8558 \| 0.8566 \| 0.0597 \| 0.0299 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
ShawnLJW/harry-GPTter	ShawnLJW	2023-07-26T04:03:42Z	0	1	null	[ "text-generation", "en", "region:us" ]	text-generation	2023-04-09T00:38:20Z	--- language: - en pipeline_tag: text-generation --- # harry-GPTter harry-GPTter is a transformer text generation model implemented in PyTorch. It has been trained on text from all 7 books from from all 7 books of the Harry Potter series. In only 10 minutes of training with the free tier of [Google Colaboratory](https://colab.research.google.com/), the model learnt to generate coherent and grammatically correct sentences. - Code and more information in the [GitHub Repository](https://github.com/ShawnLJW/harry-GPTter) - Download the [weights](https://huggingface.co/ShawnLJW/harry-GPTter/resolve/main/checkpoint.pt) ## Text Generation with harry-GPTter > “Ah,” said Mrs. Weasley, hiscolored lips looking unpleasant. “He wasn’t talking about her, he has tried to think he was saying he had looked up. The bleers were flooding.” > > “My master died?” whispered Voldemort, but the wasnoddenbling until he are, making to be seeing him. > > “I’ll see you, Professor Lockhart,” said Hermione, “but so surely now to have solid on it out of her whole bed! You’re thinking — > > “Oh hello the unconscious!” > > “And now blimey,” said Harry, “it was a very serious for an enormous mother. ...” ## Model Details harry-GPTter is a relatively small language model with 56M parameters (less than 1/2x of smallest gpt-2). It contains 8 layers of 8 headed attention with a hidden size of 384. It supports a maximum sequence length of 128. For tokenization, we use the same tokenizer as text-davinci-003, which has a vocabulary of 50,280 in total. The model was trained for 2000 epochs in about 10 minutes with the free tier of Google Colab GPU Runtime. It achieves a cross-entropy loss of 3.1189. This model was built for learning purposes. You can probably get better performance by finetuning a pre-trained model.
InductiveGrub/halo-grunt	InductiveGrub	2023-07-26T03:59:55Z	0	0	null	[ "video games", "halo", "halo combat evolved", "en", "region:us" ]	null	2023-07-26T03:57:31Z	--- language: - en tags: - video games - halo - halo combat evolved ---
nebulae7/max	nebulae7	2023-07-26T03:58:08Z	0	0	peft	[ "peft", "RefinedWebModel", "custom_code", "4-bit", "region:us" ]	null	2023-07-24T05:33:09Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
LearnItAnyway/YOLO_LLaMa_7B_VisNav	LearnItAnyway	2023-07-26T03:57:32Z	13	0	transformers	[ "transformers", "pytorch", "llava", "text-generation", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-26T01:50:13Z	--- license: other --- # Overview This project aims to support visually impaired individuals in their daily navigation. This project combines the [YOLO](https://ultralytics.com/yolov8) model and [LLaMa 2 7b](https://huggingface.co/meta-llama/Llama-2-7b) for the navigation. YOLO is trained on the bounding box data from the [AI Hub](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=189), Output of YOLO (bbox data) is converted as lists like `[[class_of_obj_1, xmin, xmax, ymin, ymax, size], [class_of...] ...]` then added to the input of question. The LLM is trained to navigate using [LearnItAnyway/Visual-Navigation-21k](https://huggingface.co/datasets/LearnItAnyway/Visual-Navigation-21k) multi-turn dataset ## Usage We show how to use the model in [yolo_llama_visnav_test.ipynb](https://huggingface.co/LearnItAnyway/YOLO_LLaMa_7B_VisNav/blob/main/yolo_llama_visnav_test.ipynb)
nebulae7/seven	nebulae7	2023-07-26T03:57:22Z	5	0	peft	[ "peft", "RefinedWebModel", "custom_code", "4-bit", "region:us" ]	null	2023-07-24T05:53:59Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
nebulae7/eight	nebulae7	2023-07-26T03:55:19Z	3	0	peft	[ "peft", "RefinedWebModel", "custom_code", "4-bit", "region:us" ]	null	2023-07-24T07:34:02Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
vicbguti/bloom-1b7-lora-tagger	vicbguti	2023-07-26T03:54:23Z	2	0	peft	[ "peft", "region:us" ]	null	2023-07-26T02:45:04Z	--- 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.dev0
datasistah/20230725_STAR_interview_model	datasistah	2023-07-26T03:49:32Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-26T03:09:51Z	--- 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.dev0
PascalY/bert-finetuned-ner	PascalY	2023-07-26T03:43:26Z	116	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-26T03:10:26Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9374896093100582 - name: Recall type: recall value: 0.9490070683271625 - name: F1 type: f1 value: 0.9432131805636865 - name: Accuracy type: accuracy value: 0.9873862137989102 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0579 - Precision: 0.9375 - Recall: 0.9490 - F1: 0.9432 - Accuracy: 0.9874 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0729 \| 1.0 \| 1756 \| 0.0607 \| 0.9117 \| 0.9366 \| 0.9240 \| 0.9839 \| \| 0.0361 \| 2.0 \| 3512 \| 0.0538 \| 0.9250 \| 0.9468 \| 0.9358 \| 0.9864 \| \| 0.0205 \| 3.0 \| 5268 \| 0.0579 \| 0.9375 \| 0.9490 \| 0.9432 \| 0.9874 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
Sukmin/q-FrozenLake-v1-4x4-noSlippery	Sukmin	2023-07-26T03:36:39Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-26T03:36:36Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Sukmin/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Sukmin/ppo-LunarLander-v2	Sukmin	2023-07-26T03:29:20Z	2	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-26T03:28:58Z	--- 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: 251.64 +/- 15.24 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 ... ```
catbox/llama2-qlora-finetunined-french	catbox	2023-07-26T03:26:29Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-26T03:26:20Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
Sukmin/rl_course_vizdoom_health_gathering_supreme	Sukmin	2023-07-26T03:23:14Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-26T03:00:00Z	--- 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: 8.86 +/- 2.70 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 Sukmin/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.
Jonathaniu/llama2-breast-cancer-7b-knowledge-epoch-3	Jonathaniu	2023-07-26T02:56:42Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-26T02:56:29Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False ### Framework versions - PEFT 0.4.0.dev0
simonycl/best_model-sst-2-64-100	simonycl	2023-07-26T02:38:25Z	107	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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	2023-07-26T02:27:10Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-64-100 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. --> # best_model-sst-2-64-100 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: - Loss: 0.9480 - Accuracy: 0.8906 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 4 \| 0.8613 \| 0.9141 \| \| No log \| 2.0 \| 8 \| 0.8613 \| 0.9141 \| \| 0.6496 \| 3.0 \| 12 \| 0.8614 \| 0.9141 \| \| 0.6496 \| 4.0 \| 16 \| 0.8614 \| 0.9141 \| \| 0.6483 \| 5.0 \| 20 \| 0.8596 \| 0.9141 \| \| 0.6483 \| 6.0 \| 24 \| 0.8575 \| 0.9141 \| \| 0.6483 \| 7.0 \| 28 \| 0.8557 \| 0.9141 \| \| 0.6867 \| 8.0 \| 32 \| 0.8528 \| 0.9141 \| \| 0.6867 \| 9.0 \| 36 \| 0.8506 \| 0.9141 \| \| 0.3821 \| 10.0 \| 40 \| 0.8542 \| 0.9062 \| \| 0.3821 \| 11.0 \| 44 \| 0.8721 \| 0.8984 \| \| 0.3821 \| 12.0 \| 48 \| 0.8877 \| 0.8984 \| \| 0.4452 \| 13.0 \| 52 \| 0.8920 \| 0.8984 \| \| 0.4452 \| 14.0 \| 56 \| 0.8952 \| 0.8984 \| \| 0.3224 \| 15.0 \| 60 \| 0.8920 \| 0.9062 \| \| 0.3224 \| 16.0 \| 64 \| 0.8833 \| 0.9062 \| \| 0.3224 \| 17.0 \| 68 \| 0.8727 \| 0.9062 \| \| 0.2699 \| 18.0 \| 72 \| 0.8284 \| 0.8984 \| \| 0.2699 \| 19.0 \| 76 \| 0.7829 \| 0.9062 \| \| 0.1873 \| 20.0 \| 80 \| 0.7713 \| 0.9062 \| \| 0.1873 \| 21.0 \| 84 \| 0.7646 \| 0.8984 \| \| 0.1873 \| 22.0 \| 88 \| 0.7517 \| 0.8984 \| \| 0.1282 \| 23.0 \| 92 \| 0.7379 \| 0.9062 \| \| 0.1282 \| 24.0 \| 96 \| 0.7295 \| 0.9062 \| \| 0.0438 \| 25.0 \| 100 \| 0.7243 \| 0.8984 \| \| 0.0438 \| 26.0 \| 104 \| 0.7038 \| 0.9141 \| \| 0.0438 \| 27.0 \| 108 \| 0.6994 \| 0.9219 \| \| 0.0154 \| 28.0 \| 112 \| 0.6997 \| 0.9062 \| \| 0.0154 \| 29.0 \| 116 \| 0.7184 \| 0.8984 \| \| 0.0019 \| 30.0 \| 120 \| 0.7601 \| 0.9062 \| \| 0.0019 \| 31.0 \| 124 \| 0.7739 \| 0.9062 \| \| 0.0019 \| 32.0 \| 128 \| 0.7854 \| 0.9062 \| \| 0.0003 \| 33.0 \| 132 \| 0.7934 \| 0.9062 \| \| 0.0003 \| 34.0 \| 136 \| 0.7945 \| 0.9062 \| \| 0.0002 \| 35.0 \| 140 \| 0.7896 \| 0.9062 \| \| 0.0002 \| 36.0 \| 144 \| 0.7711 \| 0.9062 \| \| 0.0002 \| 37.0 \| 148 \| 0.7503 \| 0.9062 \| \| 0.0004 \| 38.0 \| 152 \| 0.7436 \| 0.9062 \| \| 0.0004 \| 39.0 \| 156 \| 0.7464 \| 0.9062 \| \| 0.0001 \| 40.0 \| 160 \| 0.7492 \| 0.9062 \| \| 0.0001 \| 41.0 \| 164 \| 0.7990 \| 0.9062 \| \| 0.0001 \| 42.0 \| 168 \| 0.8244 \| 0.9062 \| \| 0.0059 \| 43.0 \| 172 \| 0.8377 \| 0.9062 \| \| 0.0059 \| 44.0 \| 176 \| 0.8496 \| 0.9062 \| \| 0.0001 \| 45.0 \| 180 \| 0.8582 \| 0.9062 \| \| 0.0001 \| 46.0 \| 184 \| 0.8646 \| 0.9062 \| \| 0.0001 \| 47.0 \| 188 \| 0.8286 \| 0.9062 \| \| 0.0005 \| 48.0 \| 192 \| 0.8002 \| 0.9062 \| \| 0.0005 \| 49.0 \| 196 \| 0.7854 \| 0.9062 \| \| 0.0001 \| 50.0 \| 200 \| 0.7691 \| 0.9062 \| \| 0.0001 \| 51.0 \| 204 \| 0.7594 \| 0.9062 \| \| 0.0001 \| 52.0 \| 208 \| 0.7618 \| 0.9062 \| \| 0.0003 \| 53.0 \| 212 \| 0.8175 \| 0.9062 \| \| 0.0003 \| 54.0 \| 216 \| 0.8539 \| 0.9062 \| \| 0.0001 \| 55.0 \| 220 \| 0.8737 \| 0.9062 \| \| 0.0001 \| 56.0 \| 224 \| 0.8661 \| 0.9062 \| \| 0.0001 \| 57.0 \| 228 \| 0.8398 \| 0.9062 \| \| 0.0038 \| 58.0 \| 232 \| 0.8162 \| 0.9062 \| \| 0.0038 \| 59.0 \| 236 \| 0.7946 \| 0.9062 \| \| 0.0001 \| 60.0 \| 240 \| 0.7866 \| 0.9062 \| \| 0.0001 \| 61.0 \| 244 \| 0.7776 \| 0.9141 \| \| 0.0001 \| 62.0 \| 248 \| 0.7781 \| 0.9141 \| \| 0.0001 \| 63.0 \| 252 \| 0.7963 \| 0.9062 \| \| 0.0001 \| 64.0 \| 256 \| 0.8099 \| 0.9062 \| \| 0.0 \| 65.0 \| 260 \| 0.8196 \| 0.9062 \| \| 0.0 \| 66.0 \| 264 \| 0.8284 \| 0.9062 \| \| 0.0 \| 67.0 \| 268 \| 0.8880 \| 0.9062 \| \| 0.0045 \| 68.0 \| 272 \| 0.9217 \| 0.9062 \| \| 0.0045 \| 69.0 \| 276 \| 0.9374 \| 0.8984 \| \| 0.0082 \| 70.0 \| 280 \| 0.9364 \| 0.9062 \| \| 0.0082 \| 71.0 \| 284 \| 0.8651 \| 0.9062 \| \| 0.0082 \| 72.0 \| 288 \| 0.7849 \| 0.8984 \| \| 0.0003 \| 73.0 \| 292 \| 0.7981 \| 0.8984 \| \| 0.0003 \| 74.0 \| 296 \| 0.7808 \| 0.9141 \| \| 0.021 \| 75.0 \| 300 \| 0.8438 \| 0.9062 \| \| 0.021 \| 76.0 \| 304 \| 0.8882 \| 0.8984 \| \| 0.021 \| 77.0 \| 308 \| 0.9214 \| 0.8984 \| \| 0.0001 \| 78.0 \| 312 \| 0.9396 \| 0.8984 \| \| 0.0001 \| 79.0 \| 316 \| 0.9493 \| 0.8984 \| \| 0.0 \| 80.0 \| 320 \| 0.9549 \| 0.8984 \| \| 0.0 \| 81.0 \| 324 \| 0.9466 \| 0.8984 \| \| 0.0 \| 82.0 \| 328 \| 0.9041 \| 0.8984 \| \| 0.0001 \| 83.0 \| 332 \| 0.8993 \| 0.8984 \| \| 0.0001 \| 84.0 \| 336 \| 0.9616 \| 0.8984 \| \| 0.0001 \| 85.0 \| 340 \| 0.9844 \| 0.8984 \| \| 0.0001 \| 86.0 \| 344 \| 0.9934 \| 0.8906 \| \| 0.0001 \| 87.0 \| 348 \| 0.9999 \| 0.8906 \| \| 0.0001 \| 88.0 \| 352 \| 0.9973 \| 0.8906 \| \| 0.0001 \| 89.0 \| 356 \| 0.9943 \| 0.8984 \| \| 0.0 \| 90.0 \| 360 \| 0.9929 \| 0.8984 \| \| 0.0 \| 91.0 \| 364 \| 0.9921 \| 0.8984 \| \| 0.0 \| 92.0 \| 368 \| 0.9915 \| 0.8984 \| \| 0.0 \| 93.0 \| 372 \| 0.9916 \| 0.8984 \| \| 0.0 \| 94.0 \| 376 \| 0.9924 \| 0.8984 \| \| 0.0 \| 95.0 \| 380 \| 0.9930 \| 0.8984 \| \| 0.0 \| 96.0 \| 384 \| 0.9936 \| 0.8984 \| \| 0.0 \| 97.0 \| 388 \| 0.9940 \| 0.8984 \| \| 0.0 \| 98.0 \| 392 \| 0.9946 \| 0.8984 \| \| 0.0 \| 99.0 \| 396 \| 0.9950 \| 0.8984 \| \| 0.0006 \| 100.0 \| 400 \| 0.9869 \| 0.8984 \| \| 0.0006 \| 101.0 \| 404 \| 0.8625 \| 0.8984 \| \| 0.0006 \| 102.0 \| 408 \| 0.7755 \| 0.9219 \| \| 0.0 \| 103.0 \| 412 \| 0.7887 \| 0.8984 \| \| 0.0 \| 104.0 \| 416 \| 0.7844 \| 0.9062 \| \| 0.0062 \| 105.0 \| 420 \| 0.8504 \| 0.8984 \| \| 0.0062 \| 106.0 \| 424 \| 0.9449 \| 0.8984 \| \| 0.0062 \| 107.0 \| 428 \| 0.9568 \| 0.8906 \| \| 0.0 \| 108.0 \| 432 \| 0.9504 \| 0.8984 \| \| 0.0 \| 109.0 \| 436 \| 0.9700 \| 0.8984 \| \| 0.0 \| 110.0 \| 440 \| 0.9875 \| 0.8906 \| \| 0.0 \| 111.0 \| 444 \| 1.0002 \| 0.8906 \| \| 0.0 \| 112.0 \| 448 \| 1.0095 \| 0.8828 \| \| 0.0 \| 113.0 \| 452 \| 1.0156 \| 0.8828 \| \| 0.0 \| 114.0 \| 456 \| 0.8995 \| 0.8984 \| \| 0.0144 \| 115.0 \| 460 \| 0.8017 \| 0.8984 \| \| 0.0144 \| 116.0 \| 464 \| 0.7774 \| 0.9062 \| \| 0.0144 \| 117.0 \| 468 \| 0.7913 \| 0.9062 \| \| 0.0 \| 118.0 \| 472 \| 0.8033 \| 0.8984 \| \| 0.0 \| 119.0 \| 476 \| 0.8244 \| 0.8906 \| \| 0.0001 \| 120.0 \| 480 \| 0.9148 \| 0.8984 \| \| 0.0001 \| 121.0 \| 484 \| 1.0038 \| 0.8828 \| \| 0.0001 \| 122.0 \| 488 \| 1.1128 \| 0.875 \| \| 0.0 \| 123.0 \| 492 \| 1.1276 \| 0.875 \| \| 0.0 \| 124.0 \| 496 \| 1.1209 \| 0.8828 \| \| 0.0 \| 125.0 \| 500 \| 1.1161 \| 0.8828 \| \| 0.0 \| 126.0 \| 504 \| 1.1119 \| 0.8828 \| \| 0.0 \| 127.0 \| 508 \| 1.1037 \| 0.8828 \| \| 0.0 \| 128.0 \| 512 \| 1.0644 \| 0.8828 \| \| 0.0 \| 129.0 \| 516 \| 1.0175 \| 0.875 \| \| 0.0 \| 130.0 \| 520 \| 0.9819 \| 0.8828 \| \| 0.0 \| 131.0 \| 524 \| 0.9613 \| 0.8906 \| \| 0.0 \| 132.0 \| 528 \| 0.9509 \| 0.8906 \| \| 0.0 \| 133.0 \| 532 \| 0.9463 \| 0.8906 \| \| 0.0 \| 134.0 \| 536 \| 0.9441 \| 0.875 \| \| 0.0 \| 135.0 \| 540 \| 0.9432 \| 0.875 \| \| 0.0 \| 136.0 \| 544 \| 0.9429 \| 0.875 \| \| 0.0 \| 137.0 \| 548 \| 0.9429 \| 0.8828 \| \| 0.0 \| 138.0 \| 552 \| 0.9430 \| 0.8828 \| \| 0.0 \| 139.0 \| 556 \| 0.9432 \| 0.8828 \| \| 0.0 \| 140.0 \| 560 \| 0.9434 \| 0.8828 \| \| 0.0 \| 141.0 \| 564 \| 0.9436 \| 0.8828 \| \| 0.0 \| 142.0 \| 568 \| 0.9438 \| 0.8906 \| \| 0.0 \| 143.0 \| 572 \| 0.9439 \| 0.8906 \| \| 0.0 \| 144.0 \| 576 \| 0.9448 \| 0.8906 \| \| 0.0 \| 145.0 \| 580 \| 0.9461 \| 0.8906 \| \| 0.0 \| 146.0 \| 584 \| 0.9470 \| 0.8906 \| \| 0.0 \| 147.0 \| 588 \| 0.9476 \| 0.8906 \| \| 0.0 \| 148.0 \| 592 \| 0.9478 \| 0.8906 \| \| 0.0 \| 149.0 \| 596 \| 0.9480 \| 0.8906 \| \| 0.0 \| 150.0 \| 600 \| 0.9480 \| 0.8906 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3
simonycl/best_model-sst-2-64-87	simonycl	2023-07-26T02:26:54Z	113	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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	2023-07-26T02:15:17Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-64-87 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. --> # best_model-sst-2-64-87 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: - Loss: 1.2746 - Accuracy: 0.8438 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 4 \| 1.3247 \| 0.8438 \| \| No log \| 2.0 \| 8 \| 1.3227 \| 0.8438 \| \| 0.7148 \| 3.0 \| 12 \| 1.3195 \| 0.8438 \| \| 0.7148 \| 4.0 \| 16 \| 1.3169 \| 0.8359 \| \| 0.6114 \| 5.0 \| 20 \| 1.3149 \| 0.8359 \| \| 0.6114 \| 6.0 \| 24 \| 1.3101 \| 0.8359 \| \| 0.6114 \| 7.0 \| 28 \| 1.2982 \| 0.8438 \| \| 0.5794 \| 8.0 \| 32 \| 1.2836 \| 0.8438 \| \| 0.5794 \| 9.0 \| 36 \| 1.2655 \| 0.8438 \| \| 0.5231 \| 10.0 \| 40 \| 1.2497 \| 0.8438 \| \| 0.5231 \| 11.0 \| 44 \| 1.2410 \| 0.8438 \| \| 0.5231 \| 12.0 \| 48 \| 1.2307 \| 0.8438 \| \| 0.4052 \| 13.0 \| 52 \| 1.2154 \| 0.8438 \| \| 0.4052 \| 14.0 \| 56 \| 1.2001 \| 0.8438 \| \| 0.363 \| 15.0 \| 60 \| 1.1877 \| 0.8438 \| \| 0.363 \| 16.0 \| 64 \| 1.1760 \| 0.8516 \| \| 0.363 \| 17.0 \| 68 \| 1.1836 \| 0.8516 \| \| 0.2969 \| 18.0 \| 72 \| 1.1848 \| 0.8594 \| \| 0.2969 \| 19.0 \| 76 \| 1.1823 \| 0.8516 \| \| 0.1866 \| 20.0 \| 80 \| 1.1867 \| 0.8516 \| \| 0.1866 \| 21.0 \| 84 \| 1.1795 \| 0.8516 \| \| 0.1866 \| 22.0 \| 88 \| 1.1756 \| 0.8516 \| \| 0.1502 \| 23.0 \| 92 \| 1.1731 \| 0.8516 \| \| 0.1502 \| 24.0 \| 96 \| 1.1680 \| 0.8516 \| \| 0.0974 \| 25.0 \| 100 \| 1.1489 \| 0.8516 \| \| 0.0974 \| 26.0 \| 104 \| 1.1088 \| 0.8516 \| \| 0.0974 \| 27.0 \| 108 \| 1.0986 \| 0.8594 \| \| 0.0992 \| 28.0 \| 112 \| 1.0879 \| 0.8594 \| \| 0.0992 \| 29.0 \| 116 \| 1.0850 \| 0.8594 \| \| 0.0065 \| 30.0 \| 120 \| 1.1056 \| 0.8594 \| \| 0.0065 \| 31.0 \| 124 \| 1.1355 \| 0.8516 \| \| 0.0065 \| 32.0 \| 128 \| 1.1457 \| 0.8438 \| \| 0.0185 \| 33.0 \| 132 \| 1.1518 \| 0.8438 \| \| 0.0185 \| 34.0 \| 136 \| 1.1437 \| 0.8438 \| \| 0.0123 \| 35.0 \| 140 \| 1.1230 \| 0.8516 \| \| 0.0123 \| 36.0 \| 144 \| 1.1109 \| 0.8516 \| \| 0.0123 \| 37.0 \| 148 \| 1.1093 \| 0.8594 \| \| 0.0001 \| 38.0 \| 152 \| 1.1085 \| 0.8594 \| \| 0.0001 \| 39.0 \| 156 \| 1.1092 \| 0.8594 \| \| 0.008 \| 40.0 \| 160 \| 1.1163 \| 0.8594 \| \| 0.008 \| 41.0 \| 164 \| 1.1272 \| 0.8516 \| \| 0.008 \| 42.0 \| 168 \| 1.1351 \| 0.8516 \| \| 0.0001 \| 43.0 \| 172 \| 1.1365 \| 0.8516 \| \| 0.0001 \| 44.0 \| 176 \| 1.1287 \| 0.8516 \| \| 0.0007 \| 45.0 \| 180 \| 1.1195 \| 0.8594 \| \| 0.0007 \| 46.0 \| 184 \| 1.1110 \| 0.8594 \| \| 0.0007 \| 47.0 \| 188 \| 1.1261 \| 0.8594 \| \| 0.0003 \| 48.0 \| 192 \| 1.1236 \| 0.8594 \| \| 0.0003 \| 49.0 \| 196 \| 1.1083 \| 0.8594 \| \| 0.0018 \| 50.0 \| 200 \| 1.1057 \| 0.8594 \| \| 0.0018 \| 51.0 \| 204 \| 1.1077 \| 0.8594 \| \| 0.0018 \| 52.0 \| 208 \| 1.1095 \| 0.8516 \| \| 0.0001 \| 53.0 \| 212 \| 1.1116 \| 0.8594 \| \| 0.0001 \| 54.0 \| 216 \| 1.1149 \| 0.8594 \| \| 0.0017 \| 55.0 \| 220 \| 1.1500 \| 0.8516 \| \| 0.0017 \| 56.0 \| 224 \| 1.1396 \| 0.8516 \| \| 0.0017 \| 57.0 \| 228 \| 1.1474 \| 0.8516 \| \| 0.0002 \| 58.0 \| 232 \| 1.1402 \| 0.8594 \| \| 0.0002 \| 59.0 \| 236 \| 1.1367 \| 0.8594 \| \| 0.0001 \| 60.0 \| 240 \| 1.1349 \| 0.8516 \| \| 0.0001 \| 61.0 \| 244 \| 1.1350 \| 0.8516 \| \| 0.0001 \| 62.0 \| 248 \| 1.1366 \| 0.8516 \| \| 0.0001 \| 63.0 \| 252 \| 1.1389 \| 0.8594 \| \| 0.0001 \| 64.0 \| 256 \| 1.1395 \| 0.8594 \| \| 0.0001 \| 65.0 \| 260 \| 1.1380 \| 0.8594 \| \| 0.0001 \| 66.0 \| 264 \| 1.1378 \| 0.8594 \| \| 0.0001 \| 67.0 \| 268 \| 1.1411 \| 0.8594 \| \| 0.0001 \| 68.0 \| 272 \| 1.1439 \| 0.8594 \| \| 0.0001 \| 69.0 \| 276 \| 1.1452 \| 0.8594 \| \| 0.0122 \| 70.0 \| 280 \| 1.1270 \| 0.8594 \| \| 0.0122 \| 71.0 \| 284 \| 1.1514 \| 0.8594 \| \| 0.0122 \| 72.0 \| 288 \| 1.1908 \| 0.8516 \| \| 0.0001 \| 73.0 \| 292 \| 1.2155 \| 0.8516 \| \| 0.0001 \| 74.0 \| 296 \| 1.2281 \| 0.8516 \| \| 0.0001 \| 75.0 \| 300 \| 1.2353 \| 0.8516 \| \| 0.0001 \| 76.0 \| 304 \| 1.2387 \| 0.8516 \| \| 0.0001 \| 77.0 \| 308 \| 1.2380 \| 0.8516 \| \| 0.0177 \| 78.0 \| 312 \| 1.1050 \| 0.8594 \| \| 0.0177 \| 79.0 \| 316 \| 1.1201 \| 0.8594 \| \| 0.0123 \| 80.0 \| 320 \| 1.1227 \| 0.8516 \| \| 0.0123 \| 81.0 \| 324 \| 1.1249 \| 0.8594 \| \| 0.0123 \| 82.0 \| 328 \| 1.1305 \| 0.8594 \| \| 0.0001 \| 83.0 \| 332 \| 1.1371 \| 0.8672 \| \| 0.0001 \| 84.0 \| 336 \| 1.1424 \| 0.8672 \| \| 0.0001 \| 85.0 \| 340 \| 1.1449 \| 0.8672 \| \| 0.0001 \| 86.0 \| 344 \| 1.1464 \| 0.8672 \| \| 0.0001 \| 87.0 \| 348 \| 1.1469 \| 0.8672 \| \| 0.0001 \| 88.0 \| 352 \| 1.1448 \| 0.8594 \| \| 0.0001 \| 89.0 \| 356 \| 1.1444 \| 0.8594 \| \| 0.0 \| 90.0 \| 360 \| 1.1452 \| 0.8594 \| \| 0.0 \| 91.0 \| 364 \| 1.1464 \| 0.8594 \| \| 0.0 \| 92.0 \| 368 \| 1.1484 \| 0.8594 \| \| 0.0001 \| 93.0 \| 372 \| 1.1504 \| 0.8594 \| \| 0.0001 \| 94.0 \| 376 \| 1.1521 \| 0.8516 \| \| 0.0 \| 95.0 \| 380 \| 1.1537 \| 0.8516 \| \| 0.0 \| 96.0 \| 384 \| 1.1553 \| 0.8516 \| \| 0.0 \| 97.0 \| 388 \| 1.1571 \| 0.8516 \| \| 0.0001 \| 98.0 \| 392 \| 1.1605 \| 0.8594 \| \| 0.0001 \| 99.0 \| 396 \| 1.1645 \| 0.8594 \| \| 0.0 \| 100.0 \| 400 \| 1.1678 \| 0.8594 \| \| 0.0 \| 101.0 \| 404 \| 1.1706 \| 0.8594 \| \| 0.0 \| 102.0 \| 408 \| 1.1729 \| 0.8594 \| \| 0.0 \| 103.0 \| 412 \| 1.1747 \| 0.8594 \| \| 0.0 \| 104.0 \| 416 \| 1.1762 \| 0.8594 \| \| 0.0001 \| 105.0 \| 420 \| 1.1777 \| 0.8594 \| \| 0.0001 \| 106.0 \| 424 \| 1.1792 \| 0.8594 \| \| 0.0001 \| 107.0 \| 428 \| 1.1808 \| 0.8594 \| \| 0.0034 \| 108.0 \| 432 \| 1.2561 \| 0.8516 \| \| 0.0034 \| 109.0 \| 436 \| 1.3098 \| 0.8516 \| \| 0.0063 \| 110.0 \| 440 \| 1.2197 \| 0.8516 \| \| 0.0063 \| 111.0 \| 444 \| 1.1982 \| 0.8516 \| \| 0.0063 \| 112.0 \| 448 \| 1.2230 \| 0.8516 \| \| 0.0 \| 113.0 \| 452 \| 1.2172 \| 0.8594 \| \| 0.0 \| 114.0 \| 456 \| 1.2165 \| 0.8516 \| \| 0.0 \| 115.0 \| 460 \| 1.2187 \| 0.8516 \| \| 0.0 \| 116.0 \| 464 \| 1.2213 \| 0.8516 \| \| 0.0 \| 117.0 \| 468 \| 1.2234 \| 0.8516 \| \| 0.0 \| 118.0 \| 472 \| 1.2248 \| 0.8516 \| \| 0.0 \| 119.0 \| 476 \| 1.2267 \| 0.8516 \| \| 0.0 \| 120.0 \| 480 \| 1.2288 \| 0.8594 \| \| 0.0 \| 121.0 \| 484 \| 1.2316 \| 0.8594 \| \| 0.0 \| 122.0 \| 488 \| 1.2342 \| 0.8594 \| \| 0.0 \| 123.0 \| 492 \| 1.2364 \| 0.8594 \| \| 0.0 \| 124.0 \| 496 \| 1.2436 \| 0.8594 \| \| 0.001 \| 125.0 \| 500 \| 1.2770 \| 0.8438 \| \| 0.001 \| 126.0 \| 504 \| 1.3138 \| 0.8594 \| \| 0.001 \| 127.0 \| 508 \| 1.3084 \| 0.8594 \| \| 0.0 \| 128.0 \| 512 \| 1.3102 \| 0.8438 \| \| 0.0 \| 129.0 \| 516 \| 1.3333 \| 0.8438 \| \| 0.0002 \| 130.0 \| 520 \| 1.3251 \| 0.8516 \| \| 0.0002 \| 131.0 \| 524 \| 1.2928 \| 0.8594 \| \| 0.0002 \| 132.0 \| 528 \| 1.2468 \| 0.8438 \| \| 0.0 \| 133.0 \| 532 \| 1.2295 \| 0.8438 \| \| 0.0 \| 134.0 \| 536 \| 1.2483 \| 0.8438 \| \| 0.0 \| 135.0 \| 540 \| 1.2652 \| 0.8438 \| \| 0.0 \| 136.0 \| 544 \| 1.2741 \| 0.8438 \| \| 0.0 \| 137.0 \| 548 \| 1.2786 \| 0.8438 \| \| 0.0 \| 138.0 \| 552 \| 1.2811 \| 0.8438 \| \| 0.0 \| 139.0 \| 556 \| 1.2824 \| 0.8438 \| \| 0.0 \| 140.0 \| 560 \| 1.2833 \| 0.8438 \| \| 0.0 \| 141.0 \| 564 \| 1.2837 \| 0.8438 \| \| 0.0 \| 142.0 \| 568 \| 1.2833 \| 0.8438 \| \| 0.0 \| 143.0 \| 572 \| 1.2830 \| 0.8438 \| \| 0.0 \| 144.0 \| 576 \| 1.2828 \| 0.8438 \| \| 0.0 \| 145.0 \| 580 \| 1.2827 \| 0.8438 \| \| 0.0 \| 146.0 \| 584 \| 1.2827 \| 0.8438 \| \| 0.0 \| 147.0 \| 588 \| 1.2827 \| 0.8438 \| \| 0.0001 \| 148.0 \| 592 \| 1.2786 \| 0.8438 \| \| 0.0001 \| 149.0 \| 596 \| 1.2755 \| 0.8438 \| \| 0.0 \| 150.0 \| 600 \| 1.2746 \| 0.8438 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3
xiaojuntime/final-ppo	xiaojuntime	2023-07-26T02:21:10Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-26T02:20:55Z	--- 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 0.4.0
simonycl/best_model-sst-2-64-42	simonycl	2023-07-26T02:15:01Z	106	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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	2023-07-26T02:03:35Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-64-42 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. --> # best_model-sst-2-64-42 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: - Loss: 1.4849 - Accuracy: 0.8281 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 4 \| 1.3914 \| 0.8125 \| \| No log \| 2.0 \| 8 \| 1.3910 \| 0.8203 \| \| 0.3843 \| 3.0 \| 12 \| 1.3922 \| 0.8203 \| \| 0.3843 \| 4.0 \| 16 \| 1.3920 \| 0.8203 \| \| 0.5793 \| 5.0 \| 20 \| 1.3923 \| 0.8203 \| \| 0.5793 \| 6.0 \| 24 \| 1.3989 \| 0.8203 \| \| 0.5793 \| 7.0 \| 28 \| 1.4029 \| 0.8281 \| \| 0.3663 \| 8.0 \| 32 \| 1.4103 \| 0.8281 \| \| 0.3663 \| 9.0 \| 36 \| 1.3999 \| 0.8281 \| \| 0.2779 \| 10.0 \| 40 \| 1.4010 \| 0.8281 \| \| 0.2779 \| 11.0 \| 44 \| 1.3978 \| 0.8281 \| \| 0.2779 \| 12.0 \| 48 \| 1.3963 \| 0.8203 \| \| 0.3589 \| 13.0 \| 52 \| 1.4087 \| 0.8203 \| \| 0.3589 \| 14.0 \| 56 \| 1.4067 \| 0.8281 \| \| 0.3185 \| 15.0 \| 60 \| 1.4148 \| 0.8281 \| \| 0.3185 \| 16.0 \| 64 \| 1.4171 \| 0.8359 \| \| 0.3185 \| 17.0 \| 68 \| 1.4140 \| 0.8359 \| \| 0.1743 \| 18.0 \| 72 \| 1.3982 \| 0.8359 \| \| 0.1743 \| 19.0 \| 76 \| 1.3650 \| 0.8359 \| \| 0.1416 \| 20.0 \| 80 \| 1.3456 \| 0.8359 \| \| 0.1416 \| 21.0 \| 84 \| 1.3210 \| 0.8359 \| \| 0.1416 \| 22.0 \| 88 \| 1.3070 \| 0.8359 \| \| 0.0354 \| 23.0 \| 92 \| 1.3015 \| 0.8359 \| \| 0.0354 \| 24.0 \| 96 \| 1.3319 \| 0.8438 \| \| 0.0035 \| 25.0 \| 100 \| 1.3656 \| 0.8281 \| \| 0.0035 \| 26.0 \| 104 \| 1.3587 \| 0.8281 \| \| 0.0035 \| 27.0 \| 108 \| 1.3243 \| 0.8359 \| \| 0.0006 \| 28.0 \| 112 \| 1.2945 \| 0.8438 \| \| 0.0006 \| 29.0 \| 116 \| 1.2898 \| 0.8438 \| \| 0.0028 \| 30.0 \| 120 \| 1.3066 \| 0.8438 \| \| 0.0028 \| 31.0 \| 124 \| 1.3055 \| 0.8438 \| \| 0.0028 \| 32.0 \| 128 \| 1.3202 \| 0.8438 \| \| 0.0049 \| 33.0 \| 132 \| 1.3351 \| 0.8438 \| \| 0.0049 \| 34.0 \| 136 \| 1.3190 \| 0.8438 \| \| 0.0102 \| 35.0 \| 140 \| 1.3141 \| 0.8438 \| \| 0.0102 \| 36.0 \| 144 \| 1.3142 \| 0.8438 \| \| 0.0102 \| 37.0 \| 148 \| 1.3647 \| 0.8281 \| \| 0.0034 \| 38.0 \| 152 \| 1.4250 \| 0.8203 \| \| 0.0034 \| 39.0 \| 156 \| 1.4708 \| 0.8203 \| \| 0.0001 \| 40.0 \| 160 \| 1.4570 \| 0.8203 \| \| 0.0001 \| 41.0 \| 164 \| 1.4446 \| 0.8203 \| \| 0.0001 \| 42.0 \| 168 \| 1.4345 \| 0.8281 \| \| 0.0001 \| 43.0 \| 172 \| 1.4272 \| 0.8281 \| \| 0.0001 \| 44.0 \| 176 \| 1.4185 \| 0.8281 \| \| 0.0001 \| 45.0 \| 180 \| 1.4048 \| 0.8281 \| \| 0.0001 \| 46.0 \| 184 \| 1.3962 \| 0.8281 \| \| 0.0001 \| 47.0 \| 188 \| 1.4924 \| 0.8203 \| \| 0.0002 \| 48.0 \| 192 \| 1.5361 \| 0.8125 \| \| 0.0002 \| 49.0 \| 196 \| 1.5831 \| 0.8125 \| \| 0.0292 \| 50.0 \| 200 \| 1.4789 \| 0.8281 \| \| 0.0292 \| 51.0 \| 204 \| 1.2642 \| 0.8359 \| \| 0.0292 \| 52.0 \| 208 \| 1.2154 \| 0.8516 \| \| 0.0001 \| 53.0 \| 212 \| 1.1895 \| 0.8516 \| \| 0.0001 \| 54.0 \| 216 \| 1.1775 \| 0.8438 \| \| 0.0001 \| 55.0 \| 220 \| 1.1730 \| 0.8438 \| \| 0.0001 \| 56.0 \| 224 \| 1.1746 \| 0.8438 \| \| 0.0001 \| 57.0 \| 228 \| 1.1782 \| 0.8516 \| \| 0.0001 \| 58.0 \| 232 \| 1.1838 \| 0.8516 \| \| 0.0001 \| 59.0 \| 236 \| 1.2456 \| 0.8281 \| \| 0.025 \| 60.0 \| 240 \| 1.3887 \| 0.8281 \| \| 0.025 \| 61.0 \| 244 \| 1.4950 \| 0.8125 \| \| 0.025 \| 62.0 \| 248 \| 1.5753 \| 0.8047 \| \| 0.0001 \| 63.0 \| 252 \| 1.6287 \| 0.8047 \| \| 0.0001 \| 64.0 \| 256 \| 1.6608 \| 0.8047 \| \| 0.0001 \| 65.0 \| 260 \| 1.6803 \| 0.8047 \| \| 0.0001 \| 66.0 \| 264 \| 1.6919 \| 0.7969 \| \| 0.0001 \| 67.0 \| 268 \| 1.5961 \| 0.8047 \| \| 0.0001 \| 68.0 \| 272 \| 1.4858 \| 0.8125 \| \| 0.0001 \| 69.0 \| 276 \| 1.4104 \| 0.8281 \| \| 0.0001 \| 70.0 \| 280 \| 1.3623 \| 0.8281 \| \| 0.0001 \| 71.0 \| 284 \| 1.3333 \| 0.8359 \| \| 0.0001 \| 72.0 \| 288 \| 1.3172 \| 0.8359 \| \| 0.0 \| 73.0 \| 292 \| 1.3107 \| 0.8359 \| \| 0.0 \| 74.0 \| 296 \| 1.5801 \| 0.8047 \| \| 0.0014 \| 75.0 \| 300 \| 1.7857 \| 0.8047 \| \| 0.0014 \| 76.0 \| 304 \| 1.8724 \| 0.7969 \| \| 0.0014 \| 77.0 \| 308 \| 1.9146 \| 0.7969 \| \| 0.0001 \| 78.0 \| 312 \| 1.9250 \| 0.7969 \| \| 0.0001 \| 79.0 \| 316 \| 1.9265 \| 0.7969 \| \| 0.0001 \| 80.0 \| 320 \| 1.9268 \| 0.7969 \| \| 0.0001 \| 81.0 \| 324 \| 1.9243 \| 0.7969 \| \| 0.0001 \| 82.0 \| 328 \| 1.9215 \| 0.7969 \| \| 0.0 \| 83.0 \| 332 \| 1.9188 \| 0.7969 \| \| 0.0 \| 84.0 \| 336 \| 1.9159 \| 0.7969 \| \| 0.0 \| 85.0 \| 340 \| 1.9137 \| 0.7969 \| \| 0.0 \| 86.0 \| 344 \| 1.9119 \| 0.7969 \| \| 0.0 \| 87.0 \| 348 \| 1.9103 \| 0.7969 \| \| 0.0009 \| 88.0 \| 352 \| 1.6541 \| 0.8047 \| \| 0.0009 \| 89.0 \| 356 \| 1.2749 \| 0.8438 \| \| 0.0 \| 90.0 \| 360 \| 1.2046 \| 0.8438 \| \| 0.0 \| 91.0 \| 364 \| 1.1909 \| 0.8438 \| \| 0.0 \| 92.0 \| 368 \| 1.1860 \| 0.8594 \| \| 0.0 \| 93.0 \| 372 \| 1.1901 \| 0.8594 \| \| 0.0 \| 94.0 \| 376 \| 1.1966 \| 0.8516 \| \| 0.0001 \| 95.0 \| 380 \| 1.2014 \| 0.8516 \| \| 0.0001 \| 96.0 \| 384 \| 1.2061 \| 0.8438 \| \| 0.0001 \| 97.0 \| 388 \| 1.2109 \| 0.8438 \| \| 0.0 \| 98.0 \| 392 \| 1.2170 \| 0.8516 \| \| 0.0 \| 99.0 \| 396 \| 1.2210 \| 0.8516 \| \| 0.0 \| 100.0 \| 400 \| 1.2237 \| 0.8516 \| \| 0.0 \| 101.0 \| 404 \| 1.2258 \| 0.8516 \| \| 0.0 \| 102.0 \| 408 \| 1.2276 \| 0.8438 \| \| 0.0 \| 103.0 \| 412 \| 1.2290 \| 0.8438 \| \| 0.0 \| 104.0 \| 416 \| 1.2301 \| 0.8438 \| \| 0.0 \| 105.0 \| 420 \| 1.2313 \| 0.8438 \| \| 0.0 \| 106.0 \| 424 \| 1.2324 \| 0.8438 \| \| 0.0 \| 107.0 \| 428 \| 1.2334 \| 0.8438 \| \| 0.0 \| 108.0 \| 432 \| 1.2345 \| 0.8438 \| \| 0.0 \| 109.0 \| 436 \| 1.2356 \| 0.8438 \| \| 0.0 \| 110.0 \| 440 \| 1.2366 \| 0.8438 \| \| 0.0 \| 111.0 \| 444 \| 1.2375 \| 0.8516 \| \| 0.0 \| 112.0 \| 448 \| 1.2384 \| 0.8516 \| \| 0.0 \| 113.0 \| 452 \| 1.2400 \| 0.8516 \| \| 0.0 \| 114.0 \| 456 \| 1.2415 \| 0.8516 \| \| 0.0 \| 115.0 \| 460 \| 1.2428 \| 0.8516 \| \| 0.0 \| 116.0 \| 464 \| 1.2439 \| 0.8516 \| \| 0.0 \| 117.0 \| 468 \| 1.2450 \| 0.8516 \| \| 0.0 \| 118.0 \| 472 \| 1.2459 \| 0.8516 \| \| 0.0 \| 119.0 \| 476 \| 1.2467 \| 0.8516 \| \| 0.0 \| 120.0 \| 480 \| 1.2476 \| 0.8516 \| \| 0.0 \| 121.0 \| 484 \| 1.2485 \| 0.8516 \| \| 0.0 \| 122.0 \| 488 \| 1.2495 \| 0.8516 \| \| 0.0 \| 123.0 \| 492 \| 1.2495 \| 0.8516 \| \| 0.0 \| 124.0 \| 496 \| 1.2491 \| 0.8516 \| \| 0.0 \| 125.0 \| 500 \| 1.2491 \| 0.8516 \| \| 0.0 \| 126.0 \| 504 \| 1.2494 \| 0.8516 \| \| 0.0 \| 127.0 \| 508 \| 1.2498 \| 0.8516 \| \| 0.0 \| 128.0 \| 512 \| 1.2503 \| 0.8516 \| \| 0.0 \| 129.0 \| 516 \| 1.2509 \| 0.8516 \| \| 0.0 \| 130.0 \| 520 \| 1.2514 \| 0.8516 \| \| 0.0 \| 131.0 \| 524 \| 1.2519 \| 0.8516 \| \| 0.0 \| 132.0 \| 528 \| 1.2527 \| 0.8516 \| \| 0.0 \| 133.0 \| 532 \| 1.2535 \| 0.8516 \| \| 0.0 \| 134.0 \| 536 \| 1.2542 \| 0.8516 \| \| 0.0 \| 135.0 \| 540 \| 1.2549 \| 0.8516 \| \| 0.0 \| 136.0 \| 544 \| 1.2554 \| 0.8516 \| \| 0.0 \| 137.0 \| 548 \| 1.3879 \| 0.8359 \| \| 0.0001 \| 138.0 \| 552 \| 1.6893 \| 0.7969 \| \| 0.0001 \| 139.0 \| 556 \| 1.8348 \| 0.7969 \| \| 0.0 \| 140.0 \| 560 \| 1.8942 \| 0.7969 \| \| 0.0 \| 141.0 \| 564 \| 1.8778 \| 0.7969 \| \| 0.0 \| 142.0 \| 568 \| 1.7187 \| 0.8047 \| \| 0.0001 \| 143.0 \| 572 \| 1.6119 \| 0.8203 \| \| 0.0001 \| 144.0 \| 576 \| 1.5523 \| 0.8281 \| \| 0.0 \| 145.0 \| 580 \| 1.5189 \| 0.8281 \| \| 0.0 \| 146.0 \| 584 \| 1.5008 \| 0.8281 \| \| 0.0 \| 147.0 \| 588 \| 1.4916 \| 0.8281 \| \| 0.0 \| 148.0 \| 592 \| 1.4872 \| 0.8281 \| \| 0.0 \| 149.0 \| 596 \| 1.4854 \| 0.8281 \| \| 0.0 \| 150.0 \| 600 \| 1.4849 \| 0.8281 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3
Sukmin/ppo-u8	Sukmin	2023-07-26T02:11:19Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-07-20T05:10:04Z	--- 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: -164.28 +/- 84.41 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': 'Sukmin/ppo-u8' 'batch_size': 512 'minibatch_size': 128} ```
The-matt/law-qlora-polyglot-12.8b	The-matt	2023-07-26T02:09:02Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-26T01:35:09Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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 0.5.0.dev0
jonalkw/ppo_LunarLander-v2	jonalkw	2023-07-26T02:03:23Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-29T23:12:11Z	--- 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: 247.79 +/- 19.77 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 ... ```
simonycl/best_model-sst-2-64-21	simonycl	2023-07-26T02:03:20Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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	2023-07-26T01:52:00Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-64-21 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. --> # best_model-sst-2-64-21 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: - Loss: 1.0374 - Accuracy: 0.8906 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 4 \| 1.1068 \| 0.8672 \| \| No log \| 2.0 \| 8 \| 1.1055 \| 0.8672 \| \| 0.5789 \| 3.0 \| 12 \| 1.1002 \| 0.8672 \| \| 0.5789 \| 4.0 \| 16 \| 1.0902 \| 0.8672 \| \| 0.4952 \| 5.0 \| 20 \| 1.0797 \| 0.8672 \| \| 0.4952 \| 6.0 \| 24 \| 1.0662 \| 0.8672 \| \| 0.4952 \| 7.0 \| 28 \| 1.0461 \| 0.8672 \| \| 0.4202 \| 8.0 \| 32 \| 1.0329 \| 0.8672 \| \| 0.4202 \| 9.0 \| 36 \| 1.0326 \| 0.8672 \| \| 0.5159 \| 10.0 \| 40 \| 1.0217 \| 0.8672 \| \| 0.5159 \| 11.0 \| 44 \| 1.0053 \| 0.8672 \| \| 0.5159 \| 12.0 \| 48 \| 0.9908 \| 0.875 \| \| 0.4018 \| 13.0 \| 52 \| 0.9818 \| 0.8828 \| \| 0.4018 \| 14.0 \| 56 \| 0.9686 \| 0.8828 \| \| 0.2452 \| 15.0 \| 60 \| 0.9591 \| 0.8828 \| \| 0.2452 \| 16.0 \| 64 \| 0.9489 \| 0.8828 \| \| 0.2452 \| 17.0 \| 68 \| 0.9421 \| 0.8828 \| \| 0.1966 \| 18.0 \| 72 \| 0.9354 \| 0.8828 \| \| 0.1966 \| 19.0 \| 76 \| 0.9318 \| 0.8906 \| \| 0.1955 \| 20.0 \| 80 \| 0.9353 \| 0.8828 \| \| 0.1955 \| 21.0 \| 84 \| 0.9552 \| 0.8828 \| \| 0.1955 \| 22.0 \| 88 \| 0.9728 \| 0.875 \| \| 0.1316 \| 23.0 \| 92 \| 0.9686 \| 0.875 \| \| 0.1316 \| 24.0 \| 96 \| 0.9555 \| 0.875 \| \| 0.0488 \| 25.0 \| 100 \| 0.9442 \| 0.8828 \| \| 0.0488 \| 26.0 \| 104 \| 0.9410 \| 0.8828 \| \| 0.0488 \| 27.0 \| 108 \| 0.9413 \| 0.8828 \| \| 0.0023 \| 28.0 \| 112 \| 0.9522 \| 0.8828 \| \| 0.0023 \| 29.0 \| 116 \| 0.9614 \| 0.8828 \| \| 0.0019 \| 30.0 \| 120 \| 0.9603 \| 0.8828 \| \| 0.0019 \| 31.0 \| 124 \| 0.9474 \| 0.8828 \| \| 0.0019 \| 32.0 \| 128 \| 0.9408 \| 0.8906 \| \| 0.0136 \| 33.0 \| 132 \| 0.9417 \| 0.8906 \| \| 0.0136 \| 34.0 \| 136 \| 0.9433 \| 0.8906 \| \| 0.0037 \| 35.0 \| 140 \| 0.9412 \| 0.8906 \| \| 0.0037 \| 36.0 \| 144 \| 0.9529 \| 0.8906 \| \| 0.0037 \| 37.0 \| 148 \| 0.9641 \| 0.8828 \| \| 0.0003 \| 38.0 \| 152 \| 0.9868 \| 0.8828 \| \| 0.0003 \| 39.0 \| 156 \| 0.9985 \| 0.875 \| \| 0.0002 \| 40.0 \| 160 \| 1.0006 \| 0.875 \| \| 0.0002 \| 41.0 \| 164 \| 1.0009 \| 0.875 \| \| 0.0002 \| 42.0 \| 168 \| 1.0038 \| 0.875 \| \| 0.0013 \| 43.0 \| 172 \| 0.9982 \| 0.8828 \| \| 0.0013 \| 44.0 \| 176 \| 0.9853 \| 0.8828 \| \| 0.0102 \| 45.0 \| 180 \| 0.9790 \| 0.8828 \| \| 0.0102 \| 46.0 \| 184 \| 0.9900 \| 0.8828 \| \| 0.0102 \| 47.0 \| 188 \| 1.0004 \| 0.8828 \| \| 0.0002 \| 48.0 \| 192 \| 1.0063 \| 0.875 \| \| 0.0002 \| 49.0 \| 196 \| 1.0095 \| 0.875 \| \| 0.0001 \| 50.0 \| 200 \| 1.0136 \| 0.875 \| \| 0.0001 \| 51.0 \| 204 \| 1.0180 \| 0.8672 \| \| 0.0001 \| 52.0 \| 208 \| 1.0206 \| 0.8672 \| \| 0.0001 \| 53.0 \| 212 \| 1.0178 \| 0.8672 \| \| 0.0001 \| 54.0 \| 216 \| 1.0157 \| 0.8672 \| \| 0.0001 \| 55.0 \| 220 \| 1.0140 \| 0.875 \| \| 0.0001 \| 56.0 \| 224 \| 1.0128 \| 0.875 \| \| 0.0001 \| 57.0 \| 228 \| 1.0117 \| 0.875 \| \| 0.0001 \| 58.0 \| 232 \| 1.0097 \| 0.875 \| \| 0.0001 \| 59.0 \| 236 \| 1.0082 \| 0.875 \| \| 0.0001 \| 60.0 \| 240 \| 1.0002 \| 0.8828 \| \| 0.0001 \| 61.0 \| 244 \| 0.9944 \| 0.8828 \| \| 0.0001 \| 62.0 \| 248 \| 0.9913 \| 0.8906 \| \| 0.0001 \| 63.0 \| 252 \| 0.9897 \| 0.8906 \| \| 0.0001 \| 64.0 \| 256 \| 0.9893 \| 0.8906 \| \| 0.0001 \| 65.0 \| 260 \| 0.9895 \| 0.8906 \| \| 0.0001 \| 66.0 \| 264 \| 0.9899 \| 0.8906 \| \| 0.0001 \| 67.0 \| 268 \| 0.9905 \| 0.8906 \| \| 0.0001 \| 68.0 \| 272 \| 0.9913 \| 0.8906 \| \| 0.0001 \| 69.0 \| 276 \| 0.9962 \| 0.8906 \| \| 0.0001 \| 70.0 \| 280 \| 1.0023 \| 0.8828 \| \| 0.0001 \| 71.0 \| 284 \| 1.0079 \| 0.8828 \| \| 0.0001 \| 72.0 \| 288 \| 1.0118 \| 0.875 \| \| 0.0001 \| 73.0 \| 292 \| 1.0144 \| 0.875 \| \| 0.0001 \| 74.0 \| 296 \| 1.0161 \| 0.875 \| \| 0.0001 \| 75.0 \| 300 \| 1.0172 \| 0.875 \| \| 0.0001 \| 76.0 \| 304 \| 1.0178 \| 0.875 \| \| 0.0001 \| 77.0 \| 308 \| 1.0241 \| 0.875 \| \| 0.0183 \| 78.0 \| 312 \| 1.0549 \| 0.8672 \| \| 0.0183 \| 79.0 \| 316 \| 1.0631 \| 0.8672 \| \| 0.0001 \| 80.0 \| 320 \| 1.0629 \| 0.875 \| \| 0.0001 \| 81.0 \| 324 \| 1.0650 \| 0.875 \| \| 0.0001 \| 82.0 \| 328 \| 1.0672 \| 0.8594 \| \| 0.0001 \| 83.0 \| 332 \| 1.0686 \| 0.8594 \| \| 0.0001 \| 84.0 \| 336 \| 1.0632 \| 0.875 \| \| 0.0131 \| 85.0 \| 340 \| 1.0157 \| 0.8672 \| \| 0.0131 \| 86.0 \| 344 \| 0.9959 \| 0.8828 \| \| 0.0131 \| 87.0 \| 348 \| 0.9946 \| 0.8906 \| \| 0.0001 \| 88.0 \| 352 \| 0.9933 \| 0.8906 \| \| 0.0001 \| 89.0 \| 356 \| 0.9933 \| 0.8906 \| \| 0.0001 \| 90.0 \| 360 \| 0.9941 \| 0.8828 \| \| 0.0001 \| 91.0 \| 364 \| 0.9949 \| 0.8828 \| \| 0.0001 \| 92.0 \| 368 \| 0.9954 \| 0.8828 \| \| 0.0001 \| 93.0 \| 372 \| 0.9959 \| 0.8828 \| \| 0.0001 \| 94.0 \| 376 \| 0.9962 \| 0.8828 \| \| 0.0001 \| 95.0 \| 380 \| 0.9961 \| 0.8828 \| \| 0.0001 \| 96.0 \| 384 \| 0.9963 \| 0.8828 \| \| 0.0001 \| 97.0 \| 388 \| 0.9967 \| 0.8828 \| \| 0.0001 \| 98.0 \| 392 \| 0.9987 \| 0.8906 \| \| 0.0001 \| 99.0 \| 396 \| 1.0214 \| 0.8828 \| \| 0.0105 \| 100.0 \| 400 \| 1.0346 \| 0.875 \| \| 0.0105 \| 101.0 \| 404 \| 1.0406 \| 0.875 \| \| 0.0105 \| 102.0 \| 408 \| 1.0435 \| 0.875 \| \| 0.0001 \| 103.0 \| 412 \| 1.0444 \| 0.875 \| \| 0.0001 \| 104.0 \| 416 \| 1.0446 \| 0.875 \| \| 0.0001 \| 105.0 \| 420 \| 1.0447 \| 0.875 \| \| 0.0001 \| 106.0 \| 424 \| 1.0448 \| 0.875 \| \| 0.0001 \| 107.0 \| 428 \| 1.0453 \| 0.8828 \| \| 0.0001 \| 108.0 \| 432 \| 1.0457 \| 0.8828 \| \| 0.0001 \| 109.0 \| 436 \| 1.0488 \| 0.875 \| \| 0.0184 \| 110.0 \| 440 \| 1.0597 \| 0.875 \| \| 0.0184 \| 111.0 \| 444 \| 1.0939 \| 0.8594 \| \| 0.0184 \| 112.0 \| 448 \| 1.1410 \| 0.8438 \| \| 0.0001 \| 113.0 \| 452 \| 1.1659 \| 0.8438 \| \| 0.0001 \| 114.0 \| 456 \| 1.1104 \| 0.8594 \| \| 0.0001 \| 115.0 \| 460 \| 1.0816 \| 0.8672 \| \| 0.0001 \| 116.0 \| 464 \| 1.0695 \| 0.875 \| \| 0.0001 \| 117.0 \| 468 \| 1.0702 \| 0.875 \| \| 0.0 \| 118.0 \| 472 \| 1.0709 \| 0.875 \| \| 0.0 \| 119.0 \| 476 \| 1.0704 \| 0.875 \| \| 0.0 \| 120.0 \| 480 \| 1.0693 \| 0.875 \| \| 0.0 \| 121.0 \| 484 \| 1.0684 \| 0.875 \| \| 0.0 \| 122.0 \| 488 \| 1.0677 \| 0.875 \| \| 0.0 \| 123.0 \| 492 \| 1.0676 \| 0.875 \| \| 0.0 \| 124.0 \| 496 \| 1.0676 \| 0.875 \| \| 0.0 \| 125.0 \| 500 \| 1.0675 \| 0.875 \| \| 0.0 \| 126.0 \| 504 \| 1.0675 \| 0.875 \| \| 0.0 \| 127.0 \| 508 \| 1.0676 \| 0.875 \| \| 0.0 \| 128.0 \| 512 \| 1.0687 \| 0.875 \| \| 0.0 \| 129.0 \| 516 \| 1.0694 \| 0.875 \| \| 0.0 \| 130.0 \| 520 \| 1.0701 \| 0.875 \| \| 0.0 \| 131.0 \| 524 \| 1.0707 \| 0.875 \| \| 0.0 \| 132.0 \| 528 \| 1.0661 \| 0.875 \| \| 0.0001 \| 133.0 \| 532 \| 1.0391 \| 0.8906 \| \| 0.0001 \| 134.0 \| 536 \| 1.0258 \| 0.8906 \| \| 0.0 \| 135.0 \| 540 \| 1.0188 \| 0.8906 \| \| 0.0 \| 136.0 \| 544 \| 1.0171 \| 0.8906 \| \| 0.0 \| 137.0 \| 548 \| 1.0188 \| 0.8828 \| \| 0.0 \| 138.0 \| 552 \| 1.0210 \| 0.875 \| \| 0.0 \| 139.0 \| 556 \| 1.0223 \| 0.875 \| \| 0.0001 \| 140.0 \| 560 \| 1.0202 \| 0.8828 \| \| 0.0001 \| 141.0 \| 564 \| 1.0235 \| 0.8906 \| \| 0.0001 \| 142.0 \| 568 \| 1.0288 \| 0.8906 \| \| 0.0 \| 143.0 \| 572 \| 1.0322 \| 0.8906 \| \| 0.0 \| 144.0 \| 576 \| 1.0343 \| 0.8906 \| \| 0.0 \| 145.0 \| 580 \| 1.0356 \| 0.8906 \| \| 0.0 \| 146.0 \| 584 \| 1.0364 \| 0.8906 \| \| 0.0 \| 147.0 \| 588 \| 1.0369 \| 0.8906 \| \| 0.0 \| 148.0 \| 592 \| 1.0372 \| 0.8906 \| \| 0.0 \| 149.0 \| 596 \| 1.0374 \| 0.8906 \| \| 0.0 \| 150.0 \| 600 \| 1.0374 \| 0.8906 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3
simonycl/best_model-sst-2-32-100	simonycl	2023-07-26T01:39:26Z	107	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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	2023-07-26T01:33:39Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-32-100 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. --> # best_model-sst-2-32-100 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: - Loss: 0.5168 - Accuracy: 0.9219 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 2 \| 0.8101 \| 0.9062 \| \| No log \| 2.0 \| 4 \| 0.8102 \| 0.9062 \| \| No log \| 3.0 \| 6 \| 0.8102 \| 0.9062 \| \| No log \| 4.0 \| 8 \| 0.8100 \| 0.9062 \| \| 0.6019 \| 5.0 \| 10 \| 0.8098 \| 0.9062 \| \| 0.6019 \| 6.0 \| 12 \| 0.8095 \| 0.9062 \| \| 0.6019 \| 7.0 \| 14 \| 0.8090 \| 0.9062 \| \| 0.6019 \| 8.0 \| 16 \| 0.8085 \| 0.9062 \| \| 0.6019 \| 9.0 \| 18 \| 0.8079 \| 0.9062 \| \| 0.6181 \| 10.0 \| 20 \| 0.8073 \| 0.9062 \| \| 0.6181 \| 11.0 \| 22 \| 0.8066 \| 0.9062 \| \| 0.6181 \| 12.0 \| 24 \| 0.8061 \| 0.9062 \| \| 0.6181 \| 13.0 \| 26 \| 0.8055 \| 0.9062 \| \| 0.6181 \| 14.0 \| 28 \| 0.8048 \| 0.9062 \| \| 0.5045 \| 15.0 \| 30 \| 0.8037 \| 0.9062 \| \| 0.5045 \| 16.0 \| 32 \| 0.8020 \| 0.9062 \| \| 0.5045 \| 17.0 \| 34 \| 0.8003 \| 0.9062 \| \| 0.5045 \| 18.0 \| 36 \| 0.7978 \| 0.9062 \| \| 0.5045 \| 19.0 \| 38 \| 0.7955 \| 0.9062 \| \| 0.4784 \| 20.0 \| 40 \| 0.7928 \| 0.9062 \| \| 0.4784 \| 21.0 \| 42 \| 0.7902 \| 0.9062 \| \| 0.4784 \| 22.0 \| 44 \| 0.7868 \| 0.9062 \| \| 0.4784 \| 23.0 \| 46 \| 0.7824 \| 0.9062 \| \| 0.4784 \| 24.0 \| 48 \| 0.7764 \| 0.9062 \| \| 0.3582 \| 25.0 \| 50 \| 0.7695 \| 0.9062 \| \| 0.3582 \| 26.0 \| 52 \| 0.7628 \| 0.9062 \| \| 0.3582 \| 27.0 \| 54 \| 0.7548 \| 0.9062 \| \| 0.3582 \| 28.0 \| 56 \| 0.7473 \| 0.9062 \| \| 0.3582 \| 29.0 \| 58 \| 0.7388 \| 0.9062 \| \| 0.3152 \| 30.0 \| 60 \| 0.7286 \| 0.9062 \| \| 0.3152 \| 31.0 \| 62 \| 0.7145 \| 0.9062 \| \| 0.3152 \| 32.0 \| 64 \| 0.7007 \| 0.9062 \| \| 0.3152 \| 33.0 \| 66 \| 0.6860 \| 0.9062 \| \| 0.3152 \| 34.0 \| 68 \| 0.6662 \| 0.9062 \| \| 0.2403 \| 35.0 \| 70 \| 0.6377 \| 0.9062 \| \| 0.2403 \| 36.0 \| 72 \| 0.5941 \| 0.9062 \| \| 0.2403 \| 37.0 \| 74 \| 0.5458 \| 0.8906 \| \| 0.2403 \| 38.0 \| 76 \| 0.4985 \| 0.8906 \| \| 0.2403 \| 39.0 \| 78 \| 0.4676 \| 0.9219 \| \| 0.1021 \| 40.0 \| 80 \| 0.4598 \| 0.9219 \| \| 0.1021 \| 41.0 \| 82 \| 0.4572 \| 0.9375 \| \| 0.1021 \| 42.0 \| 84 \| 0.4521 \| 0.9375 \| \| 0.1021 \| 43.0 \| 86 \| 0.4493 \| 0.9375 \| \| 0.1021 \| 44.0 \| 88 \| 0.4420 \| 0.9375 \| \| 0.016 \| 45.0 \| 90 \| 0.4264 \| 0.9375 \| \| 0.016 \| 46.0 \| 92 \| 0.4104 \| 0.9375 \| \| 0.016 \| 47.0 \| 94 \| 0.4008 \| 0.9375 \| \| 0.016 \| 48.0 \| 96 \| 0.4056 \| 0.9062 \| \| 0.016 \| 49.0 \| 98 \| 0.4256 \| 0.9219 \| \| 0.0016 \| 50.0 \| 100 \| 0.4450 \| 0.9062 \| \| 0.0016 \| 51.0 \| 102 \| 0.4667 \| 0.9062 \| \| 0.0016 \| 52.0 \| 104 \| 0.4946 \| 0.9062 \| \| 0.0016 \| 53.0 \| 106 \| 0.5189 \| 0.9062 \| \| 0.0016 \| 54.0 \| 108 \| 0.5347 \| 0.9062 \| \| 0.0008 \| 55.0 \| 110 \| 0.5434 \| 0.9062 \| \| 0.0008 \| 56.0 \| 112 \| 0.5500 \| 0.9062 \| \| 0.0008 \| 57.0 \| 114 \| 0.5545 \| 0.9062 \| \| 0.0008 \| 58.0 \| 116 \| 0.5557 \| 0.9062 \| \| 0.0008 \| 59.0 \| 118 \| 0.5535 \| 0.9062 \| \| 0.0005 \| 60.0 \| 120 \| 0.5492 \| 0.9062 \| \| 0.0005 \| 61.0 \| 122 \| 0.5389 \| 0.9062 \| \| 0.0005 \| 62.0 \| 124 \| 0.5249 \| 0.9062 \| \| 0.0005 \| 63.0 \| 126 \| 0.5044 \| 0.9062 \| \| 0.0005 \| 64.0 \| 128 \| 0.4804 \| 0.9062 \| \| 0.0008 \| 65.0 \| 130 \| 0.4611 \| 0.9219 \| \| 0.0008 \| 66.0 \| 132 \| 0.4474 \| 0.9375 \| \| 0.0008 \| 67.0 \| 134 \| 0.4373 \| 0.9375 \| \| 0.0008 \| 68.0 \| 136 \| 0.4299 \| 0.9375 \| \| 0.0008 \| 69.0 \| 138 \| 0.4246 \| 0.9219 \| \| 0.0003 \| 70.0 \| 140 \| 0.4213 \| 0.9219 \| \| 0.0003 \| 71.0 \| 142 \| 0.4191 \| 0.9219 \| \| 0.0003 \| 72.0 \| 144 \| 0.4177 \| 0.9219 \| \| 0.0003 \| 73.0 \| 146 \| 0.4283 \| 0.9219 \| \| 0.0003 \| 74.0 \| 148 \| 0.4393 \| 0.9375 \| \| 0.0011 \| 75.0 \| 150 \| 0.4489 \| 0.9375 \| \| 0.0011 \| 76.0 \| 152 \| 0.4577 \| 0.9375 \| \| 0.0011 \| 77.0 \| 154 \| 0.4659 \| 0.9375 \| \| 0.0011 \| 78.0 \| 156 \| 0.4734 \| 0.9219 \| \| 0.0011 \| 79.0 \| 158 \| 0.4803 \| 0.9219 \| \| 0.0003 \| 80.0 \| 160 \| 0.4866 \| 0.9219 \| \| 0.0003 \| 81.0 \| 162 \| 0.4924 \| 0.9062 \| \| 0.0003 \| 82.0 \| 164 \| 0.4845 \| 0.9219 \| \| 0.0003 \| 83.0 \| 166 \| 0.4663 \| 0.9375 \| \| 0.0003 \| 84.0 \| 168 \| 0.4532 \| 0.9375 \| \| 0.0072 \| 85.0 \| 170 \| 0.4429 \| 0.9375 \| \| 0.0072 \| 86.0 \| 172 \| 0.4352 \| 0.9375 \| \| 0.0072 \| 87.0 \| 174 \| 0.4297 \| 0.9375 \| \| 0.0072 \| 88.0 \| 176 \| 0.4255 \| 0.9219 \| \| 0.0072 \| 89.0 \| 178 \| 0.4223 \| 0.9219 \| \| 0.0002 \| 90.0 \| 180 \| 0.4201 \| 0.9219 \| \| 0.0002 \| 91.0 \| 182 \| 0.4184 \| 0.9219 \| \| 0.0002 \| 92.0 \| 184 \| 0.4171 \| 0.9219 \| \| 0.0002 \| 93.0 \| 186 \| 0.4163 \| 0.9219 \| \| 0.0002 \| 94.0 \| 188 \| 0.4231 \| 0.9219 \| \| 0.0002 \| 95.0 \| 190 \| 0.4306 \| 0.9375 \| \| 0.0002 \| 96.0 \| 192 \| 0.4377 \| 0.9375 \| \| 0.0002 \| 97.0 \| 194 \| 0.4440 \| 0.9375 \| \| 0.0002 \| 98.0 \| 196 \| 0.4494 \| 0.9375 \| \| 0.0002 \| 99.0 \| 198 \| 0.4542 \| 0.9375 \| \| 0.0002 \| 100.0 \| 200 \| 0.4582 \| 0.9375 \| \| 0.0002 \| 101.0 \| 202 \| 0.4617 \| 0.9375 \| \| 0.0002 \| 102.0 \| 204 \| 0.4646 \| 0.9375 \| \| 0.0002 \| 103.0 \| 206 \| 0.4676 \| 0.9375 \| \| 0.0002 \| 104.0 \| 208 \| 0.4705 \| 0.9375 \| \| 0.0002 \| 105.0 \| 210 \| 0.4729 \| 0.9375 \| \| 0.0002 \| 106.0 \| 212 \| 0.4749 \| 0.9375 \| \| 0.0002 \| 107.0 \| 214 \| 0.4769 \| 0.9375 \| \| 0.0002 \| 108.0 \| 216 \| 0.4788 \| 0.9375 \| \| 0.0002 \| 109.0 \| 218 \| 0.4803 \| 0.9375 \| \| 0.0002 \| 110.0 \| 220 \| 0.4810 \| 0.9375 \| \| 0.0002 \| 111.0 \| 222 \| 0.4817 \| 0.9375 \| \| 0.0002 \| 112.0 \| 224 \| 0.4825 \| 0.9375 \| \| 0.0002 \| 113.0 \| 226 \| 0.4837 \| 0.9375 \| \| 0.0002 \| 114.0 \| 228 \| 0.4849 \| 0.9375 \| \| 0.0002 \| 115.0 \| 230 \| 0.4857 \| 0.9219 \| \| 0.0002 \| 116.0 \| 232 \| 0.4679 \| 0.9375 \| \| 0.0002 \| 117.0 \| 234 \| 0.4374 \| 0.9375 \| \| 0.0002 \| 118.0 \| 236 \| 0.4225 \| 0.9375 \| \| 0.0002 \| 119.0 \| 238 \| 0.4275 \| 0.9375 \| \| 0.0004 \| 120.0 \| 240 \| 0.4352 \| 0.9375 \| \| 0.0004 \| 121.0 \| 242 \| 0.4423 \| 0.9375 \| \| 0.0004 \| 122.0 \| 244 \| 0.4481 \| 0.9375 \| \| 0.0004 \| 123.0 \| 246 \| 0.4509 \| 0.9375 \| \| 0.0004 \| 124.0 \| 248 \| 0.4527 \| 0.9375 \| \| 0.0002 \| 125.0 \| 250 \| 0.4528 \| 0.9375 \| \| 0.0002 \| 126.0 \| 252 \| 0.4530 \| 0.9375 \| \| 0.0002 \| 127.0 \| 254 \| 0.4531 \| 0.9375 \| \| 0.0002 \| 128.0 \| 256 \| 0.4531 \| 0.9375 \| \| 0.0002 \| 129.0 \| 258 \| 0.4530 \| 0.9375 \| \| 0.0014 \| 130.0 \| 260 \| 0.4188 \| 0.9375 \| \| 0.0014 \| 131.0 \| 262 \| 0.4099 \| 0.9531 \| \| 0.0014 \| 132.0 \| 264 \| 0.4306 \| 0.9219 \| \| 0.0014 \| 133.0 \| 266 \| 0.4583 \| 0.9219 \| \| 0.0014 \| 134.0 \| 268 \| 0.4801 \| 0.9219 \| \| 0.0001 \| 135.0 \| 270 \| 0.4951 \| 0.9219 \| \| 0.0001 \| 136.0 \| 272 \| 0.5056 \| 0.9219 \| \| 0.0001 \| 137.0 \| 274 \| 0.5134 \| 0.9062 \| \| 0.0001 \| 138.0 \| 276 \| 0.5179 \| 0.9062 \| \| 0.0001 \| 139.0 \| 278 \| 0.5215 \| 0.9062 \| \| 0.0001 \| 140.0 \| 280 \| 0.5243 \| 0.9062 \| \| 0.0001 \| 141.0 \| 282 \| 0.5255 \| 0.9062 \| \| 0.0001 \| 142.0 \| 284 \| 0.5258 \| 0.9062 \| \| 0.0001 \| 143.0 \| 286 \| 0.5261 \| 0.9062 \| \| 0.0001 \| 144.0 \| 288 \| 0.5262 \| 0.9062 \| \| 0.0001 \| 145.0 \| 290 \| 0.5261 \| 0.9062 \| \| 0.0001 \| 146.0 \| 292 \| 0.5236 \| 0.9062 \| \| 0.0001 \| 147.0 \| 294 \| 0.5214 \| 0.9062 \| \| 0.0001 \| 148.0 \| 296 \| 0.5194 \| 0.9219 \| \| 0.0001 \| 149.0 \| 298 \| 0.5177 \| 0.9219 \| \| 0.0001 \| 150.0 \| 300 \| 0.5168 \| 0.9219 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3
simonycl/best_model-sst-2-32-87	simonycl	2023-07-26T01:33:23Z	108	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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	2023-07-26T01:27:32Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-32-87 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. --> # best_model-sst-2-32-87 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: - Loss: 1.0406 - Accuracy: 0.8438 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 2 \| 1.2928 \| 0.8438 \| \| No log \| 2.0 \| 4 \| 1.2923 \| 0.8438 \| \| No log \| 3.0 \| 6 \| 1.2917 \| 0.8438 \| \| No log \| 4.0 \| 8 \| 1.2902 \| 0.8438 \| \| 0.7235 \| 5.0 \| 10 \| 1.2884 \| 0.8438 \| \| 0.7235 \| 6.0 \| 12 \| 1.2856 \| 0.8438 \| \| 0.7235 \| 7.0 \| 14 \| 1.2829 \| 0.8438 \| \| 0.7235 \| 8.0 \| 16 \| 1.2800 \| 0.8281 \| \| 0.7235 \| 9.0 \| 18 \| 1.2769 \| 0.8281 \| \| 0.5899 \| 10.0 \| 20 \| 1.2742 \| 0.8281 \| \| 0.5899 \| 11.0 \| 22 \| 1.2710 \| 0.8281 \| \| 0.5899 \| 12.0 \| 24 \| 1.2662 \| 0.8281 \| \| 0.5899 \| 13.0 \| 26 \| 1.2590 \| 0.8281 \| \| 0.5899 \| 14.0 \| 28 \| 1.2466 \| 0.8281 \| \| 0.6318 \| 15.0 \| 30 \| 1.2287 \| 0.8281 \| \| 0.6318 \| 16.0 \| 32 \| 1.2138 \| 0.8281 \| \| 0.6318 \| 17.0 \| 34 \| 1.2024 \| 0.8281 \| \| 0.6318 \| 18.0 \| 36 \| 1.1924 \| 0.8281 \| \| 0.6318 \| 19.0 \| 38 \| 1.1838 \| 0.8281 \| \| 0.4743 \| 20.0 \| 40 \| 1.1729 \| 0.8281 \| \| 0.4743 \| 21.0 \| 42 \| 1.1591 \| 0.8281 \| \| 0.4743 \| 22.0 \| 44 \| 1.1527 \| 0.8281 \| \| 0.4743 \| 23.0 \| 46 \| 1.1459 \| 0.8281 \| \| 0.4743 \| 24.0 \| 48 \| 1.1407 \| 0.8281 \| \| 0.3414 \| 25.0 \| 50 \| 1.1351 \| 0.8281 \| \| 0.3414 \| 26.0 \| 52 \| 1.1305 \| 0.8281 \| \| 0.3414 \| 27.0 \| 54 \| 1.1230 \| 0.8281 \| \| 0.3414 \| 28.0 \| 56 \| 1.1087 \| 0.8281 \| \| 0.3414 \| 29.0 \| 58 \| 1.0831 \| 0.8281 \| \| 0.3141 \| 30.0 \| 60 \| 1.0555 \| 0.8281 \| \| 0.3141 \| 31.0 \| 62 \| 1.0313 \| 0.8438 \| \| 0.3141 \| 32.0 \| 64 \| 1.0141 \| 0.8594 \| \| 0.3141 \| 33.0 \| 66 \| 1.0063 \| 0.8438 \| \| 0.3141 \| 34.0 \| 68 \| 0.9990 \| 0.8438 \| \| 0.1594 \| 35.0 \| 70 \| 0.9916 \| 0.8438 \| \| 0.1594 \| 36.0 \| 72 \| 0.9884 \| 0.8438 \| \| 0.1594 \| 37.0 \| 74 \| 0.9922 \| 0.8438 \| \| 0.1594 \| 38.0 \| 76 \| 1.0013 \| 0.8281 \| \| 0.1594 \| 39.0 \| 78 \| 1.0097 \| 0.8281 \| \| 0.1018 \| 40.0 \| 80 \| 1.0209 \| 0.8281 \| \| 0.1018 \| 41.0 \| 82 \| 1.0341 \| 0.8281 \| \| 0.1018 \| 42.0 \| 84 \| 1.0352 \| 0.8281 \| \| 0.1018 \| 43.0 \| 86 \| 1.0284 \| 0.8281 \| \| 0.1018 \| 44.0 \| 88 \| 1.0236 \| 0.8281 \| \| 0.0404 \| 45.0 \| 90 \| 1.0214 \| 0.8438 \| \| 0.0404 \| 46.0 \| 92 \| 1.0237 \| 0.8594 \| \| 0.0404 \| 47.0 \| 94 \| 1.0233 \| 0.875 \| \| 0.0404 \| 48.0 \| 96 \| 1.0223 \| 0.875 \| \| 0.0404 \| 49.0 \| 98 \| 1.0187 \| 0.875 \| \| 0.0052 \| 50.0 \| 100 \| 1.0160 \| 0.8594 \| \| 0.0052 \| 51.0 \| 102 \| 1.0134 \| 0.8594 \| \| 0.0052 \| 52.0 \| 104 \| 1.0107 \| 0.8438 \| \| 0.0052 \| 53.0 \| 106 \| 1.0083 \| 0.8438 \| \| 0.0052 \| 54.0 \| 108 \| 1.0061 \| 0.8438 \| \| 0.0003 \| 55.0 \| 110 \| 1.0043 \| 0.8438 \| \| 0.0003 \| 56.0 \| 112 \| 1.0016 \| 0.8438 \| \| 0.0003 \| 57.0 \| 114 \| 0.9994 \| 0.8438 \| \| 0.0003 \| 58.0 \| 116 \| 0.9955 \| 0.8438 \| \| 0.0003 \| 59.0 \| 118 \| 0.9902 \| 0.8438 \| \| 0.0003 \| 60.0 \| 120 \| 0.9852 \| 0.8438 \| \| 0.0003 \| 61.0 \| 122 \| 0.9806 \| 0.8438 \| \| 0.0003 \| 62.0 \| 124 \| 0.9791 \| 0.8438 \| \| 0.0003 \| 63.0 \| 126 \| 0.9794 \| 0.8438 \| \| 0.0003 \| 64.0 \| 128 \| 0.9802 \| 0.8438 \| \| 0.0003 \| 65.0 \| 130 \| 0.9809 \| 0.8438 \| \| 0.0003 \| 66.0 \| 132 \| 0.9816 \| 0.8438 \| \| 0.0003 \| 67.0 \| 134 \| 0.9821 \| 0.8438 \| \| 0.0003 \| 68.0 \| 136 \| 0.9779 \| 0.8438 \| \| 0.0003 \| 69.0 \| 138 \| 0.9746 \| 0.8281 \| \| 0.0003 \| 70.0 \| 140 \| 0.9719 \| 0.8281 \| \| 0.0003 \| 71.0 \| 142 \| 0.9699 \| 0.8281 \| \| 0.0003 \| 72.0 \| 144 \| 0.9684 \| 0.8438 \| \| 0.0003 \| 73.0 \| 146 \| 0.9673 \| 0.8438 \| \| 0.0003 \| 74.0 \| 148 \| 0.9665 \| 0.8438 \| \| 0.0002 \| 75.0 \| 150 \| 0.9660 \| 0.8438 \| \| 0.0002 \| 76.0 \| 152 \| 0.9657 \| 0.8438 \| \| 0.0002 \| 77.0 \| 154 \| 0.9605 \| 0.8438 \| \| 0.0002 \| 78.0 \| 156 \| 0.9545 \| 0.8438 \| \| 0.0002 \| 79.0 \| 158 \| 0.9485 \| 0.8438 \| \| 0.0004 \| 80.0 \| 160 \| 0.9431 \| 0.8438 \| \| 0.0004 \| 81.0 \| 162 \| 0.9384 \| 0.8438 \| \| 0.0004 \| 82.0 \| 164 \| 0.9349 \| 0.8438 \| \| 0.0004 \| 83.0 \| 166 \| 0.9324 \| 0.8438 \| \| 0.0004 \| 84.0 \| 168 \| 0.9309 \| 0.8438 \| \| 0.0002 \| 85.0 \| 170 \| 0.9309 \| 0.8438 \| \| 0.0002 \| 86.0 \| 172 \| 0.9313 \| 0.8438 \| \| 0.0002 \| 87.0 \| 174 \| 0.9331 \| 0.8438 \| \| 0.0002 \| 88.0 \| 176 \| 0.9357 \| 0.8438 \| \| 0.0002 \| 89.0 \| 178 \| 0.9380 \| 0.8438 \| \| 0.0002 \| 90.0 \| 180 \| 0.9404 \| 0.8438 \| \| 0.0002 \| 91.0 \| 182 \| 0.9428 \| 0.8438 \| \| 0.0002 \| 92.0 \| 184 \| 0.9449 \| 0.8438 \| \| 0.0002 \| 93.0 \| 186 \| 0.9472 \| 0.8438 \| \| 0.0002 \| 94.0 \| 188 \| 0.9495 \| 0.8438 \| \| 0.0002 \| 95.0 \| 190 \| 0.9521 \| 0.8438 \| \| 0.0002 \| 96.0 \| 192 \| 0.9545 \| 0.8438 \| \| 0.0002 \| 97.0 \| 194 \| 0.9576 \| 0.8438 \| \| 0.0002 \| 98.0 \| 196 \| 0.9619 \| 0.8438 \| \| 0.0002 \| 99.0 \| 198 \| 0.9658 \| 0.8438 \| \| 0.0002 \| 100.0 \| 200 \| 0.9692 \| 0.8438 \| \| 0.0002 \| 101.0 \| 202 \| 0.9723 \| 0.8438 \| \| 0.0002 \| 102.0 \| 204 \| 0.9748 \| 0.8438 \| \| 0.0002 \| 103.0 \| 206 \| 0.9781 \| 0.8438 \| \| 0.0002 \| 104.0 \| 208 \| 0.9808 \| 0.8438 \| \| 0.0001 \| 105.0 \| 210 \| 0.9832 \| 0.8438 \| \| 0.0001 \| 106.0 \| 212 \| 0.9856 \| 0.8438 \| \| 0.0001 \| 107.0 \| 214 \| 0.9884 \| 0.8438 \| \| 0.0001 \| 108.0 \| 216 \| 0.9906 \| 0.8438 \| \| 0.0001 \| 109.0 \| 218 \| 0.9903 \| 0.8438 \| \| 0.0002 \| 110.0 \| 220 \| 0.9888 \| 0.8438 \| \| 0.0002 \| 111.0 \| 222 \| 0.9874 \| 0.8438 \| \| 0.0002 \| 112.0 \| 224 \| 0.9863 \| 0.8438 \| \| 0.0002 \| 113.0 \| 226 \| 0.9854 \| 0.8438 \| \| 0.0002 \| 114.0 \| 228 \| 0.9848 \| 0.8438 \| \| 0.0001 \| 115.0 \| 230 \| 0.9878 \| 0.8438 \| \| 0.0001 \| 116.0 \| 232 \| 0.9905 \| 0.8438 \| \| 0.0001 \| 117.0 \| 234 \| 0.9926 \| 0.8438 \| \| 0.0001 \| 118.0 \| 236 \| 0.9952 \| 0.8438 \| \| 0.0001 \| 119.0 \| 238 \| 1.0010 \| 0.8438 \| \| 0.0001 \| 120.0 \| 240 \| 1.0054 \| 0.8438 \| \| 0.0001 \| 121.0 \| 242 \| 1.0086 \| 0.8438 \| \| 0.0001 \| 122.0 \| 244 \| 1.0124 \| 0.8438 \| \| 0.0001 \| 123.0 \| 246 \| 1.0155 \| 0.8438 \| \| 0.0001 \| 124.0 \| 248 \| 1.0180 \| 0.8438 \| \| 0.0001 \| 125.0 \| 250 \| 1.0201 \| 0.8438 \| \| 0.0001 \| 126.0 \| 252 \| 1.0219 \| 0.8438 \| \| 0.0001 \| 127.0 \| 254 \| 1.0235 \| 0.8438 \| \| 0.0001 \| 128.0 \| 256 \| 1.0249 \| 0.8438 \| \| 0.0001 \| 129.0 \| 258 \| 1.0261 \| 0.8438 \| \| 0.0001 \| 130.0 \| 260 \| 1.0271 \| 0.8438 \| \| 0.0001 \| 131.0 \| 262 \| 1.0279 \| 0.8438 \| \| 0.0001 \| 132.0 \| 264 \| 1.0287 \| 0.8438 \| \| 0.0001 \| 133.0 \| 266 \| 1.0293 \| 0.8438 \| \| 0.0001 \| 134.0 \| 268 \| 1.0297 \| 0.8438 \| \| 0.0001 \| 135.0 \| 270 \| 1.0301 \| 0.8438 \| \| 0.0001 \| 136.0 \| 272 \| 1.0305 \| 0.8438 \| \| 0.0001 \| 137.0 \| 274 \| 1.0309 \| 0.8438 \| \| 0.0001 \| 138.0 \| 276 \| 1.0314 \| 0.8438 \| \| 0.0001 \| 139.0 \| 278 \| 1.0324 \| 0.8438 \| \| 0.0001 \| 140.0 \| 280 \| 1.0339 \| 0.8438 \| \| 0.0001 \| 141.0 \| 282 \| 1.0352 \| 0.8438 \| \| 0.0001 \| 142.0 \| 284 \| 1.0364 \| 0.8438 \| \| 0.0001 \| 143.0 \| 286 \| 1.0373 \| 0.8438 \| \| 0.0001 \| 144.0 \| 288 \| 1.0381 \| 0.8438 \| \| 0.0001 \| 145.0 \| 290 \| 1.0388 \| 0.8438 \| \| 0.0001 \| 146.0 \| 292 \| 1.0394 \| 0.8438 \| \| 0.0001 \| 147.0 \| 294 \| 1.0401 \| 0.8438 \| \| 0.0001 \| 148.0 \| 296 \| 1.0404 \| 0.8438 \| \| 0.0001 \| 149.0 \| 298 \| 1.0404 \| 0.8438 \| \| 0.0001 \| 150.0 \| 300 \| 1.0406 \| 0.8438 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3
simonycl/best_model-sst-2-32-42	simonycl	2023-07-26T01:27:17Z	108	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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	2023-07-26T01:21:29Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-32-42 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. --> # best_model-sst-2-32-42 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: - Loss: 1.2575 - Accuracy: 0.8281 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 2 \| 1.2950 \| 0.8281 \| \| No log \| 2.0 \| 4 \| 1.2965 \| 0.8281 \| \| No log \| 3.0 \| 6 \| 1.2971 \| 0.8281 \| \| No log \| 4.0 \| 8 \| 1.2972 \| 0.8281 \| \| 0.3346 \| 5.0 \| 10 \| 1.2994 \| 0.8281 \| \| 0.3346 \| 6.0 \| 12 \| 1.3037 \| 0.8281 \| \| 0.3346 \| 7.0 \| 14 \| 1.3082 \| 0.8281 \| \| 0.3346 \| 8.0 \| 16 \| 1.3140 \| 0.8281 \| \| 0.3346 \| 9.0 \| 18 \| 1.3212 \| 0.8281 \| \| 0.2586 \| 10.0 \| 20 \| 1.3285 \| 0.8281 \| \| 0.2586 \| 11.0 \| 22 \| 1.3346 \| 0.8281 \| \| 0.2586 \| 12.0 \| 24 \| 1.3404 \| 0.8281 \| \| 0.2586 \| 13.0 \| 26 \| 1.3443 \| 0.8281 \| \| 0.2586 \| 14.0 \| 28 \| 1.3499 \| 0.8281 \| \| 0.2171 \| 15.0 \| 30 \| 1.3534 \| 0.8281 \| \| 0.2171 \| 16.0 \| 32 \| 1.3551 \| 0.8281 \| \| 0.2171 \| 17.0 \| 34 \| 1.3544 \| 0.8281 \| \| 0.2171 \| 18.0 \| 36 \| 1.3531 \| 0.8281 \| \| 0.2171 \| 19.0 \| 38 \| 1.3516 \| 0.8281 \| \| 0.1549 \| 20.0 \| 40 \| 1.3494 \| 0.8281 \| \| 0.1549 \| 21.0 \| 42 \| 1.3471 \| 0.8281 \| \| 0.1549 \| 22.0 \| 44 \| 1.3446 \| 0.8281 \| \| 0.1549 \| 23.0 \| 46 \| 1.3414 \| 0.8281 \| \| 0.1549 \| 24.0 \| 48 \| 1.3351 \| 0.8281 \| \| 0.0613 \| 25.0 \| 50 \| 1.3277 \| 0.8281 \| \| 0.0613 \| 26.0 \| 52 \| 1.3201 \| 0.8281 \| \| 0.0613 \| 27.0 \| 54 \| 1.3110 \| 0.8281 \| \| 0.0613 \| 28.0 \| 56 \| 1.2974 \| 0.8281 \| \| 0.0613 \| 29.0 \| 58 \| 1.2847 \| 0.8281 \| \| 0.0094 \| 30.0 \| 60 \| 1.2767 \| 0.8281 \| \| 0.0094 \| 31.0 \| 62 \| 1.2697 \| 0.8281 \| \| 0.0094 \| 32.0 \| 64 \| 1.2638 \| 0.8281 \| \| 0.0094 \| 33.0 \| 66 \| 1.2625 \| 0.8281 \| \| 0.0094 \| 34.0 \| 68 \| 1.2633 \| 0.8281 \| \| 0.0004 \| 35.0 \| 70 \| 1.2642 \| 0.8281 \| \| 0.0004 \| 36.0 \| 72 \| 1.2757 \| 0.8281 \| \| 0.0004 \| 37.0 \| 74 \| 1.2783 \| 0.8281 \| \| 0.0004 \| 38.0 \| 76 \| 1.2813 \| 0.8281 \| \| 0.0004 \| 39.0 \| 78 \| 1.2892 \| 0.8281 \| \| 0.0074 \| 40.0 \| 80 \| 1.2990 \| 0.8281 \| \| 0.0074 \| 41.0 \| 82 \| 1.3111 \| 0.8281 \| \| 0.0074 \| 42.0 \| 84 \| 1.3233 \| 0.8281 \| \| 0.0074 \| 43.0 \| 86 \| 1.3317 \| 0.8281 \| \| 0.0074 \| 44.0 \| 88 \| 1.3371 \| 0.8281 \| \| 0.0004 \| 45.0 \| 90 \| 1.3410 \| 0.8281 \| \| 0.0004 \| 46.0 \| 92 \| 1.3436 \| 0.8281 \| \| 0.0004 \| 47.0 \| 94 \| 1.3456 \| 0.8281 \| \| 0.0004 \| 48.0 \| 96 \| 1.3471 \| 0.8281 \| \| 0.0004 \| 49.0 \| 98 \| 1.3489 \| 0.8281 \| \| 0.0005 \| 50.0 \| 100 \| 1.3488 \| 0.8281 \| \| 0.0005 \| 51.0 \| 102 \| 1.3429 \| 0.8281 \| \| 0.0005 \| 52.0 \| 104 \| 1.3365 \| 0.8281 \| \| 0.0005 \| 53.0 \| 106 \| 1.3305 \| 0.8281 \| \| 0.0005 \| 54.0 \| 108 \| 1.3247 \| 0.8281 \| \| 0.0003 \| 55.0 \| 110 \| 1.3195 \| 0.8281 \| \| 0.0003 \| 56.0 \| 112 \| 1.3151 \| 0.8281 \| \| 0.0003 \| 57.0 \| 114 \| 1.2921 \| 0.8281 \| \| 0.0003 \| 58.0 \| 116 \| 1.2717 \| 0.8281 \| \| 0.0003 \| 59.0 \| 118 \| 1.2551 \| 0.8281 \| \| 0.0166 \| 60.0 \| 120 \| 1.2421 \| 0.8281 \| \| 0.0166 \| 61.0 \| 122 \| 1.2590 \| 0.8281 \| \| 0.0166 \| 62.0 \| 124 \| 1.2739 \| 0.8281 \| \| 0.0166 \| 63.0 \| 126 \| 1.2861 \| 0.8281 \| \| 0.0166 \| 64.0 \| 128 \| 1.2958 \| 0.8281 \| \| 0.0003 \| 65.0 \| 130 \| 1.3039 \| 0.8281 \| \| 0.0003 \| 66.0 \| 132 \| 1.3103 \| 0.8281 \| \| 0.0003 \| 67.0 \| 134 \| 1.3126 \| 0.8281 \| \| 0.0003 \| 68.0 \| 136 \| 1.3125 \| 0.8281 \| \| 0.0003 \| 69.0 \| 138 \| 1.3125 \| 0.8281 \| \| 0.0002 \| 70.0 \| 140 \| 1.3128 \| 0.8281 \| \| 0.0002 \| 71.0 \| 142 \| 1.3131 \| 0.8281 \| \| 0.0002 \| 72.0 \| 144 \| 1.3135 \| 0.8281 \| \| 0.0002 \| 73.0 \| 146 \| 1.3141 \| 0.8281 \| \| 0.0002 \| 74.0 \| 148 \| 1.3147 \| 0.8281 \| \| 0.0004 \| 75.0 \| 150 \| 1.3289 \| 0.8281 \| \| 0.0004 \| 76.0 \| 152 \| 1.3274 \| 0.8281 \| \| 0.0004 \| 77.0 \| 154 \| 1.3260 \| 0.8281 \| \| 0.0004 \| 78.0 \| 156 \| 1.3251 \| 0.8281 \| \| 0.0004 \| 79.0 \| 158 \| 1.3523 \| 0.8281 \| \| 0.0008 \| 80.0 \| 160 \| 1.3691 \| 0.8281 \| \| 0.0008 \| 81.0 \| 162 \| 1.3789 \| 0.8281 \| \| 0.0008 \| 82.0 \| 164 \| 1.3844 \| 0.8281 \| \| 0.0008 \| 83.0 \| 166 \| 1.3873 \| 0.8281 \| \| 0.0008 \| 84.0 \| 168 \| 1.3885 \| 0.8281 \| \| 0.0002 \| 85.0 \| 170 \| 1.3889 \| 0.8281 \| \| 0.0002 \| 86.0 \| 172 \| 1.3889 \| 0.8281 \| \| 0.0002 \| 87.0 \| 174 \| 1.3888 \| 0.8281 \| \| 0.0002 \| 88.0 \| 176 \| 1.3888 \| 0.8281 \| \| 0.0002 \| 89.0 \| 178 \| 1.3890 \| 0.8281 \| \| 0.0002 \| 90.0 \| 180 \| 1.3893 \| 0.8281 \| \| 0.0002 \| 91.0 \| 182 \| 1.3898 \| 0.8281 \| \| 0.0002 \| 92.0 \| 184 \| 1.3905 \| 0.8281 \| \| 0.0002 \| 93.0 \| 186 \| 1.3913 \| 0.8281 \| \| 0.0002 \| 94.0 \| 188 \| 1.3927 \| 0.8281 \| \| 0.0002 \| 95.0 \| 190 \| 1.3938 \| 0.8281 \| \| 0.0002 \| 96.0 \| 192 \| 1.3947 \| 0.8281 \| \| 0.0002 \| 97.0 \| 194 \| 1.3954 \| 0.8281 \| \| 0.0002 \| 98.0 \| 196 \| 1.3960 \| 0.8281 \| \| 0.0002 \| 99.0 \| 198 \| 1.3967 \| 0.8281 \| \| 0.0002 \| 100.0 \| 200 \| 1.3975 \| 0.8281 \| \| 0.0002 \| 101.0 \| 202 \| 1.3984 \| 0.8281 \| \| 0.0002 \| 102.0 \| 204 \| 1.3993 \| 0.8281 \| \| 0.0002 \| 103.0 \| 206 \| 1.4001 \| 0.8281 \| \| 0.0002 \| 104.0 \| 208 \| 1.4008 \| 0.8281 \| \| 0.0001 \| 105.0 \| 210 \| 1.4014 \| 0.8281 \| \| 0.0001 \| 106.0 \| 212 \| 1.4020 \| 0.8281 \| \| 0.0001 \| 107.0 \| 214 \| 1.4026 \| 0.8281 \| \| 0.0001 \| 108.0 \| 216 \| 1.4030 \| 0.8281 \| \| 0.0001 \| 109.0 \| 218 \| 1.4035 \| 0.8281 \| \| 0.0001 \| 110.0 \| 220 \| 1.4040 \| 0.8281 \| \| 0.0001 \| 111.0 \| 222 \| 1.4046 \| 0.8281 \| \| 0.0001 \| 112.0 \| 224 \| 1.4051 \| 0.8281 \| \| 0.0001 \| 113.0 \| 226 \| 1.4057 \| 0.8281 \| \| 0.0001 \| 114.0 \| 228 \| 1.4064 \| 0.8281 \| \| 0.0001 \| 115.0 \| 230 \| 1.4071 \| 0.8281 \| \| 0.0001 \| 116.0 \| 232 \| 1.4078 \| 0.8281 \| \| 0.0001 \| 117.0 \| 234 \| 1.4085 \| 0.8281 \| \| 0.0001 \| 118.0 \| 236 \| 1.4092 \| 0.8281 \| \| 0.0001 \| 119.0 \| 238 \| 1.4099 \| 0.8281 \| \| 0.0001 \| 120.0 \| 240 \| 1.4106 \| 0.8281 \| \| 0.0001 \| 121.0 \| 242 \| 1.4108 \| 0.8281 \| \| 0.0001 \| 122.0 \| 244 \| 1.4081 \| 0.8281 \| \| 0.0001 \| 123.0 \| 246 \| 1.4055 \| 0.8281 \| \| 0.0001 \| 124.0 \| 248 \| 1.4032 \| 0.8281 \| \| 0.0001 \| 125.0 \| 250 \| 1.4011 \| 0.8281 \| \| 0.0001 \| 126.0 \| 252 \| 1.3995 \| 0.8281 \| \| 0.0001 \| 127.0 \| 254 \| 1.3982 \| 0.8281 \| \| 0.0001 \| 128.0 \| 256 \| 1.3973 \| 0.8281 \| \| 0.0001 \| 129.0 \| 258 \| 1.3967 \| 0.8281 \| \| 0.0001 \| 130.0 \| 260 \| 1.3963 \| 0.8281 \| \| 0.0001 \| 131.0 \| 262 \| 1.3962 \| 0.8281 \| \| 0.0001 \| 132.0 \| 264 \| 1.3962 \| 0.8281 \| \| 0.0001 \| 133.0 \| 266 \| 1.3965 \| 0.8281 \| \| 0.0001 \| 134.0 \| 268 \| 1.3970 \| 0.8281 \| \| 0.0001 \| 135.0 \| 270 \| 1.3989 \| 0.8281 \| \| 0.0001 \| 136.0 \| 272 \| 1.4012 \| 0.8281 \| \| 0.0001 \| 137.0 \| 274 \| 1.4035 \| 0.8281 \| \| 0.0001 \| 138.0 \| 276 \| 1.4052 \| 0.8281 \| \| 0.0001 \| 139.0 \| 278 \| 1.4064 \| 0.8281 \| \| 0.0002 \| 140.0 \| 280 \| 1.3703 \| 0.8281 \| \| 0.0002 \| 141.0 \| 282 \| 1.2995 \| 0.8438 \| \| 0.0002 \| 142.0 \| 284 \| 1.2572 \| 0.8281 \| \| 0.0002 \| 143.0 \| 286 \| 1.2224 \| 0.8281 \| \| 0.0002 \| 144.0 \| 288 \| 1.2120 \| 0.8438 \| \| 0.0001 \| 145.0 \| 290 \| 1.2242 \| 0.8281 \| \| 0.0001 \| 146.0 \| 292 \| 1.2377 \| 0.8281 \| \| 0.0001 \| 147.0 \| 294 \| 1.2477 \| 0.8281 \| \| 0.0001 \| 148.0 \| 296 \| 1.2542 \| 0.8281 \| \| 0.0001 \| 149.0 \| 298 \| 1.2575 \| 0.8281 \| \| 0.0002 \| 150.0 \| 300 \| 1.2575 \| 0.8281 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3
Potemkin87/llama2-qlora-finetunined-french	Potemkin87	2023-07-26T01:24:18Z	1	1	peft	[ "peft", "region:us" ]	null	2023-07-26T01:24:04Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
Guilherme34/Jennifer-beta-test-donotdownloaditsjustatest	Guilherme34	2023-07-26T01:15:29Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-25T21:42:23Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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 0.4.0
simonycl/best_model-sst-2-16-100	simonycl	2023-07-26T01:08:23Z	106	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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	2023-07-26T01:04:56Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-16-100 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. --> # best_model-sst-2-16-100 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: - Loss: 0.4760 - Accuracy: 0.9062 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 1 \| 0.3957 \| 0.875 \| \| No log \| 2.0 \| 2 \| 0.3958 \| 0.875 \| \| No log \| 3.0 \| 3 \| 0.3961 \| 0.875 \| \| No log \| 4.0 \| 4 \| 0.3964 \| 0.875 \| \| No log \| 5.0 \| 5 \| 0.3968 \| 0.875 \| \| No log \| 6.0 \| 6 \| 0.3971 \| 0.875 \| \| No log \| 7.0 \| 7 \| 0.3974 \| 0.875 \| \| No log \| 8.0 \| 8 \| 0.3976 \| 0.875 \| \| No log \| 9.0 \| 9 \| 0.3978 \| 0.875 \| \| 0.2951 \| 10.0 \| 10 \| 0.3979 \| 0.875 \| \| 0.2951 \| 11.0 \| 11 \| 0.3977 \| 0.875 \| \| 0.2951 \| 12.0 \| 12 \| 0.3971 \| 0.875 \| \| 0.2951 \| 13.0 \| 13 \| 0.3963 \| 0.875 \| \| 0.2951 \| 14.0 \| 14 \| 0.3954 \| 0.875 \| \| 0.2951 \| 15.0 \| 15 \| 0.3943 \| 0.875 \| \| 0.2951 \| 16.0 \| 16 \| 0.3929 \| 0.875 \| \| 0.2951 \| 17.0 \| 17 \| 0.3912 \| 0.875 \| \| 0.2951 \| 18.0 \| 18 \| 0.3895 \| 0.875 \| \| 0.2951 \| 19.0 \| 19 \| 0.3876 \| 0.875 \| \| 0.2889 \| 20.0 \| 20 \| 0.3854 \| 0.875 \| \| 0.2889 \| 21.0 \| 21 \| 0.3830 \| 0.875 \| \| 0.2889 \| 22.0 \| 22 \| 0.3806 \| 0.875 \| \| 0.2889 \| 23.0 \| 23 \| 0.3789 \| 0.875 \| \| 0.2889 \| 24.0 \| 24 \| 0.3770 \| 0.875 \| \| 0.2889 \| 25.0 \| 25 \| 0.3755 \| 0.9062 \| \| 0.2889 \| 26.0 \| 26 \| 0.3739 \| 0.9062 \| \| 0.2889 \| 27.0 \| 27 \| 0.3728 \| 0.9062 \| \| 0.2889 \| 28.0 \| 28 \| 0.3716 \| 0.9062 \| \| 0.2889 \| 29.0 \| 29 \| 0.3704 \| 0.9062 \| \| 0.2147 \| 30.0 \| 30 \| 0.3697 \| 0.9062 \| \| 0.2147 \| 31.0 \| 31 \| 0.3692 \| 0.9062 \| \| 0.2147 \| 32.0 \| 32 \| 0.3688 \| 0.9062 \| \| 0.2147 \| 33.0 \| 33 \| 0.3686 \| 0.9062 \| \| 0.2147 \| 34.0 \| 34 \| 0.3684 \| 0.9062 \| \| 0.2147 \| 35.0 \| 35 \| 0.3683 \| 0.9062 \| \| 0.2147 \| 36.0 \| 36 \| 0.3682 \| 0.9062 \| \| 0.2147 \| 37.0 \| 37 \| 0.3684 \| 0.9062 \| \| 0.2147 \| 38.0 \| 38 \| 0.3684 \| 0.9062 \| \| 0.2147 \| 39.0 \| 39 \| 0.3685 \| 0.9062 \| \| 0.1272 \| 40.0 \| 40 \| 0.3689 \| 0.9062 \| \| 0.1272 \| 41.0 \| 41 \| 0.3693 \| 0.9062 \| \| 0.1272 \| 42.0 \| 42 \| 0.3701 \| 0.9062 \| \| 0.1272 \| 43.0 \| 43 \| 0.3709 \| 0.875 \| \| 0.1272 \| 44.0 \| 44 \| 0.3719 \| 0.875 \| \| 0.1272 \| 45.0 \| 45 \| 0.3728 \| 0.875 \| \| 0.1272 \| 46.0 \| 46 \| 0.3731 \| 0.875 \| \| 0.1272 \| 47.0 \| 47 \| 0.3728 \| 0.875 \| \| 0.1272 \| 48.0 \| 48 \| 0.3729 \| 0.875 \| \| 0.1272 \| 49.0 \| 49 \| 0.3726 \| 0.875 \| \| 0.0531 \| 50.0 \| 50 \| 0.3726 \| 0.875 \| \| 0.0531 \| 51.0 \| 51 \| 0.3721 \| 0.875 \| \| 0.0531 \| 52.0 \| 52 \| 0.3716 \| 0.875 \| \| 0.0531 \| 53.0 \| 53 \| 0.3715 \| 0.875 \| \| 0.0531 \| 54.0 \| 54 \| 0.3707 \| 0.875 \| \| 0.0531 \| 55.0 \| 55 \| 0.3706 \| 0.875 \| \| 0.0531 \| 56.0 \| 56 \| 0.3702 \| 0.875 \| \| 0.0531 \| 57.0 \| 57 \| 0.3707 \| 0.875 \| \| 0.0531 \| 58.0 \| 58 \| 0.3716 \| 0.875 \| \| 0.0531 \| 59.0 \| 59 \| 0.3735 \| 0.875 \| \| 0.0221 \| 60.0 \| 60 \| 0.3754 \| 0.875 \| \| 0.0221 \| 61.0 \| 61 \| 0.3775 \| 0.875 \| \| 0.0221 \| 62.0 \| 62 \| 0.3801 \| 0.875 \| \| 0.0221 \| 63.0 \| 63 \| 0.3824 \| 0.875 \| \| 0.0221 \| 64.0 \| 64 \| 0.3847 \| 0.875 \| \| 0.0221 \| 65.0 \| 65 \| 0.3871 \| 0.875 \| \| 0.0221 \| 66.0 \| 66 \| 0.3883 \| 0.875 \| \| 0.0221 \| 67.0 \| 67 \| 0.3885 \| 0.875 \| \| 0.0221 \| 68.0 \| 68 \| 0.3886 \| 0.875 \| \| 0.0221 \| 69.0 \| 69 \| 0.3876 \| 0.875 \| \| 0.0151 \| 70.0 \| 70 \| 0.3869 \| 0.875 \| \| 0.0151 \| 71.0 \| 71 \| 0.3869 \| 0.875 \| \| 0.0151 \| 72.0 \| 72 \| 0.3871 \| 0.875 \| \| 0.0151 \| 73.0 \| 73 \| 0.3875 \| 0.875 \| \| 0.0151 \| 74.0 \| 74 \| 0.3872 \| 0.875 \| \| 0.0151 \| 75.0 \| 75 \| 0.3873 \| 0.875 \| \| 0.0151 \| 76.0 \| 76 \| 0.3869 \| 0.875 \| \| 0.0151 \| 77.0 \| 77 \| 0.3868 \| 0.875 \| \| 0.0151 \| 78.0 \| 78 \| 0.3876 \| 0.9062 \| \| 0.0151 \| 79.0 \| 79 \| 0.3885 \| 0.9062 \| \| 0.0099 \| 80.0 \| 80 \| 0.3896 \| 0.9062 \| \| 0.0099 \| 81.0 \| 81 \| 0.3908 \| 0.9062 \| \| 0.0099 \| 82.0 \| 82 \| 0.3921 \| 0.9062 \| \| 0.0099 \| 83.0 \| 83 \| 0.3935 \| 0.9062 \| \| 0.0099 \| 84.0 \| 84 \| 0.3952 \| 0.9062 \| \| 0.0099 \| 85.0 \| 85 \| 0.3972 \| 0.9062 \| \| 0.0099 \| 86.0 \| 86 \| 0.3992 \| 0.9062 \| \| 0.0099 \| 87.0 \| 87 \| 0.4017 \| 0.9062 \| \| 0.0099 \| 88.0 \| 88 \| 0.4042 \| 0.9062 \| \| 0.0099 \| 89.0 \| 89 \| 0.4062 \| 0.9062 \| \| 0.0074 \| 90.0 \| 90 \| 0.4082 \| 0.9062 \| \| 0.0074 \| 91.0 \| 91 \| 0.4100 \| 0.9062 \| \| 0.0074 \| 92.0 \| 92 \| 0.4118 \| 0.9062 \| \| 0.0074 \| 93.0 \| 93 \| 0.4135 \| 0.9062 \| \| 0.0074 \| 94.0 \| 94 \| 0.4152 \| 0.9062 \| \| 0.0074 \| 95.0 \| 95 \| 0.4169 \| 0.9062 \| \| 0.0074 \| 96.0 \| 96 \| 0.4185 \| 0.9062 \| \| 0.0074 \| 97.0 \| 97 \| 0.4198 \| 0.9062 \| \| 0.0074 \| 98.0 \| 98 \| 0.4211 \| 0.9062 \| \| 0.0074 \| 99.0 \| 99 \| 0.4224 \| 0.9062 \| \| 0.006 \| 100.0 \| 100 \| 0.4236 \| 0.9062 \| \| 0.006 \| 101.0 \| 101 \| 0.4248 \| 0.9062 \| \| 0.006 \| 102.0 \| 102 \| 0.4259 \| 0.9062 \| \| 0.006 \| 103.0 \| 103 \| 0.4271 \| 0.9062 \| \| 0.006 \| 104.0 \| 104 \| 0.4284 \| 0.9062 \| \| 0.006 \| 105.0 \| 105 \| 0.4296 \| 0.9062 \| \| 0.006 \| 106.0 \| 106 \| 0.4298 \| 0.9062 \| \| 0.006 \| 107.0 \| 107 \| 0.4283 \| 0.9062 \| \| 0.006 \| 108.0 \| 108 \| 0.4276 \| 0.9062 \| \| 0.006 \| 109.0 \| 109 \| 0.4275 \| 0.9062 \| \| 0.0065 \| 110.0 \| 110 \| 0.4280 \| 0.9062 \| \| 0.0065 \| 111.0 \| 111 \| 0.4287 \| 0.9062 \| \| 0.0065 \| 112.0 \| 112 \| 0.4297 \| 0.9062 \| \| 0.0065 \| 113.0 \| 113 \| 0.4309 \| 0.9062 \| \| 0.0065 \| 114.0 \| 114 \| 0.4322 \| 0.9062 \| \| 0.0065 \| 115.0 \| 115 \| 0.4337 \| 0.9062 \| \| 0.0065 \| 116.0 \| 116 \| 0.4352 \| 0.9062 \| \| 0.0065 \| 117.0 \| 117 \| 0.4367 \| 0.9062 \| \| 0.0065 \| 118.0 \| 118 \| 0.4383 \| 0.9062 \| \| 0.0065 \| 119.0 \| 119 \| 0.4399 \| 0.9062 \| \| 0.0046 \| 120.0 \| 120 \| 0.4413 \| 0.9062 \| \| 0.0046 \| 121.0 \| 121 \| 0.4428 \| 0.9062 \| \| 0.0046 \| 122.0 \| 122 \| 0.4443 \| 0.9062 \| \| 0.0046 \| 123.0 \| 123 \| 0.4457 \| 0.9062 \| \| 0.0046 \| 124.0 \| 124 \| 0.4470 \| 0.9062 \| \| 0.0046 \| 125.0 \| 125 \| 0.4483 \| 0.9062 \| \| 0.0046 \| 126.0 \| 126 \| 0.4495 \| 0.9062 \| \| 0.0046 \| 127.0 \| 127 \| 0.4508 \| 0.9062 \| \| 0.0046 \| 128.0 \| 128 \| 0.4520 \| 0.9062 \| \| 0.0046 \| 129.0 \| 129 \| 0.4531 \| 0.9062 \| \| 0.0037 \| 130.0 \| 130 \| 0.4543 \| 0.9062 \| \| 0.0037 \| 131.0 \| 131 \| 0.4555 \| 0.9062 \| \| 0.0037 \| 132.0 \| 132 \| 0.4566 \| 0.9062 \| \| 0.0037 \| 133.0 \| 133 \| 0.4577 \| 0.9062 \| \| 0.0037 \| 134.0 \| 134 \| 0.4588 \| 0.9062 \| \| 0.0037 \| 135.0 \| 135 \| 0.4599 \| 0.9062 \| \| 0.0037 \| 136.0 \| 136 \| 0.4610 \| 0.9062 \| \| 0.0037 \| 137.0 \| 137 \| 0.4622 \| 0.9062 \| \| 0.0037 \| 138.0 \| 138 \| 0.4633 \| 0.9062 \| \| 0.0037 \| 139.0 \| 139 \| 0.4644 \| 0.9062 \| \| 0.0033 \| 140.0 \| 140 \| 0.4655 \| 0.9062 \| \| 0.0033 \| 141.0 \| 141 \| 0.4666 \| 0.9062 \| \| 0.0033 \| 142.0 \| 142 \| 0.4677 \| 0.9062 \| \| 0.0033 \| 143.0 \| 143 \| 0.4688 \| 0.9062 \| \| 0.0033 \| 144.0 \| 144 \| 0.4700 \| 0.9062 \| \| 0.0033 \| 145.0 \| 145 \| 0.4712 \| 0.9062 \| \| 0.0033 \| 146.0 \| 146 \| 0.4725 \| 0.9062 \| \| 0.0033 \| 147.0 \| 147 \| 0.4733 \| 0.9062 \| \| 0.0033 \| 148.0 \| 148 \| 0.4742 \| 0.9062 \| \| 0.0033 \| 149.0 \| 149 \| 0.4751 \| 0.9062 \| \| 0.0029 \| 150.0 \| 150 \| 0.4760 \| 0.9062 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3
simonycl/best_model-sst-2-16-87	simonycl	2023-07-26T01:04:41Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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	2023-07-26T01:01:18Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-16-87 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. --> # best_model-sst-2-16-87 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: - Loss: 0.6392 - Accuracy: 0.875 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 1 \| 0.5816 \| 0.8438 \| \| No log \| 2.0 \| 2 \| 0.5813 \| 0.8438 \| \| No log \| 3.0 \| 3 \| 0.5807 \| 0.8438 \| \| No log \| 4.0 \| 4 \| 0.5798 \| 0.8438 \| \| No log \| 5.0 \| 5 \| 0.5786 \| 0.8438 \| \| No log \| 6.0 \| 6 \| 0.5770 \| 0.8438 \| \| No log \| 7.0 \| 7 \| 0.5750 \| 0.8438 \| \| No log \| 8.0 \| 8 \| 0.5726 \| 0.8438 \| \| No log \| 9.0 \| 9 \| 0.5701 \| 0.8438 \| \| 0.4546 \| 10.0 \| 10 \| 0.5672 \| 0.8438 \| \| 0.4546 \| 11.0 \| 11 \| 0.5641 \| 0.8438 \| \| 0.4546 \| 12.0 \| 12 \| 0.5614 \| 0.8438 \| \| 0.4546 \| 13.0 \| 13 \| 0.5586 \| 0.8438 \| \| 0.4546 \| 14.0 \| 14 \| 0.5560 \| 0.8438 \| \| 0.4546 \| 15.0 \| 15 \| 0.5530 \| 0.8438 \| \| 0.4546 \| 16.0 \| 16 \| 0.5501 \| 0.8438 \| \| 0.4546 \| 17.0 \| 17 \| 0.5470 \| 0.8438 \| \| 0.4546 \| 18.0 \| 18 \| 0.5438 \| 0.8438 \| \| 0.4546 \| 19.0 \| 19 \| 0.5407 \| 0.8438 \| \| 0.4413 \| 20.0 \| 20 \| 0.5369 \| 0.8438 \| \| 0.4413 \| 21.0 \| 21 \| 0.5325 \| 0.8438 \| \| 0.4413 \| 22.0 \| 22 \| 0.5280 \| 0.8438 \| \| 0.4413 \| 23.0 \| 23 \| 0.5230 \| 0.8438 \| \| 0.4413 \| 24.0 \| 24 \| 0.5180 \| 0.8438 \| \| 0.4413 \| 25.0 \| 25 \| 0.5132 \| 0.8438 \| \| 0.4413 \| 26.0 \| 26 \| 0.5088 \| 0.8438 \| \| 0.4413 \| 27.0 \| 27 \| 0.5049 \| 0.8438 \| \| 0.4413 \| 28.0 \| 28 \| 0.5014 \| 0.8438 \| \| 0.4413 \| 29.0 \| 29 \| 0.4985 \| 0.8438 \| \| 0.3899 \| 30.0 \| 30 \| 0.4964 \| 0.8438 \| \| 0.3899 \| 31.0 \| 31 \| 0.4951 \| 0.8438 \| \| 0.3899 \| 32.0 \| 32 \| 0.4937 \| 0.8438 \| \| 0.3899 \| 33.0 \| 33 \| 0.4919 \| 0.8438 \| \| 0.3899 \| 34.0 \| 34 \| 0.4902 \| 0.8438 \| \| 0.3899 \| 35.0 \| 35 \| 0.4884 \| 0.8438 \| \| 0.3899 \| 36.0 \| 36 \| 0.4870 \| 0.8438 \| \| 0.3899 \| 37.0 \| 37 \| 0.4854 \| 0.8438 \| \| 0.3899 \| 38.0 \| 38 \| 0.4844 \| 0.8438 \| \| 0.3899 \| 39.0 \| 39 \| 0.4832 \| 0.875 \| \| 0.3672 \| 40.0 \| 40 \| 0.4821 \| 0.875 \| \| 0.3672 \| 41.0 \| 41 \| 0.4817 \| 0.875 \| \| 0.3672 \| 42.0 \| 42 \| 0.4817 \| 0.875 \| \| 0.3672 \| 43.0 \| 43 \| 0.4820 \| 0.875 \| \| 0.3672 \| 44.0 \| 44 \| 0.4830 \| 0.875 \| \| 0.3672 \| 45.0 \| 45 \| 0.4838 \| 0.875 \| \| 0.3672 \| 46.0 \| 46 \| 0.4848 \| 0.875 \| \| 0.3672 \| 47.0 \| 47 \| 0.4855 \| 0.875 \| \| 0.3672 \| 48.0 \| 48 \| 0.4854 \| 0.875 \| \| 0.3672 \| 49.0 \| 49 \| 0.4860 \| 0.875 \| \| 0.2765 \| 50.0 \| 50 \| 0.4872 \| 0.875 \| \| 0.2765 \| 51.0 \| 51 \| 0.4878 \| 0.875 \| \| 0.2765 \| 52.0 \| 52 \| 0.4892 \| 0.875 \| \| 0.2765 \| 53.0 \| 53 \| 0.4913 \| 0.875 \| \| 0.2765 \| 54.0 \| 54 \| 0.4942 \| 0.8438 \| \| 0.2765 \| 55.0 \| 55 \| 0.4977 \| 0.8438 \| \| 0.2765 \| 56.0 \| 56 \| 0.5017 \| 0.8438 \| \| 0.2765 \| 57.0 \| 57 \| 0.5074 \| 0.8438 \| \| 0.2765 \| 58.0 \| 58 \| 0.5148 \| 0.8438 \| \| 0.2765 \| 59.0 \| 59 \| 0.5211 \| 0.8438 \| \| 0.2106 \| 60.0 \| 60 \| 0.5286 \| 0.8438 \| \| 0.2106 \| 61.0 \| 61 \| 0.5361 \| 0.8438 \| \| 0.2106 \| 62.0 \| 62 \| 0.5429 \| 0.8438 \| \| 0.2106 \| 63.0 \| 63 \| 0.5497 \| 0.8438 \| \| 0.2106 \| 64.0 \| 64 \| 0.5551 \| 0.8438 \| \| 0.2106 \| 65.0 \| 65 \| 0.5569 \| 0.8438 \| \| 0.2106 \| 66.0 \| 66 \| 0.5556 \| 0.8438 \| \| 0.2106 \| 67.0 \| 67 \| 0.5522 \| 0.8438 \| \| 0.2106 \| 68.0 \| 68 \| 0.5465 \| 0.8438 \| \| 0.2106 \| 69.0 \| 69 \| 0.5400 \| 0.8438 \| \| 0.1587 \| 70.0 \| 70 \| 0.5359 \| 0.8438 \| \| 0.1587 \| 71.0 \| 71 \| 0.5311 \| 0.8438 \| \| 0.1587 \| 72.0 \| 72 \| 0.5252 \| 0.8438 \| \| 0.1587 \| 73.0 \| 73 \| 0.5217 \| 0.8438 \| \| 0.1587 \| 74.0 \| 74 \| 0.5192 \| 0.8438 \| \| 0.1587 \| 75.0 \| 75 \| 0.5158 \| 0.8438 \| \| 0.1587 \| 76.0 \| 76 \| 0.5128 \| 0.8438 \| \| 0.1587 \| 77.0 \| 77 \| 0.5113 \| 0.8438 \| \| 0.1587 \| 78.0 \| 78 \| 0.5105 \| 0.8438 \| \| 0.1587 \| 79.0 \| 79 \| 0.5091 \| 0.8438 \| \| 0.122 \| 80.0 \| 80 \| 0.5090 \| 0.8438 \| \| 0.122 \| 81.0 \| 81 \| 0.5100 \| 0.8438 \| \| 0.122 \| 82.0 \| 82 \| 0.5126 \| 0.8438 \| \| 0.122 \| 83.0 \| 83 \| 0.5167 \| 0.8438 \| \| 0.122 \| 84.0 \| 84 \| 0.5215 \| 0.8438 \| \| 0.122 \| 85.0 \| 85 \| 0.5274 \| 0.8438 \| \| 0.122 \| 86.0 \| 86 \| 0.5351 \| 0.8438 \| \| 0.122 \| 87.0 \| 87 \| 0.5439 \| 0.8438 \| \| 0.122 \| 88.0 \| 88 \| 0.5547 \| 0.8438 \| \| 0.122 \| 89.0 \| 89 \| 0.5658 \| 0.8438 \| \| 0.0738 \| 90.0 \| 90 \| 0.5778 \| 0.8438 \| \| 0.0738 \| 91.0 \| 91 \| 0.5872 \| 0.8438 \| \| 0.0738 \| 92.0 \| 92 \| 0.5963 \| 0.8438 \| \| 0.0738 \| 93.0 \| 93 \| 0.6027 \| 0.8438 \| \| 0.0738 \| 94.0 \| 94 \| 0.6059 \| 0.8438 \| \| 0.0738 \| 95.0 \| 95 \| 0.6070 \| 0.8438 \| \| 0.0738 \| 96.0 \| 96 \| 0.6052 \| 0.8438 \| \| 0.0738 \| 97.0 \| 97 \| 0.6020 \| 0.8438 \| \| 0.0738 \| 98.0 \| 98 \| 0.5950 \| 0.8438 \| \| 0.0738 \| 99.0 \| 99 \| 0.5870 \| 0.8438 \| \| 0.0328 \| 100.0 \| 100 \| 0.5788 \| 0.8438 \| \| 0.0328 \| 101.0 \| 101 \| 0.5706 \| 0.8438 \| \| 0.0328 \| 102.0 \| 102 \| 0.5638 \| 0.8438 \| \| 0.0328 \| 103.0 \| 103 \| 0.5578 \| 0.8438 \| \| 0.0328 \| 104.0 \| 104 \| 0.5530 \| 0.8438 \| \| 0.0328 \| 105.0 \| 105 \| 0.5491 \| 0.875 \| \| 0.0328 \| 106.0 \| 106 \| 0.5465 \| 0.875 \| \| 0.0328 \| 107.0 \| 107 \| 0.5457 \| 0.875 \| \| 0.0328 \| 108.0 \| 108 \| 0.5456 \| 0.875 \| \| 0.0328 \| 109.0 \| 109 \| 0.5462 \| 0.875 \| \| 0.0221 \| 110.0 \| 110 \| 0.5473 \| 0.875 \| \| 0.0221 \| 111.0 \| 111 \| 0.5486 \| 0.875 \| \| 0.0221 \| 112.0 \| 112 \| 0.5500 \| 0.875 \| \| 0.0221 \| 113.0 \| 113 \| 0.5521 \| 0.875 \| \| 0.0221 \| 114.0 \| 114 \| 0.5543 \| 0.875 \| \| 0.0221 \| 115.0 \| 115 \| 0.5564 \| 0.875 \| \| 0.0221 \| 116.0 \| 116 \| 0.5589 \| 0.875 \| \| 0.0221 \| 117.0 \| 117 \| 0.5613 \| 0.875 \| \| 0.0221 \| 118.0 \| 118 \| 0.5637 \| 0.875 \| \| 0.0221 \| 119.0 \| 119 \| 0.5660 \| 0.875 \| \| 0.017 \| 120.0 \| 120 \| 0.5682 \| 0.875 \| \| 0.017 \| 121.0 \| 121 \| 0.5704 \| 0.875 \| \| 0.017 \| 122.0 \| 122 \| 0.5727 \| 0.875 \| \| 0.017 \| 123.0 \| 123 \| 0.5748 \| 0.875 \| \| 0.017 \| 124.0 \| 124 \| 0.5772 \| 0.875 \| \| 0.017 \| 125.0 \| 125 \| 0.5796 \| 0.875 \| \| 0.017 \| 126.0 \| 126 \| 0.5820 \| 0.875 \| \| 0.017 \| 127.0 \| 127 \| 0.5847 \| 0.875 \| \| 0.017 \| 128.0 \| 128 \| 0.5874 \| 0.875 \| \| 0.017 \| 129.0 \| 129 \| 0.5900 \| 0.875 \| \| 0.0129 \| 130.0 \| 130 \| 0.5926 \| 0.875 \| \| 0.0129 \| 131.0 \| 131 \| 0.5951 \| 0.875 \| \| 0.0129 \| 132.0 \| 132 \| 0.5976 \| 0.875 \| \| 0.0129 \| 133.0 \| 133 \| 0.6001 \| 0.875 \| \| 0.0129 \| 134.0 \| 134 \| 0.6027 \| 0.875 \| \| 0.0129 \| 135.0 \| 135 \| 0.6051 \| 0.875 \| \| 0.0129 \| 136.0 \| 136 \| 0.6076 \| 0.875 \| \| 0.0129 \| 137.0 \| 137 \| 0.6099 \| 0.875 \| \| 0.0129 \| 138.0 \| 138 \| 0.6123 \| 0.875 \| \| 0.0129 \| 139.0 \| 139 \| 0.6146 \| 0.875 \| \| 0.0103 \| 140.0 \| 140 \| 0.6169 \| 0.875 \| \| 0.0103 \| 141.0 \| 141 \| 0.6192 \| 0.875 \| \| 0.0103 \| 142.0 \| 142 \| 0.6216 \| 0.875 \| \| 0.0103 \| 143.0 \| 143 \| 0.6239 \| 0.875 \| \| 0.0103 \| 144.0 \| 144 \| 0.6261 \| 0.875 \| \| 0.0103 \| 145.0 \| 145 \| 0.6284 \| 0.875 \| \| 0.0103 \| 146.0 \| 146 \| 0.6306 \| 0.875 \| \| 0.0103 \| 147.0 \| 147 \| 0.6328 \| 0.875 \| \| 0.0103 \| 148.0 \| 148 \| 0.6350 \| 0.875 \| \| 0.0103 \| 149.0 \| 149 \| 0.6371 \| 0.875 \| \| 0.0084 \| 150.0 \| 150 \| 0.6392 \| 0.875 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3
simonycl/best_model-sst-2-16-42	simonycl	2023-07-26T01:01:03Z	106	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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	2023-07-26T00:57:40Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-16-42 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. --> # best_model-sst-2-16-42 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: - Loss: 0.4301 - Accuracy: 0.875 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 1 \| 0.4423 \| 0.7812 \| \| No log \| 2.0 \| 2 \| 0.4424 \| 0.7812 \| \| No log \| 3.0 \| 3 \| 0.4425 \| 0.7812 \| \| No log \| 4.0 \| 4 \| 0.4427 \| 0.7812 \| \| No log \| 5.0 \| 5 \| 0.4430 \| 0.7812 \| \| No log \| 6.0 \| 6 \| 0.4433 \| 0.7812 \| \| No log \| 7.0 \| 7 \| 0.4437 \| 0.7812 \| \| No log \| 8.0 \| 8 \| 0.4442 \| 0.7812 \| \| No log \| 9.0 \| 9 \| 0.4448 \| 0.7812 \| \| 0.458 \| 10.0 \| 10 \| 0.4454 \| 0.7812 \| \| 0.458 \| 11.0 \| 11 \| 0.4461 \| 0.7812 \| \| 0.458 \| 12.0 \| 12 \| 0.4467 \| 0.7812 \| \| 0.458 \| 13.0 \| 13 \| 0.4475 \| 0.7812 \| \| 0.458 \| 14.0 \| 14 \| 0.4482 \| 0.7812 \| \| 0.458 \| 15.0 \| 15 \| 0.4489 \| 0.7812 \| \| 0.458 \| 16.0 \| 16 \| 0.4497 \| 0.7812 \| \| 0.458 \| 17.0 \| 17 \| 0.4506 \| 0.7812 \| \| 0.458 \| 18.0 \| 18 \| 0.4517 \| 0.7812 \| \| 0.458 \| 19.0 \| 19 \| 0.4528 \| 0.7812 \| \| 0.4251 \| 20.0 \| 20 \| 0.4540 \| 0.7812 \| \| 0.4251 \| 21.0 \| 21 \| 0.4553 \| 0.7812 \| \| 0.4251 \| 22.0 \| 22 \| 0.4565 \| 0.7812 \| \| 0.4251 \| 23.0 \| 23 \| 0.4574 \| 0.7812 \| \| 0.4251 \| 24.0 \| 24 \| 0.4582 \| 0.7812 \| \| 0.4251 \| 25.0 \| 25 \| 0.4589 \| 0.7812 \| \| 0.4251 \| 26.0 \| 26 \| 0.4595 \| 0.7812 \| \| 0.4251 \| 27.0 \| 27 \| 0.4600 \| 0.7812 \| \| 0.4251 \| 28.0 \| 28 \| 0.4606 \| 0.7812 \| \| 0.4251 \| 29.0 \| 29 \| 0.4608 \| 0.7812 \| \| 0.3723 \| 30.0 \| 30 \| 0.4610 \| 0.7812 \| \| 0.3723 \| 31.0 \| 31 \| 0.4612 \| 0.7812 \| \| 0.3723 \| 32.0 \| 32 \| 0.4612 \| 0.7812 \| \| 0.3723 \| 33.0 \| 33 \| 0.4611 \| 0.7812 \| \| 0.3723 \| 34.0 \| 34 \| 0.4610 \| 0.7812 \| \| 0.3723 \| 35.0 \| 35 \| 0.4609 \| 0.7812 \| \| 0.3723 \| 36.0 \| 36 \| 0.4604 \| 0.7812 \| \| 0.3723 \| 37.0 \| 37 \| 0.4601 \| 0.7812 \| \| 0.3723 \| 38.0 \| 38 \| 0.4592 \| 0.7812 \| \| 0.3723 \| 39.0 \| 39 \| 0.4583 \| 0.7812 \| \| 0.3304 \| 40.0 \| 40 \| 0.4572 \| 0.7812 \| \| 0.3304 \| 41.0 \| 41 \| 0.4568 \| 0.7812 \| \| 0.3304 \| 42.0 \| 42 \| 0.4562 \| 0.7812 \| \| 0.3304 \| 43.0 \| 43 \| 0.4557 \| 0.7812 \| \| 0.3304 \| 44.0 \| 44 \| 0.4552 \| 0.7812 \| \| 0.3304 \| 45.0 \| 45 \| 0.4543 \| 0.7812 \| \| 0.3304 \| 46.0 \| 46 \| 0.4541 \| 0.7812 \| \| 0.3304 \| 47.0 \| 47 \| 0.4536 \| 0.7812 \| \| 0.3304 \| 48.0 \| 48 \| 0.4534 \| 0.7812 \| \| 0.3304 \| 49.0 \| 49 \| 0.4528 \| 0.7812 \| \| 0.2724 \| 50.0 \| 50 \| 0.4526 \| 0.7812 \| \| 0.2724 \| 51.0 \| 51 \| 0.4533 \| 0.7812 \| \| 0.2724 \| 52.0 \| 52 \| 0.4544 \| 0.7812 \| \| 0.2724 \| 53.0 \| 53 \| 0.4554 \| 0.7812 \| \| 0.2724 \| 54.0 \| 54 \| 0.4563 \| 0.7812 \| \| 0.2724 \| 55.0 \| 55 \| 0.4570 \| 0.7812 \| \| 0.2724 \| 56.0 \| 56 \| 0.4578 \| 0.7812 \| \| 0.2724 \| 57.0 \| 57 \| 0.4587 \| 0.7812 \| \| 0.2724 \| 58.0 \| 58 \| 0.4588 \| 0.7812 \| \| 0.2724 \| 59.0 \| 59 \| 0.4580 \| 0.7812 \| \| 0.2089 \| 60.0 \| 60 \| 0.4569 \| 0.7812 \| \| 0.2089 \| 61.0 \| 61 \| 0.4553 \| 0.7812 \| \| 0.2089 \| 62.0 \| 62 \| 0.4531 \| 0.7812 \| \| 0.2089 \| 63.0 \| 63 \| 0.4509 \| 0.7812 \| \| 0.2089 \| 64.0 \| 64 \| 0.4478 \| 0.7812 \| \| 0.2089 \| 65.0 \| 65 \| 0.4449 \| 0.7812 \| \| 0.2089 \| 66.0 \| 66 \| 0.4425 \| 0.7812 \| \| 0.2089 \| 67.0 \| 67 \| 0.4414 \| 0.7812 \| \| 0.2089 \| 68.0 \| 68 \| 0.4399 \| 0.7812 \| \| 0.2089 \| 69.0 \| 69 \| 0.4391 \| 0.7812 \| \| 0.163 \| 70.0 \| 70 \| 0.4382 \| 0.7812 \| \| 0.163 \| 71.0 \| 71 \| 0.4366 \| 0.7812 \| \| 0.163 \| 72.0 \| 72 \| 0.4356 \| 0.7812 \| \| 0.163 \| 73.0 \| 73 \| 0.4344 \| 0.7812 \| \| 0.163 \| 74.0 \| 74 \| 0.4331 \| 0.7812 \| \| 0.163 \| 75.0 \| 75 \| 0.4320 \| 0.7812 \| \| 0.163 \| 76.0 \| 76 \| 0.4310 \| 0.7812 \| \| 0.163 \| 77.0 \| 77 \| 0.4294 \| 0.7812 \| \| 0.163 \| 78.0 \| 78 \| 0.4285 \| 0.7812 \| \| 0.163 \| 79.0 \| 79 \| 0.4273 \| 0.7812 \| \| 0.1282 \| 80.0 \| 80 \| 0.4267 \| 0.7812 \| \| 0.1282 \| 81.0 \| 81 \| 0.4262 \| 0.7812 \| \| 0.1282 \| 82.0 \| 82 \| 0.4271 \| 0.7812 \| \| 0.1282 \| 83.0 \| 83 \| 0.4275 \| 0.7812 \| \| 0.1282 \| 84.0 \| 84 \| 0.4289 \| 0.7812 \| \| 0.1282 \| 85.0 \| 85 \| 0.4295 \| 0.7812 \| \| 0.1282 \| 86.0 \| 86 \| 0.4293 \| 0.7812 \| \| 0.1282 \| 87.0 \| 87 \| 0.4284 \| 0.7812 \| \| 0.1282 \| 88.0 \| 88 \| 0.4275 \| 0.7812 \| \| 0.1282 \| 89.0 \| 89 \| 0.4263 \| 0.7812 \| \| 0.1021 \| 90.0 \| 90 \| 0.4249 \| 0.7812 \| \| 0.1021 \| 91.0 \| 91 \| 0.4233 \| 0.7812 \| \| 0.1021 \| 92.0 \| 92 \| 0.4210 \| 0.7812 \| \| 0.1021 \| 93.0 \| 93 \| 0.4188 \| 0.7812 \| \| 0.1021 \| 94.0 \| 94 \| 0.4166 \| 0.7812 \| \| 0.1021 \| 95.0 \| 95 \| 0.4162 \| 0.7812 \| \| 0.1021 \| 96.0 \| 96 \| 0.4154 \| 0.7812 \| \| 0.1021 \| 97.0 \| 97 \| 0.4139 \| 0.7812 \| \| 0.1021 \| 98.0 \| 98 \| 0.4126 \| 0.8125 \| \| 0.1021 \| 99.0 \| 99 \| 0.4117 \| 0.8125 \| \| 0.0862 \| 100.0 \| 100 \| 0.4115 \| 0.8125 \| \| 0.0862 \| 101.0 \| 101 \| 0.4119 \| 0.8125 \| \| 0.0862 \| 102.0 \| 102 \| 0.4116 \| 0.8125 \| \| 0.0862 \| 103.0 \| 103 \| 0.4119 \| 0.8125 \| \| 0.0862 \| 104.0 \| 104 \| 0.4141 \| 0.8125 \| \| 0.0862 \| 105.0 \| 105 \| 0.4156 \| 0.8125 \| \| 0.0862 \| 106.0 \| 106 \| 0.4165 \| 0.8438 \| \| 0.0862 \| 107.0 \| 107 \| 0.4170 \| 0.8438 \| \| 0.0862 \| 108.0 \| 108 \| 0.4183 \| 0.8438 \| \| 0.0862 \| 109.0 \| 109 \| 0.4200 \| 0.8438 \| \| 0.0708 \| 110.0 \| 110 \| 0.4212 \| 0.8438 \| \| 0.0708 \| 111.0 \| 111 \| 0.4216 \| 0.8438 \| \| 0.0708 \| 112.0 \| 112 \| 0.4213 \| 0.8438 \| \| 0.0708 \| 113.0 \| 113 \| 0.4205 \| 0.8438 \| \| 0.0708 \| 114.0 \| 114 \| 0.4191 \| 0.8438 \| \| 0.0708 \| 115.0 \| 115 \| 0.4180 \| 0.8438 \| \| 0.0708 \| 116.0 \| 116 \| 0.4167 \| 0.8438 \| \| 0.0708 \| 117.0 \| 117 \| 0.4154 \| 0.8438 \| \| 0.0708 \| 118.0 \| 118 \| 0.4143 \| 0.8438 \| \| 0.0708 \| 119.0 \| 119 \| 0.4125 \| 0.8438 \| \| 0.056 \| 120.0 \| 120 \| 0.4109 \| 0.8438 \| \| 0.056 \| 121.0 \| 121 \| 0.4090 \| 0.8438 \| \| 0.056 \| 122.0 \| 122 \| 0.4092 \| 0.8438 \| \| 0.056 \| 123.0 \| 123 \| 0.4093 \| 0.8438 \| \| 0.056 \| 124.0 \| 124 \| 0.4094 \| 0.8438 \| \| 0.056 \| 125.0 \| 125 \| 0.4095 \| 0.8438 \| \| 0.056 \| 126.0 \| 126 \| 0.4096 \| 0.8438 \| \| 0.056 \| 127.0 \| 127 \| 0.4103 \| 0.8438 \| \| 0.056 \| 128.0 \| 128 \| 0.4109 \| 0.8438 \| \| 0.056 \| 129.0 \| 129 \| 0.4111 \| 0.8438 \| \| 0.0436 \| 130.0 \| 130 \| 0.4110 \| 0.8438 \| \| 0.0436 \| 131.0 \| 131 \| 0.4114 \| 0.8438 \| \| 0.0436 \| 132.0 \| 132 \| 0.4119 \| 0.8438 \| \| 0.0436 \| 133.0 \| 133 \| 0.4121 \| 0.8438 \| \| 0.0436 \| 134.0 \| 134 \| 0.4119 \| 0.875 \| \| 0.0436 \| 135.0 \| 135 \| 0.4119 \| 0.875 \| \| 0.0436 \| 136.0 \| 136 \| 0.4121 \| 0.875 \| \| 0.0436 \| 137.0 \| 137 \| 0.4131 \| 0.875 \| \| 0.0436 \| 138.0 \| 138 \| 0.4138 \| 0.875 \| \| 0.0436 \| 139.0 \| 139 \| 0.4150 \| 0.875 \| \| 0.0326 \| 140.0 \| 140 \| 0.4167 \| 0.875 \| \| 0.0326 \| 141.0 \| 141 \| 0.4181 \| 0.875 \| \| 0.0326 \| 142.0 \| 142 \| 0.4194 \| 0.875 \| \| 0.0326 \| 143.0 \| 143 \| 0.4206 \| 0.875 \| \| 0.0326 \| 144.0 \| 144 \| 0.4217 \| 0.875 \| \| 0.0326 \| 145.0 \| 145 \| 0.4228 \| 0.875 \| \| 0.0326 \| 146.0 \| 146 \| 0.4242 \| 0.875 \| \| 0.0326 \| 147.0 \| 147 \| 0.4256 \| 0.875 \| \| 0.0326 \| 148.0 \| 148 \| 0.4268 \| 0.875 \| \| 0.0326 \| 149.0 \| 149 \| 0.4280 \| 0.875 \| \| 0.0247 \| 150.0 \| 150 \| 0.4301 \| 0.875 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3
simonycl/best_model-sst-2-16-13	simonycl	2023-07-26T00:53:47Z	108	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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	2023-07-25T23:05:09Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-16-13 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. --> # best_model-sst-2-16-13 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: - Loss: 0.5420 - Accuracy: 0.8125 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 1 \| 0.6945 \| 0.5938 \| \| No log \| 2.0 \| 2 \| 0.6945 \| 0.5938 \| \| No log \| 3.0 \| 3 \| 0.6945 \| 0.5938 \| \| No log \| 4.0 \| 4 \| 0.6944 \| 0.5938 \| \| No log \| 5.0 \| 5 \| 0.6944 \| 0.5938 \| \| No log \| 6.0 \| 6 \| 0.6944 \| 0.5938 \| \| No log \| 7.0 \| 7 \| 0.6944 \| 0.5938 \| \| No log \| 8.0 \| 8 \| 0.6943 \| 0.5938 \| \| No log \| 9.0 \| 9 \| 0.6943 \| 0.5938 \| \| 0.7032 \| 10.0 \| 10 \| 0.6943 \| 0.5938 \| \| 0.7032 \| 11.0 \| 11 \| 0.6942 \| 0.5938 \| \| 0.7032 \| 12.0 \| 12 \| 0.6942 \| 0.5938 \| \| 0.7032 \| 13.0 \| 13 \| 0.6941 \| 0.5938 \| \| 0.7032 \| 14.0 \| 14 \| 0.6940 \| 0.5938 \| \| 0.7032 \| 15.0 \| 15 \| 0.6940 \| 0.5938 \| \| 0.7032 \| 16.0 \| 16 \| 0.6939 \| 0.5938 \| \| 0.7032 \| 17.0 \| 17 \| 0.6938 \| 0.5938 \| \| 0.7032 \| 18.0 \| 18 \| 0.6937 \| 0.5938 \| \| 0.7032 \| 19.0 \| 19 \| 0.6936 \| 0.5938 \| \| 0.709 \| 20.0 \| 20 \| 0.6935 \| 0.5938 \| \| 0.709 \| 21.0 \| 21 \| 0.6934 \| 0.5938 \| \| 0.709 \| 22.0 \| 22 \| 0.6933 \| 0.5938 \| \| 0.709 \| 23.0 \| 23 \| 0.6932 \| 0.5938 \| \| 0.709 \| 24.0 \| 24 \| 0.6931 \| 0.5938 \| \| 0.709 \| 25.0 \| 25 \| 0.6930 \| 0.5938 \| \| 0.709 \| 26.0 \| 26 \| 0.6928 \| 0.5938 \| \| 0.709 \| 27.0 \| 27 \| 0.6927 \| 0.5938 \| \| 0.709 \| 28.0 \| 28 \| 0.6926 \| 0.5938 \| \| 0.709 \| 29.0 \| 29 \| 0.6924 \| 0.5938 \| \| 0.6984 \| 30.0 \| 30 \| 0.6923 \| 0.5938 \| \| 0.6984 \| 31.0 \| 31 \| 0.6921 \| 0.5938 \| \| 0.6984 \| 32.0 \| 32 \| 0.6920 \| 0.5938 \| \| 0.6984 \| 33.0 \| 33 \| 0.6918 \| 0.5938 \| \| 0.6984 \| 34.0 \| 34 \| 0.6916 \| 0.5938 \| \| 0.6984 \| 35.0 \| 35 \| 0.6915 \| 0.5938 \| \| 0.6984 \| 36.0 \| 36 \| 0.6913 \| 0.5938 \| \| 0.6984 \| 37.0 \| 37 \| 0.6911 \| 0.5938 \| \| 0.6984 \| 38.0 \| 38 \| 0.6909 \| 0.5938 \| \| 0.6984 \| 39.0 \| 39 \| 0.6907 \| 0.5938 \| \| 0.6833 \| 40.0 \| 40 \| 0.6905 \| 0.5938 \| \| 0.6833 \| 41.0 \| 41 \| 0.6903 \| 0.5938 \| \| 0.6833 \| 42.0 \| 42 \| 0.6901 \| 0.5938 \| \| 0.6833 \| 43.0 \| 43 \| 0.6899 \| 0.5938 \| \| 0.6833 \| 44.0 \| 44 \| 0.6897 \| 0.5938 \| \| 0.6833 \| 45.0 \| 45 \| 0.6895 \| 0.5938 \| \| 0.6833 \| 46.0 \| 46 \| 0.6893 \| 0.5938 \| \| 0.6833 \| 47.0 \| 47 \| 0.6890 \| 0.5938 \| \| 0.6833 \| 48.0 \| 48 \| 0.6888 \| 0.5938 \| \| 0.6833 \| 49.0 \| 49 \| 0.6885 \| 0.5938 \| \| 0.6831 \| 50.0 \| 50 \| 0.6882 \| 0.5938 \| \| 0.6831 \| 51.0 \| 51 \| 0.6879 \| 0.5938 \| \| 0.6831 \| 52.0 \| 52 \| 0.6876 \| 0.5938 \| \| 0.6831 \| 53.0 \| 53 \| 0.6873 \| 0.5938 \| \| 0.6831 \| 54.0 \| 54 \| 0.6870 \| 0.5938 \| \| 0.6831 \| 55.0 \| 55 \| 0.6867 \| 0.625 \| \| 0.6831 \| 56.0 \| 56 \| 0.6863 \| 0.625 \| \| 0.6831 \| 57.0 \| 57 \| 0.6860 \| 0.625 \| \| 0.6831 \| 58.0 \| 58 \| 0.6856 \| 0.625 \| \| 0.6831 \| 59.0 \| 59 \| 0.6852 \| 0.625 \| \| 0.669 \| 60.0 \| 60 \| 0.6848 \| 0.625 \| \| 0.669 \| 61.0 \| 61 \| 0.6844 \| 0.625 \| \| 0.669 \| 62.0 \| 62 \| 0.6839 \| 0.625 \| \| 0.669 \| 63.0 \| 63 \| 0.6835 \| 0.625 \| \| 0.669 \| 64.0 \| 64 \| 0.6830 \| 0.625 \| \| 0.669 \| 65.0 \| 65 \| 0.6824 \| 0.625 \| \| 0.669 \| 66.0 \| 66 \| 0.6819 \| 0.625 \| \| 0.669 \| 67.0 \| 67 \| 0.6814 \| 0.625 \| \| 0.669 \| 68.0 \| 68 \| 0.6808 \| 0.625 \| \| 0.669 \| 69.0 \| 69 \| 0.6802 \| 0.625 \| \| 0.6556 \| 70.0 \| 70 \| 0.6796 \| 0.625 \| \| 0.6556 \| 71.0 \| 71 \| 0.6789 \| 0.625 \| \| 0.6556 \| 72.0 \| 72 \| 0.6782 \| 0.625 \| \| 0.6556 \| 73.0 \| 73 \| 0.6774 \| 0.625 \| \| 0.6556 \| 74.0 \| 74 \| 0.6766 \| 0.6562 \| \| 0.6556 \| 75.0 \| 75 \| 0.6757 \| 0.6562 \| \| 0.6556 \| 76.0 \| 76 \| 0.6747 \| 0.6562 \| \| 0.6556 \| 77.0 \| 77 \| 0.6736 \| 0.6562 \| \| 0.6556 \| 78.0 \| 78 \| 0.6725 \| 0.6562 \| \| 0.6556 \| 79.0 \| 79 \| 0.6713 \| 0.6562 \| \| 0.6248 \| 80.0 \| 80 \| 0.6700 \| 0.6562 \| \| 0.6248 \| 81.0 \| 81 \| 0.6687 \| 0.6562 \| \| 0.6248 \| 82.0 \| 82 \| 0.6673 \| 0.6562 \| \| 0.6248 \| 83.0 \| 83 \| 0.6660 \| 0.6562 \| \| 0.6248 \| 84.0 \| 84 \| 0.6647 \| 0.6562 \| \| 0.6248 \| 85.0 \| 85 \| 0.6635 \| 0.6562 \| \| 0.6248 \| 86.0 \| 86 \| 0.6622 \| 0.6562 \| \| 0.6248 \| 87.0 \| 87 \| 0.6603 \| 0.5938 \| \| 0.6248 \| 88.0 \| 88 \| 0.6586 \| 0.5938 \| \| 0.6248 \| 89.0 \| 89 \| 0.6574 \| 0.5938 \| \| 0.6013 \| 90.0 \| 90 \| 0.6565 \| 0.5938 \| \| 0.6013 \| 91.0 \| 91 \| 0.6557 \| 0.5938 \| \| 0.6013 \| 92.0 \| 92 \| 0.6548 \| 0.5938 \| \| 0.6013 \| 93.0 \| 93 \| 0.6541 \| 0.625 \| \| 0.6013 \| 94.0 \| 94 \| 0.6538 \| 0.625 \| \| 0.6013 \| 95.0 \| 95 \| 0.6537 \| 0.625 \| \| 0.6013 \| 96.0 \| 96 \| 0.6531 \| 0.625 \| \| 0.6013 \| 97.0 \| 97 \| 0.6522 \| 0.625 \| \| 0.6013 \| 98.0 \| 98 \| 0.6518 \| 0.625 \| \| 0.6013 \| 99.0 \| 99 \| 0.6515 \| 0.6562 \| \| 0.5622 \| 100.0 \| 100 \| 0.6502 \| 0.6562 \| \| 0.5622 \| 101.0 \| 101 \| 0.6481 \| 0.6562 \| \| 0.5622 \| 102.0 \| 102 \| 0.6457 \| 0.6562 \| \| 0.5622 \| 103.0 \| 103 \| 0.6434 \| 0.6562 \| \| 0.5622 \| 104.0 \| 104 \| 0.6411 \| 0.6562 \| \| 0.5622 \| 105.0 \| 105 \| 0.6384 \| 0.7188 \| \| 0.5622 \| 106.0 \| 106 \| 0.6362 \| 0.6875 \| \| 0.5622 \| 107.0 \| 107 \| 0.6338 \| 0.6875 \| \| 0.5622 \| 108.0 \| 108 \| 0.6311 \| 0.6875 \| \| 0.5622 \| 109.0 \| 109 \| 0.6281 \| 0.6562 \| \| 0.5022 \| 110.0 \| 110 \| 0.6236 \| 0.6875 \| \| 0.5022 \| 111.0 \| 111 \| 0.6193 \| 0.6875 \| \| 0.5022 \| 112.0 \| 112 \| 0.6141 \| 0.6562 \| \| 0.5022 \| 113.0 \| 113 \| 0.6088 \| 0.6875 \| \| 0.5022 \| 114.0 \| 114 \| 0.6046 \| 0.6875 \| \| 0.5022 \| 115.0 \| 115 \| 0.6024 \| 0.6875 \| \| 0.5022 \| 116.0 \| 116 \| 0.6014 \| 0.6875 \| \| 0.5022 \| 117.0 \| 117 \| 0.6004 \| 0.6875 \| \| 0.5022 \| 118.0 \| 118 \| 0.5993 \| 0.6875 \| \| 0.5022 \| 119.0 \| 119 \| 0.5982 \| 0.6875 \| \| 0.4576 \| 120.0 \| 120 \| 0.5969 \| 0.6875 \| \| 0.4576 \| 121.0 \| 121 \| 0.5957 \| 0.6875 \| \| 0.4576 \| 122.0 \| 122 \| 0.5944 \| 0.7188 \| \| 0.4576 \| 123.0 \| 123 \| 0.5929 \| 0.7188 \| \| 0.4576 \| 124.0 \| 124 \| 0.5916 \| 0.7188 \| \| 0.4576 \| 125.0 \| 125 \| 0.5903 \| 0.7188 \| \| 0.4576 \| 126.0 \| 126 \| 0.5887 \| 0.7188 \| \| 0.4576 \| 127.0 \| 127 \| 0.5873 \| 0.7188 \| \| 0.4576 \| 128.0 \| 128 \| 0.5857 \| 0.75 \| \| 0.4576 \| 129.0 \| 129 \| 0.5837 \| 0.75 \| \| 0.4105 \| 130.0 \| 130 \| 0.5819 \| 0.75 \| \| 0.4105 \| 131.0 \| 131 \| 0.5797 \| 0.75 \| \| 0.4105 \| 132.0 \| 132 \| 0.5781 \| 0.75 \| \| 0.4105 \| 133.0 \| 133 \| 0.5770 \| 0.75 \| \| 0.4105 \| 134.0 \| 134 \| 0.5756 \| 0.75 \| \| 0.4105 \| 135.0 \| 135 \| 0.5734 \| 0.75 \| \| 0.4105 \| 136.0 \| 136 \| 0.5714 \| 0.75 \| \| 0.4105 \| 137.0 \| 137 \| 0.5694 \| 0.75 \| \| 0.4105 \| 138.0 \| 138 \| 0.5673 \| 0.75 \| \| 0.4105 \| 139.0 \| 139 \| 0.5651 \| 0.75 \| \| 0.3744 \| 140.0 \| 140 \| 0.5628 \| 0.75 \| \| 0.3744 \| 141.0 \| 141 \| 0.5605 \| 0.7812 \| \| 0.3744 \| 142.0 \| 142 \| 0.5581 \| 0.7812 \| \| 0.3744 \| 143.0 \| 143 \| 0.5555 \| 0.7812 \| \| 0.3744 \| 144.0 \| 144 \| 0.5532 \| 0.7812 \| \| 0.3744 \| 145.0 \| 145 \| 0.5510 \| 0.7812 \| \| 0.3744 \| 146.0 \| 146 \| 0.5489 \| 0.7812 \| \| 0.3744 \| 147.0 \| 147 \| 0.5470 \| 0.7812 \| \| 0.3744 \| 148.0 \| 148 \| 0.5453 \| 0.7812 \| \| 0.3744 \| 149.0 \| 149 \| 0.5435 \| 0.7812 \| \| 0.3294 \| 150.0 \| 150 \| 0.5420 \| 0.8125 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3
Yaxin1992/llama2-13b-1800-eos	Yaxin1992	2023-07-26T00:28:16Z	0	0	null	[ "tensorboard", "generated_from_trainer", "base_model:meta-llama/Llama-2-13b-hf", "base_model:finetune:meta-llama/Llama-2-13b-hf", "region:us" ]	null	2023-07-25T21:29:55Z	--- base_model: meta-llama/Llama-2-13b-hf tags: - generated_from_trainer model-index: - name: llama2-13b-1800-eos 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. --> # llama2-13b-1800-eos This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1800 ### Training results ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
richardr1126/spider-skeleton-wizard-coder-8bit	richardr1126	2023-07-26T00:11:51Z	5	1	transformers	[ "transformers", "pytorch", "gpt_bigcode", "text-generation", "sql", "spider", "text-to-sql", "sql finetune", "8bit", "dataset:spider", "dataset:richardr1126/spider-skeleton-context-instruct", "arxiv:1809.08887", "arxiv:2305.14314", "license:bigcode-openrail-m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "region:us" ]	text-generation	2023-07-21T01:33:43Z	--- tags: - sql - spider - text-to-sql - sql finetune - 8bit datasets: - spider - richardr1126/spider-skeleton-context-instruct library_name: transformers license: bigcode-openrail-m --- ### Spider Skeleton Wizard Coder 8bit Summary - This model was created by finetuning [WizardLM/WizardCoder-15B-V1.0](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0) on an enhanced Spider context training dataset: [richardr1126/spider-skeleton-context-instruct](https://huggingface.co/datasets/richardr1126/spider-skeleton-context-instruct). - Finetuning was performed using QLoRa on 3x RTX6000 48GB. - This is the bitsandbytes 8-bit version of the model. It needs to be loaded onto a GPU for it to work. - If you want just the QLoRa/LoRA adapter: [richardr1126/spider-skeleton-wizard-coder-qlora](https://huggingface.co/richardr1126/spider-skeleton-wizard-coder-qlora) ### Running the GGML model - The best way to run this model is to use the [4-bit GGML version](https://huggingface.co/richardr1126/spider-skeleton-wizard-coder-ggml) on [koboldcpp](https://github.com/LostRuins/koboldcpp), with CuBlas support. ### Spider Dataset [Spider](https://arxiv.org/abs/1809.08887) is a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 Yale students The goal of the Spider challenge is to develop natural language interfaces to cross-domain databases. This dataset was used to finetune this model. ### Spider Skeleton WizardCoder - [test-suite-sql-eval](https://github.com/taoyds/test-suite-sql-eval) Results With temperature set to 0.0, top_p set to 0.9, and top_k set to 0, the model achieves 61% execution accuracy on the Spider dev set. <img src="https://raw.githubusercontent.com/cuplv/text-to-sql-wizardcoder/main/eval/plots/spiderwizard-plus-chatgpt.svg" height="300"> <img src="https://raw.githubusercontent.com/cuplv/text-to-sql-wizardcoder/main/eval/plots/spiderwizard-vs-chatgpt.svg" height="300"> Note: - ChatGPT was evaluated with the default hyperparameters and with the system message `You are a sophisticated AI assistant capable of converting text into SQL queries. You can only output SQL, don't add any other text.` - Both models were evaluated with `--plug_value` in `evaluation.py` using the Spider dev set with database context. - `--plug_value`: If set, the gold value will be plugged into the predicted query. This is suitable if your model does not predict values. This is set to `False` by default. ## Citations ``` @misc{luo2023wizardcoder, title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct}, author={Ziyang Luo and Can Xu and Pu Zhao and Qingfeng Sun and Xiubo Geng and Wenxiang Hu and Chongyang Tao and Jing Ma and Qingwei Lin and Daxin Jiang}, year={2023}, } ``` ``` @article{yu2018spider, title={Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task}, author={Yu, Tao and Zhang, Rui and Yang, Kai and Yasunaga, Michihiro and Wang, Dongxu and Li, Zifan and Ma, James and Li, Irene and Yao, Qingning and Roman, Shanelle and others}, journal={arXiv preprint arXiv:1809.08887}, year={2018} } ``` ``` @article{dettmers2023qlora, title={QLoRA: Efficient Finetuning of Quantized LLMs}, author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke}, journal={arXiv preprint arXiv:2305.14314}, year={2023} } ``` ## Disclaimer The resources, including code, data, and model weights, associated with this project are restricted for academic research purposes only and cannot be used for commercial purposes. The content produced by any version of WizardCoder is influenced by uncontrollable variables such as randomness, and therefore, the accuracy of the output cannot be guaranteed by this project. This project does not accept any legal liability for the content of the model output, nor does it assume responsibility for any losses incurred due to the use of associated resources and output results.
gyuri2020/kw-classification-setfithead-head-only-model	gyuri2020	2023-07-26T00:11:14Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-07-26T00:08:21Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # gyuri2020/kw-classification-setfithead-head-only-model This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("gyuri2020/kw-classification-setfithead-head-only-model") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
Jungwonchang/whisper_finetune_ksponspeech_2000steps	Jungwonchang	2023-07-25T23:59:42Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "kr", "dataset:Jungwonchang/ksponspeech", "base_model:openai/whisper-large-v2", "base_model:finetune:openai/whisper-large-v2", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-25T18:18:25Z	--- language: - kr license: apache-2.0 base_model: openai/whisper-large-v2 tags: - generated_from_trainer datasets: - Jungwonchang/ksponspeech metrics: - wer model-index: - name: Whisper large-v2, KsponSpeech results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: KsponSpeech type: Jungwonchang/ksponspeech config: dev split: validation args: dev metrics: - name: Wer type: wer value: 42.225687000584685 --- <!-- 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 large-v2, KsponSpeech This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the KsponSpeech dataset. It achieves the following results on the evaluation set: - Loss: 0.2946 - Wer: 42.2257 ## 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: linear - lr_scheduler_warmup_steps: 50 - training_steps: 2000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.3315 \| 0.25 \| 500 \| 0.3446 \| 41.5319 \| \| 0.3204 \| 0.5 \| 1000 \| 0.3229 \| 37.7003 \| \| 0.2967 \| 0.75 \| 1500 \| 0.3054 \| 38.3980 \| \| 0.2859 \| 1.0 \| 2000 \| 0.2946 \| 42.2257 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.12.1+cu116 - Datasets 2.14.0 - Tokenizers 0.12.1
LonyGohan/ReiRei_Rei_Ayanami_RVC_IA_BR	LonyGohan	2023-07-25T23:53:27Z	0	3	null	[ "music", "audio-to-audio", "pt", "license:openrail", "region:us" ]	audio-to-audio	2023-07-01T04:41:45Z	--- license: openrail language: - pt metrics: - character pipeline_tag: audio-to-audio tags: - music --- ![rei-ayanami-doing-side-eye-zgajjszogzasaz6p.gif](https://cdn-uploads.huggingface.co/production/uploads/649fad444812ea6b2d3447a0/Ml7zvJOHryeA51x1uRL_B.gif) ⠀ ⠀ ⠀ ⠀ # ReiRei e ReiReiV2 • ReiRei é IA de voz da Rei Ayanami Brasileira feita no RVC ambas na V2, mais especificamente no EasyGui. Existem dois modelos que eu fiz, cada um apresentou uma vantagem para coisas diferentes, ReiRei é o Medelo mais velho enquanto ReiReiV2 é o modelo mais recente. • A dublagem escolhida foi a da Priscilla Concepcion feita pela netiflix em 2019 ⠀ ⠀ ⠀ ⠀ ⠀ ⠀ ![☆.jpg](https://cdn-uploads.huggingface.co/production/uploads/649fad444812ea6b2d3447a0/QgTNhZRv-eOJiXsMTm5pn.jpeg) # ReiRei • A primeira versão consegue cantar muito melhor do que sua sucessora, Isso acontece devido dela ter sido treinada apenas com um único áudio de Rei de duração de mais ou menos 1:28. • Sua dubladora costuma muito sussurrar a voz de Rei por conta da personalidade da personagem, e o áudio que ela foi treinada foi baseado na Voz da Rei mais solta e um timbre mais agudo e alto. • Ela foi treinada cerca de 540 vezes, um número bem alto, acho que eu não devia ter treinado ela tanto assim. # ReiReiv2 • A segunda versão consegue ser muito mais fiel em questão de fala da Rei Ayanami, a mesma foi treinada com 3 áudios de Rei, 01:28m, 01:23m e 02:08. Porém essa versão não fica tão bem cantando que sua voz fica em um tom mais grave e baixo do sua antecessora. • Acredito que a baixa compatibilidade da versão ReiReiv2 vem dela ser baseado em muitos outros áudio onde Rei sussurra e respira muito, porém é bem mais fiel na hora de falar frases que a Rei falaria, o modelo combina bem mais se você está pensando em dublar algo usando o modelo. • Ela foi treinada bem menos que ReiRei, cerca 320x, mas ainda é um número alto, tendo em vista que não precisa de muito por ter 6 minutos de áudio da Rei falando constantemente. # ReiRei.pht + ReiReiV2.index • Fiz alguns teste, e impressionantemente, ReiRei.pht com o treinamento da ReiReiv2.index melhora em muito alguns aspectos, principalmente na hora de cantar que sua pronuncia melhora e alguns tons e em linguas estrangeiras, porem o inverso não funciona. ![75a.gif](https://cdn-uploads.huggingface.co/production/uploads/649fad444812ea6b2d3447a0/n3U9eJqNg8o_0HER37rSy.gif) # Modelos pra download em Zip • Os modelos estão prontos para serem usados no RVC do EasyGui • Os modelos também podem ser usando no RCV original, é só colocar os arquivos .pht e .index nas suas respectivas pastas que eles são detectados automaticamente ao dar Refresh # Demonstração [Rei Ayanami Cantando Ligia - anna (Lofi Remix) IA COVER](https://youtu.be/E-dxn2Mj1lQ)
Bellaaazzzzz/model_wireframe	Bellaaazzzzz	2023-07-25T23:38:46Z	1	3	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "controlnet", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-07-12T21:32:45Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet inference: true --- # controlnet-Bellaaazzzzz/model_wireframe These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images below. Validation result of 1 round. ![images_0_0)](./images_0_0.png) Validation result of 2 round. ![images_1_0)](./images_1_0.png) Validation result of 3 round. ![images_2_0)](./images_2_0.png)
ashercn97/awesome-prompts-merged	ashercn97	2023-07-25T23:17:53Z	10	0	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-25T22:07:38Z	--- pipeline_tag: text2text-generation --- This is a merged version of my previous chatgpt prompt generator model, but this time it isnt in LoRA form so you can use it easier (and so i can make a space for it)
ChairWorm/ppo-Huggy	ChairWorm	2023-07-25T22:57:16Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-25T22:57:04Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy 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: ChairWorm/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
CordwainerSmith/distilhubert-finetuned-gtzan-v3	CordwainerSmith	2023-07-25T22:55:17Z	159	0	transformers	[ "transformers", "pytorch", "tensorboard", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-25T21:37:49Z	--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan-v3 results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.87 --- <!-- 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. --> # distilhubert-finetuned-gtzan-v3 This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5053 - Accuracy: 0.87 ## 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.9855 \| 1.0 \| 113 \| 1.7934 \| 0.52 \| \| 1.3551 \| 2.0 \| 226 \| 1.2638 \| 0.68 \| \| 1.0094 \| 3.0 \| 339 \| 0.9340 \| 0.76 \| \| 0.9176 \| 4.0 \| 452 \| 0.7845 \| 0.78 \| \| 0.6402 \| 5.0 \| 565 \| 0.6458 \| 0.81 \| \| 0.3626 \| 6.0 \| 678 \| 0.5620 \| 0.85 \| \| 0.4944 \| 7.0 \| 791 \| 0.5078 \| 0.82 \| \| 0.1754 \| 8.0 \| 904 \| 0.4793 \| 0.81 \| \| 0.2203 \| 9.0 \| 1017 \| 0.4875 \| 0.84 \| \| 0.1121 \| 10.0 \| 1130 \| 0.5053 \| 0.87 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
bhenrym14/airophin-13b-pntk-16k-fp16	bhenrym14	2023-07-25T22:40:27Z	1,390	4	transformers	[ "transformers", "safetensors", "llama", "text-generation", "dataset:jondurbin/airoboros-gpt4-1.4.1", "dataset:ehartford/dolphin", "arxiv:2306.15595", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-25T22:16:03Z	--- datasets: - jondurbin/airoboros-gpt4-1.4.1 - ehartford/dolphin --- # Airophin: A NTK-by-Parts RoPE Scaled QLoRA Fine-tune of Llama-2-13b (fp16 weights) <!-- LoRA Weights can be found here: https://huggingface.co/bhenrym14/airophin-13b-pntk-16k-LoRA --> GPTQ weights can be found here: https://huggingface.co/bhenrym14/airophin-13b-pntk-16k-GPTQ ## Overview This is a finetune of Llama-2-13b, intended to extend the useful context window to 16384 tokens. There are two training phases: 1. It is first trained on a long-context (7000-8192 tokens) subset of [dolphin](https://huggingface.co/datasets/ehartford/dolphin), an orca-like dataset (GPT4 split only). This amounts to roughly 110mm tokens. Airoboros-like training prompt was used instead of the dolphin system prompt. Training was done with partial NTK scaling applied (scale factor of 4). This took ~20 hours. 2. The model was then finetuned on [Jon Durbin's Airoboros GPT4 1.4.1](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1), with same scaling approach, for 2 epochs. This took ~15 hours. This is a QLoRA fine-tune (rank 64). All training was performed with 1x RTX 6000 Ada. For the 4096 context length model using airoboros-gpt4-1.4.1 see: [Jon Durbin's airoboros-l2-13b-gpt4-1.4.1](https://huggingface.co/jondurbin/airoboros-l2-13b-gpt4-1.4.1) ## How to Use This model employs [Partial NTK Rope Scaling](https://github.com/jquesnelle/scaled-rope/pull/1). This methodology is not yet implemented natively in Transformers or Exllama (as of 7/21). There are three options to run this. 1. Transformers (use bnb for quantization). Use [fp16 weights](https://huggingface.co/bhenrym14/airophin-13b-pntk-16k-fp16). This will require replacing the `LlamaEmbedding` with `LlamaPartNTKScaledRotaryEmbedding`, with `max_position_embeddings=16384` and `original_max_position_embeddings=4096`. A monkeypatch can be found [here](https://github.com/bhenrym14/qlora-airoboros-longcontext/blob/main/scaledllama/llama_pntk_monkey_patch.py). 2. Autogptq/GPTQ-for-Llama. See the [GPTQ weights](https://huggingface.co/bhenrym14/airophin-13b-pntk-16k-GPTQ) 3. Use ExLLama, see the [GPTQ weights](https://huggingface.co/bhenrym14/airophin-13b-pntk-16k-GPTQ) Please comment with any questions. This hasn't been extensively tested. ## Motivation Methods of extending the useful context window of LLM's have gained significant traction. Several methods requiring little to no finetuning/retraining have emerged. Among these is linear position interpolation [kaiokendev](https://kaiokendev.github.io/til#extending-context-to-8k) and [meta AI)](https://arxiv.org/abs/2306.15595)) and [NTK aware scaling](https://github.com/jquesnelle/scaled-rope). My prior experiments demonstrate significant performance improvements both from finetuning with these scaling adjustments implemented and with longer sequences. Unfortunately it has also been shown that LLM's frequently struggle to attend to salient information in the middle of the context window. Attending to nearby tokens is essential to producing syntactically correct and semantically coherent sentences. Essential context is also most commonly found at the beginning of a context window. With this in mind, it is unsurprising LLMs often attend more strongly to these areas. Does this learned model behavior result in an "extrapolated deemphasis" when such embeddings are scaled? This hypothesis may be supported by the material improvements in perplexity achieved by training on long sequences (not just including the RoPE scaling during the fine-tune). Here I explore whether training on long sequences that have clear conceptual dependencies residing in the middle of the context helps attenuate the difficulties in attending to middle-context tokens. When/if I have time, I hope to perform a more rigorous assessment of the peformance with respect to this specific issue. ## Relative Performance (perplexity) \| Context (tokens) \| bhenrym14/airophin-13b-pntk-16k-fp16\| bhenrym14/airoboros-13b-gpt4-1.4.1-PI-8192-fp16 \|bhenrym14/airoboros-33b-gpt4-1.4.1-lxctx-PI-16384-fp16 \| jondurbin/airoboros-l2-13b-gpt4-1.4.1 \| \| ---\| ----- \| -----\| ------\| --- \| \| 512 \| 7.62 \| 8.24 \| 7.90 \| 7.23 \| \| 1024 \| 6.20 \| 6.71 \| 6.17 \| 5.85 \| \| 2048 \| 5.38 \| 5.87 \| 5.23 \| 5.07 \| \| 4096 \| 5.08 \| 5.50 \| 4.91 \| 4.77 \| \| 8192 \| 4.90 \| 5.32 \| Not Tested \| 57.1 \| \| 12000 \| 4.82 \| 56.1 \| Not Tested \| Not Tested \| - This model is very competitive with the Llama-1 33b extended context variants. In particular, at 512 tokens it has lower perplexity. This is probably an improvement imparted (in part) by the NTK by parts scaling method. - Not presented here, but this model outperforms the base llama-2-13b on MMLU-fs with a score of 54.9. While perhaps an insignificant difference, the fact there isn't a clear performance regression despite the context extension is notable. - Perplexity continues to decline to 12000 tokens, the longest context length I tested due to VRAM constraints. - Feedback regarding real-world performance is appreciated. I don't know if the first dolphin training phase really contributed much beyond what pile did for the 33b-lxctx model; many relevant modeling components changed here, so it's difficult to make any specific attributions. The base model improvement may very well be the most dominant change. ## Prompting: airoboros-like prompting remains. See the following from one of Jon Durbin's airoboros model cards: ### Context obedient question answering By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations. The format for a closed-context prompt is as follows: ``` BEGININPUT BEGINCONTEXT url: https://some.web.site/123 date: 2023-06-01 ... other metdata ... ENDCONTEXT [insert your text blocks here] ENDINPUT [add as many other blocks, in the exact same format] BEGININSTRUCTION [insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.] ENDINSTRUCTION ``` It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up. The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not! I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it. - `BEGININPUT` - denotes a new input block - `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block - `ENDCONTEXT` - denotes the end of the metadata block for the current input - [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context. - `ENDINPUT` - denotes the end of the current input block - [repeat as many input blocks in this format as you want] - `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above. - [instruction(s)] - `ENDINSTRUCTION` - denotes the end of instruction set It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to. Here's a trivial, but important example to prove the point: ``` BEGININPUT BEGINCONTEXT date: 2021-01-01 url: https://web.site/123 ENDCONTEXT In a shocking turn of events, blueberries are now green, but will be sticking with the same name. ENDINPUT BEGININSTRUCTION What color are bluberries? Source? ENDINSTRUCTION ``` And the response: ``` Blueberries are now green. Source: date: 2021-01-01 url: https://web.site/123 ``` The prompt itself should be wrapped in the vicuna1.1 template if you aren't using fastchat with the conv-template vicuna_v1.1 as described: ``` USER: BEGININPUT BEGINCONTEXT date: 2021-01-01 url: https://web.site/123 ENDCONTEXT In a shocking turn of events, blueberries are now green, but will be sticking with the same name. ENDINPUT BEGININSTRUCTION What color are bluberries? Source? ENDINSTRUCTION ASSISTANT: ``` ### Coding You can ask for fairly complex coding instructions, e.g. (be sure to wrap with vicuna style prompt): ``` Create a python application with the following requirements: - Asyncio FastAPI webserver - ping endpoint that returns the current date in JSON format - file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate ``` Or: ``` Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values. ``` You can optionally add a single space and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.: ``` Write a websocket application in node.js. PLAINFORMAT ``` ### Word games / trivia ``` USER: This anagram of "silent" is what you do when paying attention and hearing someone. ASSISTANT: listen ``` ``` USER: Give me a list of 7 words that start with EN ASSISTANT: 1. Endlessly 2. Enthusiastically 3. Enormously 4. Enveloping 5. Enduring 6. Enlightening 7. Encouraging ``` ### Multiple choice ``` USER: Which of the following astronomical objects is composed primarily of ice and rock, and is located in the Kuiper Belt? A) Mercury B) Venus C) Pluto D) Jupiter ASSISTANT: C, Pluto ``` ### Writing <details> <summary>Write a resignation letter in the style of a pirate captain, in which you resign from your position as janitor.</summary> Response: ``` ASSISTANT: Captain Jack Sparrow here, as ye be knowin', has been forced to abandon me post as Janitor o' th' Festering Cesspool, which be a filthy hole o' water that we be callin' home. Ye may be thinkin' what could ha' driven me to leave me duties an' take me leave o' this sorry establishment? Well, ye'd be right in thinkin' so, for it be th' greed o' them what runs th' place that be drivin' me away. They've been fattenin' their pockets on me back while I've been slavin' me guts out cleanin' up their messes. No more, me hearties! Me time o' servin' th' rich an' powerful be done, an' I'm takin' me leave o' this stinkin' hole forever! Me destiny lies elsewhere, an' it be time I pursued it. Ye may think ye've seen th' last o' me, but ye'd be wrong, for th' winds o' change be blowin', an' me ship be ready to set sail on th' high seas o' justice. So, farewell ye scurvy dogs, ye who've made me life a living hell. I leave ye with me favorite saying: "ARRRGGGHHH, ye scurvy dogs!" An' remember, ye ain't never gonna see me comin', for me shadow be castin' long an' dark on th' horizon</summary> ``` </details> ### Jokes ``` USER: Tell me a joke about bananas. ASSISTANT: What did the banana say when it got hit by a bus? R.I.P.E. ``` ### Riddles (not great, but slightly better) ``` USER: What has a neck but no head? ASSISTANT: A bottle. ``` ### Multi-character conversations ``` USER: Characters: - Yoda, the ever clever jedi master. - Homer Simpson, you know, the goofy yellow guy. - USER/Jon - don't generate any speech or actions on behalf of Jon Rules: - be sure to use the manerisms and speaking style of the characters correctly - surround character actions with asterisks - always put quotes around words spoken by the characters - use a new line for each character's actions or speech - always prefix actions/speech with the character's name and colon - only generate text and actions on behalf of Yoda or Homer, and never anyone else Conversation will revolve around the grapes, in a local cafe with delicious coffee. Generate a single interaction between Yoda and Homer to start, with one sentence each. ASSISTANT: Yoda: "Mmm, these grapes are quite delicious, Master Homer." Homer: "Oh yeah, Yoda! They're the best grapes I've ever had!" USER: enters the cafe Sorry I'm late guys! ASSISTANT: Yoda: "No need to apologize, Jon. We were just enjoying these delightful grapes." Homer: "Yeah, man! It's not every day you get to eat grapes with a real-life Jedi Master!" Yoda raises an eyebrow ```
Adel-Elwan/msmarco-bert-base-dot-v5-fine-tuned-AI	Adel-Elwan	2023-07-25T22:16:26Z	69	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "semantic-search", "sentence-similarity", "transformers", "artificial-intelligence", "computer-science", "question-answering", "en", "dataset:Adel-Elwan/Artificial-intelligence-dataset-for-IR-systems", "model-index", "region:us" ]	question-answering	2023-07-24T16:29:06Z	--- pipeline_tag: question-answering tags: - semantic-search - sentence-similarity - sentence-transformers - transformers - artificial-intelligence - computer-science language: - en metrics: - accuracy datasets: - Adel-Elwan/Artificial-intelligence-dataset-for-IR-systems model-index: - name: Adel-Elwan/msmarco-bert-base-dot-v5-fine-tuned-AI results: - task: type: semantic-search # Required. Example: automatic-speech-recognition name: Semantic Search # Optional. Example: Speech Recognition dataset: type: Adel-Elwan/Artificial-intelligence-dataset-for-IR-systems name: Artificial intelligence dataset for IR systems split: test # Optional. Example: test metrics: - type: accuracy value: 83.45% name: Accuracy@5 - type: accuracy value: 87.78% name: Accuracy@10 - type: precision value: 16.69% name: Precision@5 - type: recall value: 83.45% name: Recall@5 - type: recall value: 87.78% name: Recall@10 - type: mean_reciprocal_rank value: 0.7327 name: MRR@10 verified: true --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 6563 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'dot_score'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 5000, "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 656, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
WhoTookMyAmogusNickname/WizardLM-13B-V1.2-GGML	WhoTookMyAmogusNickname	2023-07-25T22:04:16Z	0	1	null	[ "region:us" ]	null	2023-07-25T16:51:07Z	GGML quantizations of [WizardLM-13B-V1.2](https://huggingface.co/WizardLM/WizardLM-13B-V1.2) Prompt template: ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT: ```
FashionAI4Wholesale/segformer-b2-finetuned-segments-dresses-071123	FashionAI4Wholesale	2023-07-25T21:58:17Z	197	0	transformers	[ "transformers", "pytorch", "tensorboard", "segformer", "image-segmentation", "en", "dataset:Latiste", "arxiv:1910.09700", "license:openrail", "endpoints_compatible", "region:us" ]	image-segmentation	2023-07-11T17:24:31Z	--- language: - en tags: - image-segmentation license: "openrail" datasets: - Latiste metrics: - iou --- # 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 A SegFormer-b2 model fine tuned with private dress on mannequin datasets <!-- 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]
Habana/swin	Habana	2023-07-25T21:36:24Z	2,088	0	null	[ "optimum_habana", "license:apache-2.0", "region:us" ]	null	2022-08-23T08:10:57Z	--- license: apache-2.0 --- [Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU). It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks. Learn more about how to take advantage of the power of Habana HPUs to train and deploy Transformers and Diffusers models at [hf.co/hardware/habana](https://huggingface.co/hardware/habana). ## Swin Transformer model HPU configuration This model only contains the `GaudiConfig` file for running the [Swin Transformer](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) model on Habana's Gaudi processors (HPU). This model contains no model weights, only a GaudiConfig. This enables to specify: - `use_fused_adam`: whether to use Habana's custom AdamW implementation - `use_fused_clip_norm`: whether to use Habana's fused gradient norm clipping operator - `use_torch_autocast`: whether to use Torch Autocast for managing mixed precision ## Usage The model is instantiated the same way as in the Transformers library. The only difference is that there are a few new training arguments specific to HPUs.\ It is strongly recommended to train this model doing bf16 mixed-precision training for optimal performance and accuracy. [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/image-classification/run_image_classification.py) is an image classification example script to fine-tune a model. You can run it with Swin with the following command: ```bash python run_image_classification.py \ --model_name_or_path microsoft/swin-base-patch4-window7-224-in22k \ --dataset_name cifar10 \ --output_dir /tmp/outputs/ \ --remove_unused_columns False \ --do_train \ --do_eval \ --learning_rate 3e-5 \ --num_train_epochs 5 \ --per_device_train_batch_size 64 \ --per_device_eval_batch_size 64 \ --evaluation_strategy epoch \ --save_strategy epoch \ --load_best_model_at_end True \ --save_total_limit 3 \ --seed 1337 \ --use_habana \ --use_lazy_mode \ --gaudi_config_name Habana/swin \ --throughput_warmup_steps 3 \ --ignore_mismatched_sizes \ --bf16 ``` Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.
zpattdev/Reinforce-cartpoleV1	zpattdev	2023-07-25T21:31:30Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-25T21:31:20Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpoleV1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
grace-pro/no_repeats	grace-pro	2023-07-25T20:55:53Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:Davlan/afro-xlmr-base", "base_model:finetune:Davlan/afro-xlmr-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-25T19:38:27Z	--- license: mit base_model: Davlan/afro-xlmr-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: no_repeats 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. --> # no_repeats This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1658 - Precision: 0.7350 - Recall: 0.5701 - F1: 0.6421 - Accuracy: 0.9596 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1578 \| 1.0 \| 1283 \| 0.1410 \| 0.7141 \| 0.4748 \| 0.5704 \| 0.9540 \| \| 0.1189 \| 2.0 \| 2566 \| 0.1336 \| 0.7023 \| 0.5501 \| 0.6170 \| 0.9568 \| \| 0.0929 \| 3.0 \| 3849 \| 0.1406 \| 0.7380 \| 0.5433 \| 0.6259 \| 0.9584 \| \| 0.0725 \| 4.0 \| 5132 \| 0.1512 \| 0.7283 \| 0.5751 \| 0.6427 \| 0.9591 \| \| 0.057 \| 5.0 \| 6415 \| 0.1658 \| 0.7350 \| 0.5701 \| 0.6421 \| 0.9596 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
hcrystalwang/bloom-3b-lora-Step10	hcrystalwang	2023-07-25T20:38:41Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-24T19:36:08Z	--- 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.dev0
wyx-ucl/bart-EDGAR-CORPUS	wyx-ucl	2023-07-25T20:28:25Z	104	1	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large-cnn", "base_model:finetune:facebook/bart-large-cnn", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-25T19:29:49Z	--- license: mit base_model: facebook/bart-large-cnn tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-EDGAR-CORPUS results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-EDGAR-CORPUS This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4876 - Rouge1: 0.5298 - Rouge2: 0.3439 - Rougel: 0.4251 - Rougelsum: 0.5210 ## 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: 3 - eval_batch_size: 3 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| 0.9531 \| 1.0 \| 6 \| 0.4876 \| 0.5298 \| 0.3439 \| 0.4251 \| 0.5210 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
ATrapenard/Discord-Impersonation-Bot	ATrapenard	2023-07-25T20:09:51Z	157	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "quite rude on purpose", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-25T19:13:46Z	--- tags: - conversational - quite rude on purpose --- #Specifically created for use in attempting to clone the speech consistencies of a particular person
cwiz/llama-saiga-7b-gofman	cwiz	2023-07-25T20:09:21Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-10T04:27:35Z	--- library_name: peft --- # LLaMa-Saiga-7b-Gofman [llama-7b-saiga-merged](https://huggingface.co/cwiz/llama-7b-saiga-merged) trained on [Igor Gofman](https://github.com/Shoe-Eye/gofman-digital-oracle) dataset.
dariowsz/dqn-SpaceInvadersNoFrameskip-v4	dariowsz	2023-07-25T20:03:49Z	11	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-25T20:03:13Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 702.00 +/- 172.35 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dariowsz -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dariowsz -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga dariowsz ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
jordyvl/cdip-tiny_rvl_cdip-NK1000_kd_test	jordyvl	2023-07-25T20:00:20Z	166	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-25T13:58:28Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: cdip-tiny_rvl_cdip-NK1000_kd_test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cdip-tiny_rvl_cdip-NK1000_kd_test This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4588 - Accuracy: 0.8227 - Brier Loss: 0.2552 - Nll: 1.9117 - F1 Micro: 0.8227 - F1 Macro: 0.8239 - Ece: 0.0507 - Aurc: 0.0420 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 125 \| 1.3371 \| 0.5423 \| 0.5838 \| 2.6217 \| 0.5423 \| 0.5341 \| 0.0583 \| 0.2219 \| \| No log \| 2.0 \| 250 \| 0.9983 \| 0.646 \| 0.4725 \| 2.3143 \| 0.646 \| 0.6407 \| 0.0490 \| 0.1432 \| \| No log \| 3.0 \| 375 \| 0.8094 \| 0.7085 \| 0.3977 \| 2.2571 \| 0.7085 \| 0.7034 \| 0.0500 \| 0.1017 \| \| 1.2477 \| 4.0 \| 500 \| 0.7633 \| 0.7215 \| 0.3806 \| 2.2013 \| 0.7215 \| 0.7275 \| 0.0447 \| 0.0926 \| \| 1.2477 \| 5.0 \| 625 \| 0.7295 \| 0.7505 \| 0.3565 \| 2.1741 \| 0.7505 \| 0.7417 \| 0.0631 \| 0.0775 \| \| 1.2477 \| 6.0 \| 750 \| 0.6706 \| 0.7692 \| 0.3321 \| 2.1869 \| 0.7692 \| 0.7669 \| 0.0614 \| 0.0690 \| \| 1.2477 \| 7.0 \| 875 \| 0.6933 \| 0.767 \| 0.3344 \| 2.1685 \| 0.767 \| 0.7650 \| 0.0811 \| 0.0676 \| \| 0.3434 \| 8.0 \| 1000 \| 0.6640 \| 0.7778 \| 0.3251 \| 2.1666 \| 0.7778 \| 0.7795 \| 0.0681 \| 0.0650 \| \| 0.3434 \| 9.0 \| 1125 \| 0.6874 \| 0.774 \| 0.3328 \| 2.1401 \| 0.774 \| 0.7739 \| 0.0863 \| 0.0660 \| \| 0.3434 \| 10.0 \| 1250 \| 0.6639 \| 0.7795 \| 0.3194 \| 2.1335 \| 0.7795 \| 0.7800 \| 0.0800 \| 0.0618 \| \| 0.3434 \| 11.0 \| 1375 \| 0.6827 \| 0.7728 \| 0.3332 \| 2.1140 \| 0.7728 \| 0.7771 \| 0.0891 \| 0.0650 \| \| 0.1507 \| 12.0 \| 1500 \| 0.6197 \| 0.786 \| 0.3106 \| 2.1052 \| 0.786 \| 0.7873 \| 0.0716 \| 0.0586 \| \| 0.1507 \| 13.0 \| 1625 \| 0.6264 \| 0.7823 \| 0.3133 \| 2.1077 \| 0.7823 \| 0.7834 \| 0.0784 \| 0.0595 \| \| 0.1507 \| 14.0 \| 1750 \| 0.5822 \| 0.796 \| 0.2964 \| 2.0567 \| 0.796 \| 0.7983 \| 0.0676 \| 0.0549 \| \| 0.1507 \| 15.0 \| 1875 \| 0.5900 \| 0.7923 \| 0.3016 \| 2.0704 \| 0.7923 \| 0.7936 \| 0.0724 \| 0.0541 \| \| 0.107 \| 16.0 \| 2000 \| 0.6044 \| 0.7855 \| 0.3099 \| 2.0625 \| 0.7855 \| 0.7901 \| 0.0730 \| 0.0617 \| \| 0.107 \| 17.0 \| 2125 \| 0.5692 \| 0.7973 \| 0.2930 \| 2.0627 \| 0.7973 \| 0.7990 \| 0.0676 \| 0.0528 \| \| 0.107 \| 18.0 \| 2250 \| 0.5836 \| 0.7907 \| 0.2984 \| 2.0575 \| 0.7907 \| 0.7922 \| 0.0749 \| 0.0554 \| \| 0.107 \| 19.0 \| 2375 \| 0.5469 \| 0.806 \| 0.2835 \| 2.0754 \| 0.806 \| 0.8060 \| 0.0576 \| 0.0498 \| \| 0.0879 \| 20.0 \| 2500 \| 0.5427 \| 0.804 \| 0.2892 \| 2.0655 \| 0.804 \| 0.8089 \| 0.0593 \| 0.0528 \| \| 0.0879 \| 21.0 \| 2625 \| 0.5305 \| 0.806 \| 0.2777 \| 2.0213 \| 0.806 \| 0.8070 \| 0.0604 \| 0.0495 \| \| 0.0879 \| 22.0 \| 2750 \| 0.5146 \| 0.8113 \| 0.2741 \| 2.0127 \| 0.8113 \| 0.8121 \| 0.0534 \| 0.0480 \| \| 0.0879 \| 23.0 \| 2875 \| 0.5196 \| 0.8107 \| 0.2750 \| 2.0261 \| 0.8108 \| 0.8117 \| 0.0541 \| 0.0489 \| \| 0.0755 \| 24.0 \| 3000 \| 0.5169 \| 0.8123 \| 0.2743 \| 1.9561 \| 0.8123 \| 0.8127 \| 0.0595 \| 0.0478 \| \| 0.0755 \| 25.0 \| 3125 \| 0.5129 \| 0.8073 \| 0.2777 \| 2.0020 \| 0.8073 \| 0.8089 \| 0.0552 \| 0.0491 \| \| 0.0755 \| 26.0 \| 3250 \| 0.4898 \| 0.8177 \| 0.2649 \| 1.9710 \| 0.8178 \| 0.8177 \| 0.0474 \| 0.0451 \| \| 0.0755 \| 27.0 \| 3375 \| 0.4966 \| 0.8155 \| 0.2682 \| 2.0075 \| 0.8155 \| 0.8163 \| 0.0514 \| 0.0458 \| \| 0.0652 \| 28.0 \| 3500 \| 0.4883 \| 0.813 \| 0.2690 \| 1.9655 \| 0.813 \| 0.8141 \| 0.0557 \| 0.0465 \| \| 0.0652 \| 29.0 \| 3625 \| 0.4860 \| 0.8185 \| 0.2659 \| 1.9593 \| 0.8185 \| 0.8194 \| 0.0481 \| 0.0456 \| \| 0.0652 \| 30.0 \| 3750 \| 0.4760 \| 0.818 \| 0.2600 \| 1.9517 \| 0.818 \| 0.8194 \| 0.0505 \| 0.0441 \| \| 0.0652 \| 31.0 \| 3875 \| 0.4755 \| 0.8195 \| 0.2611 \| 1.9593 \| 0.8195 \| 0.8196 \| 0.0507 \| 0.0440 \| \| 0.0568 \| 32.0 \| 4000 \| 0.4763 \| 0.8155 \| 0.2628 \| 1.9508 \| 0.8155 \| 0.8161 \| 0.0484 \| 0.0451 \| \| 0.0568 \| 33.0 \| 4125 \| 0.4675 \| 0.8225 \| 0.2574 \| 1.9474 \| 0.8225 \| 0.8238 \| 0.0477 \| 0.0433 \| \| 0.0568 \| 34.0 \| 4250 \| 0.4664 \| 0.8207 \| 0.2579 \| 1.9478 \| 0.8207 \| 0.8220 \| 0.0498 \| 0.0431 \| \| 0.0568 \| 35.0 \| 4375 \| 0.4635 \| 0.8213 \| 0.2567 \| 1.9233 \| 0.8213 \| 0.8219 \| 0.0481 \| 0.0427 \| \| 0.0514 \| 36.0 \| 4500 \| 0.4584 \| 0.8245 \| 0.2551 \| 1.9196 \| 0.8245 \| 0.8260 \| 0.0461 \| 0.0424 \| \| 0.0514 \| 37.0 \| 4625 \| 0.4627 \| 0.825 \| 0.2557 \| 1.9274 \| 0.825 \| 0.8256 \| 0.0454 \| 0.0424 \| \| 0.0514 \| 38.0 \| 4750 \| 0.4603 \| 0.8213 \| 0.2552 \| 1.9319 \| 0.8213 \| 0.8221 \| 0.0478 \| 0.0425 \| \| 0.0514 \| 39.0 \| 4875 \| 0.4610 \| 0.8245 \| 0.2560 \| 1.9337 \| 0.8245 \| 0.8252 \| 0.0476 \| 0.0424 \| \| 0.0483 \| 40.0 \| 5000 \| 0.4603 \| 0.825 \| 0.2559 \| 1.9319 \| 0.825 \| 0.8262 \| 0.0460 \| 0.0421 \| \| 0.0483 \| 41.0 \| 5125 \| 0.4589 \| 0.8253 \| 0.2545 \| 1.9317 \| 0.8253 \| 0.8260 \| 0.0459 \| 0.0421 \| \| 0.0483 \| 42.0 \| 5250 \| 0.4586 \| 0.8245 \| 0.2552 \| 1.9192 \| 0.8245 \| 0.8260 \| 0.0524 \| 0.0420 \| \| 0.0483 \| 43.0 \| 5375 \| 0.4581 \| 0.825 \| 0.2552 \| 1.9179 \| 0.825 \| 0.8263 \| 0.0477 \| 0.0421 \| \| 0.0465 \| 44.0 \| 5500 \| 0.4573 \| 0.8245 \| 0.2543 \| 1.9187 \| 0.8245 \| 0.8257 \| 0.0457 \| 0.0417 \| \| 0.0465 \| 45.0 \| 5625 \| 0.4589 \| 0.8225 \| 0.2554 \| 1.9184 \| 0.8225 \| 0.8235 \| 0.0549 \| 0.0421 \| \| 0.0465 \| 46.0 \| 5750 \| 0.4582 \| 0.823 \| 0.2547 \| 1.9128 \| 0.823 \| 0.8242 \| 0.0512 \| 0.0420 \| \| 0.0465 \| 47.0 \| 5875 \| 0.4587 \| 0.823 \| 0.2551 \| 1.9135 \| 0.823 \| 0.8241 \| 0.0484 \| 0.0420 \| \| 0.0458 \| 48.0 \| 6000 \| 0.4585 \| 0.8235 \| 0.2550 \| 1.9127 \| 0.8235 \| 0.8246 \| 0.0479 \| 0.0420 \| \| 0.0458 \| 49.0 \| 6125 \| 0.4589 \| 0.8227 \| 0.2553 \| 1.9117 \| 0.8227 \| 0.8238 \| 0.0490 \| 0.0421 \| \| 0.0458 \| 50.0 \| 6250 \| 0.4588 \| 0.8227 \| 0.2552 \| 1.9117 \| 0.8227 \| 0.8239 \| 0.0507 \| 0.0420 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
reginaboateng/pfeiffer_Bioasq_adapter	reginaboateng	2023-07-25T19:58:04Z	1	0	adapter-transformers	[ "adapter-transformers", "adapterhub:biaoasq", "bert", "dataset:bioasq7b", "region:us" ]	null	2023-07-25T19:58:01Z	--- tags: - adapter-transformers - adapterhub:biaoasq - bert datasets: - bioasq7b --- # Adapter `reginaboateng/pfeiffer_Bioasq_adapter` for allenai/scibert_scivocab_uncased An [adapter](https://adapterhub.ml) for the `allenai/scibert_scivocab_uncased` model that was trained on the [biaoasq](https://adapterhub.ml/explore/biaoasq/) dataset. This adapter was created for usage with the [adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers) library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoAdapterModel model = AutoAdapterModel.from_pretrained("allenai/scibert_scivocab_uncased") adapter_name = model.load_adapter("reginaboateng/pfeiffer_Bioasq_adapter", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
CordwainerSmith/distilhubert-finetuned-gtzan-v2	CordwainerSmith	2023-07-25T19:57:06Z	162	0	transformers	[ "transformers", "pytorch", "tensorboard", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-25T18:16:05Z	--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.83 --- <!-- 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. --> # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5786 - Accuracy: 0.83 ## 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.9933 \| 1.0 \| 113 \| 1.8547 \| 0.55 \| \| 1.2871 \| 2.0 \| 226 \| 1.2257 \| 0.65 \| \| 0.9868 \| 3.0 \| 339 \| 0.9143 \| 0.73 \| \| 0.9468 \| 4.0 \| 452 \| 0.7964 \| 0.76 \| \| 0.6634 \| 5.0 \| 565 \| 0.6592 \| 0.82 \| \| 0.3877 \| 6.0 \| 678 \| 0.6870 \| 0.77 \| \| 0.426 \| 7.0 \| 791 \| 0.5259 \| 0.85 \| \| 0.1165 \| 8.0 \| 904 \| 0.5274 \| 0.86 \| \| 0.2397 \| 9.0 \| 1017 \| 0.5487 \| 0.84 \| \| 0.1039 \| 10.0 \| 1130 \| 0.5786 \| 0.83 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
BrianS15/prot_bert-finetuned-tchard	BrianS15	2023-07-25T19:52:26Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "base_model:Rostlab/prot_bert", "base_model:finetune:Rostlab/prot_bert", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-25T15:35:43Z	--- base_model: Rostlab/prot_bert tags: - generated_from_trainer model-index: - name: prot_bert-finetuned-tchard 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. --> # prot_bert-finetuned-tchard This model is a fine-tuned version of [Rostlab/prot_bert](https://huggingface.co/Rostlab/prot_bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0390 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 800 \| 0.0454 \| \| 0.0667 \| 2.0 \| 1600 \| 0.0376 \| \| 0.0394 \| 3.0 \| 2400 \| 0.0390 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
annazhong/vit-base-patch16-224-finetuned-feature-map-v2	annazhong	2023-07-25T19:52:21Z	166	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-25T08:03:24Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-patch16-224-finetuned-feature-map-v2 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. --> # vit-base-patch16-224-finetuned-feature-map-v2 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9026 - Accuracy: 0.22 ## 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: 150 - eval_batch_size: 150 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 600 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 1 \| 2.1272 \| 0.21 \| \| No log \| 2.0 \| 3 \| 1.9026 \| 0.22 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
Nerozud/ppo-LunarLander-v2	Nerozud	2023-07-25T19:45:00Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-25T19:44:40Z	--- 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: 270.79 +/- 18.07 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 ... ```
Za88yes/Icis	Za88yes	2023-07-25T19:44:52Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-25T19:42:24Z	--- license: creativeml-openrail-m ---
alvarote26/llama2-qlora-finetunined-french	alvarote26	2023-07-25T19:37:10Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-25T19:37:06Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
ShekDass/donut-base-cord-smart-43	ShekDass	2023-07-25T19:37:07Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base-finetuned-cord-v2", "base_model:finetune:naver-clova-ix/donut-base-finetuned-cord-v2", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-07-25T18:30:50Z	--- license: mit base_model: naver-clova-ix/donut-base-finetuned-cord-v2 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-base-cord-smart-43 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. --> # donut-base-cord-smart-43 This model is a fine-tuned version of [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2) on the imagefolder 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: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
NasimB/aochildes-rarity	NasimB	2023-07-25T19:24:03Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-25T15:06:23Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: aochildes-rarity 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. --> # aochildes-rarity This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.0570 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.3684 \| 0.29 \| 500 \| 5.3161 \| \| 5.0527 \| 0.58 \| 1000 \| 4.8912 \| \| 4.716 \| 0.87 \| 1500 \| 4.6588 \| \| 4.4472 \| 1.16 \| 2000 \| 4.5116 \| \| 4.3056 \| 1.46 \| 2500 \| 4.3936 \| \| 4.2065 \| 1.75 \| 3000 \| 4.3008 \| \| 4.0872 \| 2.04 \| 3500 \| 4.2252 \| \| 3.8993 \| 2.33 \| 4000 \| 4.1825 \| \| 3.8715 \| 2.62 \| 4500 \| 4.1240 \| \| 3.8388 \| 2.91 \| 5000 \| 4.0762 \| \| 3.6552 \| 3.2 \| 5500 \| 4.0680 \| \| 3.592 \| 3.49 \| 6000 \| 4.0385 \| \| 3.5701 \| 3.79 \| 6500 \| 4.0111 \| \| 3.4953 \| 4.08 \| 7000 \| 4.0047 \| \| 3.3186 \| 4.37 \| 7500 \| 4.0017 \| \| 3.3174 \| 4.66 \| 8000 \| 3.9888 \| \| 3.3059 \| 4.95 \| 8500 \| 3.9759 \| \| 3.1594 \| 5.24 \| 9000 \| 3.9886 \| \| 3.1356 \| 5.53 \| 9500 \| 3.9881 \| \| 3.1353 \| 5.82 \| 10000 \| 3.9870 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
zacdennis/dqn-SpaceInvadersNoFrameskip-v4	zacdennis	2023-07-25T19:09:29Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-25T19:08:57Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 379.00 +/- 138.00 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga zacdennis -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga zacdennis -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga zacdennis ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 10000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 10000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 100), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
NasimB/aochildes-log-rarity	NasimB	2023-07-25T19:07:06Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-25T14:48:27Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: aochildes-log-rarity 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. --> # aochildes-log-rarity This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.0784 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.3735 \| 0.29 \| 500 \| 5.3132 \| \| 5.0467 \| 0.58 \| 1000 \| 4.8995 \| \| 4.7113 \| 0.87 \| 1500 \| 4.6669 \| \| 4.4637 \| 1.16 \| 2000 \| 4.5216 \| \| 4.3059 \| 1.46 \| 2500 \| 4.4059 \| \| 4.2053 \| 1.75 \| 3000 \| 4.3042 \| \| 4.092 \| 2.04 \| 3500 \| 4.2347 \| \| 3.9015 \| 2.33 \| 4000 \| 4.1896 \| \| 3.8711 \| 2.62 \| 4500 \| 4.1381 \| \| 3.8373 \| 2.91 \| 5000 \| 4.0894 \| \| 3.6562 \| 3.2 \| 5500 \| 4.0843 \| \| 3.5884 \| 3.49 \| 6000 \| 4.0512 \| \| 3.5745 \| 3.79 \| 6500 \| 4.0281 \| \| 3.494 \| 4.08 \| 7000 \| 4.0312 \| \| 3.3149 \| 4.37 \| 7500 \| 4.0223 \| \| 3.3155 \| 4.66 \| 8000 \| 4.0143 \| \| 3.3071 \| 4.95 \| 8500 \| 3.9997 \| \| 3.1576 \| 5.24 \| 9000 \| 4.0174 \| \| 3.138 \| 5.53 \| 9500 \| 4.0160 \| \| 3.1384 \| 5.82 \| 10000 \| 4.0158 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
Envoid/MindFlay-22B	Envoid	2023-07-25T18:37:50Z	15	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-25T16:15:19Z	This is the original FP16 result of the model created using chargoddard's frankenllama script so that others interested in further experimentation with the results may do so. # WARNING: this model is very unpredictable. This model is an experiment using the frankenstein script from https://huggingface.co/chargoddard/llama2-22b Except I decided to use it with two models that have already been extensively finetuned. With https://huggingface.co/TheBloke/Llama-2-13B-Chat-fp16 as the base model and https://huggingface.co/Aeala/Enterredaas-33b as the donor model. The resulting model is surprisingly coherent and still responds well to the llama2chat prompt format ```[INST]<<SYS>><</SYS>>[/INST]``` and still has most of llama2chat's bubbly/giddy personality but more gritty and visceral. It makes occasional "typos" along with some other quirks so it was not completely unscathed by the frankensteining process. I plan to massage it over with a LoRA in the near future to bring it into more harmony but in the meantime it is available now for your enjoyment. Use cases: Chat/RP not much else.
rossellison/kpop-face-generator	rossellison	2023-07-25T18:35:54Z	0	2	null	[ "kpop", "face-generation", "gan", "stylegan3", "en", "license:apache-2.0", "region:us" ]	null	2023-07-20T23:41:47Z	--- language: - en thumbnail: "https://huggingface.co/rossellison/kpop-face-generator/raw/main/finalthumb2-01.jpg" tags: - kpop - face-generation - gan - stylegan3 license: "apache-2.0" datasets: - https://www.kaggle.com/datasets/rossellison/kpop-idol-faces --- # Kpop Face Generator Welcome to the Kpop Face Generator! Generate new, unique faces. The model `kpopGG.pkl` file is trained using Stylegan. ## Dataset Available on Kaggle: [Kpop Idol Faces Dataset](https://www.kaggle.com/datasets/rossellison/kpop-idol-faces) ## Code Code to generate the data and notebooks: [Face Data Creation Repository](https://github.com/rossellison/face-data-creation) ## Try it Out! Hugging Face Space: [Kpop Face Generator](https://huggingface.co/spaces/rossellison/kpop-face-generator)
Adi0010/ppo-sb-LunarLander-v2	Adi0010	2023-07-25T18:12:30Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-25T18:12:09Z	--- 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: 243.64 +/- 20.51 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 ... ```
GeneralShan/fwap_models_20230725	GeneralShan	2023-07-25T18:06:58Z	0	0	null	[ "onnx", "region:us" ]	null	2023-07-25T17:59:17Z	Model for FWAP project, - includes INSIGHTFACE model - CODEFORMER conversion TO ONNX - also uses OPENNSFW to prevent abuse
naveenkarakavalasa/t5-small-finetunesmallT5	naveenkarakavalasa	2023-07-25T18:06:09Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-25T17:50:43Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetunesmallT5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetunesmallT5 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 22.8504 - Rouge1: 80.2116 - Rouge2: 70.3704 - Rougel: 80.2116 - Rougelsum: 80.2116 - Gen Len: 4.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 4 \| 27.3213 \| 80.2116 \| 70.3704 \| 80.2116 \| 80.2116 \| 4.0 \| \| No log \| 2.0 \| 8 \| 25.8240 \| 80.2116 \| 70.3704 \| 80.2116 \| 80.2116 \| 4.0 \| \| No log \| 3.0 \| 12 \| 24.2754 \| 80.2116 \| 70.3704 \| 80.2116 \| 80.2116 \| 4.0 \| \| No log \| 4.0 \| 16 \| 23.4084 \| 80.2116 \| 70.3704 \| 80.2116 \| 80.2116 \| 4.0 \| \| No log \| 5.0 \| 20 \| 22.8504 \| 80.2116 \| 70.3704 \| 80.2116 \| 80.2116 \| 4.0 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
admin-dev/Llama2-7b-hf-resume-formatter-modgen	admin-dev	2023-07-25T17:57:46Z	2	0	peft	[ "peft", "region:us" ]	null	2023-07-25T17:57:38Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
brunoboat/dqn-SpaceInvadersNoFrameskip	brunoboat	2023-07-25T17:53:44Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-25T17:53:06Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 550.00 +/- 153.70 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga brunoboat -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga brunoboat -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga brunoboat ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 600000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
gaioNL/Pixelcopter-PLE-v2-r3	gaioNL	2023-07-25T17:38:52Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-25T17:38:44Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v2-r3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 97.80 +/- 70.54 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
jakariamd/opp_115_privacy_contact_information	jakariamd	2023-07-25T17:12:31Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-25T17:05:06Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: opp_115_privacy_contact_information 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. --> # opp_115_privacy_contact_information This model is a fine-tuned version of [mukund/privbert](https://huggingface.co/mukund/privbert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0744 - Accuracy: 0.9788 ## 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: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 165 \| 0.0795 \| 0.9788 \| \| No log \| 2.0 \| 330 \| 0.0744 \| 0.9788 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
Za88yes/Iccis	Za88yes	2023-07-25T17:11:51Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-25T17:09:46Z	--- license: creativeml-openrail-m ---

EXrRor3/PixelCopter

EXrRor3

2023-07-26T07:06:08Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-07-26T07:06:04Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 40.90 +/- 32.40 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

ezzzz22/CTRN

ezzzz22

2023-07-26T06:56:00Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-26T06:54:29Z

--- license: creativeml-openrail-m ---

bangnbx/t5.1.1.lm100k.large-384

bangnbx

2023-07-26T06:46:24Z

105

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-26T06:36:28Z

acc 67.02% https://wandb.ai/bangnbx/t5.1.1.lm100k.large-relax/runs/3d15uut5?workspace=user-bangnbx

NasimB/guten-no-merge-log-rarity

NasimB

2023-07-26T06:44:33Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-25T23:58:20Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: guten-no-merge-log-rarity results: [] ---  # guten-no-merge-log-rarity This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.0835 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 7.5185 | 0.12 | 200 | 5.9953 | | 5.702 | 0.23 | 400 | 5.4671 | | 5.3132 | 0.35 | 600 | 5.2102 | | 5.0736 | 0.46 | 800 | 5.0308 | | 4.9325 | 0.58 | 1000 | 4.9096 | | 4.7986 | 0.69 | 1200 | 4.7945 | | 4.6851 | 0.81 | 1400 | 4.7064 | | 4.6142 | 0.93 | 1600 | 4.6351 | | 4.4995 | 1.04 | 1800 | 4.5776 | | 4.3886 | 1.16 | 2000 | 4.5242 | | 4.3455 | 1.27 | 2200 | 4.4778 | | 4.3119 | 1.39 | 2400 | 4.4343 | | 4.2646 | 1.5 | 2600 | 4.3910 | | 4.2227 | 1.62 | 2800 | 4.3531 | | 4.1925 | 1.74 | 3000 | 4.3114 | | 4.1501 | 1.85 | 3200 | 4.2712 | | 4.129 | 1.97 | 3400 | 4.2403 | | 3.9673 | 2.08 | 3600 | 4.2321 | | 3.9145 | 2.2 | 3800 | 4.2166 | | 3.9146 | 2.31 | 4000 | 4.1956 | | 3.8992 | 2.43 | 4200 | 4.1732 | | 3.8932 | 2.55 | 4400 | 4.1494 | | 3.8646 | 2.66 | 4600 | 4.1281 | | 3.8627 | 2.78 | 4800 | 4.1104 | | 3.8537 | 2.89 | 5000 | 4.0890 | | 3.8128 | 3.01 | 5200 | 4.0839 | | 3.6101 | 3.12 | 5400 | 4.0845 | | 3.611 | 3.24 | 5600 | 4.0771 | | 3.6168 | 3.36 | 5800 | 4.0707 | | 3.6047 | 3.47 | 6000 | 4.0544 | | 3.6015 | 3.59 | 6200 | 4.0471 | | 3.5941 | 3.7 | 6400 | 4.0331 | | 3.5878 | 3.82 | 6600 | 4.0210 | | 3.5797 | 3.94 | 6800 | 4.0098 | | 3.4728 | 4.05 | 7000 | 4.0174 | | 3.3399 | 4.17 | 7200 | 4.0220 | | 3.3436 | 4.28 | 7400 | 4.0194 | | 3.3467 | 4.4 | 7600 | 4.0145 | | 3.3501 | 4.51 | 7800 | 4.0088 | | 3.3493 | 4.63 | 8000 | 4.0028 | | 3.3374 | 4.75 | 8200 | 3.9991 | | 3.3364 | 4.86 | 8400 | 3.9946 | | 3.3261 | 4.98 | 8600 | 3.9902 | | 3.204 | 5.09 | 8800 | 4.0007 | | 3.1715 | 5.21 | 9000 | 4.0032 | | 3.1683 | 5.32 | 9200 | 4.0025 | | 3.1708 | 5.44 | 9400 | 4.0026 | | 3.1649 | 5.56 | 9600 | 4.0019 | | 3.1773 | 5.67 | 9800 | 4.0012 | | 3.1608 | 5.79 | 10000 | 4.0014 | | 3.1538 | 5.9 | 10200 | 4.0012 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

nkpz/llama2-22b-frankenwizard

nkpz

2023-07-26T06:44:04Z

15

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-26T04:13:21Z

--- license: other --- Thanks to chargoddard for the original 22b model and merge script: https://huggingface.co/chargoddard/llama2-22b This uses https://huggingface.co/Blackroot/FrankensteinsMonster-13B as a base, with https://huggingface.co/ehartford/WizardLM-33B-V1.0-Uncensored as the donor model. This an experimental model and it sometimes forgets its context or says something nonsensical, but maybe some further fine tuning would smooth things out. This has less guardrails than my prior merge, https://huggingface.co/nkpz/llama2-22b-chat-wizard-uncensored Took around 2 hours to merge with 32gb of ram and about 115gb of swap used.

zhyemmmm/FantasticAnime

zhyemmmm

2023-07-26T06:35:56Z

29

0

diffusers

[ "diffusers", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-06-29T08:36:09Z

--- license: creativeml-openrail-m ---

NHNDQ/nllb-finetuned-en2ko

NHNDQ

2023-07-26T06:28:11Z

1,325

20

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "translation", "en", "ko", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-05-15T00:49:33Z

--- license: cc-by-4.0 language: - en - ko tags: - translation --- ## Model Details * Model Description: Fine-tuned facebook/nllb-200-distilled-600M model * Developed by: Jisu Kim, Juhwan Lee, TakSung Heo, and Minsu Jeong * Model Type: Translation * Language(s): * Source Language: English * Target Language: Korean * License: CC-BY-4.0 ## Dataset * [AI-hub dataset](https://www.aihub.or.kr/) ## BLEU Score * Deepl translation: 22.83 * Fine-tune nllb: 33.66 ## Uses This model can be used for translation and text-to-text generation ## Data Augmentation with backtranslation task You can exercise korean data augmentation task with python package [KoTAN](https://github.com/KoJLabs/KoTAN/tree/main)

opm29/mauren

opm29

2023-07-26T06:27:53Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-26T06:11:28Z

--- license: creativeml-openrail-m ---

zhyemmmm/CuriousMerge

zhyemmmm

2023-07-26T06:24:41Z

29

0

diffusers

[ "diffusers", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-06-29T03:16:22Z

--- license: creativeml-openrail-m ---

zhyemmmm/ManmaruMix

zhyemmmm

2023-07-26T06:23:36Z

30

2

diffusers

[ "diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-06-26T07:47:44Z

--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image ---

uomnf97/LawBot-level2-final-preprocessing-v3

uomnf97

2023-07-26T06:19:45Z

0

peft

[ "peft", "region:us" ]

null

2023-07-26T06:19:29Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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 0.5.0.dev0

hksul1024/llama2-qlora-finetunined-french

hksul1024

2023-07-26T06:17:15Z

0

peft

[ "peft", "region:us" ]

null

2023-07-26T06:16:58Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

zhyemmmm/Cutecolor

zhyemmmm

2023-07-26T06:08:00Z

29

1

diffusers

[ "diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-06-29T03:43:25Z

--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image ---

min9805/bert-base-finetuned-ynat

min9805

2023-07-26T06:01:26Z

106

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:klue/bert-base", "base_model:finetune:klue/bert-base", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-18T09:07:37Z

--- license: cc-by-sa-4.0 base_model: klue/bert-base tags: - generated_from_trainer metrics: - f1 model-index: - name: bert-base-finetuned-ynat results: [] ---  # bert-base-finetuned-ynat This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6806 - F1: 0.2273 ## 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: 256 - eval_batch_size: 256 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 2 | 1.8609 | 0.0476 | | No log | 2.0 | 4 | 1.7637 | 0.0476 | | No log | 3.0 | 6 | 1.6806 | 0.2273 | | No log | 4.0 | 8 | 1.6409 | 0.2273 | | No log | 5.0 | 10 | 1.6236 | 0.2273 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

111wwwww/2DWDWFCFSV

111wwwww

2023-07-26T06:01:17Z

0

null

[ "license:bsd-3-clause-clear", "region:us" ]

null

2023-07-26T06:01:17Z

--- license: bsd-3-clause-clear ---

junkmind/my_awesome_mind

junkmind

2023-07-26T05:57:41Z

161

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-26T05:49:56Z

--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: my_awesome_mind results: - task: name: Audio Classification type: audio-classification dataset: name: minds14 type: minds14 config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.061946902654867256 ---  # my_awesome_mind This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6657 - Accuracy: 0.0619 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.8 | 3 | 2.6406 | 0.0708 | | No log | 1.87 | 7 | 2.6497 | 0.0885 | | 2.635 | 2.93 | 11 | 2.6532 | 0.0531 | | 2.635 | 4.0 | 15 | 2.6612 | 0.0265 | | 2.635 | 4.8 | 18 | 2.6621 | 0.0265 | | 2.6225 | 5.87 | 22 | 2.6622 | 0.0531 | | 2.6225 | 6.93 | 26 | 2.6650 | 0.0442 | | 2.6054 | 8.0 | 30 | 2.6657 | 0.0619 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

111wwwww/111

111wwwww

2023-07-26T05:56:35Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-26T05:56:35Z

--- license: creativeml-openrail-m ---

VCool22/bert-finetuned-ner

VCool22

2023-07-26T05:47:17Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-25T22:11:17Z

--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.936092715231788 - name: Recall type: recall value: 0.9515314708852238 - name: F1 type: f1 value: 0.9437489567684861 - name: Accuracy type: accuracy value: 0.986504385706717 ---  # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0597 - Precision: 0.9361 - Recall: 0.9515 - F1: 0.9437 - Accuracy: 0.9865 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0794 | 1.0 | 1756 | 0.0703 | 0.9143 | 0.9317 | 0.9229 | 0.9808 | | 0.0346 | 2.0 | 3512 | 0.0573 | 0.9331 | 0.9490 | 0.9410 | 0.9861 | | 0.0191 | 3.0 | 5268 | 0.0597 | 0.9361 | 0.9515 | 0.9437 | 0.9865 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

koochikoo25/Whisper-medium-pashto

koochikoo25

2023-07-26T05:40:26Z

9

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ps", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-07-20T13:39:17Z

--- language: - ps license: apache-2.0 base_model: openai/whisper-medium tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Pashto - Awais Nawaz results: [] ---  # Whisper Medium Pashto - Awais Nawaz This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. - Loss: 1.021 - Wer: 30.731 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

hhs8746/qlora-koalpaca-polyglot-5.8b-10000stepplus

hhs8746

2023-07-26T05:36:40Z

0

peft

[ "peft", "region:us" ]

null

2023-07-26T05:36:35Z

--- 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.dev0

jamesthong/whisper-small-dv

jamesthong

2023-07-26T05:32:48Z

81

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "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

2023-07-25T13:55:01Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-small-dv results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train[450:] args: en-US metrics: - name: Wer type: wer value: 0.3530106257378985 ---  # whisper-small-dv This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6457 - Wer Ortho: 0.3572 - Wer: 0.3530 ## 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 | 0.6457 | 0.3572 | 0.3530 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

sanitas/q-FrozenLake-v1-4x4-noSlippery

sanitas

2023-07-26T05:12:39Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-26T05:12:37Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="sanitas/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

CordwainerSmith/speecht5_finetuned_voxpopuli_nl-v2

CordwainerSmith

2023-07-26T04:50:36Z

82

0

transformers

[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "dataset:voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-speech

2023-07-25T22:21:32Z

--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl-v2 results: [] pipeline_tag: text-to-speech ---  # speecht5_finetuned_voxpopuli_nl-v2 This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4601 ## 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: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5213 | 4.3 | 1000 | 0.4805 | | 0.4946 | 8.61 | 2000 | 0.4651 | | 0.4928 | 12.91 | 3000 | 0.4627 | | 0.4923 | 17.21 | 4000 | 0.4601 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

liuyt75/t5-small_prefix_tuning_sentences_allagree_10

liuyt75

2023-07-26T04:48:11Z

0

peft

[ "peft", "region:us" ]

null

2023-07-26T04:48:10Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0

camargoj/llama2-qlora-finetunined-french

camargoj

2023-07-26T04:24:23Z

0

peft

[ "peft", "region:us" ]

null

2023-07-26T04:24:18Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

jordyvl/cdip-small_rvl_cdip-NK1000_kd_test

jordyvl

2023-07-26T04:09:21Z

165

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-25T14:00:54Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: cdip-small_rvl_cdip-NK1000_kd_test results: [] ---  # cdip-small_rvl_cdip-NK1000_kd_test This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3813 - Accuracy: 0.8558 - Brier Loss: 0.2176 - Nll: 1.4251 - F1 Micro: 0.8558 - F1 Macro: 0.8566 - Ece: 0.0597 - Aurc: 0.0299 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | 1.7536 | 1.0 | 667 | 0.9652 | 0.6695 | 0.4474 | 2.2965 | 0.6695 | 0.6595 | 0.0494 | 0.1257 | | 0.8802 | 2.0 | 1334 | 0.7683 | 0.7195 | 0.3806 | 2.0303 | 0.7195 | 0.7116 | 0.0473 | 0.0920 | | 0.5767 | 3.0 | 2001 | 0.6276 | 0.7698 | 0.3253 | 1.9446 | 0.7698 | 0.7711 | 0.0436 | 0.0684 | | 0.4263 | 4.0 | 2668 | 0.6095 | 0.7785 | 0.3110 | 1.9810 | 0.7785 | 0.7810 | 0.0474 | 0.0624 | | 0.3987 | 5.0 | 3335 | 0.5608 | 0.791 | 0.2939 | 1.8539 | 0.791 | 0.7918 | 0.0504 | 0.0557 | | 0.3179 | 6.0 | 4002 | 0.6057 | 0.7935 | 0.3027 | 1.8778 | 0.7935 | 0.7940 | 0.0811 | 0.0548 | | 0.2428 | 7.0 | 4669 | 0.5828 | 0.8043 | 0.2905 | 1.8616 | 0.8043 | 0.8050 | 0.0662 | 0.0520 | | 0.2094 | 8.0 | 5336 | 0.5812 | 0.7957 | 0.2973 | 1.8459 | 0.7957 | 0.8019 | 0.0783 | 0.0532 | | 0.1715 | 9.0 | 6003 | 0.6152 | 0.7987 | 0.2993 | 1.9533 | 0.7987 | 0.7998 | 0.0723 | 0.0539 | | 0.1508 | 10.0 | 6670 | 0.5442 | 0.808 | 0.2820 | 1.8159 | 0.808 | 0.8097 | 0.0836 | 0.0476 | | 0.1434 | 11.0 | 7337 | 0.4881 | 0.828 | 0.2549 | 1.6938 | 0.828 | 0.8286 | 0.0610 | 0.0410 | | 0.1267 | 12.0 | 8004 | 0.4720 | 0.8365 | 0.2465 | 1.6878 | 0.8365 | 0.8360 | 0.0576 | 0.0400 | | 0.115 | 13.0 | 8671 | 0.4648 | 0.8335 | 0.2482 | 1.6871 | 0.8335 | 0.8353 | 0.0630 | 0.0387 | | 0.1112 | 14.0 | 9338 | 0.4777 | 0.8317 | 0.2509 | 1.6393 | 0.8317 | 0.8312 | 0.0614 | 0.0418 | | 0.1002 | 15.0 | 10005 | 0.4684 | 0.8333 | 0.2484 | 1.6054 | 0.8333 | 0.8335 | 0.0657 | 0.0392 | | 0.0944 | 16.0 | 10672 | 0.4693 | 0.8365 | 0.2480 | 1.6381 | 0.8365 | 0.8366 | 0.0658 | 0.0383 | | 0.0934 | 17.0 | 11339 | 0.4534 | 0.8323 | 0.2465 | 1.6420 | 0.8323 | 0.8343 | 0.0561 | 0.0373 | | 0.0835 | 18.0 | 12006 | 0.4512 | 0.8357 | 0.2435 | 1.6301 | 0.8357 | 0.8367 | 0.0575 | 0.0372 | | 0.08 | 19.0 | 12673 | 0.4345 | 0.838 | 0.2394 | 1.6382 | 0.838 | 0.8398 | 0.0562 | 0.0366 | | 0.0819 | 20.0 | 13340 | 0.4356 | 0.838 | 0.2374 | 1.5973 | 0.838 | 0.8384 | 0.0588 | 0.0364 | | 0.0709 | 21.0 | 14007 | 0.4484 | 0.8415 | 0.2368 | 1.6231 | 0.8415 | 0.8411 | 0.0595 | 0.0368 | | 0.0691 | 22.0 | 14674 | 0.4194 | 0.8495 | 0.2287 | 1.5968 | 0.8495 | 0.8505 | 0.0531 | 0.0335 | | 0.068 | 23.0 | 15341 | 0.4308 | 0.8413 | 0.2346 | 1.5599 | 0.8413 | 0.8410 | 0.0542 | 0.0360 | | 0.0641 | 24.0 | 16008 | 0.4209 | 0.8405 | 0.2336 | 1.5539 | 0.8405 | 0.8422 | 0.0590 | 0.0339 | | 0.0617 | 25.0 | 16675 | 0.4181 | 0.841 | 0.2352 | 1.5735 | 0.841 | 0.8435 | 0.0568 | 0.0356 | | 0.0633 | 26.0 | 17342 | 0.4193 | 0.8508 | 0.2286 | 1.5299 | 0.8508 | 0.8510 | 0.0650 | 0.0348 | | 0.0569 | 27.0 | 18009 | 0.4065 | 0.8468 | 0.2278 | 1.5267 | 0.8468 | 0.8479 | 0.0546 | 0.0332 | | 0.0571 | 28.0 | 18676 | 0.4109 | 0.8498 | 0.2255 | 1.5147 | 0.8498 | 0.8499 | 0.0590 | 0.0331 | | 0.0543 | 29.0 | 19343 | 0.4026 | 0.8482 | 0.2250 | 1.5187 | 0.8482 | 0.8498 | 0.0623 | 0.0327 | | 0.0543 | 30.0 | 20010 | 0.4124 | 0.847 | 0.2293 | 1.5125 | 0.847 | 0.8473 | 0.0605 | 0.0330 | | 0.0536 | 31.0 | 20677 | 0.4022 | 0.851 | 0.2238 | 1.5100 | 0.851 | 0.8527 | 0.0594 | 0.0323 | | 0.0522 | 32.0 | 21344 | 0.4120 | 0.8475 | 0.2290 | 1.5044 | 0.8475 | 0.8483 | 0.0633 | 0.0327 | | 0.0493 | 33.0 | 22011 | 0.3990 | 0.8492 | 0.2258 | 1.5197 | 0.8492 | 0.8503 | 0.0589 | 0.0318 | | 0.0512 | 34.0 | 22678 | 0.3983 | 0.85 | 0.2251 | 1.4644 | 0.85 | 0.8503 | 0.0597 | 0.0319 | | 0.0517 | 35.0 | 23345 | 0.3969 | 0.8465 | 0.2257 | 1.4814 | 0.8465 | 0.8479 | 0.0630 | 0.0309 | | 0.0477 | 36.0 | 24012 | 0.3939 | 0.8528 | 0.2237 | 1.4797 | 0.8528 | 0.8531 | 0.0604 | 0.0316 | | 0.0482 | 37.0 | 24679 | 0.3934 | 0.852 | 0.2218 | 1.4595 | 0.852 | 0.8527 | 0.0613 | 0.0316 | | 0.0481 | 38.0 | 25346 | 0.3930 | 0.8532 | 0.2217 | 1.4561 | 0.8532 | 0.8544 | 0.0593 | 0.0306 | | 0.0477 | 39.0 | 26013 | 0.3875 | 0.8512 | 0.2202 | 1.4610 | 0.8512 | 0.8523 | 0.0609 | 0.0310 | | 0.048 | 40.0 | 26680 | 0.3900 | 0.8538 | 0.2202 | 1.4541 | 0.8537 | 0.8546 | 0.0629 | 0.0307 | | 0.0448 | 41.0 | 27347 | 0.3901 | 0.8525 | 0.2221 | 1.4519 | 0.8525 | 0.8532 | 0.0621 | 0.0308 | | 0.0454 | 42.0 | 28014 | 0.3858 | 0.851 | 0.2186 | 1.4554 | 0.851 | 0.8519 | 0.0633 | 0.0298 | | 0.0464 | 43.0 | 28681 | 0.3861 | 0.8528 | 0.2197 | 1.4516 | 0.8528 | 0.8535 | 0.0618 | 0.0307 | | 0.0444 | 44.0 | 29348 | 0.3824 | 0.8548 | 0.2176 | 1.4288 | 0.8547 | 0.8557 | 0.0607 | 0.0299 | | 0.0461 | 45.0 | 30015 | 0.3833 | 0.8555 | 0.2181 | 1.4330 | 0.8555 | 0.8566 | 0.0606 | 0.0302 | | 0.0442 | 46.0 | 30682 | 0.3830 | 0.8552 | 0.2174 | 1.4358 | 0.8552 | 0.8560 | 0.0604 | 0.0302 | | 0.0456 | 47.0 | 31349 | 0.3797 | 0.8552 | 0.2173 | 1.4264 | 0.8552 | 0.8560 | 0.0596 | 0.0297 | | 0.0447 | 48.0 | 32016 | 0.3811 | 0.8558 | 0.2176 | 1.4273 | 0.8558 | 0.8566 | 0.0595 | 0.0300 | | 0.0439 | 49.0 | 32683 | 0.3814 | 0.856 | 0.2176 | 1.4252 | 0.856 | 0.8568 | 0.0600 | 0.0300 | | 0.0437 | 50.0 | 33350 | 0.3813 | 0.8558 | 0.2176 | 1.4251 | 0.8558 | 0.8566 | 0.0597 | 0.0299 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

ShawnLJW/harry-GPTter

ShawnLJW

2023-07-26T04:03:42Z

0

1

null

[ "text-generation", "en", "region:us" ]

text-generation

2023-04-09T00:38:20Z

--- language: - en pipeline_tag: text-generation --- # harry-GPTter harry-GPTter is a transformer text generation model implemented in PyTorch. It has been trained on text from all 7 books from from all 7 books of the Harry Potter series. In only 10 minutes of training with the free tier of [Google Colaboratory](https://colab.research.google.com/), the model learnt to generate coherent and grammatically correct sentences. - Code and more information in the [GitHub Repository](https://github.com/ShawnLJW/harry-GPTter) - Download the [weights](https://huggingface.co/ShawnLJW/harry-GPTter/resolve/main/checkpoint.pt) ## Text Generation with harry-GPTter > “Ah,” said Mrs. Weasley, hiscolored lips looking unpleasant. “He wasn’t talking about her, he has tried to think he was saying he had looked up. The bleers were flooding.” > > “My master died?” whispered Voldemort, but the wasnoddenbling until he are, making to be seeing him. > > “I’ll see you, Professor Lockhart,” said Hermione, “but so surely now to have solid on it out of her whole bed! You’re thinking — > > “Oh hello the unconscious!” > > “And now blimey,” said Harry, “it was a very serious for an enormous mother. ...” ## Model Details harry-GPTter is a relatively small language model with 56M parameters (less than 1/2x of smallest gpt-2). It contains 8 layers of 8 headed attention with a hidden size of 384. It supports a maximum sequence length of 128. For tokenization, we use the same tokenizer as text-davinci-003, which has a vocabulary of 50,280 in total. The model was trained for 2000 epochs in about 10 minutes with the free tier of Google Colab GPU Runtime. It achieves a cross-entropy loss of 3.1189. This model was built for learning purposes. You can probably get better performance by finetuning a pre-trained model.

InductiveGrub/halo-grunt

InductiveGrub

2023-07-26T03:59:55Z

0

null

[ "video games", "halo", "halo combat evolved", "en", "region:us" ]

null

2023-07-26T03:57:31Z

--- language: - en tags: - video games - halo - halo combat evolved ---

nebulae7/max

nebulae7

2023-07-26T03:58:08Z

0

peft

[ "peft", "RefinedWebModel", "custom_code", "4-bit", "region:us" ]

null

2023-07-24T05:33:09Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

LearnItAnyway/YOLO_LLaMa_7B_VisNav

LearnItAnyway

2023-07-26T03:57:32Z

13

0

transformers

[ "transformers", "pytorch", "llava", "text-generation", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-07-26T01:50:13Z

--- license: other --- # Overview This project aims to support visually impaired individuals in their daily navigation. This project combines the [YOLO](https://ultralytics.com/yolov8) model and [LLaMa 2 7b](https://huggingface.co/meta-llama/Llama-2-7b) for the navigation. YOLO is trained on the bounding box data from the [AI Hub](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=189), Output of YOLO (bbox data) is converted as lists like `[[class_of_obj_1, xmin, xmax, ymin, ymax, size], [class_of...] ...]` then added to the input of question. The LLM is trained to navigate using [LearnItAnyway/Visual-Navigation-21k](https://huggingface.co/datasets/LearnItAnyway/Visual-Navigation-21k) multi-turn dataset ## Usage We show how to use the model in [yolo_llama_visnav_test.ipynb](https://huggingface.co/LearnItAnyway/YOLO_LLaMa_7B_VisNav/blob/main/yolo_llama_visnav_test.ipynb)

nebulae7/seven

nebulae7

2023-07-26T03:57:22Z

5

0

peft

[ "peft", "RefinedWebModel", "custom_code", "4-bit", "region:us" ]

null

2023-07-24T05:53:59Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

nebulae7/eight

nebulae7

2023-07-26T03:55:19Z

3

0

peft

[ "peft", "RefinedWebModel", "custom_code", "4-bit", "region:us" ]

null

2023-07-24T07:34:02Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

vicbguti/bloom-1b7-lora-tagger

vicbguti

2023-07-26T03:54:23Z

2

0

peft

[ "peft", "region:us" ]

null

2023-07-26T02:45:04Z

--- 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.dev0

datasistah/20230725_STAR_interview_model

datasistah

2023-07-26T03:49:32Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-26T03:09:51Z

--- 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.dev0

PascalY/bert-finetuned-ner

PascalY

2023-07-26T03:43:26Z

116

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-26T03:10:26Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9374896093100582 - name: Recall type: recall value: 0.9490070683271625 - name: F1 type: f1 value: 0.9432131805636865 - name: Accuracy type: accuracy value: 0.9873862137989102 ---  # bert-finetuned-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0579 - Precision: 0.9375 - Recall: 0.9490 - F1: 0.9432 - Accuracy: 0.9874 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0729 | 1.0 | 1756 | 0.0607 | 0.9117 | 0.9366 | 0.9240 | 0.9839 | | 0.0361 | 2.0 | 3512 | 0.0538 | 0.9250 | 0.9468 | 0.9358 | 0.9864 | | 0.0205 | 3.0 | 5268 | 0.0579 | 0.9375 | 0.9490 | 0.9432 | 0.9874 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

Sukmin/q-FrozenLake-v1-4x4-noSlippery

Sukmin

2023-07-26T03:36:39Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-26T03:36:36Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="Sukmin/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Sukmin/ppo-LunarLander-v2

Sukmin

2023-07-26T03:29:20Z

2

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-26T03:28:58Z

--- 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: 251.64 +/- 15.24 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 ... ```

catbox/llama2-qlora-finetunined-french

catbox

2023-07-26T03:26:29Z

0

peft

[ "peft", "region:us" ]

null

2023-07-26T03:26:20Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

Sukmin/rl_course_vizdoom_health_gathering_supreme

Sukmin

2023-07-26T03:23:14Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-26T03:00:00Z

--- 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: 8.86 +/- 2.70 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 Sukmin/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.

Jonathaniu/llama2-breast-cancer-7b-knowledge-epoch-3

Jonathaniu

2023-07-26T02:56:42Z

0

peft

[ "peft", "region:us" ]

null

2023-07-26T02:56:29Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False ### Framework versions - PEFT 0.4.0.dev0

simonycl/best_model-sst-2-64-100

simonycl

2023-07-26T02:38:25Z

107

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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

2023-07-26T02:27:10Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-64-100 results: [] ---  # best_model-sst-2-64-100 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: - Loss: 0.9480 - Accuracy: 0.8906 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 4 | 0.8613 | 0.9141 | | No log | 2.0 | 8 | 0.8613 | 0.9141 | | 0.6496 | 3.0 | 12 | 0.8614 | 0.9141 | | 0.6496 | 4.0 | 16 | 0.8614 | 0.9141 | | 0.6483 | 5.0 | 20 | 0.8596 | 0.9141 | | 0.6483 | 6.0 | 24 | 0.8575 | 0.9141 | | 0.6483 | 7.0 | 28 | 0.8557 | 0.9141 | | 0.6867 | 8.0 | 32 | 0.8528 | 0.9141 | | 0.6867 | 9.0 | 36 | 0.8506 | 0.9141 | | 0.3821 | 10.0 | 40 | 0.8542 | 0.9062 | | 0.3821 | 11.0 | 44 | 0.8721 | 0.8984 | | 0.3821 | 12.0 | 48 | 0.8877 | 0.8984 | | 0.4452 | 13.0 | 52 | 0.8920 | 0.8984 | | 0.4452 | 14.0 | 56 | 0.8952 | 0.8984 | | 0.3224 | 15.0 | 60 | 0.8920 | 0.9062 | | 0.3224 | 16.0 | 64 | 0.8833 | 0.9062 | | 0.3224 | 17.0 | 68 | 0.8727 | 0.9062 | | 0.2699 | 18.0 | 72 | 0.8284 | 0.8984 | | 0.2699 | 19.0 | 76 | 0.7829 | 0.9062 | | 0.1873 | 20.0 | 80 | 0.7713 | 0.9062 | | 0.1873 | 21.0 | 84 | 0.7646 | 0.8984 | | 0.1873 | 22.0 | 88 | 0.7517 | 0.8984 | | 0.1282 | 23.0 | 92 | 0.7379 | 0.9062 | | 0.1282 | 24.0 | 96 | 0.7295 | 0.9062 | | 0.0438 | 25.0 | 100 | 0.7243 | 0.8984 | | 0.0438 | 26.0 | 104 | 0.7038 | 0.9141 | | 0.0438 | 27.0 | 108 | 0.6994 | 0.9219 | | 0.0154 | 28.0 | 112 | 0.6997 | 0.9062 | | 0.0154 | 29.0 | 116 | 0.7184 | 0.8984 | | 0.0019 | 30.0 | 120 | 0.7601 | 0.9062 | | 0.0019 | 31.0 | 124 | 0.7739 | 0.9062 | | 0.0019 | 32.0 | 128 | 0.7854 | 0.9062 | | 0.0003 | 33.0 | 132 | 0.7934 | 0.9062 | | 0.0003 | 34.0 | 136 | 0.7945 | 0.9062 | | 0.0002 | 35.0 | 140 | 0.7896 | 0.9062 | | 0.0002 | 36.0 | 144 | 0.7711 | 0.9062 | | 0.0002 | 37.0 | 148 | 0.7503 | 0.9062 | | 0.0004 | 38.0 | 152 | 0.7436 | 0.9062 | | 0.0004 | 39.0 | 156 | 0.7464 | 0.9062 | | 0.0001 | 40.0 | 160 | 0.7492 | 0.9062 | | 0.0001 | 41.0 | 164 | 0.7990 | 0.9062 | | 0.0001 | 42.0 | 168 | 0.8244 | 0.9062 | | 0.0059 | 43.0 | 172 | 0.8377 | 0.9062 | | 0.0059 | 44.0 | 176 | 0.8496 | 0.9062 | | 0.0001 | 45.0 | 180 | 0.8582 | 0.9062 | | 0.0001 | 46.0 | 184 | 0.8646 | 0.9062 | | 0.0001 | 47.0 | 188 | 0.8286 | 0.9062 | | 0.0005 | 48.0 | 192 | 0.8002 | 0.9062 | | 0.0005 | 49.0 | 196 | 0.7854 | 0.9062 | | 0.0001 | 50.0 | 200 | 0.7691 | 0.9062 | | 0.0001 | 51.0 | 204 | 0.7594 | 0.9062 | | 0.0001 | 52.0 | 208 | 0.7618 | 0.9062 | | 0.0003 | 53.0 | 212 | 0.8175 | 0.9062 | | 0.0003 | 54.0 | 216 | 0.8539 | 0.9062 | | 0.0001 | 55.0 | 220 | 0.8737 | 0.9062 | | 0.0001 | 56.0 | 224 | 0.8661 | 0.9062 | | 0.0001 | 57.0 | 228 | 0.8398 | 0.9062 | | 0.0038 | 58.0 | 232 | 0.8162 | 0.9062 | | 0.0038 | 59.0 | 236 | 0.7946 | 0.9062 | | 0.0001 | 60.0 | 240 | 0.7866 | 0.9062 | | 0.0001 | 61.0 | 244 | 0.7776 | 0.9141 | | 0.0001 | 62.0 | 248 | 0.7781 | 0.9141 | | 0.0001 | 63.0 | 252 | 0.7963 | 0.9062 | | 0.0001 | 64.0 | 256 | 0.8099 | 0.9062 | | 0.0 | 65.0 | 260 | 0.8196 | 0.9062 | | 0.0 | 66.0 | 264 | 0.8284 | 0.9062 | | 0.0 | 67.0 | 268 | 0.8880 | 0.9062 | | 0.0045 | 68.0 | 272 | 0.9217 | 0.9062 | | 0.0045 | 69.0 | 276 | 0.9374 | 0.8984 | | 0.0082 | 70.0 | 280 | 0.9364 | 0.9062 | | 0.0082 | 71.0 | 284 | 0.8651 | 0.9062 | | 0.0082 | 72.0 | 288 | 0.7849 | 0.8984 | | 0.0003 | 73.0 | 292 | 0.7981 | 0.8984 | | 0.0003 | 74.0 | 296 | 0.7808 | 0.9141 | | 0.021 | 75.0 | 300 | 0.8438 | 0.9062 | | 0.021 | 76.0 | 304 | 0.8882 | 0.8984 | | 0.021 | 77.0 | 308 | 0.9214 | 0.8984 | | 0.0001 | 78.0 | 312 | 0.9396 | 0.8984 | | 0.0001 | 79.0 | 316 | 0.9493 | 0.8984 | | 0.0 | 80.0 | 320 | 0.9549 | 0.8984 | | 0.0 | 81.0 | 324 | 0.9466 | 0.8984 | | 0.0 | 82.0 | 328 | 0.9041 | 0.8984 | | 0.0001 | 83.0 | 332 | 0.8993 | 0.8984 | | 0.0001 | 84.0 | 336 | 0.9616 | 0.8984 | | 0.0001 | 85.0 | 340 | 0.9844 | 0.8984 | | 0.0001 | 86.0 | 344 | 0.9934 | 0.8906 | | 0.0001 | 87.0 | 348 | 0.9999 | 0.8906 | | 0.0001 | 88.0 | 352 | 0.9973 | 0.8906 | | 0.0001 | 89.0 | 356 | 0.9943 | 0.8984 | | 0.0 | 90.0 | 360 | 0.9929 | 0.8984 | | 0.0 | 91.0 | 364 | 0.9921 | 0.8984 | | 0.0 | 92.0 | 368 | 0.9915 | 0.8984 | | 0.0 | 93.0 | 372 | 0.9916 | 0.8984 | | 0.0 | 94.0 | 376 | 0.9924 | 0.8984 | | 0.0 | 95.0 | 380 | 0.9930 | 0.8984 | | 0.0 | 96.0 | 384 | 0.9936 | 0.8984 | | 0.0 | 97.0 | 388 | 0.9940 | 0.8984 | | 0.0 | 98.0 | 392 | 0.9946 | 0.8984 | | 0.0 | 99.0 | 396 | 0.9950 | 0.8984 | | 0.0006 | 100.0 | 400 | 0.9869 | 0.8984 | | 0.0006 | 101.0 | 404 | 0.8625 | 0.8984 | | 0.0006 | 102.0 | 408 | 0.7755 | 0.9219 | | 0.0 | 103.0 | 412 | 0.7887 | 0.8984 | | 0.0 | 104.0 | 416 | 0.7844 | 0.9062 | | 0.0062 | 105.0 | 420 | 0.8504 | 0.8984 | | 0.0062 | 106.0 | 424 | 0.9449 | 0.8984 | | 0.0062 | 107.0 | 428 | 0.9568 | 0.8906 | | 0.0 | 108.0 | 432 | 0.9504 | 0.8984 | | 0.0 | 109.0 | 436 | 0.9700 | 0.8984 | | 0.0 | 110.0 | 440 | 0.9875 | 0.8906 | | 0.0 | 111.0 | 444 | 1.0002 | 0.8906 | | 0.0 | 112.0 | 448 | 1.0095 | 0.8828 | | 0.0 | 113.0 | 452 | 1.0156 | 0.8828 | | 0.0 | 114.0 | 456 | 0.8995 | 0.8984 | | 0.0144 | 115.0 | 460 | 0.8017 | 0.8984 | | 0.0144 | 116.0 | 464 | 0.7774 | 0.9062 | | 0.0144 | 117.0 | 468 | 0.7913 | 0.9062 | | 0.0 | 118.0 | 472 | 0.8033 | 0.8984 | | 0.0 | 119.0 | 476 | 0.8244 | 0.8906 | | 0.0001 | 120.0 | 480 | 0.9148 | 0.8984 | | 0.0001 | 121.0 | 484 | 1.0038 | 0.8828 | | 0.0001 | 122.0 | 488 | 1.1128 | 0.875 | | 0.0 | 123.0 | 492 | 1.1276 | 0.875 | | 0.0 | 124.0 | 496 | 1.1209 | 0.8828 | | 0.0 | 125.0 | 500 | 1.1161 | 0.8828 | | 0.0 | 126.0 | 504 | 1.1119 | 0.8828 | | 0.0 | 127.0 | 508 | 1.1037 | 0.8828 | | 0.0 | 128.0 | 512 | 1.0644 | 0.8828 | | 0.0 | 129.0 | 516 | 1.0175 | 0.875 | | 0.0 | 130.0 | 520 | 0.9819 | 0.8828 | | 0.0 | 131.0 | 524 | 0.9613 | 0.8906 | | 0.0 | 132.0 | 528 | 0.9509 | 0.8906 | | 0.0 | 133.0 | 532 | 0.9463 | 0.8906 | | 0.0 | 134.0 | 536 | 0.9441 | 0.875 | | 0.0 | 135.0 | 540 | 0.9432 | 0.875 | | 0.0 | 136.0 | 544 | 0.9429 | 0.875 | | 0.0 | 137.0 | 548 | 0.9429 | 0.8828 | | 0.0 | 138.0 | 552 | 0.9430 | 0.8828 | | 0.0 | 139.0 | 556 | 0.9432 | 0.8828 | | 0.0 | 140.0 | 560 | 0.9434 | 0.8828 | | 0.0 | 141.0 | 564 | 0.9436 | 0.8828 | | 0.0 | 142.0 | 568 | 0.9438 | 0.8906 | | 0.0 | 143.0 | 572 | 0.9439 | 0.8906 | | 0.0 | 144.0 | 576 | 0.9448 | 0.8906 | | 0.0 | 145.0 | 580 | 0.9461 | 0.8906 | | 0.0 | 146.0 | 584 | 0.9470 | 0.8906 | | 0.0 | 147.0 | 588 | 0.9476 | 0.8906 | | 0.0 | 148.0 | 592 | 0.9478 | 0.8906 | | 0.0 | 149.0 | 596 | 0.9480 | 0.8906 | | 0.0 | 150.0 | 600 | 0.9480 | 0.8906 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3

simonycl/best_model-sst-2-64-87

simonycl

2023-07-26T02:26:54Z

113

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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

2023-07-26T02:15:17Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-64-87 results: [] ---  # best_model-sst-2-64-87 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: - Loss: 1.2746 - Accuracy: 0.8438 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 4 | 1.3247 | 0.8438 | | No log | 2.0 | 8 | 1.3227 | 0.8438 | | 0.7148 | 3.0 | 12 | 1.3195 | 0.8438 | | 0.7148 | 4.0 | 16 | 1.3169 | 0.8359 | | 0.6114 | 5.0 | 20 | 1.3149 | 0.8359 | | 0.6114 | 6.0 | 24 | 1.3101 | 0.8359 | | 0.6114 | 7.0 | 28 | 1.2982 | 0.8438 | | 0.5794 | 8.0 | 32 | 1.2836 | 0.8438 | | 0.5794 | 9.0 | 36 | 1.2655 | 0.8438 | | 0.5231 | 10.0 | 40 | 1.2497 | 0.8438 | | 0.5231 | 11.0 | 44 | 1.2410 | 0.8438 | | 0.5231 | 12.0 | 48 | 1.2307 | 0.8438 | | 0.4052 | 13.0 | 52 | 1.2154 | 0.8438 | | 0.4052 | 14.0 | 56 | 1.2001 | 0.8438 | | 0.363 | 15.0 | 60 | 1.1877 | 0.8438 | | 0.363 | 16.0 | 64 | 1.1760 | 0.8516 | | 0.363 | 17.0 | 68 | 1.1836 | 0.8516 | | 0.2969 | 18.0 | 72 | 1.1848 | 0.8594 | | 0.2969 | 19.0 | 76 | 1.1823 | 0.8516 | | 0.1866 | 20.0 | 80 | 1.1867 | 0.8516 | | 0.1866 | 21.0 | 84 | 1.1795 | 0.8516 | | 0.1866 | 22.0 | 88 | 1.1756 | 0.8516 | | 0.1502 | 23.0 | 92 | 1.1731 | 0.8516 | | 0.1502 | 24.0 | 96 | 1.1680 | 0.8516 | | 0.0974 | 25.0 | 100 | 1.1489 | 0.8516 | | 0.0974 | 26.0 | 104 | 1.1088 | 0.8516 | | 0.0974 | 27.0 | 108 | 1.0986 | 0.8594 | | 0.0992 | 28.0 | 112 | 1.0879 | 0.8594 | | 0.0992 | 29.0 | 116 | 1.0850 | 0.8594 | | 0.0065 | 30.0 | 120 | 1.1056 | 0.8594 | | 0.0065 | 31.0 | 124 | 1.1355 | 0.8516 | | 0.0065 | 32.0 | 128 | 1.1457 | 0.8438 | | 0.0185 | 33.0 | 132 | 1.1518 | 0.8438 | | 0.0185 | 34.0 | 136 | 1.1437 | 0.8438 | | 0.0123 | 35.0 | 140 | 1.1230 | 0.8516 | | 0.0123 | 36.0 | 144 | 1.1109 | 0.8516 | | 0.0123 | 37.0 | 148 | 1.1093 | 0.8594 | | 0.0001 | 38.0 | 152 | 1.1085 | 0.8594 | | 0.0001 | 39.0 | 156 | 1.1092 | 0.8594 | | 0.008 | 40.0 | 160 | 1.1163 | 0.8594 | | 0.008 | 41.0 | 164 | 1.1272 | 0.8516 | | 0.008 | 42.0 | 168 | 1.1351 | 0.8516 | | 0.0001 | 43.0 | 172 | 1.1365 | 0.8516 | | 0.0001 | 44.0 | 176 | 1.1287 | 0.8516 | | 0.0007 | 45.0 | 180 | 1.1195 | 0.8594 | | 0.0007 | 46.0 | 184 | 1.1110 | 0.8594 | | 0.0007 | 47.0 | 188 | 1.1261 | 0.8594 | | 0.0003 | 48.0 | 192 | 1.1236 | 0.8594 | | 0.0003 | 49.0 | 196 | 1.1083 | 0.8594 | | 0.0018 | 50.0 | 200 | 1.1057 | 0.8594 | | 0.0018 | 51.0 | 204 | 1.1077 | 0.8594 | | 0.0018 | 52.0 | 208 | 1.1095 | 0.8516 | | 0.0001 | 53.0 | 212 | 1.1116 | 0.8594 | | 0.0001 | 54.0 | 216 | 1.1149 | 0.8594 | | 0.0017 | 55.0 | 220 | 1.1500 | 0.8516 | | 0.0017 | 56.0 | 224 | 1.1396 | 0.8516 | | 0.0017 | 57.0 | 228 | 1.1474 | 0.8516 | | 0.0002 | 58.0 | 232 | 1.1402 | 0.8594 | | 0.0002 | 59.0 | 236 | 1.1367 | 0.8594 | | 0.0001 | 60.0 | 240 | 1.1349 | 0.8516 | | 0.0001 | 61.0 | 244 | 1.1350 | 0.8516 | | 0.0001 | 62.0 | 248 | 1.1366 | 0.8516 | | 0.0001 | 63.0 | 252 | 1.1389 | 0.8594 | | 0.0001 | 64.0 | 256 | 1.1395 | 0.8594 | | 0.0001 | 65.0 | 260 | 1.1380 | 0.8594 | | 0.0001 | 66.0 | 264 | 1.1378 | 0.8594 | | 0.0001 | 67.0 | 268 | 1.1411 | 0.8594 | | 0.0001 | 68.0 | 272 | 1.1439 | 0.8594 | | 0.0001 | 69.0 | 276 | 1.1452 | 0.8594 | | 0.0122 | 70.0 | 280 | 1.1270 | 0.8594 | | 0.0122 | 71.0 | 284 | 1.1514 | 0.8594 | | 0.0122 | 72.0 | 288 | 1.1908 | 0.8516 | | 0.0001 | 73.0 | 292 | 1.2155 | 0.8516 | | 0.0001 | 74.0 | 296 | 1.2281 | 0.8516 | | 0.0001 | 75.0 | 300 | 1.2353 | 0.8516 | | 0.0001 | 76.0 | 304 | 1.2387 | 0.8516 | | 0.0001 | 77.0 | 308 | 1.2380 | 0.8516 | | 0.0177 | 78.0 | 312 | 1.1050 | 0.8594 | | 0.0177 | 79.0 | 316 | 1.1201 | 0.8594 | | 0.0123 | 80.0 | 320 | 1.1227 | 0.8516 | | 0.0123 | 81.0 | 324 | 1.1249 | 0.8594 | | 0.0123 | 82.0 | 328 | 1.1305 | 0.8594 | | 0.0001 | 83.0 | 332 | 1.1371 | 0.8672 | | 0.0001 | 84.0 | 336 | 1.1424 | 0.8672 | | 0.0001 | 85.0 | 340 | 1.1449 | 0.8672 | | 0.0001 | 86.0 | 344 | 1.1464 | 0.8672 | | 0.0001 | 87.0 | 348 | 1.1469 | 0.8672 | | 0.0001 | 88.0 | 352 | 1.1448 | 0.8594 | | 0.0001 | 89.0 | 356 | 1.1444 | 0.8594 | | 0.0 | 90.0 | 360 | 1.1452 | 0.8594 | | 0.0 | 91.0 | 364 | 1.1464 | 0.8594 | | 0.0 | 92.0 | 368 | 1.1484 | 0.8594 | | 0.0001 | 93.0 | 372 | 1.1504 | 0.8594 | | 0.0001 | 94.0 | 376 | 1.1521 | 0.8516 | | 0.0 | 95.0 | 380 | 1.1537 | 0.8516 | | 0.0 | 96.0 | 384 | 1.1553 | 0.8516 | | 0.0 | 97.0 | 388 | 1.1571 | 0.8516 | | 0.0001 | 98.0 | 392 | 1.1605 | 0.8594 | | 0.0001 | 99.0 | 396 | 1.1645 | 0.8594 | | 0.0 | 100.0 | 400 | 1.1678 | 0.8594 | | 0.0 | 101.0 | 404 | 1.1706 | 0.8594 | | 0.0 | 102.0 | 408 | 1.1729 | 0.8594 | | 0.0 | 103.0 | 412 | 1.1747 | 0.8594 | | 0.0 | 104.0 | 416 | 1.1762 | 0.8594 | | 0.0001 | 105.0 | 420 | 1.1777 | 0.8594 | | 0.0001 | 106.0 | 424 | 1.1792 | 0.8594 | | 0.0001 | 107.0 | 428 | 1.1808 | 0.8594 | | 0.0034 | 108.0 | 432 | 1.2561 | 0.8516 | | 0.0034 | 109.0 | 436 | 1.3098 | 0.8516 | | 0.0063 | 110.0 | 440 | 1.2197 | 0.8516 | | 0.0063 | 111.0 | 444 | 1.1982 | 0.8516 | | 0.0063 | 112.0 | 448 | 1.2230 | 0.8516 | | 0.0 | 113.0 | 452 | 1.2172 | 0.8594 | | 0.0 | 114.0 | 456 | 1.2165 | 0.8516 | | 0.0 | 115.0 | 460 | 1.2187 | 0.8516 | | 0.0 | 116.0 | 464 | 1.2213 | 0.8516 | | 0.0 | 117.0 | 468 | 1.2234 | 0.8516 | | 0.0 | 118.0 | 472 | 1.2248 | 0.8516 | | 0.0 | 119.0 | 476 | 1.2267 | 0.8516 | | 0.0 | 120.0 | 480 | 1.2288 | 0.8594 | | 0.0 | 121.0 | 484 | 1.2316 | 0.8594 | | 0.0 | 122.0 | 488 | 1.2342 | 0.8594 | | 0.0 | 123.0 | 492 | 1.2364 | 0.8594 | | 0.0 | 124.0 | 496 | 1.2436 | 0.8594 | | 0.001 | 125.0 | 500 | 1.2770 | 0.8438 | | 0.001 | 126.0 | 504 | 1.3138 | 0.8594 | | 0.001 | 127.0 | 508 | 1.3084 | 0.8594 | | 0.0 | 128.0 | 512 | 1.3102 | 0.8438 | | 0.0 | 129.0 | 516 | 1.3333 | 0.8438 | | 0.0002 | 130.0 | 520 | 1.3251 | 0.8516 | | 0.0002 | 131.0 | 524 | 1.2928 | 0.8594 | | 0.0002 | 132.0 | 528 | 1.2468 | 0.8438 | | 0.0 | 133.0 | 532 | 1.2295 | 0.8438 | | 0.0 | 134.0 | 536 | 1.2483 | 0.8438 | | 0.0 | 135.0 | 540 | 1.2652 | 0.8438 | | 0.0 | 136.0 | 544 | 1.2741 | 0.8438 | | 0.0 | 137.0 | 548 | 1.2786 | 0.8438 | | 0.0 | 138.0 | 552 | 1.2811 | 0.8438 | | 0.0 | 139.0 | 556 | 1.2824 | 0.8438 | | 0.0 | 140.0 | 560 | 1.2833 | 0.8438 | | 0.0 | 141.0 | 564 | 1.2837 | 0.8438 | | 0.0 | 142.0 | 568 | 1.2833 | 0.8438 | | 0.0 | 143.0 | 572 | 1.2830 | 0.8438 | | 0.0 | 144.0 | 576 | 1.2828 | 0.8438 | | 0.0 | 145.0 | 580 | 1.2827 | 0.8438 | | 0.0 | 146.0 | 584 | 1.2827 | 0.8438 | | 0.0 | 147.0 | 588 | 1.2827 | 0.8438 | | 0.0001 | 148.0 | 592 | 1.2786 | 0.8438 | | 0.0001 | 149.0 | 596 | 1.2755 | 0.8438 | | 0.0 | 150.0 | 600 | 1.2746 | 0.8438 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3

xiaojuntime/final-ppo

xiaojuntime

2023-07-26T02:21:10Z

0

peft

[ "peft", "region:us" ]

null

2023-07-26T02:20:55Z

--- 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 0.4.0

simonycl/best_model-sst-2-64-42

simonycl

2023-07-26T02:15:01Z

106

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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

2023-07-26T02:03:35Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-64-42 results: [] ---  # best_model-sst-2-64-42 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: - Loss: 1.4849 - Accuracy: 0.8281 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 4 | 1.3914 | 0.8125 | | No log | 2.0 | 8 | 1.3910 | 0.8203 | | 0.3843 | 3.0 | 12 | 1.3922 | 0.8203 | | 0.3843 | 4.0 | 16 | 1.3920 | 0.8203 | | 0.5793 | 5.0 | 20 | 1.3923 | 0.8203 | | 0.5793 | 6.0 | 24 | 1.3989 | 0.8203 | | 0.5793 | 7.0 | 28 | 1.4029 | 0.8281 | | 0.3663 | 8.0 | 32 | 1.4103 | 0.8281 | | 0.3663 | 9.0 | 36 | 1.3999 | 0.8281 | | 0.2779 | 10.0 | 40 | 1.4010 | 0.8281 | | 0.2779 | 11.0 | 44 | 1.3978 | 0.8281 | | 0.2779 | 12.0 | 48 | 1.3963 | 0.8203 | | 0.3589 | 13.0 | 52 | 1.4087 | 0.8203 | | 0.3589 | 14.0 | 56 | 1.4067 | 0.8281 | | 0.3185 | 15.0 | 60 | 1.4148 | 0.8281 | | 0.3185 | 16.0 | 64 | 1.4171 | 0.8359 | | 0.3185 | 17.0 | 68 | 1.4140 | 0.8359 | | 0.1743 | 18.0 | 72 | 1.3982 | 0.8359 | | 0.1743 | 19.0 | 76 | 1.3650 | 0.8359 | | 0.1416 | 20.0 | 80 | 1.3456 | 0.8359 | | 0.1416 | 21.0 | 84 | 1.3210 | 0.8359 | | 0.1416 | 22.0 | 88 | 1.3070 | 0.8359 | | 0.0354 | 23.0 | 92 | 1.3015 | 0.8359 | | 0.0354 | 24.0 | 96 | 1.3319 | 0.8438 | | 0.0035 | 25.0 | 100 | 1.3656 | 0.8281 | | 0.0035 | 26.0 | 104 | 1.3587 | 0.8281 | | 0.0035 | 27.0 | 108 | 1.3243 | 0.8359 | | 0.0006 | 28.0 | 112 | 1.2945 | 0.8438 | | 0.0006 | 29.0 | 116 | 1.2898 | 0.8438 | | 0.0028 | 30.0 | 120 | 1.3066 | 0.8438 | | 0.0028 | 31.0 | 124 | 1.3055 | 0.8438 | | 0.0028 | 32.0 | 128 | 1.3202 | 0.8438 | | 0.0049 | 33.0 | 132 | 1.3351 | 0.8438 | | 0.0049 | 34.0 | 136 | 1.3190 | 0.8438 | | 0.0102 | 35.0 | 140 | 1.3141 | 0.8438 | | 0.0102 | 36.0 | 144 | 1.3142 | 0.8438 | | 0.0102 | 37.0 | 148 | 1.3647 | 0.8281 | | 0.0034 | 38.0 | 152 | 1.4250 | 0.8203 | | 0.0034 | 39.0 | 156 | 1.4708 | 0.8203 | | 0.0001 | 40.0 | 160 | 1.4570 | 0.8203 | | 0.0001 | 41.0 | 164 | 1.4446 | 0.8203 | | 0.0001 | 42.0 | 168 | 1.4345 | 0.8281 | | 0.0001 | 43.0 | 172 | 1.4272 | 0.8281 | | 0.0001 | 44.0 | 176 | 1.4185 | 0.8281 | | 0.0001 | 45.0 | 180 | 1.4048 | 0.8281 | | 0.0001 | 46.0 | 184 | 1.3962 | 0.8281 | | 0.0001 | 47.0 | 188 | 1.4924 | 0.8203 | | 0.0002 | 48.0 | 192 | 1.5361 | 0.8125 | | 0.0002 | 49.0 | 196 | 1.5831 | 0.8125 | | 0.0292 | 50.0 | 200 | 1.4789 | 0.8281 | | 0.0292 | 51.0 | 204 | 1.2642 | 0.8359 | | 0.0292 | 52.0 | 208 | 1.2154 | 0.8516 | | 0.0001 | 53.0 | 212 | 1.1895 | 0.8516 | | 0.0001 | 54.0 | 216 | 1.1775 | 0.8438 | | 0.0001 | 55.0 | 220 | 1.1730 | 0.8438 | | 0.0001 | 56.0 | 224 | 1.1746 | 0.8438 | | 0.0001 | 57.0 | 228 | 1.1782 | 0.8516 | | 0.0001 | 58.0 | 232 | 1.1838 | 0.8516 | | 0.0001 | 59.0 | 236 | 1.2456 | 0.8281 | | 0.025 | 60.0 | 240 | 1.3887 | 0.8281 | | 0.025 | 61.0 | 244 | 1.4950 | 0.8125 | | 0.025 | 62.0 | 248 | 1.5753 | 0.8047 | | 0.0001 | 63.0 | 252 | 1.6287 | 0.8047 | | 0.0001 | 64.0 | 256 | 1.6608 | 0.8047 | | 0.0001 | 65.0 | 260 | 1.6803 | 0.8047 | | 0.0001 | 66.0 | 264 | 1.6919 | 0.7969 | | 0.0001 | 67.0 | 268 | 1.5961 | 0.8047 | | 0.0001 | 68.0 | 272 | 1.4858 | 0.8125 | | 0.0001 | 69.0 | 276 | 1.4104 | 0.8281 | | 0.0001 | 70.0 | 280 | 1.3623 | 0.8281 | | 0.0001 | 71.0 | 284 | 1.3333 | 0.8359 | | 0.0001 | 72.0 | 288 | 1.3172 | 0.8359 | | 0.0 | 73.0 | 292 | 1.3107 | 0.8359 | | 0.0 | 74.0 | 296 | 1.5801 | 0.8047 | | 0.0014 | 75.0 | 300 | 1.7857 | 0.8047 | | 0.0014 | 76.0 | 304 | 1.8724 | 0.7969 | | 0.0014 | 77.0 | 308 | 1.9146 | 0.7969 | | 0.0001 | 78.0 | 312 | 1.9250 | 0.7969 | | 0.0001 | 79.0 | 316 | 1.9265 | 0.7969 | | 0.0001 | 80.0 | 320 | 1.9268 | 0.7969 | | 0.0001 | 81.0 | 324 | 1.9243 | 0.7969 | | 0.0001 | 82.0 | 328 | 1.9215 | 0.7969 | | 0.0 | 83.0 | 332 | 1.9188 | 0.7969 | | 0.0 | 84.0 | 336 | 1.9159 | 0.7969 | | 0.0 | 85.0 | 340 | 1.9137 | 0.7969 | | 0.0 | 86.0 | 344 | 1.9119 | 0.7969 | | 0.0 | 87.0 | 348 | 1.9103 | 0.7969 | | 0.0009 | 88.0 | 352 | 1.6541 | 0.8047 | | 0.0009 | 89.0 | 356 | 1.2749 | 0.8438 | | 0.0 | 90.0 | 360 | 1.2046 | 0.8438 | | 0.0 | 91.0 | 364 | 1.1909 | 0.8438 | | 0.0 | 92.0 | 368 | 1.1860 | 0.8594 | | 0.0 | 93.0 | 372 | 1.1901 | 0.8594 | | 0.0 | 94.0 | 376 | 1.1966 | 0.8516 | | 0.0001 | 95.0 | 380 | 1.2014 | 0.8516 | | 0.0001 | 96.0 | 384 | 1.2061 | 0.8438 | | 0.0001 | 97.0 | 388 | 1.2109 | 0.8438 | | 0.0 | 98.0 | 392 | 1.2170 | 0.8516 | | 0.0 | 99.0 | 396 | 1.2210 | 0.8516 | | 0.0 | 100.0 | 400 | 1.2237 | 0.8516 | | 0.0 | 101.0 | 404 | 1.2258 | 0.8516 | | 0.0 | 102.0 | 408 | 1.2276 | 0.8438 | | 0.0 | 103.0 | 412 | 1.2290 | 0.8438 | | 0.0 | 104.0 | 416 | 1.2301 | 0.8438 | | 0.0 | 105.0 | 420 | 1.2313 | 0.8438 | | 0.0 | 106.0 | 424 | 1.2324 | 0.8438 | | 0.0 | 107.0 | 428 | 1.2334 | 0.8438 | | 0.0 | 108.0 | 432 | 1.2345 | 0.8438 | | 0.0 | 109.0 | 436 | 1.2356 | 0.8438 | | 0.0 | 110.0 | 440 | 1.2366 | 0.8438 | | 0.0 | 111.0 | 444 | 1.2375 | 0.8516 | | 0.0 | 112.0 | 448 | 1.2384 | 0.8516 | | 0.0 | 113.0 | 452 | 1.2400 | 0.8516 | | 0.0 | 114.0 | 456 | 1.2415 | 0.8516 | | 0.0 | 115.0 | 460 | 1.2428 | 0.8516 | | 0.0 | 116.0 | 464 | 1.2439 | 0.8516 | | 0.0 | 117.0 | 468 | 1.2450 | 0.8516 | | 0.0 | 118.0 | 472 | 1.2459 | 0.8516 | | 0.0 | 119.0 | 476 | 1.2467 | 0.8516 | | 0.0 | 120.0 | 480 | 1.2476 | 0.8516 | | 0.0 | 121.0 | 484 | 1.2485 | 0.8516 | | 0.0 | 122.0 | 488 | 1.2495 | 0.8516 | | 0.0 | 123.0 | 492 | 1.2495 | 0.8516 | | 0.0 | 124.0 | 496 | 1.2491 | 0.8516 | | 0.0 | 125.0 | 500 | 1.2491 | 0.8516 | | 0.0 | 126.0 | 504 | 1.2494 | 0.8516 | | 0.0 | 127.0 | 508 | 1.2498 | 0.8516 | | 0.0 | 128.0 | 512 | 1.2503 | 0.8516 | | 0.0 | 129.0 | 516 | 1.2509 | 0.8516 | | 0.0 | 130.0 | 520 | 1.2514 | 0.8516 | | 0.0 | 131.0 | 524 | 1.2519 | 0.8516 | | 0.0 | 132.0 | 528 | 1.2527 | 0.8516 | | 0.0 | 133.0 | 532 | 1.2535 | 0.8516 | | 0.0 | 134.0 | 536 | 1.2542 | 0.8516 | | 0.0 | 135.0 | 540 | 1.2549 | 0.8516 | | 0.0 | 136.0 | 544 | 1.2554 | 0.8516 | | 0.0 | 137.0 | 548 | 1.3879 | 0.8359 | | 0.0001 | 138.0 | 552 | 1.6893 | 0.7969 | | 0.0001 | 139.0 | 556 | 1.8348 | 0.7969 | | 0.0 | 140.0 | 560 | 1.8942 | 0.7969 | | 0.0 | 141.0 | 564 | 1.8778 | 0.7969 | | 0.0 | 142.0 | 568 | 1.7187 | 0.8047 | | 0.0001 | 143.0 | 572 | 1.6119 | 0.8203 | | 0.0001 | 144.0 | 576 | 1.5523 | 0.8281 | | 0.0 | 145.0 | 580 | 1.5189 | 0.8281 | | 0.0 | 146.0 | 584 | 1.5008 | 0.8281 | | 0.0 | 147.0 | 588 | 1.4916 | 0.8281 | | 0.0 | 148.0 | 592 | 1.4872 | 0.8281 | | 0.0 | 149.0 | 596 | 1.4854 | 0.8281 | | 0.0 | 150.0 | 600 | 1.4849 | 0.8281 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3

Sukmin/ppo-u8

Sukmin

2023-07-26T02:11:19Z

0

null

[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]

reinforcement-learning

2023-07-20T05:10:04Z

--- 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: -164.28 +/- 84.41 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': 'Sukmin/ppo-u8' 'batch_size': 512 'minibatch_size': 128} ```

The-matt/law-qlora-polyglot-12.8b

The-matt

2023-07-26T02:09:02Z

0

peft

[ "peft", "region:us" ]

null

2023-07-26T01:35:09Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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 0.5.0.dev0

jonalkw/ppo_LunarLander-v2

jonalkw

2023-07-26T02:03:23Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-29T23:12:11Z

--- 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: 247.79 +/- 19.77 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 ... ```

simonycl/best_model-sst-2-64-21

simonycl

2023-07-26T02:03:20Z

105

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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

2023-07-26T01:52:00Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-64-21 results: [] ---  # best_model-sst-2-64-21 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: - Loss: 1.0374 - Accuracy: 0.8906 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 4 | 1.1068 | 0.8672 | | No log | 2.0 | 8 | 1.1055 | 0.8672 | | 0.5789 | 3.0 | 12 | 1.1002 | 0.8672 | | 0.5789 | 4.0 | 16 | 1.0902 | 0.8672 | | 0.4952 | 5.0 | 20 | 1.0797 | 0.8672 | | 0.4952 | 6.0 | 24 | 1.0662 | 0.8672 | | 0.4952 | 7.0 | 28 | 1.0461 | 0.8672 | | 0.4202 | 8.0 | 32 | 1.0329 | 0.8672 | | 0.4202 | 9.0 | 36 | 1.0326 | 0.8672 | | 0.5159 | 10.0 | 40 | 1.0217 | 0.8672 | | 0.5159 | 11.0 | 44 | 1.0053 | 0.8672 | | 0.5159 | 12.0 | 48 | 0.9908 | 0.875 | | 0.4018 | 13.0 | 52 | 0.9818 | 0.8828 | | 0.4018 | 14.0 | 56 | 0.9686 | 0.8828 | | 0.2452 | 15.0 | 60 | 0.9591 | 0.8828 | | 0.2452 | 16.0 | 64 | 0.9489 | 0.8828 | | 0.2452 | 17.0 | 68 | 0.9421 | 0.8828 | | 0.1966 | 18.0 | 72 | 0.9354 | 0.8828 | | 0.1966 | 19.0 | 76 | 0.9318 | 0.8906 | | 0.1955 | 20.0 | 80 | 0.9353 | 0.8828 | | 0.1955 | 21.0 | 84 | 0.9552 | 0.8828 | | 0.1955 | 22.0 | 88 | 0.9728 | 0.875 | | 0.1316 | 23.0 | 92 | 0.9686 | 0.875 | | 0.1316 | 24.0 | 96 | 0.9555 | 0.875 | | 0.0488 | 25.0 | 100 | 0.9442 | 0.8828 | | 0.0488 | 26.0 | 104 | 0.9410 | 0.8828 | | 0.0488 | 27.0 | 108 | 0.9413 | 0.8828 | | 0.0023 | 28.0 | 112 | 0.9522 | 0.8828 | | 0.0023 | 29.0 | 116 | 0.9614 | 0.8828 | | 0.0019 | 30.0 | 120 | 0.9603 | 0.8828 | | 0.0019 | 31.0 | 124 | 0.9474 | 0.8828 | | 0.0019 | 32.0 | 128 | 0.9408 | 0.8906 | | 0.0136 | 33.0 | 132 | 0.9417 | 0.8906 | | 0.0136 | 34.0 | 136 | 0.9433 | 0.8906 | | 0.0037 | 35.0 | 140 | 0.9412 | 0.8906 | | 0.0037 | 36.0 | 144 | 0.9529 | 0.8906 | | 0.0037 | 37.0 | 148 | 0.9641 | 0.8828 | | 0.0003 | 38.0 | 152 | 0.9868 | 0.8828 | | 0.0003 | 39.0 | 156 | 0.9985 | 0.875 | | 0.0002 | 40.0 | 160 | 1.0006 | 0.875 | | 0.0002 | 41.0 | 164 | 1.0009 | 0.875 | | 0.0002 | 42.0 | 168 | 1.0038 | 0.875 | | 0.0013 | 43.0 | 172 | 0.9982 | 0.8828 | | 0.0013 | 44.0 | 176 | 0.9853 | 0.8828 | | 0.0102 | 45.0 | 180 | 0.9790 | 0.8828 | | 0.0102 | 46.0 | 184 | 0.9900 | 0.8828 | | 0.0102 | 47.0 | 188 | 1.0004 | 0.8828 | | 0.0002 | 48.0 | 192 | 1.0063 | 0.875 | | 0.0002 | 49.0 | 196 | 1.0095 | 0.875 | | 0.0001 | 50.0 | 200 | 1.0136 | 0.875 | | 0.0001 | 51.0 | 204 | 1.0180 | 0.8672 | | 0.0001 | 52.0 | 208 | 1.0206 | 0.8672 | | 0.0001 | 53.0 | 212 | 1.0178 | 0.8672 | | 0.0001 | 54.0 | 216 | 1.0157 | 0.8672 | | 0.0001 | 55.0 | 220 | 1.0140 | 0.875 | | 0.0001 | 56.0 | 224 | 1.0128 | 0.875 | | 0.0001 | 57.0 | 228 | 1.0117 | 0.875 | | 0.0001 | 58.0 | 232 | 1.0097 | 0.875 | | 0.0001 | 59.0 | 236 | 1.0082 | 0.875 | | 0.0001 | 60.0 | 240 | 1.0002 | 0.8828 | | 0.0001 | 61.0 | 244 | 0.9944 | 0.8828 | | 0.0001 | 62.0 | 248 | 0.9913 | 0.8906 | | 0.0001 | 63.0 | 252 | 0.9897 | 0.8906 | | 0.0001 | 64.0 | 256 | 0.9893 | 0.8906 | | 0.0001 | 65.0 | 260 | 0.9895 | 0.8906 | | 0.0001 | 66.0 | 264 | 0.9899 | 0.8906 | | 0.0001 | 67.0 | 268 | 0.9905 | 0.8906 | | 0.0001 | 68.0 | 272 | 0.9913 | 0.8906 | | 0.0001 | 69.0 | 276 | 0.9962 | 0.8906 | | 0.0001 | 70.0 | 280 | 1.0023 | 0.8828 | | 0.0001 | 71.0 | 284 | 1.0079 | 0.8828 | | 0.0001 | 72.0 | 288 | 1.0118 | 0.875 | | 0.0001 | 73.0 | 292 | 1.0144 | 0.875 | | 0.0001 | 74.0 | 296 | 1.0161 | 0.875 | | 0.0001 | 75.0 | 300 | 1.0172 | 0.875 | | 0.0001 | 76.0 | 304 | 1.0178 | 0.875 | | 0.0001 | 77.0 | 308 | 1.0241 | 0.875 | | 0.0183 | 78.0 | 312 | 1.0549 | 0.8672 | | 0.0183 | 79.0 | 316 | 1.0631 | 0.8672 | | 0.0001 | 80.0 | 320 | 1.0629 | 0.875 | | 0.0001 | 81.0 | 324 | 1.0650 | 0.875 | | 0.0001 | 82.0 | 328 | 1.0672 | 0.8594 | | 0.0001 | 83.0 | 332 | 1.0686 | 0.8594 | | 0.0001 | 84.0 | 336 | 1.0632 | 0.875 | | 0.0131 | 85.0 | 340 | 1.0157 | 0.8672 | | 0.0131 | 86.0 | 344 | 0.9959 | 0.8828 | | 0.0131 | 87.0 | 348 | 0.9946 | 0.8906 | | 0.0001 | 88.0 | 352 | 0.9933 | 0.8906 | | 0.0001 | 89.0 | 356 | 0.9933 | 0.8906 | | 0.0001 | 90.0 | 360 | 0.9941 | 0.8828 | | 0.0001 | 91.0 | 364 | 0.9949 | 0.8828 | | 0.0001 | 92.0 | 368 | 0.9954 | 0.8828 | | 0.0001 | 93.0 | 372 | 0.9959 | 0.8828 | | 0.0001 | 94.0 | 376 | 0.9962 | 0.8828 | | 0.0001 | 95.0 | 380 | 0.9961 | 0.8828 | | 0.0001 | 96.0 | 384 | 0.9963 | 0.8828 | | 0.0001 | 97.0 | 388 | 0.9967 | 0.8828 | | 0.0001 | 98.0 | 392 | 0.9987 | 0.8906 | | 0.0001 | 99.0 | 396 | 1.0214 | 0.8828 | | 0.0105 | 100.0 | 400 | 1.0346 | 0.875 | | 0.0105 | 101.0 | 404 | 1.0406 | 0.875 | | 0.0105 | 102.0 | 408 | 1.0435 | 0.875 | | 0.0001 | 103.0 | 412 | 1.0444 | 0.875 | | 0.0001 | 104.0 | 416 | 1.0446 | 0.875 | | 0.0001 | 105.0 | 420 | 1.0447 | 0.875 | | 0.0001 | 106.0 | 424 | 1.0448 | 0.875 | | 0.0001 | 107.0 | 428 | 1.0453 | 0.8828 | | 0.0001 | 108.0 | 432 | 1.0457 | 0.8828 | | 0.0001 | 109.0 | 436 | 1.0488 | 0.875 | | 0.0184 | 110.0 | 440 | 1.0597 | 0.875 | | 0.0184 | 111.0 | 444 | 1.0939 | 0.8594 | | 0.0184 | 112.0 | 448 | 1.1410 | 0.8438 | | 0.0001 | 113.0 | 452 | 1.1659 | 0.8438 | | 0.0001 | 114.0 | 456 | 1.1104 | 0.8594 | | 0.0001 | 115.0 | 460 | 1.0816 | 0.8672 | | 0.0001 | 116.0 | 464 | 1.0695 | 0.875 | | 0.0001 | 117.0 | 468 | 1.0702 | 0.875 | | 0.0 | 118.0 | 472 | 1.0709 | 0.875 | | 0.0 | 119.0 | 476 | 1.0704 | 0.875 | | 0.0 | 120.0 | 480 | 1.0693 | 0.875 | | 0.0 | 121.0 | 484 | 1.0684 | 0.875 | | 0.0 | 122.0 | 488 | 1.0677 | 0.875 | | 0.0 | 123.0 | 492 | 1.0676 | 0.875 | | 0.0 | 124.0 | 496 | 1.0676 | 0.875 | | 0.0 | 125.0 | 500 | 1.0675 | 0.875 | | 0.0 | 126.0 | 504 | 1.0675 | 0.875 | | 0.0 | 127.0 | 508 | 1.0676 | 0.875 | | 0.0 | 128.0 | 512 | 1.0687 | 0.875 | | 0.0 | 129.0 | 516 | 1.0694 | 0.875 | | 0.0 | 130.0 | 520 | 1.0701 | 0.875 | | 0.0 | 131.0 | 524 | 1.0707 | 0.875 | | 0.0 | 132.0 | 528 | 1.0661 | 0.875 | | 0.0001 | 133.0 | 532 | 1.0391 | 0.8906 | | 0.0001 | 134.0 | 536 | 1.0258 | 0.8906 | | 0.0 | 135.0 | 540 | 1.0188 | 0.8906 | | 0.0 | 136.0 | 544 | 1.0171 | 0.8906 | | 0.0 | 137.0 | 548 | 1.0188 | 0.8828 | | 0.0 | 138.0 | 552 | 1.0210 | 0.875 | | 0.0 | 139.0 | 556 | 1.0223 | 0.875 | | 0.0001 | 140.0 | 560 | 1.0202 | 0.8828 | | 0.0001 | 141.0 | 564 | 1.0235 | 0.8906 | | 0.0001 | 142.0 | 568 | 1.0288 | 0.8906 | | 0.0 | 143.0 | 572 | 1.0322 | 0.8906 | | 0.0 | 144.0 | 576 | 1.0343 | 0.8906 | | 0.0 | 145.0 | 580 | 1.0356 | 0.8906 | | 0.0 | 146.0 | 584 | 1.0364 | 0.8906 | | 0.0 | 147.0 | 588 | 1.0369 | 0.8906 | | 0.0 | 148.0 | 592 | 1.0372 | 0.8906 | | 0.0 | 149.0 | 596 | 1.0374 | 0.8906 | | 0.0 | 150.0 | 600 | 1.0374 | 0.8906 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3

simonycl/best_model-sst-2-32-100

simonycl

2023-07-26T01:39:26Z

107

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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

2023-07-26T01:33:39Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-32-100 results: [] ---  # best_model-sst-2-32-100 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: - Loss: 0.5168 - Accuracy: 0.9219 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 2 | 0.8101 | 0.9062 | | No log | 2.0 | 4 | 0.8102 | 0.9062 | | No log | 3.0 | 6 | 0.8102 | 0.9062 | | No log | 4.0 | 8 | 0.8100 | 0.9062 | | 0.6019 | 5.0 | 10 | 0.8098 | 0.9062 | | 0.6019 | 6.0 | 12 | 0.8095 | 0.9062 | | 0.6019 | 7.0 | 14 | 0.8090 | 0.9062 | | 0.6019 | 8.0 | 16 | 0.8085 | 0.9062 | | 0.6019 | 9.0 | 18 | 0.8079 | 0.9062 | | 0.6181 | 10.0 | 20 | 0.8073 | 0.9062 | | 0.6181 | 11.0 | 22 | 0.8066 | 0.9062 | | 0.6181 | 12.0 | 24 | 0.8061 | 0.9062 | | 0.6181 | 13.0 | 26 | 0.8055 | 0.9062 | | 0.6181 | 14.0 | 28 | 0.8048 | 0.9062 | | 0.5045 | 15.0 | 30 | 0.8037 | 0.9062 | | 0.5045 | 16.0 | 32 | 0.8020 | 0.9062 | | 0.5045 | 17.0 | 34 | 0.8003 | 0.9062 | | 0.5045 | 18.0 | 36 | 0.7978 | 0.9062 | | 0.5045 | 19.0 | 38 | 0.7955 | 0.9062 | | 0.4784 | 20.0 | 40 | 0.7928 | 0.9062 | | 0.4784 | 21.0 | 42 | 0.7902 | 0.9062 | | 0.4784 | 22.0 | 44 | 0.7868 | 0.9062 | | 0.4784 | 23.0 | 46 | 0.7824 | 0.9062 | | 0.4784 | 24.0 | 48 | 0.7764 | 0.9062 | | 0.3582 | 25.0 | 50 | 0.7695 | 0.9062 | | 0.3582 | 26.0 | 52 | 0.7628 | 0.9062 | | 0.3582 | 27.0 | 54 | 0.7548 | 0.9062 | | 0.3582 | 28.0 | 56 | 0.7473 | 0.9062 | | 0.3582 | 29.0 | 58 | 0.7388 | 0.9062 | | 0.3152 | 30.0 | 60 | 0.7286 | 0.9062 | | 0.3152 | 31.0 | 62 | 0.7145 | 0.9062 | | 0.3152 | 32.0 | 64 | 0.7007 | 0.9062 | | 0.3152 | 33.0 | 66 | 0.6860 | 0.9062 | | 0.3152 | 34.0 | 68 | 0.6662 | 0.9062 | | 0.2403 | 35.0 | 70 | 0.6377 | 0.9062 | | 0.2403 | 36.0 | 72 | 0.5941 | 0.9062 | | 0.2403 | 37.0 | 74 | 0.5458 | 0.8906 | | 0.2403 | 38.0 | 76 | 0.4985 | 0.8906 | | 0.2403 | 39.0 | 78 | 0.4676 | 0.9219 | | 0.1021 | 40.0 | 80 | 0.4598 | 0.9219 | | 0.1021 | 41.0 | 82 | 0.4572 | 0.9375 | | 0.1021 | 42.0 | 84 | 0.4521 | 0.9375 | | 0.1021 | 43.0 | 86 | 0.4493 | 0.9375 | | 0.1021 | 44.0 | 88 | 0.4420 | 0.9375 | | 0.016 | 45.0 | 90 | 0.4264 | 0.9375 | | 0.016 | 46.0 | 92 | 0.4104 | 0.9375 | | 0.016 | 47.0 | 94 | 0.4008 | 0.9375 | | 0.016 | 48.0 | 96 | 0.4056 | 0.9062 | | 0.016 | 49.0 | 98 | 0.4256 | 0.9219 | | 0.0016 | 50.0 | 100 | 0.4450 | 0.9062 | | 0.0016 | 51.0 | 102 | 0.4667 | 0.9062 | | 0.0016 | 52.0 | 104 | 0.4946 | 0.9062 | | 0.0016 | 53.0 | 106 | 0.5189 | 0.9062 | | 0.0016 | 54.0 | 108 | 0.5347 | 0.9062 | | 0.0008 | 55.0 | 110 | 0.5434 | 0.9062 | | 0.0008 | 56.0 | 112 | 0.5500 | 0.9062 | | 0.0008 | 57.0 | 114 | 0.5545 | 0.9062 | | 0.0008 | 58.0 | 116 | 0.5557 | 0.9062 | | 0.0008 | 59.0 | 118 | 0.5535 | 0.9062 | | 0.0005 | 60.0 | 120 | 0.5492 | 0.9062 | | 0.0005 | 61.0 | 122 | 0.5389 | 0.9062 | | 0.0005 | 62.0 | 124 | 0.5249 | 0.9062 | | 0.0005 | 63.0 | 126 | 0.5044 | 0.9062 | | 0.0005 | 64.0 | 128 | 0.4804 | 0.9062 | | 0.0008 | 65.0 | 130 | 0.4611 | 0.9219 | | 0.0008 | 66.0 | 132 | 0.4474 | 0.9375 | | 0.0008 | 67.0 | 134 | 0.4373 | 0.9375 | | 0.0008 | 68.0 | 136 | 0.4299 | 0.9375 | | 0.0008 | 69.0 | 138 | 0.4246 | 0.9219 | | 0.0003 | 70.0 | 140 | 0.4213 | 0.9219 | | 0.0003 | 71.0 | 142 | 0.4191 | 0.9219 | | 0.0003 | 72.0 | 144 | 0.4177 | 0.9219 | | 0.0003 | 73.0 | 146 | 0.4283 | 0.9219 | | 0.0003 | 74.0 | 148 | 0.4393 | 0.9375 | | 0.0011 | 75.0 | 150 | 0.4489 | 0.9375 | | 0.0011 | 76.0 | 152 | 0.4577 | 0.9375 | | 0.0011 | 77.0 | 154 | 0.4659 | 0.9375 | | 0.0011 | 78.0 | 156 | 0.4734 | 0.9219 | | 0.0011 | 79.0 | 158 | 0.4803 | 0.9219 | | 0.0003 | 80.0 | 160 | 0.4866 | 0.9219 | | 0.0003 | 81.0 | 162 | 0.4924 | 0.9062 | | 0.0003 | 82.0 | 164 | 0.4845 | 0.9219 | | 0.0003 | 83.0 | 166 | 0.4663 | 0.9375 | | 0.0003 | 84.0 | 168 | 0.4532 | 0.9375 | | 0.0072 | 85.0 | 170 | 0.4429 | 0.9375 | | 0.0072 | 86.0 | 172 | 0.4352 | 0.9375 | | 0.0072 | 87.0 | 174 | 0.4297 | 0.9375 | | 0.0072 | 88.0 | 176 | 0.4255 | 0.9219 | | 0.0072 | 89.0 | 178 | 0.4223 | 0.9219 | | 0.0002 | 90.0 | 180 | 0.4201 | 0.9219 | | 0.0002 | 91.0 | 182 | 0.4184 | 0.9219 | | 0.0002 | 92.0 | 184 | 0.4171 | 0.9219 | | 0.0002 | 93.0 | 186 | 0.4163 | 0.9219 | | 0.0002 | 94.0 | 188 | 0.4231 | 0.9219 | | 0.0002 | 95.0 | 190 | 0.4306 | 0.9375 | | 0.0002 | 96.0 | 192 | 0.4377 | 0.9375 | | 0.0002 | 97.0 | 194 | 0.4440 | 0.9375 | | 0.0002 | 98.0 | 196 | 0.4494 | 0.9375 | | 0.0002 | 99.0 | 198 | 0.4542 | 0.9375 | | 0.0002 | 100.0 | 200 | 0.4582 | 0.9375 | | 0.0002 | 101.0 | 202 | 0.4617 | 0.9375 | | 0.0002 | 102.0 | 204 | 0.4646 | 0.9375 | | 0.0002 | 103.0 | 206 | 0.4676 | 0.9375 | | 0.0002 | 104.0 | 208 | 0.4705 | 0.9375 | | 0.0002 | 105.0 | 210 | 0.4729 | 0.9375 | | 0.0002 | 106.0 | 212 | 0.4749 | 0.9375 | | 0.0002 | 107.0 | 214 | 0.4769 | 0.9375 | | 0.0002 | 108.0 | 216 | 0.4788 | 0.9375 | | 0.0002 | 109.0 | 218 | 0.4803 | 0.9375 | | 0.0002 | 110.0 | 220 | 0.4810 | 0.9375 | | 0.0002 | 111.0 | 222 | 0.4817 | 0.9375 | | 0.0002 | 112.0 | 224 | 0.4825 | 0.9375 | | 0.0002 | 113.0 | 226 | 0.4837 | 0.9375 | | 0.0002 | 114.0 | 228 | 0.4849 | 0.9375 | | 0.0002 | 115.0 | 230 | 0.4857 | 0.9219 | | 0.0002 | 116.0 | 232 | 0.4679 | 0.9375 | | 0.0002 | 117.0 | 234 | 0.4374 | 0.9375 | | 0.0002 | 118.0 | 236 | 0.4225 | 0.9375 | | 0.0002 | 119.0 | 238 | 0.4275 | 0.9375 | | 0.0004 | 120.0 | 240 | 0.4352 | 0.9375 | | 0.0004 | 121.0 | 242 | 0.4423 | 0.9375 | | 0.0004 | 122.0 | 244 | 0.4481 | 0.9375 | | 0.0004 | 123.0 | 246 | 0.4509 | 0.9375 | | 0.0004 | 124.0 | 248 | 0.4527 | 0.9375 | | 0.0002 | 125.0 | 250 | 0.4528 | 0.9375 | | 0.0002 | 126.0 | 252 | 0.4530 | 0.9375 | | 0.0002 | 127.0 | 254 | 0.4531 | 0.9375 | | 0.0002 | 128.0 | 256 | 0.4531 | 0.9375 | | 0.0002 | 129.0 | 258 | 0.4530 | 0.9375 | | 0.0014 | 130.0 | 260 | 0.4188 | 0.9375 | | 0.0014 | 131.0 | 262 | 0.4099 | 0.9531 | | 0.0014 | 132.0 | 264 | 0.4306 | 0.9219 | | 0.0014 | 133.0 | 266 | 0.4583 | 0.9219 | | 0.0014 | 134.0 | 268 | 0.4801 | 0.9219 | | 0.0001 | 135.0 | 270 | 0.4951 | 0.9219 | | 0.0001 | 136.0 | 272 | 0.5056 | 0.9219 | | 0.0001 | 137.0 | 274 | 0.5134 | 0.9062 | | 0.0001 | 138.0 | 276 | 0.5179 | 0.9062 | | 0.0001 | 139.0 | 278 | 0.5215 | 0.9062 | | 0.0001 | 140.0 | 280 | 0.5243 | 0.9062 | | 0.0001 | 141.0 | 282 | 0.5255 | 0.9062 | | 0.0001 | 142.0 | 284 | 0.5258 | 0.9062 | | 0.0001 | 143.0 | 286 | 0.5261 | 0.9062 | | 0.0001 | 144.0 | 288 | 0.5262 | 0.9062 | | 0.0001 | 145.0 | 290 | 0.5261 | 0.9062 | | 0.0001 | 146.0 | 292 | 0.5236 | 0.9062 | | 0.0001 | 147.0 | 294 | 0.5214 | 0.9062 | | 0.0001 | 148.0 | 296 | 0.5194 | 0.9219 | | 0.0001 | 149.0 | 298 | 0.5177 | 0.9219 | | 0.0001 | 150.0 | 300 | 0.5168 | 0.9219 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3

simonycl/best_model-sst-2-32-87

simonycl

2023-07-26T01:33:23Z

108

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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

2023-07-26T01:27:32Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-32-87 results: [] ---  # best_model-sst-2-32-87 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: - Loss: 1.0406 - Accuracy: 0.8438 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 2 | 1.2928 | 0.8438 | | No log | 2.0 | 4 | 1.2923 | 0.8438 | | No log | 3.0 | 6 | 1.2917 | 0.8438 | | No log | 4.0 | 8 | 1.2902 | 0.8438 | | 0.7235 | 5.0 | 10 | 1.2884 | 0.8438 | | 0.7235 | 6.0 | 12 | 1.2856 | 0.8438 | | 0.7235 | 7.0 | 14 | 1.2829 | 0.8438 | | 0.7235 | 8.0 | 16 | 1.2800 | 0.8281 | | 0.7235 | 9.0 | 18 | 1.2769 | 0.8281 | | 0.5899 | 10.0 | 20 | 1.2742 | 0.8281 | | 0.5899 | 11.0 | 22 | 1.2710 | 0.8281 | | 0.5899 | 12.0 | 24 | 1.2662 | 0.8281 | | 0.5899 | 13.0 | 26 | 1.2590 | 0.8281 | | 0.5899 | 14.0 | 28 | 1.2466 | 0.8281 | | 0.6318 | 15.0 | 30 | 1.2287 | 0.8281 | | 0.6318 | 16.0 | 32 | 1.2138 | 0.8281 | | 0.6318 | 17.0 | 34 | 1.2024 | 0.8281 | | 0.6318 | 18.0 | 36 | 1.1924 | 0.8281 | | 0.6318 | 19.0 | 38 | 1.1838 | 0.8281 | | 0.4743 | 20.0 | 40 | 1.1729 | 0.8281 | | 0.4743 | 21.0 | 42 | 1.1591 | 0.8281 | | 0.4743 | 22.0 | 44 | 1.1527 | 0.8281 | | 0.4743 | 23.0 | 46 | 1.1459 | 0.8281 | | 0.4743 | 24.0 | 48 | 1.1407 | 0.8281 | | 0.3414 | 25.0 | 50 | 1.1351 | 0.8281 | | 0.3414 | 26.0 | 52 | 1.1305 | 0.8281 | | 0.3414 | 27.0 | 54 | 1.1230 | 0.8281 | | 0.3414 | 28.0 | 56 | 1.1087 | 0.8281 | | 0.3414 | 29.0 | 58 | 1.0831 | 0.8281 | | 0.3141 | 30.0 | 60 | 1.0555 | 0.8281 | | 0.3141 | 31.0 | 62 | 1.0313 | 0.8438 | | 0.3141 | 32.0 | 64 | 1.0141 | 0.8594 | | 0.3141 | 33.0 | 66 | 1.0063 | 0.8438 | | 0.3141 | 34.0 | 68 | 0.9990 | 0.8438 | | 0.1594 | 35.0 | 70 | 0.9916 | 0.8438 | | 0.1594 | 36.0 | 72 | 0.9884 | 0.8438 | | 0.1594 | 37.0 | 74 | 0.9922 | 0.8438 | | 0.1594 | 38.0 | 76 | 1.0013 | 0.8281 | | 0.1594 | 39.0 | 78 | 1.0097 | 0.8281 | | 0.1018 | 40.0 | 80 | 1.0209 | 0.8281 | | 0.1018 | 41.0 | 82 | 1.0341 | 0.8281 | | 0.1018 | 42.0 | 84 | 1.0352 | 0.8281 | | 0.1018 | 43.0 | 86 | 1.0284 | 0.8281 | | 0.1018 | 44.0 | 88 | 1.0236 | 0.8281 | | 0.0404 | 45.0 | 90 | 1.0214 | 0.8438 | | 0.0404 | 46.0 | 92 | 1.0237 | 0.8594 | | 0.0404 | 47.0 | 94 | 1.0233 | 0.875 | | 0.0404 | 48.0 | 96 | 1.0223 | 0.875 | | 0.0404 | 49.0 | 98 | 1.0187 | 0.875 | | 0.0052 | 50.0 | 100 | 1.0160 | 0.8594 | | 0.0052 | 51.0 | 102 | 1.0134 | 0.8594 | | 0.0052 | 52.0 | 104 | 1.0107 | 0.8438 | | 0.0052 | 53.0 | 106 | 1.0083 | 0.8438 | | 0.0052 | 54.0 | 108 | 1.0061 | 0.8438 | | 0.0003 | 55.0 | 110 | 1.0043 | 0.8438 | | 0.0003 | 56.0 | 112 | 1.0016 | 0.8438 | | 0.0003 | 57.0 | 114 | 0.9994 | 0.8438 | | 0.0003 | 58.0 | 116 | 0.9955 | 0.8438 | | 0.0003 | 59.0 | 118 | 0.9902 | 0.8438 | | 0.0003 | 60.0 | 120 | 0.9852 | 0.8438 | | 0.0003 | 61.0 | 122 | 0.9806 | 0.8438 | | 0.0003 | 62.0 | 124 | 0.9791 | 0.8438 | | 0.0003 | 63.0 | 126 | 0.9794 | 0.8438 | | 0.0003 | 64.0 | 128 | 0.9802 | 0.8438 | | 0.0003 | 65.0 | 130 | 0.9809 | 0.8438 | | 0.0003 | 66.0 | 132 | 0.9816 | 0.8438 | | 0.0003 | 67.0 | 134 | 0.9821 | 0.8438 | | 0.0003 | 68.0 | 136 | 0.9779 | 0.8438 | | 0.0003 | 69.0 | 138 | 0.9746 | 0.8281 | | 0.0003 | 70.0 | 140 | 0.9719 | 0.8281 | | 0.0003 | 71.0 | 142 | 0.9699 | 0.8281 | | 0.0003 | 72.0 | 144 | 0.9684 | 0.8438 | | 0.0003 | 73.0 | 146 | 0.9673 | 0.8438 | | 0.0003 | 74.0 | 148 | 0.9665 | 0.8438 | | 0.0002 | 75.0 | 150 | 0.9660 | 0.8438 | | 0.0002 | 76.0 | 152 | 0.9657 | 0.8438 | | 0.0002 | 77.0 | 154 | 0.9605 | 0.8438 | | 0.0002 | 78.0 | 156 | 0.9545 | 0.8438 | | 0.0002 | 79.0 | 158 | 0.9485 | 0.8438 | | 0.0004 | 80.0 | 160 | 0.9431 | 0.8438 | | 0.0004 | 81.0 | 162 | 0.9384 | 0.8438 | | 0.0004 | 82.0 | 164 | 0.9349 | 0.8438 | | 0.0004 | 83.0 | 166 | 0.9324 | 0.8438 | | 0.0004 | 84.0 | 168 | 0.9309 | 0.8438 | | 0.0002 | 85.0 | 170 | 0.9309 | 0.8438 | | 0.0002 | 86.0 | 172 | 0.9313 | 0.8438 | | 0.0002 | 87.0 | 174 | 0.9331 | 0.8438 | | 0.0002 | 88.0 | 176 | 0.9357 | 0.8438 | | 0.0002 | 89.0 | 178 | 0.9380 | 0.8438 | | 0.0002 | 90.0 | 180 | 0.9404 | 0.8438 | | 0.0002 | 91.0 | 182 | 0.9428 | 0.8438 | | 0.0002 | 92.0 | 184 | 0.9449 | 0.8438 | | 0.0002 | 93.0 | 186 | 0.9472 | 0.8438 | | 0.0002 | 94.0 | 188 | 0.9495 | 0.8438 | | 0.0002 | 95.0 | 190 | 0.9521 | 0.8438 | | 0.0002 | 96.0 | 192 | 0.9545 | 0.8438 | | 0.0002 | 97.0 | 194 | 0.9576 | 0.8438 | | 0.0002 | 98.0 | 196 | 0.9619 | 0.8438 | | 0.0002 | 99.0 | 198 | 0.9658 | 0.8438 | | 0.0002 | 100.0 | 200 | 0.9692 | 0.8438 | | 0.0002 | 101.0 | 202 | 0.9723 | 0.8438 | | 0.0002 | 102.0 | 204 | 0.9748 | 0.8438 | | 0.0002 | 103.0 | 206 | 0.9781 | 0.8438 | | 0.0002 | 104.0 | 208 | 0.9808 | 0.8438 | | 0.0001 | 105.0 | 210 | 0.9832 | 0.8438 | | 0.0001 | 106.0 | 212 | 0.9856 | 0.8438 | | 0.0001 | 107.0 | 214 | 0.9884 | 0.8438 | | 0.0001 | 108.0 | 216 | 0.9906 | 0.8438 | | 0.0001 | 109.0 | 218 | 0.9903 | 0.8438 | | 0.0002 | 110.0 | 220 | 0.9888 | 0.8438 | | 0.0002 | 111.0 | 222 | 0.9874 | 0.8438 | | 0.0002 | 112.0 | 224 | 0.9863 | 0.8438 | | 0.0002 | 113.0 | 226 | 0.9854 | 0.8438 | | 0.0002 | 114.0 | 228 | 0.9848 | 0.8438 | | 0.0001 | 115.0 | 230 | 0.9878 | 0.8438 | | 0.0001 | 116.0 | 232 | 0.9905 | 0.8438 | | 0.0001 | 117.0 | 234 | 0.9926 | 0.8438 | | 0.0001 | 118.0 | 236 | 0.9952 | 0.8438 | | 0.0001 | 119.0 | 238 | 1.0010 | 0.8438 | | 0.0001 | 120.0 | 240 | 1.0054 | 0.8438 | | 0.0001 | 121.0 | 242 | 1.0086 | 0.8438 | | 0.0001 | 122.0 | 244 | 1.0124 | 0.8438 | | 0.0001 | 123.0 | 246 | 1.0155 | 0.8438 | | 0.0001 | 124.0 | 248 | 1.0180 | 0.8438 | | 0.0001 | 125.0 | 250 | 1.0201 | 0.8438 | | 0.0001 | 126.0 | 252 | 1.0219 | 0.8438 | | 0.0001 | 127.0 | 254 | 1.0235 | 0.8438 | | 0.0001 | 128.0 | 256 | 1.0249 | 0.8438 | | 0.0001 | 129.0 | 258 | 1.0261 | 0.8438 | | 0.0001 | 130.0 | 260 | 1.0271 | 0.8438 | | 0.0001 | 131.0 | 262 | 1.0279 | 0.8438 | | 0.0001 | 132.0 | 264 | 1.0287 | 0.8438 | | 0.0001 | 133.0 | 266 | 1.0293 | 0.8438 | | 0.0001 | 134.0 | 268 | 1.0297 | 0.8438 | | 0.0001 | 135.0 | 270 | 1.0301 | 0.8438 | | 0.0001 | 136.0 | 272 | 1.0305 | 0.8438 | | 0.0001 | 137.0 | 274 | 1.0309 | 0.8438 | | 0.0001 | 138.0 | 276 | 1.0314 | 0.8438 | | 0.0001 | 139.0 | 278 | 1.0324 | 0.8438 | | 0.0001 | 140.0 | 280 | 1.0339 | 0.8438 | | 0.0001 | 141.0 | 282 | 1.0352 | 0.8438 | | 0.0001 | 142.0 | 284 | 1.0364 | 0.8438 | | 0.0001 | 143.0 | 286 | 1.0373 | 0.8438 | | 0.0001 | 144.0 | 288 | 1.0381 | 0.8438 | | 0.0001 | 145.0 | 290 | 1.0388 | 0.8438 | | 0.0001 | 146.0 | 292 | 1.0394 | 0.8438 | | 0.0001 | 147.0 | 294 | 1.0401 | 0.8438 | | 0.0001 | 148.0 | 296 | 1.0404 | 0.8438 | | 0.0001 | 149.0 | 298 | 1.0404 | 0.8438 | | 0.0001 | 150.0 | 300 | 1.0406 | 0.8438 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3

simonycl/best_model-sst-2-32-42

simonycl

2023-07-26T01:27:17Z

108

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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

2023-07-26T01:21:29Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-32-42 results: [] ---  # best_model-sst-2-32-42 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: - Loss: 1.2575 - Accuracy: 0.8281 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 2 | 1.2950 | 0.8281 | | No log | 2.0 | 4 | 1.2965 | 0.8281 | | No log | 3.0 | 6 | 1.2971 | 0.8281 | | No log | 4.0 | 8 | 1.2972 | 0.8281 | | 0.3346 | 5.0 | 10 | 1.2994 | 0.8281 | | 0.3346 | 6.0 | 12 | 1.3037 | 0.8281 | | 0.3346 | 7.0 | 14 | 1.3082 | 0.8281 | | 0.3346 | 8.0 | 16 | 1.3140 | 0.8281 | | 0.3346 | 9.0 | 18 | 1.3212 | 0.8281 | | 0.2586 | 10.0 | 20 | 1.3285 | 0.8281 | | 0.2586 | 11.0 | 22 | 1.3346 | 0.8281 | | 0.2586 | 12.0 | 24 | 1.3404 | 0.8281 | | 0.2586 | 13.0 | 26 | 1.3443 | 0.8281 | | 0.2586 | 14.0 | 28 | 1.3499 | 0.8281 | | 0.2171 | 15.0 | 30 | 1.3534 | 0.8281 | | 0.2171 | 16.0 | 32 | 1.3551 | 0.8281 | | 0.2171 | 17.0 | 34 | 1.3544 | 0.8281 | | 0.2171 | 18.0 | 36 | 1.3531 | 0.8281 | | 0.2171 | 19.0 | 38 | 1.3516 | 0.8281 | | 0.1549 | 20.0 | 40 | 1.3494 | 0.8281 | | 0.1549 | 21.0 | 42 | 1.3471 | 0.8281 | | 0.1549 | 22.0 | 44 | 1.3446 | 0.8281 | | 0.1549 | 23.0 | 46 | 1.3414 | 0.8281 | | 0.1549 | 24.0 | 48 | 1.3351 | 0.8281 | | 0.0613 | 25.0 | 50 | 1.3277 | 0.8281 | | 0.0613 | 26.0 | 52 | 1.3201 | 0.8281 | | 0.0613 | 27.0 | 54 | 1.3110 | 0.8281 | | 0.0613 | 28.0 | 56 | 1.2974 | 0.8281 | | 0.0613 | 29.0 | 58 | 1.2847 | 0.8281 | | 0.0094 | 30.0 | 60 | 1.2767 | 0.8281 | | 0.0094 | 31.0 | 62 | 1.2697 | 0.8281 | | 0.0094 | 32.0 | 64 | 1.2638 | 0.8281 | | 0.0094 | 33.0 | 66 | 1.2625 | 0.8281 | | 0.0094 | 34.0 | 68 | 1.2633 | 0.8281 | | 0.0004 | 35.0 | 70 | 1.2642 | 0.8281 | | 0.0004 | 36.0 | 72 | 1.2757 | 0.8281 | | 0.0004 | 37.0 | 74 | 1.2783 | 0.8281 | | 0.0004 | 38.0 | 76 | 1.2813 | 0.8281 | | 0.0004 | 39.0 | 78 | 1.2892 | 0.8281 | | 0.0074 | 40.0 | 80 | 1.2990 | 0.8281 | | 0.0074 | 41.0 | 82 | 1.3111 | 0.8281 | | 0.0074 | 42.0 | 84 | 1.3233 | 0.8281 | | 0.0074 | 43.0 | 86 | 1.3317 | 0.8281 | | 0.0074 | 44.0 | 88 | 1.3371 | 0.8281 | | 0.0004 | 45.0 | 90 | 1.3410 | 0.8281 | | 0.0004 | 46.0 | 92 | 1.3436 | 0.8281 | | 0.0004 | 47.0 | 94 | 1.3456 | 0.8281 | | 0.0004 | 48.0 | 96 | 1.3471 | 0.8281 | | 0.0004 | 49.0 | 98 | 1.3489 | 0.8281 | | 0.0005 | 50.0 | 100 | 1.3488 | 0.8281 | | 0.0005 | 51.0 | 102 | 1.3429 | 0.8281 | | 0.0005 | 52.0 | 104 | 1.3365 | 0.8281 | | 0.0005 | 53.0 | 106 | 1.3305 | 0.8281 | | 0.0005 | 54.0 | 108 | 1.3247 | 0.8281 | | 0.0003 | 55.0 | 110 | 1.3195 | 0.8281 | | 0.0003 | 56.0 | 112 | 1.3151 | 0.8281 | | 0.0003 | 57.0 | 114 | 1.2921 | 0.8281 | | 0.0003 | 58.0 | 116 | 1.2717 | 0.8281 | | 0.0003 | 59.0 | 118 | 1.2551 | 0.8281 | | 0.0166 | 60.0 | 120 | 1.2421 | 0.8281 | | 0.0166 | 61.0 | 122 | 1.2590 | 0.8281 | | 0.0166 | 62.0 | 124 | 1.2739 | 0.8281 | | 0.0166 | 63.0 | 126 | 1.2861 | 0.8281 | | 0.0166 | 64.0 | 128 | 1.2958 | 0.8281 | | 0.0003 | 65.0 | 130 | 1.3039 | 0.8281 | | 0.0003 | 66.0 | 132 | 1.3103 | 0.8281 | | 0.0003 | 67.0 | 134 | 1.3126 | 0.8281 | | 0.0003 | 68.0 | 136 | 1.3125 | 0.8281 | | 0.0003 | 69.0 | 138 | 1.3125 | 0.8281 | | 0.0002 | 70.0 | 140 | 1.3128 | 0.8281 | | 0.0002 | 71.0 | 142 | 1.3131 | 0.8281 | | 0.0002 | 72.0 | 144 | 1.3135 | 0.8281 | | 0.0002 | 73.0 | 146 | 1.3141 | 0.8281 | | 0.0002 | 74.0 | 148 | 1.3147 | 0.8281 | | 0.0004 | 75.0 | 150 | 1.3289 | 0.8281 | | 0.0004 | 76.0 | 152 | 1.3274 | 0.8281 | | 0.0004 | 77.0 | 154 | 1.3260 | 0.8281 | | 0.0004 | 78.0 | 156 | 1.3251 | 0.8281 | | 0.0004 | 79.0 | 158 | 1.3523 | 0.8281 | | 0.0008 | 80.0 | 160 | 1.3691 | 0.8281 | | 0.0008 | 81.0 | 162 | 1.3789 | 0.8281 | | 0.0008 | 82.0 | 164 | 1.3844 | 0.8281 | | 0.0008 | 83.0 | 166 | 1.3873 | 0.8281 | | 0.0008 | 84.0 | 168 | 1.3885 | 0.8281 | | 0.0002 | 85.0 | 170 | 1.3889 | 0.8281 | | 0.0002 | 86.0 | 172 | 1.3889 | 0.8281 | | 0.0002 | 87.0 | 174 | 1.3888 | 0.8281 | | 0.0002 | 88.0 | 176 | 1.3888 | 0.8281 | | 0.0002 | 89.0 | 178 | 1.3890 | 0.8281 | | 0.0002 | 90.0 | 180 | 1.3893 | 0.8281 | | 0.0002 | 91.0 | 182 | 1.3898 | 0.8281 | | 0.0002 | 92.0 | 184 | 1.3905 | 0.8281 | | 0.0002 | 93.0 | 186 | 1.3913 | 0.8281 | | 0.0002 | 94.0 | 188 | 1.3927 | 0.8281 | | 0.0002 | 95.0 | 190 | 1.3938 | 0.8281 | | 0.0002 | 96.0 | 192 | 1.3947 | 0.8281 | | 0.0002 | 97.0 | 194 | 1.3954 | 0.8281 | | 0.0002 | 98.0 | 196 | 1.3960 | 0.8281 | | 0.0002 | 99.0 | 198 | 1.3967 | 0.8281 | | 0.0002 | 100.0 | 200 | 1.3975 | 0.8281 | | 0.0002 | 101.0 | 202 | 1.3984 | 0.8281 | | 0.0002 | 102.0 | 204 | 1.3993 | 0.8281 | | 0.0002 | 103.0 | 206 | 1.4001 | 0.8281 | | 0.0002 | 104.0 | 208 | 1.4008 | 0.8281 | | 0.0001 | 105.0 | 210 | 1.4014 | 0.8281 | | 0.0001 | 106.0 | 212 | 1.4020 | 0.8281 | | 0.0001 | 107.0 | 214 | 1.4026 | 0.8281 | | 0.0001 | 108.0 | 216 | 1.4030 | 0.8281 | | 0.0001 | 109.0 | 218 | 1.4035 | 0.8281 | | 0.0001 | 110.0 | 220 | 1.4040 | 0.8281 | | 0.0001 | 111.0 | 222 | 1.4046 | 0.8281 | | 0.0001 | 112.0 | 224 | 1.4051 | 0.8281 | | 0.0001 | 113.0 | 226 | 1.4057 | 0.8281 | | 0.0001 | 114.0 | 228 | 1.4064 | 0.8281 | | 0.0001 | 115.0 | 230 | 1.4071 | 0.8281 | | 0.0001 | 116.0 | 232 | 1.4078 | 0.8281 | | 0.0001 | 117.0 | 234 | 1.4085 | 0.8281 | | 0.0001 | 118.0 | 236 | 1.4092 | 0.8281 | | 0.0001 | 119.0 | 238 | 1.4099 | 0.8281 | | 0.0001 | 120.0 | 240 | 1.4106 | 0.8281 | | 0.0001 | 121.0 | 242 | 1.4108 | 0.8281 | | 0.0001 | 122.0 | 244 | 1.4081 | 0.8281 | | 0.0001 | 123.0 | 246 | 1.4055 | 0.8281 | | 0.0001 | 124.0 | 248 | 1.4032 | 0.8281 | | 0.0001 | 125.0 | 250 | 1.4011 | 0.8281 | | 0.0001 | 126.0 | 252 | 1.3995 | 0.8281 | | 0.0001 | 127.0 | 254 | 1.3982 | 0.8281 | | 0.0001 | 128.0 | 256 | 1.3973 | 0.8281 | | 0.0001 | 129.0 | 258 | 1.3967 | 0.8281 | | 0.0001 | 130.0 | 260 | 1.3963 | 0.8281 | | 0.0001 | 131.0 | 262 | 1.3962 | 0.8281 | | 0.0001 | 132.0 | 264 | 1.3962 | 0.8281 | | 0.0001 | 133.0 | 266 | 1.3965 | 0.8281 | | 0.0001 | 134.0 | 268 | 1.3970 | 0.8281 | | 0.0001 | 135.0 | 270 | 1.3989 | 0.8281 | | 0.0001 | 136.0 | 272 | 1.4012 | 0.8281 | | 0.0001 | 137.0 | 274 | 1.4035 | 0.8281 | | 0.0001 | 138.0 | 276 | 1.4052 | 0.8281 | | 0.0001 | 139.0 | 278 | 1.4064 | 0.8281 | | 0.0002 | 140.0 | 280 | 1.3703 | 0.8281 | | 0.0002 | 141.0 | 282 | 1.2995 | 0.8438 | | 0.0002 | 142.0 | 284 | 1.2572 | 0.8281 | | 0.0002 | 143.0 | 286 | 1.2224 | 0.8281 | | 0.0002 | 144.0 | 288 | 1.2120 | 0.8438 | | 0.0001 | 145.0 | 290 | 1.2242 | 0.8281 | | 0.0001 | 146.0 | 292 | 1.2377 | 0.8281 | | 0.0001 | 147.0 | 294 | 1.2477 | 0.8281 | | 0.0001 | 148.0 | 296 | 1.2542 | 0.8281 | | 0.0001 | 149.0 | 298 | 1.2575 | 0.8281 | | 0.0002 | 150.0 | 300 | 1.2575 | 0.8281 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3

Potemkin87/llama2-qlora-finetunined-french

Potemkin87

2023-07-26T01:24:18Z

1

peft

[ "peft", "region:us" ]

null

2023-07-26T01:24:04Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

Guilherme34/Jennifer-beta-test-donotdownloaditsjustatest

Guilherme34

2023-07-26T01:15:29Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-25T21:42:23Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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 0.4.0

simonycl/best_model-sst-2-16-100

simonycl

2023-07-26T01:08:23Z

106

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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

2023-07-26T01:04:56Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-16-100 results: [] ---  # best_model-sst-2-16-100 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: - Loss: 0.4760 - Accuracy: 0.9062 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 0.3957 | 0.875 | | No log | 2.0 | 2 | 0.3958 | 0.875 | | No log | 3.0 | 3 | 0.3961 | 0.875 | | No log | 4.0 | 4 | 0.3964 | 0.875 | | No log | 5.0 | 5 | 0.3968 | 0.875 | | No log | 6.0 | 6 | 0.3971 | 0.875 | | No log | 7.0 | 7 | 0.3974 | 0.875 | | No log | 8.0 | 8 | 0.3976 | 0.875 | | No log | 9.0 | 9 | 0.3978 | 0.875 | | 0.2951 | 10.0 | 10 | 0.3979 | 0.875 | | 0.2951 | 11.0 | 11 | 0.3977 | 0.875 | | 0.2951 | 12.0 | 12 | 0.3971 | 0.875 | | 0.2951 | 13.0 | 13 | 0.3963 | 0.875 | | 0.2951 | 14.0 | 14 | 0.3954 | 0.875 | | 0.2951 | 15.0 | 15 | 0.3943 | 0.875 | | 0.2951 | 16.0 | 16 | 0.3929 | 0.875 | | 0.2951 | 17.0 | 17 | 0.3912 | 0.875 | | 0.2951 | 18.0 | 18 | 0.3895 | 0.875 | | 0.2951 | 19.0 | 19 | 0.3876 | 0.875 | | 0.2889 | 20.0 | 20 | 0.3854 | 0.875 | | 0.2889 | 21.0 | 21 | 0.3830 | 0.875 | | 0.2889 | 22.0 | 22 | 0.3806 | 0.875 | | 0.2889 | 23.0 | 23 | 0.3789 | 0.875 | | 0.2889 | 24.0 | 24 | 0.3770 | 0.875 | | 0.2889 | 25.0 | 25 | 0.3755 | 0.9062 | | 0.2889 | 26.0 | 26 | 0.3739 | 0.9062 | | 0.2889 | 27.0 | 27 | 0.3728 | 0.9062 | | 0.2889 | 28.0 | 28 | 0.3716 | 0.9062 | | 0.2889 | 29.0 | 29 | 0.3704 | 0.9062 | | 0.2147 | 30.0 | 30 | 0.3697 | 0.9062 | | 0.2147 | 31.0 | 31 | 0.3692 | 0.9062 | | 0.2147 | 32.0 | 32 | 0.3688 | 0.9062 | | 0.2147 | 33.0 | 33 | 0.3686 | 0.9062 | | 0.2147 | 34.0 | 34 | 0.3684 | 0.9062 | | 0.2147 | 35.0 | 35 | 0.3683 | 0.9062 | | 0.2147 | 36.0 | 36 | 0.3682 | 0.9062 | | 0.2147 | 37.0 | 37 | 0.3684 | 0.9062 | | 0.2147 | 38.0 | 38 | 0.3684 | 0.9062 | | 0.2147 | 39.0 | 39 | 0.3685 | 0.9062 | | 0.1272 | 40.0 | 40 | 0.3689 | 0.9062 | | 0.1272 | 41.0 | 41 | 0.3693 | 0.9062 | | 0.1272 | 42.0 | 42 | 0.3701 | 0.9062 | | 0.1272 | 43.0 | 43 | 0.3709 | 0.875 | | 0.1272 | 44.0 | 44 | 0.3719 | 0.875 | | 0.1272 | 45.0 | 45 | 0.3728 | 0.875 | | 0.1272 | 46.0 | 46 | 0.3731 | 0.875 | | 0.1272 | 47.0 | 47 | 0.3728 | 0.875 | | 0.1272 | 48.0 | 48 | 0.3729 | 0.875 | | 0.1272 | 49.0 | 49 | 0.3726 | 0.875 | | 0.0531 | 50.0 | 50 | 0.3726 | 0.875 | | 0.0531 | 51.0 | 51 | 0.3721 | 0.875 | | 0.0531 | 52.0 | 52 | 0.3716 | 0.875 | | 0.0531 | 53.0 | 53 | 0.3715 | 0.875 | | 0.0531 | 54.0 | 54 | 0.3707 | 0.875 | | 0.0531 | 55.0 | 55 | 0.3706 | 0.875 | | 0.0531 | 56.0 | 56 | 0.3702 | 0.875 | | 0.0531 | 57.0 | 57 | 0.3707 | 0.875 | | 0.0531 | 58.0 | 58 | 0.3716 | 0.875 | | 0.0531 | 59.0 | 59 | 0.3735 | 0.875 | | 0.0221 | 60.0 | 60 | 0.3754 | 0.875 | | 0.0221 | 61.0 | 61 | 0.3775 | 0.875 | | 0.0221 | 62.0 | 62 | 0.3801 | 0.875 | | 0.0221 | 63.0 | 63 | 0.3824 | 0.875 | | 0.0221 | 64.0 | 64 | 0.3847 | 0.875 | | 0.0221 | 65.0 | 65 | 0.3871 | 0.875 | | 0.0221 | 66.0 | 66 | 0.3883 | 0.875 | | 0.0221 | 67.0 | 67 | 0.3885 | 0.875 | | 0.0221 | 68.0 | 68 | 0.3886 | 0.875 | | 0.0221 | 69.0 | 69 | 0.3876 | 0.875 | | 0.0151 | 70.0 | 70 | 0.3869 | 0.875 | | 0.0151 | 71.0 | 71 | 0.3869 | 0.875 | | 0.0151 | 72.0 | 72 | 0.3871 | 0.875 | | 0.0151 | 73.0 | 73 | 0.3875 | 0.875 | | 0.0151 | 74.0 | 74 | 0.3872 | 0.875 | | 0.0151 | 75.0 | 75 | 0.3873 | 0.875 | | 0.0151 | 76.0 | 76 | 0.3869 | 0.875 | | 0.0151 | 77.0 | 77 | 0.3868 | 0.875 | | 0.0151 | 78.0 | 78 | 0.3876 | 0.9062 | | 0.0151 | 79.0 | 79 | 0.3885 | 0.9062 | | 0.0099 | 80.0 | 80 | 0.3896 | 0.9062 | | 0.0099 | 81.0 | 81 | 0.3908 | 0.9062 | | 0.0099 | 82.0 | 82 | 0.3921 | 0.9062 | | 0.0099 | 83.0 | 83 | 0.3935 | 0.9062 | | 0.0099 | 84.0 | 84 | 0.3952 | 0.9062 | | 0.0099 | 85.0 | 85 | 0.3972 | 0.9062 | | 0.0099 | 86.0 | 86 | 0.3992 | 0.9062 | | 0.0099 | 87.0 | 87 | 0.4017 | 0.9062 | | 0.0099 | 88.0 | 88 | 0.4042 | 0.9062 | | 0.0099 | 89.0 | 89 | 0.4062 | 0.9062 | | 0.0074 | 90.0 | 90 | 0.4082 | 0.9062 | | 0.0074 | 91.0 | 91 | 0.4100 | 0.9062 | | 0.0074 | 92.0 | 92 | 0.4118 | 0.9062 | | 0.0074 | 93.0 | 93 | 0.4135 | 0.9062 | | 0.0074 | 94.0 | 94 | 0.4152 | 0.9062 | | 0.0074 | 95.0 | 95 | 0.4169 | 0.9062 | | 0.0074 | 96.0 | 96 | 0.4185 | 0.9062 | | 0.0074 | 97.0 | 97 | 0.4198 | 0.9062 | | 0.0074 | 98.0 | 98 | 0.4211 | 0.9062 | | 0.0074 | 99.0 | 99 | 0.4224 | 0.9062 | | 0.006 | 100.0 | 100 | 0.4236 | 0.9062 | | 0.006 | 101.0 | 101 | 0.4248 | 0.9062 | | 0.006 | 102.0 | 102 | 0.4259 | 0.9062 | | 0.006 | 103.0 | 103 | 0.4271 | 0.9062 | | 0.006 | 104.0 | 104 | 0.4284 | 0.9062 | | 0.006 | 105.0 | 105 | 0.4296 | 0.9062 | | 0.006 | 106.0 | 106 | 0.4298 | 0.9062 | | 0.006 | 107.0 | 107 | 0.4283 | 0.9062 | | 0.006 | 108.0 | 108 | 0.4276 | 0.9062 | | 0.006 | 109.0 | 109 | 0.4275 | 0.9062 | | 0.0065 | 110.0 | 110 | 0.4280 | 0.9062 | | 0.0065 | 111.0 | 111 | 0.4287 | 0.9062 | | 0.0065 | 112.0 | 112 | 0.4297 | 0.9062 | | 0.0065 | 113.0 | 113 | 0.4309 | 0.9062 | | 0.0065 | 114.0 | 114 | 0.4322 | 0.9062 | | 0.0065 | 115.0 | 115 | 0.4337 | 0.9062 | | 0.0065 | 116.0 | 116 | 0.4352 | 0.9062 | | 0.0065 | 117.0 | 117 | 0.4367 | 0.9062 | | 0.0065 | 118.0 | 118 | 0.4383 | 0.9062 | | 0.0065 | 119.0 | 119 | 0.4399 | 0.9062 | | 0.0046 | 120.0 | 120 | 0.4413 | 0.9062 | | 0.0046 | 121.0 | 121 | 0.4428 | 0.9062 | | 0.0046 | 122.0 | 122 | 0.4443 | 0.9062 | | 0.0046 | 123.0 | 123 | 0.4457 | 0.9062 | | 0.0046 | 124.0 | 124 | 0.4470 | 0.9062 | | 0.0046 | 125.0 | 125 | 0.4483 | 0.9062 | | 0.0046 | 126.0 | 126 | 0.4495 | 0.9062 | | 0.0046 | 127.0 | 127 | 0.4508 | 0.9062 | | 0.0046 | 128.0 | 128 | 0.4520 | 0.9062 | | 0.0046 | 129.0 | 129 | 0.4531 | 0.9062 | | 0.0037 | 130.0 | 130 | 0.4543 | 0.9062 | | 0.0037 | 131.0 | 131 | 0.4555 | 0.9062 | | 0.0037 | 132.0 | 132 | 0.4566 | 0.9062 | | 0.0037 | 133.0 | 133 | 0.4577 | 0.9062 | | 0.0037 | 134.0 | 134 | 0.4588 | 0.9062 | | 0.0037 | 135.0 | 135 | 0.4599 | 0.9062 | | 0.0037 | 136.0 | 136 | 0.4610 | 0.9062 | | 0.0037 | 137.0 | 137 | 0.4622 | 0.9062 | | 0.0037 | 138.0 | 138 | 0.4633 | 0.9062 | | 0.0037 | 139.0 | 139 | 0.4644 | 0.9062 | | 0.0033 | 140.0 | 140 | 0.4655 | 0.9062 | | 0.0033 | 141.0 | 141 | 0.4666 | 0.9062 | | 0.0033 | 142.0 | 142 | 0.4677 | 0.9062 | | 0.0033 | 143.0 | 143 | 0.4688 | 0.9062 | | 0.0033 | 144.0 | 144 | 0.4700 | 0.9062 | | 0.0033 | 145.0 | 145 | 0.4712 | 0.9062 | | 0.0033 | 146.0 | 146 | 0.4725 | 0.9062 | | 0.0033 | 147.0 | 147 | 0.4733 | 0.9062 | | 0.0033 | 148.0 | 148 | 0.4742 | 0.9062 | | 0.0033 | 149.0 | 149 | 0.4751 | 0.9062 | | 0.0029 | 150.0 | 150 | 0.4760 | 0.9062 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3

simonycl/best_model-sst-2-16-87

simonycl

2023-07-26T01:04:41Z

105

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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

2023-07-26T01:01:18Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-16-87 results: [] ---  # best_model-sst-2-16-87 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: - Loss: 0.6392 - Accuracy: 0.875 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 0.5816 | 0.8438 | | No log | 2.0 | 2 | 0.5813 | 0.8438 | | No log | 3.0 | 3 | 0.5807 | 0.8438 | | No log | 4.0 | 4 | 0.5798 | 0.8438 | | No log | 5.0 | 5 | 0.5786 | 0.8438 | | No log | 6.0 | 6 | 0.5770 | 0.8438 | | No log | 7.0 | 7 | 0.5750 | 0.8438 | | No log | 8.0 | 8 | 0.5726 | 0.8438 | | No log | 9.0 | 9 | 0.5701 | 0.8438 | | 0.4546 | 10.0 | 10 | 0.5672 | 0.8438 | | 0.4546 | 11.0 | 11 | 0.5641 | 0.8438 | | 0.4546 | 12.0 | 12 | 0.5614 | 0.8438 | | 0.4546 | 13.0 | 13 | 0.5586 | 0.8438 | | 0.4546 | 14.0 | 14 | 0.5560 | 0.8438 | | 0.4546 | 15.0 | 15 | 0.5530 | 0.8438 | | 0.4546 | 16.0 | 16 | 0.5501 | 0.8438 | | 0.4546 | 17.0 | 17 | 0.5470 | 0.8438 | | 0.4546 | 18.0 | 18 | 0.5438 | 0.8438 | | 0.4546 | 19.0 | 19 | 0.5407 | 0.8438 | | 0.4413 | 20.0 | 20 | 0.5369 | 0.8438 | | 0.4413 | 21.0 | 21 | 0.5325 | 0.8438 | | 0.4413 | 22.0 | 22 | 0.5280 | 0.8438 | | 0.4413 | 23.0 | 23 | 0.5230 | 0.8438 | | 0.4413 | 24.0 | 24 | 0.5180 | 0.8438 | | 0.4413 | 25.0 | 25 | 0.5132 | 0.8438 | | 0.4413 | 26.0 | 26 | 0.5088 | 0.8438 | | 0.4413 | 27.0 | 27 | 0.5049 | 0.8438 | | 0.4413 | 28.0 | 28 | 0.5014 | 0.8438 | | 0.4413 | 29.0 | 29 | 0.4985 | 0.8438 | | 0.3899 | 30.0 | 30 | 0.4964 | 0.8438 | | 0.3899 | 31.0 | 31 | 0.4951 | 0.8438 | | 0.3899 | 32.0 | 32 | 0.4937 | 0.8438 | | 0.3899 | 33.0 | 33 | 0.4919 | 0.8438 | | 0.3899 | 34.0 | 34 | 0.4902 | 0.8438 | | 0.3899 | 35.0 | 35 | 0.4884 | 0.8438 | | 0.3899 | 36.0 | 36 | 0.4870 | 0.8438 | | 0.3899 | 37.0 | 37 | 0.4854 | 0.8438 | | 0.3899 | 38.0 | 38 | 0.4844 | 0.8438 | | 0.3899 | 39.0 | 39 | 0.4832 | 0.875 | | 0.3672 | 40.0 | 40 | 0.4821 | 0.875 | | 0.3672 | 41.0 | 41 | 0.4817 | 0.875 | | 0.3672 | 42.0 | 42 | 0.4817 | 0.875 | | 0.3672 | 43.0 | 43 | 0.4820 | 0.875 | | 0.3672 | 44.0 | 44 | 0.4830 | 0.875 | | 0.3672 | 45.0 | 45 | 0.4838 | 0.875 | | 0.3672 | 46.0 | 46 | 0.4848 | 0.875 | | 0.3672 | 47.0 | 47 | 0.4855 | 0.875 | | 0.3672 | 48.0 | 48 | 0.4854 | 0.875 | | 0.3672 | 49.0 | 49 | 0.4860 | 0.875 | | 0.2765 | 50.0 | 50 | 0.4872 | 0.875 | | 0.2765 | 51.0 | 51 | 0.4878 | 0.875 | | 0.2765 | 52.0 | 52 | 0.4892 | 0.875 | | 0.2765 | 53.0 | 53 | 0.4913 | 0.875 | | 0.2765 | 54.0 | 54 | 0.4942 | 0.8438 | | 0.2765 | 55.0 | 55 | 0.4977 | 0.8438 | | 0.2765 | 56.0 | 56 | 0.5017 | 0.8438 | | 0.2765 | 57.0 | 57 | 0.5074 | 0.8438 | | 0.2765 | 58.0 | 58 | 0.5148 | 0.8438 | | 0.2765 | 59.0 | 59 | 0.5211 | 0.8438 | | 0.2106 | 60.0 | 60 | 0.5286 | 0.8438 | | 0.2106 | 61.0 | 61 | 0.5361 | 0.8438 | | 0.2106 | 62.0 | 62 | 0.5429 | 0.8438 | | 0.2106 | 63.0 | 63 | 0.5497 | 0.8438 | | 0.2106 | 64.0 | 64 | 0.5551 | 0.8438 | | 0.2106 | 65.0 | 65 | 0.5569 | 0.8438 | | 0.2106 | 66.0 | 66 | 0.5556 | 0.8438 | | 0.2106 | 67.0 | 67 | 0.5522 | 0.8438 | | 0.2106 | 68.0 | 68 | 0.5465 | 0.8438 | | 0.2106 | 69.0 | 69 | 0.5400 | 0.8438 | | 0.1587 | 70.0 | 70 | 0.5359 | 0.8438 | | 0.1587 | 71.0 | 71 | 0.5311 | 0.8438 | | 0.1587 | 72.0 | 72 | 0.5252 | 0.8438 | | 0.1587 | 73.0 | 73 | 0.5217 | 0.8438 | | 0.1587 | 74.0 | 74 | 0.5192 | 0.8438 | | 0.1587 | 75.0 | 75 | 0.5158 | 0.8438 | | 0.1587 | 76.0 | 76 | 0.5128 | 0.8438 | | 0.1587 | 77.0 | 77 | 0.5113 | 0.8438 | | 0.1587 | 78.0 | 78 | 0.5105 | 0.8438 | | 0.1587 | 79.0 | 79 | 0.5091 | 0.8438 | | 0.122 | 80.0 | 80 | 0.5090 | 0.8438 | | 0.122 | 81.0 | 81 | 0.5100 | 0.8438 | | 0.122 | 82.0 | 82 | 0.5126 | 0.8438 | | 0.122 | 83.0 | 83 | 0.5167 | 0.8438 | | 0.122 | 84.0 | 84 | 0.5215 | 0.8438 | | 0.122 | 85.0 | 85 | 0.5274 | 0.8438 | | 0.122 | 86.0 | 86 | 0.5351 | 0.8438 | | 0.122 | 87.0 | 87 | 0.5439 | 0.8438 | | 0.122 | 88.0 | 88 | 0.5547 | 0.8438 | | 0.122 | 89.0 | 89 | 0.5658 | 0.8438 | | 0.0738 | 90.0 | 90 | 0.5778 | 0.8438 | | 0.0738 | 91.0 | 91 | 0.5872 | 0.8438 | | 0.0738 | 92.0 | 92 | 0.5963 | 0.8438 | | 0.0738 | 93.0 | 93 | 0.6027 | 0.8438 | | 0.0738 | 94.0 | 94 | 0.6059 | 0.8438 | | 0.0738 | 95.0 | 95 | 0.6070 | 0.8438 | | 0.0738 | 96.0 | 96 | 0.6052 | 0.8438 | | 0.0738 | 97.0 | 97 | 0.6020 | 0.8438 | | 0.0738 | 98.0 | 98 | 0.5950 | 0.8438 | | 0.0738 | 99.0 | 99 | 0.5870 | 0.8438 | | 0.0328 | 100.0 | 100 | 0.5788 | 0.8438 | | 0.0328 | 101.0 | 101 | 0.5706 | 0.8438 | | 0.0328 | 102.0 | 102 | 0.5638 | 0.8438 | | 0.0328 | 103.0 | 103 | 0.5578 | 0.8438 | | 0.0328 | 104.0 | 104 | 0.5530 | 0.8438 | | 0.0328 | 105.0 | 105 | 0.5491 | 0.875 | | 0.0328 | 106.0 | 106 | 0.5465 | 0.875 | | 0.0328 | 107.0 | 107 | 0.5457 | 0.875 | | 0.0328 | 108.0 | 108 | 0.5456 | 0.875 | | 0.0328 | 109.0 | 109 | 0.5462 | 0.875 | | 0.0221 | 110.0 | 110 | 0.5473 | 0.875 | | 0.0221 | 111.0 | 111 | 0.5486 | 0.875 | | 0.0221 | 112.0 | 112 | 0.5500 | 0.875 | | 0.0221 | 113.0 | 113 | 0.5521 | 0.875 | | 0.0221 | 114.0 | 114 | 0.5543 | 0.875 | | 0.0221 | 115.0 | 115 | 0.5564 | 0.875 | | 0.0221 | 116.0 | 116 | 0.5589 | 0.875 | | 0.0221 | 117.0 | 117 | 0.5613 | 0.875 | | 0.0221 | 118.0 | 118 | 0.5637 | 0.875 | | 0.0221 | 119.0 | 119 | 0.5660 | 0.875 | | 0.017 | 120.0 | 120 | 0.5682 | 0.875 | | 0.017 | 121.0 | 121 | 0.5704 | 0.875 | | 0.017 | 122.0 | 122 | 0.5727 | 0.875 | | 0.017 | 123.0 | 123 | 0.5748 | 0.875 | | 0.017 | 124.0 | 124 | 0.5772 | 0.875 | | 0.017 | 125.0 | 125 | 0.5796 | 0.875 | | 0.017 | 126.0 | 126 | 0.5820 | 0.875 | | 0.017 | 127.0 | 127 | 0.5847 | 0.875 | | 0.017 | 128.0 | 128 | 0.5874 | 0.875 | | 0.017 | 129.0 | 129 | 0.5900 | 0.875 | | 0.0129 | 130.0 | 130 | 0.5926 | 0.875 | | 0.0129 | 131.0 | 131 | 0.5951 | 0.875 | | 0.0129 | 132.0 | 132 | 0.5976 | 0.875 | | 0.0129 | 133.0 | 133 | 0.6001 | 0.875 | | 0.0129 | 134.0 | 134 | 0.6027 | 0.875 | | 0.0129 | 135.0 | 135 | 0.6051 | 0.875 | | 0.0129 | 136.0 | 136 | 0.6076 | 0.875 | | 0.0129 | 137.0 | 137 | 0.6099 | 0.875 | | 0.0129 | 138.0 | 138 | 0.6123 | 0.875 | | 0.0129 | 139.0 | 139 | 0.6146 | 0.875 | | 0.0103 | 140.0 | 140 | 0.6169 | 0.875 | | 0.0103 | 141.0 | 141 | 0.6192 | 0.875 | | 0.0103 | 142.0 | 142 | 0.6216 | 0.875 | | 0.0103 | 143.0 | 143 | 0.6239 | 0.875 | | 0.0103 | 144.0 | 144 | 0.6261 | 0.875 | | 0.0103 | 145.0 | 145 | 0.6284 | 0.875 | | 0.0103 | 146.0 | 146 | 0.6306 | 0.875 | | 0.0103 | 147.0 | 147 | 0.6328 | 0.875 | | 0.0103 | 148.0 | 148 | 0.6350 | 0.875 | | 0.0103 | 149.0 | 149 | 0.6371 | 0.875 | | 0.0084 | 150.0 | 150 | 0.6392 | 0.875 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3

simonycl/best_model-sst-2-16-42

simonycl

2023-07-26T01:01:03Z

106

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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

2023-07-26T00:57:40Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-16-42 results: [] ---  # best_model-sst-2-16-42 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: - Loss: 0.4301 - Accuracy: 0.875 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 0.4423 | 0.7812 | | No log | 2.0 | 2 | 0.4424 | 0.7812 | | No log | 3.0 | 3 | 0.4425 | 0.7812 | | No log | 4.0 | 4 | 0.4427 | 0.7812 | | No log | 5.0 | 5 | 0.4430 | 0.7812 | | No log | 6.0 | 6 | 0.4433 | 0.7812 | | No log | 7.0 | 7 | 0.4437 | 0.7812 | | No log | 8.0 | 8 | 0.4442 | 0.7812 | | No log | 9.0 | 9 | 0.4448 | 0.7812 | | 0.458 | 10.0 | 10 | 0.4454 | 0.7812 | | 0.458 | 11.0 | 11 | 0.4461 | 0.7812 | | 0.458 | 12.0 | 12 | 0.4467 | 0.7812 | | 0.458 | 13.0 | 13 | 0.4475 | 0.7812 | | 0.458 | 14.0 | 14 | 0.4482 | 0.7812 | | 0.458 | 15.0 | 15 | 0.4489 | 0.7812 | | 0.458 | 16.0 | 16 | 0.4497 | 0.7812 | | 0.458 | 17.0 | 17 | 0.4506 | 0.7812 | | 0.458 | 18.0 | 18 | 0.4517 | 0.7812 | | 0.458 | 19.0 | 19 | 0.4528 | 0.7812 | | 0.4251 | 20.0 | 20 | 0.4540 | 0.7812 | | 0.4251 | 21.0 | 21 | 0.4553 | 0.7812 | | 0.4251 | 22.0 | 22 | 0.4565 | 0.7812 | | 0.4251 | 23.0 | 23 | 0.4574 | 0.7812 | | 0.4251 | 24.0 | 24 | 0.4582 | 0.7812 | | 0.4251 | 25.0 | 25 | 0.4589 | 0.7812 | | 0.4251 | 26.0 | 26 | 0.4595 | 0.7812 | | 0.4251 | 27.0 | 27 | 0.4600 | 0.7812 | | 0.4251 | 28.0 | 28 | 0.4606 | 0.7812 | | 0.4251 | 29.0 | 29 | 0.4608 | 0.7812 | | 0.3723 | 30.0 | 30 | 0.4610 | 0.7812 | | 0.3723 | 31.0 | 31 | 0.4612 | 0.7812 | | 0.3723 | 32.0 | 32 | 0.4612 | 0.7812 | | 0.3723 | 33.0 | 33 | 0.4611 | 0.7812 | | 0.3723 | 34.0 | 34 | 0.4610 | 0.7812 | | 0.3723 | 35.0 | 35 | 0.4609 | 0.7812 | | 0.3723 | 36.0 | 36 | 0.4604 | 0.7812 | | 0.3723 | 37.0 | 37 | 0.4601 | 0.7812 | | 0.3723 | 38.0 | 38 | 0.4592 | 0.7812 | | 0.3723 | 39.0 | 39 | 0.4583 | 0.7812 | | 0.3304 | 40.0 | 40 | 0.4572 | 0.7812 | | 0.3304 | 41.0 | 41 | 0.4568 | 0.7812 | | 0.3304 | 42.0 | 42 | 0.4562 | 0.7812 | | 0.3304 | 43.0 | 43 | 0.4557 | 0.7812 | | 0.3304 | 44.0 | 44 | 0.4552 | 0.7812 | | 0.3304 | 45.0 | 45 | 0.4543 | 0.7812 | | 0.3304 | 46.0 | 46 | 0.4541 | 0.7812 | | 0.3304 | 47.0 | 47 | 0.4536 | 0.7812 | | 0.3304 | 48.0 | 48 | 0.4534 | 0.7812 | | 0.3304 | 49.0 | 49 | 0.4528 | 0.7812 | | 0.2724 | 50.0 | 50 | 0.4526 | 0.7812 | | 0.2724 | 51.0 | 51 | 0.4533 | 0.7812 | | 0.2724 | 52.0 | 52 | 0.4544 | 0.7812 | | 0.2724 | 53.0 | 53 | 0.4554 | 0.7812 | | 0.2724 | 54.0 | 54 | 0.4563 | 0.7812 | | 0.2724 | 55.0 | 55 | 0.4570 | 0.7812 | | 0.2724 | 56.0 | 56 | 0.4578 | 0.7812 | | 0.2724 | 57.0 | 57 | 0.4587 | 0.7812 | | 0.2724 | 58.0 | 58 | 0.4588 | 0.7812 | | 0.2724 | 59.0 | 59 | 0.4580 | 0.7812 | | 0.2089 | 60.0 | 60 | 0.4569 | 0.7812 | | 0.2089 | 61.0 | 61 | 0.4553 | 0.7812 | | 0.2089 | 62.0 | 62 | 0.4531 | 0.7812 | | 0.2089 | 63.0 | 63 | 0.4509 | 0.7812 | | 0.2089 | 64.0 | 64 | 0.4478 | 0.7812 | | 0.2089 | 65.0 | 65 | 0.4449 | 0.7812 | | 0.2089 | 66.0 | 66 | 0.4425 | 0.7812 | | 0.2089 | 67.0 | 67 | 0.4414 | 0.7812 | | 0.2089 | 68.0 | 68 | 0.4399 | 0.7812 | | 0.2089 | 69.0 | 69 | 0.4391 | 0.7812 | | 0.163 | 70.0 | 70 | 0.4382 | 0.7812 | | 0.163 | 71.0 | 71 | 0.4366 | 0.7812 | | 0.163 | 72.0 | 72 | 0.4356 | 0.7812 | | 0.163 | 73.0 | 73 | 0.4344 | 0.7812 | | 0.163 | 74.0 | 74 | 0.4331 | 0.7812 | | 0.163 | 75.0 | 75 | 0.4320 | 0.7812 | | 0.163 | 76.0 | 76 | 0.4310 | 0.7812 | | 0.163 | 77.0 | 77 | 0.4294 | 0.7812 | | 0.163 | 78.0 | 78 | 0.4285 | 0.7812 | | 0.163 | 79.0 | 79 | 0.4273 | 0.7812 | | 0.1282 | 80.0 | 80 | 0.4267 | 0.7812 | | 0.1282 | 81.0 | 81 | 0.4262 | 0.7812 | | 0.1282 | 82.0 | 82 | 0.4271 | 0.7812 | | 0.1282 | 83.0 | 83 | 0.4275 | 0.7812 | | 0.1282 | 84.0 | 84 | 0.4289 | 0.7812 | | 0.1282 | 85.0 | 85 | 0.4295 | 0.7812 | | 0.1282 | 86.0 | 86 | 0.4293 | 0.7812 | | 0.1282 | 87.0 | 87 | 0.4284 | 0.7812 | | 0.1282 | 88.0 | 88 | 0.4275 | 0.7812 | | 0.1282 | 89.0 | 89 | 0.4263 | 0.7812 | | 0.1021 | 90.0 | 90 | 0.4249 | 0.7812 | | 0.1021 | 91.0 | 91 | 0.4233 | 0.7812 | | 0.1021 | 92.0 | 92 | 0.4210 | 0.7812 | | 0.1021 | 93.0 | 93 | 0.4188 | 0.7812 | | 0.1021 | 94.0 | 94 | 0.4166 | 0.7812 | | 0.1021 | 95.0 | 95 | 0.4162 | 0.7812 | | 0.1021 | 96.0 | 96 | 0.4154 | 0.7812 | | 0.1021 | 97.0 | 97 | 0.4139 | 0.7812 | | 0.1021 | 98.0 | 98 | 0.4126 | 0.8125 | | 0.1021 | 99.0 | 99 | 0.4117 | 0.8125 | | 0.0862 | 100.0 | 100 | 0.4115 | 0.8125 | | 0.0862 | 101.0 | 101 | 0.4119 | 0.8125 | | 0.0862 | 102.0 | 102 | 0.4116 | 0.8125 | | 0.0862 | 103.0 | 103 | 0.4119 | 0.8125 | | 0.0862 | 104.0 | 104 | 0.4141 | 0.8125 | | 0.0862 | 105.0 | 105 | 0.4156 | 0.8125 | | 0.0862 | 106.0 | 106 | 0.4165 | 0.8438 | | 0.0862 | 107.0 | 107 | 0.4170 | 0.8438 | | 0.0862 | 108.0 | 108 | 0.4183 | 0.8438 | | 0.0862 | 109.0 | 109 | 0.4200 | 0.8438 | | 0.0708 | 110.0 | 110 | 0.4212 | 0.8438 | | 0.0708 | 111.0 | 111 | 0.4216 | 0.8438 | | 0.0708 | 112.0 | 112 | 0.4213 | 0.8438 | | 0.0708 | 113.0 | 113 | 0.4205 | 0.8438 | | 0.0708 | 114.0 | 114 | 0.4191 | 0.8438 | | 0.0708 | 115.0 | 115 | 0.4180 | 0.8438 | | 0.0708 | 116.0 | 116 | 0.4167 | 0.8438 | | 0.0708 | 117.0 | 117 | 0.4154 | 0.8438 | | 0.0708 | 118.0 | 118 | 0.4143 | 0.8438 | | 0.0708 | 119.0 | 119 | 0.4125 | 0.8438 | | 0.056 | 120.0 | 120 | 0.4109 | 0.8438 | | 0.056 | 121.0 | 121 | 0.4090 | 0.8438 | | 0.056 | 122.0 | 122 | 0.4092 | 0.8438 | | 0.056 | 123.0 | 123 | 0.4093 | 0.8438 | | 0.056 | 124.0 | 124 | 0.4094 | 0.8438 | | 0.056 | 125.0 | 125 | 0.4095 | 0.8438 | | 0.056 | 126.0 | 126 | 0.4096 | 0.8438 | | 0.056 | 127.0 | 127 | 0.4103 | 0.8438 | | 0.056 | 128.0 | 128 | 0.4109 | 0.8438 | | 0.056 | 129.0 | 129 | 0.4111 | 0.8438 | | 0.0436 | 130.0 | 130 | 0.4110 | 0.8438 | | 0.0436 | 131.0 | 131 | 0.4114 | 0.8438 | | 0.0436 | 132.0 | 132 | 0.4119 | 0.8438 | | 0.0436 | 133.0 | 133 | 0.4121 | 0.8438 | | 0.0436 | 134.0 | 134 | 0.4119 | 0.875 | | 0.0436 | 135.0 | 135 | 0.4119 | 0.875 | | 0.0436 | 136.0 | 136 | 0.4121 | 0.875 | | 0.0436 | 137.0 | 137 | 0.4131 | 0.875 | | 0.0436 | 138.0 | 138 | 0.4138 | 0.875 | | 0.0436 | 139.0 | 139 | 0.4150 | 0.875 | | 0.0326 | 140.0 | 140 | 0.4167 | 0.875 | | 0.0326 | 141.0 | 141 | 0.4181 | 0.875 | | 0.0326 | 142.0 | 142 | 0.4194 | 0.875 | | 0.0326 | 143.0 | 143 | 0.4206 | 0.875 | | 0.0326 | 144.0 | 144 | 0.4217 | 0.875 | | 0.0326 | 145.0 | 145 | 0.4228 | 0.875 | | 0.0326 | 146.0 | 146 | 0.4242 | 0.875 | | 0.0326 | 147.0 | 147 | 0.4256 | 0.875 | | 0.0326 | 148.0 | 148 | 0.4268 | 0.875 | | 0.0326 | 149.0 | 149 | 0.4280 | 0.875 | | 0.0247 | 150.0 | 150 | 0.4301 | 0.875 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3

simonycl/best_model-sst-2-16-13

simonycl

2023-07-26T00:53:47Z

108

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "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

2023-07-25T23:05:09Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-sst-2-16-13 results: [] ---  # best_model-sst-2-16-13 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: - Loss: 0.5420 - Accuracy: 0.8125 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 0.6945 | 0.5938 | | No log | 2.0 | 2 | 0.6945 | 0.5938 | | No log | 3.0 | 3 | 0.6945 | 0.5938 | | No log | 4.0 | 4 | 0.6944 | 0.5938 | | No log | 5.0 | 5 | 0.6944 | 0.5938 | | No log | 6.0 | 6 | 0.6944 | 0.5938 | | No log | 7.0 | 7 | 0.6944 | 0.5938 | | No log | 8.0 | 8 | 0.6943 | 0.5938 | | No log | 9.0 | 9 | 0.6943 | 0.5938 | | 0.7032 | 10.0 | 10 | 0.6943 | 0.5938 | | 0.7032 | 11.0 | 11 | 0.6942 | 0.5938 | | 0.7032 | 12.0 | 12 | 0.6942 | 0.5938 | | 0.7032 | 13.0 | 13 | 0.6941 | 0.5938 | | 0.7032 | 14.0 | 14 | 0.6940 | 0.5938 | | 0.7032 | 15.0 | 15 | 0.6940 | 0.5938 | | 0.7032 | 16.0 | 16 | 0.6939 | 0.5938 | | 0.7032 | 17.0 | 17 | 0.6938 | 0.5938 | | 0.7032 | 18.0 | 18 | 0.6937 | 0.5938 | | 0.7032 | 19.0 | 19 | 0.6936 | 0.5938 | | 0.709 | 20.0 | 20 | 0.6935 | 0.5938 | | 0.709 | 21.0 | 21 | 0.6934 | 0.5938 | | 0.709 | 22.0 | 22 | 0.6933 | 0.5938 | | 0.709 | 23.0 | 23 | 0.6932 | 0.5938 | | 0.709 | 24.0 | 24 | 0.6931 | 0.5938 | | 0.709 | 25.0 | 25 | 0.6930 | 0.5938 | | 0.709 | 26.0 | 26 | 0.6928 | 0.5938 | | 0.709 | 27.0 | 27 | 0.6927 | 0.5938 | | 0.709 | 28.0 | 28 | 0.6926 | 0.5938 | | 0.709 | 29.0 | 29 | 0.6924 | 0.5938 | | 0.6984 | 30.0 | 30 | 0.6923 | 0.5938 | | 0.6984 | 31.0 | 31 | 0.6921 | 0.5938 | | 0.6984 | 32.0 | 32 | 0.6920 | 0.5938 | | 0.6984 | 33.0 | 33 | 0.6918 | 0.5938 | | 0.6984 | 34.0 | 34 | 0.6916 | 0.5938 | | 0.6984 | 35.0 | 35 | 0.6915 | 0.5938 | | 0.6984 | 36.0 | 36 | 0.6913 | 0.5938 | | 0.6984 | 37.0 | 37 | 0.6911 | 0.5938 | | 0.6984 | 38.0 | 38 | 0.6909 | 0.5938 | | 0.6984 | 39.0 | 39 | 0.6907 | 0.5938 | | 0.6833 | 40.0 | 40 | 0.6905 | 0.5938 | | 0.6833 | 41.0 | 41 | 0.6903 | 0.5938 | | 0.6833 | 42.0 | 42 | 0.6901 | 0.5938 | | 0.6833 | 43.0 | 43 | 0.6899 | 0.5938 | | 0.6833 | 44.0 | 44 | 0.6897 | 0.5938 | | 0.6833 | 45.0 | 45 | 0.6895 | 0.5938 | | 0.6833 | 46.0 | 46 | 0.6893 | 0.5938 | | 0.6833 | 47.0 | 47 | 0.6890 | 0.5938 | | 0.6833 | 48.0 | 48 | 0.6888 | 0.5938 | | 0.6833 | 49.0 | 49 | 0.6885 | 0.5938 | | 0.6831 | 50.0 | 50 | 0.6882 | 0.5938 | | 0.6831 | 51.0 | 51 | 0.6879 | 0.5938 | | 0.6831 | 52.0 | 52 | 0.6876 | 0.5938 | | 0.6831 | 53.0 | 53 | 0.6873 | 0.5938 | | 0.6831 | 54.0 | 54 | 0.6870 | 0.5938 | | 0.6831 | 55.0 | 55 | 0.6867 | 0.625 | | 0.6831 | 56.0 | 56 | 0.6863 | 0.625 | | 0.6831 | 57.0 | 57 | 0.6860 | 0.625 | | 0.6831 | 58.0 | 58 | 0.6856 | 0.625 | | 0.6831 | 59.0 | 59 | 0.6852 | 0.625 | | 0.669 | 60.0 | 60 | 0.6848 | 0.625 | | 0.669 | 61.0 | 61 | 0.6844 | 0.625 | | 0.669 | 62.0 | 62 | 0.6839 | 0.625 | | 0.669 | 63.0 | 63 | 0.6835 | 0.625 | | 0.669 | 64.0 | 64 | 0.6830 | 0.625 | | 0.669 | 65.0 | 65 | 0.6824 | 0.625 | | 0.669 | 66.0 | 66 | 0.6819 | 0.625 | | 0.669 | 67.0 | 67 | 0.6814 | 0.625 | | 0.669 | 68.0 | 68 | 0.6808 | 0.625 | | 0.669 | 69.0 | 69 | 0.6802 | 0.625 | | 0.6556 | 70.0 | 70 | 0.6796 | 0.625 | | 0.6556 | 71.0 | 71 | 0.6789 | 0.625 | | 0.6556 | 72.0 | 72 | 0.6782 | 0.625 | | 0.6556 | 73.0 | 73 | 0.6774 | 0.625 | | 0.6556 | 74.0 | 74 | 0.6766 | 0.6562 | | 0.6556 | 75.0 | 75 | 0.6757 | 0.6562 | | 0.6556 | 76.0 | 76 | 0.6747 | 0.6562 | | 0.6556 | 77.0 | 77 | 0.6736 | 0.6562 | | 0.6556 | 78.0 | 78 | 0.6725 | 0.6562 | | 0.6556 | 79.0 | 79 | 0.6713 | 0.6562 | | 0.6248 | 80.0 | 80 | 0.6700 | 0.6562 | | 0.6248 | 81.0 | 81 | 0.6687 | 0.6562 | | 0.6248 | 82.0 | 82 | 0.6673 | 0.6562 | | 0.6248 | 83.0 | 83 | 0.6660 | 0.6562 | | 0.6248 | 84.0 | 84 | 0.6647 | 0.6562 | | 0.6248 | 85.0 | 85 | 0.6635 | 0.6562 | | 0.6248 | 86.0 | 86 | 0.6622 | 0.6562 | | 0.6248 | 87.0 | 87 | 0.6603 | 0.5938 | | 0.6248 | 88.0 | 88 | 0.6586 | 0.5938 | | 0.6248 | 89.0 | 89 | 0.6574 | 0.5938 | | 0.6013 | 90.0 | 90 | 0.6565 | 0.5938 | | 0.6013 | 91.0 | 91 | 0.6557 | 0.5938 | | 0.6013 | 92.0 | 92 | 0.6548 | 0.5938 | | 0.6013 | 93.0 | 93 | 0.6541 | 0.625 | | 0.6013 | 94.0 | 94 | 0.6538 | 0.625 | | 0.6013 | 95.0 | 95 | 0.6537 | 0.625 | | 0.6013 | 96.0 | 96 | 0.6531 | 0.625 | | 0.6013 | 97.0 | 97 | 0.6522 | 0.625 | | 0.6013 | 98.0 | 98 | 0.6518 | 0.625 | | 0.6013 | 99.0 | 99 | 0.6515 | 0.6562 | | 0.5622 | 100.0 | 100 | 0.6502 | 0.6562 | | 0.5622 | 101.0 | 101 | 0.6481 | 0.6562 | | 0.5622 | 102.0 | 102 | 0.6457 | 0.6562 | | 0.5622 | 103.0 | 103 | 0.6434 | 0.6562 | | 0.5622 | 104.0 | 104 | 0.6411 | 0.6562 | | 0.5622 | 105.0 | 105 | 0.6384 | 0.7188 | | 0.5622 | 106.0 | 106 | 0.6362 | 0.6875 | | 0.5622 | 107.0 | 107 | 0.6338 | 0.6875 | | 0.5622 | 108.0 | 108 | 0.6311 | 0.6875 | | 0.5622 | 109.0 | 109 | 0.6281 | 0.6562 | | 0.5022 | 110.0 | 110 | 0.6236 | 0.6875 | | 0.5022 | 111.0 | 111 | 0.6193 | 0.6875 | | 0.5022 | 112.0 | 112 | 0.6141 | 0.6562 | | 0.5022 | 113.0 | 113 | 0.6088 | 0.6875 | | 0.5022 | 114.0 | 114 | 0.6046 | 0.6875 | | 0.5022 | 115.0 | 115 | 0.6024 | 0.6875 | | 0.5022 | 116.0 | 116 | 0.6014 | 0.6875 | | 0.5022 | 117.0 | 117 | 0.6004 | 0.6875 | | 0.5022 | 118.0 | 118 | 0.5993 | 0.6875 | | 0.5022 | 119.0 | 119 | 0.5982 | 0.6875 | | 0.4576 | 120.0 | 120 | 0.5969 | 0.6875 | | 0.4576 | 121.0 | 121 | 0.5957 | 0.6875 | | 0.4576 | 122.0 | 122 | 0.5944 | 0.7188 | | 0.4576 | 123.0 | 123 | 0.5929 | 0.7188 | | 0.4576 | 124.0 | 124 | 0.5916 | 0.7188 | | 0.4576 | 125.0 | 125 | 0.5903 | 0.7188 | | 0.4576 | 126.0 | 126 | 0.5887 | 0.7188 | | 0.4576 | 127.0 | 127 | 0.5873 | 0.7188 | | 0.4576 | 128.0 | 128 | 0.5857 | 0.75 | | 0.4576 | 129.0 | 129 | 0.5837 | 0.75 | | 0.4105 | 130.0 | 130 | 0.5819 | 0.75 | | 0.4105 | 131.0 | 131 | 0.5797 | 0.75 | | 0.4105 | 132.0 | 132 | 0.5781 | 0.75 | | 0.4105 | 133.0 | 133 | 0.5770 | 0.75 | | 0.4105 | 134.0 | 134 | 0.5756 | 0.75 | | 0.4105 | 135.0 | 135 | 0.5734 | 0.75 | | 0.4105 | 136.0 | 136 | 0.5714 | 0.75 | | 0.4105 | 137.0 | 137 | 0.5694 | 0.75 | | 0.4105 | 138.0 | 138 | 0.5673 | 0.75 | | 0.4105 | 139.0 | 139 | 0.5651 | 0.75 | | 0.3744 | 140.0 | 140 | 0.5628 | 0.75 | | 0.3744 | 141.0 | 141 | 0.5605 | 0.7812 | | 0.3744 | 142.0 | 142 | 0.5581 | 0.7812 | | 0.3744 | 143.0 | 143 | 0.5555 | 0.7812 | | 0.3744 | 144.0 | 144 | 0.5532 | 0.7812 | | 0.3744 | 145.0 | 145 | 0.5510 | 0.7812 | | 0.3744 | 146.0 | 146 | 0.5489 | 0.7812 | | 0.3744 | 147.0 | 147 | 0.5470 | 0.7812 | | 0.3744 | 148.0 | 148 | 0.5453 | 0.7812 | | 0.3744 | 149.0 | 149 | 0.5435 | 0.7812 | | 0.3294 | 150.0 | 150 | 0.5420 | 0.8125 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3

Yaxin1992/llama2-13b-1800-eos

Yaxin1992

2023-07-26T00:28:16Z

0

null

[ "tensorboard", "generated_from_trainer", "base_model:meta-llama/Llama-2-13b-hf", "base_model:finetune:meta-llama/Llama-2-13b-hf", "region:us" ]

null

2023-07-25T21:29:55Z

--- base_model: meta-llama/Llama-2-13b-hf tags: - generated_from_trainer model-index: - name: llama2-13b-1800-eos results: [] ---  # llama2-13b-1800-eos This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1800 ### Training results ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

richardr1126/spider-skeleton-wizard-coder-8bit

richardr1126

2023-07-26T00:11:51Z

5

1

transformers

[ "transformers", "pytorch", "gpt_bigcode", "text-generation", "sql", "spider", "text-to-sql", "sql finetune", "8bit", "dataset:spider", "dataset:richardr1126/spider-skeleton-context-instruct", "arxiv:1809.08887", "arxiv:2305.14314", "license:bigcode-openrail-m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "region:us" ]

text-generation

2023-07-21T01:33:43Z

--- tags: - sql - spider - text-to-sql - sql finetune - 8bit datasets: - spider - richardr1126/spider-skeleton-context-instruct library_name: transformers license: bigcode-openrail-m --- ### Spider Skeleton Wizard Coder 8bit Summary - This model was created by finetuning [WizardLM/WizardCoder-15B-V1.0](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0) on an enhanced Spider context training dataset: [richardr1126/spider-skeleton-context-instruct](https://huggingface.co/datasets/richardr1126/spider-skeleton-context-instruct). - Finetuning was performed using QLoRa on 3x RTX6000 48GB. - This is the bitsandbytes 8-bit version of the model. It needs to be loaded onto a GPU for it to work. - If you want just the QLoRa/LoRA adapter: [richardr1126/spider-skeleton-wizard-coder-qlora](https://huggingface.co/richardr1126/spider-skeleton-wizard-coder-qlora) ### Running the GGML model - The best way to run this model is to use the [4-bit GGML version](https://huggingface.co/richardr1126/spider-skeleton-wizard-coder-ggml) on [koboldcpp](https://github.com/LostRuins/koboldcpp), with CuBlas support. ### Spider Dataset [Spider](https://arxiv.org/abs/1809.08887) is a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 Yale students The goal of the Spider challenge is to develop natural language interfaces to cross-domain databases. This dataset was used to finetune this model. ### Spider Skeleton WizardCoder - [test-suite-sql-eval](https://github.com/taoyds/test-suite-sql-eval) Results With temperature set to 0.0, top_p set to 0.9, and top_k set to 0, the model achieves **61% execution accuracy** on the Spider dev set. <img src="https://raw.githubusercontent.com/cuplv/text-to-sql-wizardcoder/main/eval/plots/spiderwizard-plus-chatgpt.svg" height="300"> <img src="https://raw.githubusercontent.com/cuplv/text-to-sql-wizardcoder/main/eval/plots/spiderwizard-vs-chatgpt.svg" height="300"> Note: - ChatGPT was evaluated with the default hyperparameters and with the system message `You are a sophisticated AI assistant capable of converting text into SQL queries. You can only output SQL, don't add any other text.` - Both models were evaluated with `--plug_value` in `evaluation.py` using the Spider dev set with database context. - `--plug_value`: If set, the gold value will be plugged into the predicted query. This is suitable if your model does not predict values. This is set to `False` by default. ## Citations ``` @misc{luo2023wizardcoder, title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct}, author={Ziyang Luo and Can Xu and Pu Zhao and Qingfeng Sun and Xiubo Geng and Wenxiang Hu and Chongyang Tao and Jing Ma and Qingwei Lin and Daxin Jiang}, year={2023}, } ``` ``` @article{yu2018spider, title={Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task}, author={Yu, Tao and Zhang, Rui and Yang, Kai and Yasunaga, Michihiro and Wang, Dongxu and Li, Zifan and Ma, James and Li, Irene and Yao, Qingning and Roman, Shanelle and others}, journal={arXiv preprint arXiv:1809.08887}, year={2018} } ``` ``` @article{dettmers2023qlora, title={QLoRA: Efficient Finetuning of Quantized LLMs}, author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke}, journal={arXiv preprint arXiv:2305.14314}, year={2023} } ``` ## Disclaimer The resources, including code, data, and model weights, associated with this project are restricted for academic research purposes only and cannot be used for commercial purposes. The content produced by any version of WizardCoder is influenced by uncontrollable variables such as randomness, and therefore, the accuracy of the output cannot be guaranteed by this project. This project does not accept any legal liability for the content of the model output, nor does it assume responsibility for any losses incurred due to the use of associated resources and output results.

gyuri2020/kw-classification-setfithead-head-only-model

gyuri2020

2023-07-26T00:11:14Z

3

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2023-07-26T00:08:21Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # gyuri2020/kw-classification-setfithead-head-only-model This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("gyuri2020/kw-classification-setfithead-head-only-model") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

Jungwonchang/whisper_finetune_ksponspeech_2000steps

Jungwonchang

2023-07-25T23:59:42Z

7

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "kr", "dataset:Jungwonchang/ksponspeech", "base_model:openai/whisper-large-v2", "base_model:finetune:openai/whisper-large-v2", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-07-25T18:18:25Z

--- language: - kr license: apache-2.0 base_model: openai/whisper-large-v2 tags: - generated_from_trainer datasets: - Jungwonchang/ksponspeech metrics: - wer model-index: - name: Whisper large-v2, KsponSpeech results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: KsponSpeech type: Jungwonchang/ksponspeech config: dev split: validation args: dev metrics: - name: Wer type: wer value: 42.225687000584685 ---  # Whisper large-v2, KsponSpeech This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the KsponSpeech dataset. It achieves the following results on the evaluation set: - Loss: 0.2946 - Wer: 42.2257 ## 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: linear - lr_scheduler_warmup_steps: 50 - training_steps: 2000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3315 | 0.25 | 500 | 0.3446 | 41.5319 | | 0.3204 | 0.5 | 1000 | 0.3229 | 37.7003 | | 0.2967 | 0.75 | 1500 | 0.3054 | 38.3980 | | 0.2859 | 1.0 | 2000 | 0.2946 | 42.2257 | ### Framework versions - Transformers 4.31.0 - Pytorch 1.12.1+cu116 - Datasets 2.14.0 - Tokenizers 0.12.1

LonyGohan/ReiRei_Rei_Ayanami_RVC_IA_BR

LonyGohan

2023-07-25T23:53:27Z

0

3

null

[ "music", "audio-to-audio", "pt", "license:openrail", "region:us" ]

audio-to-audio

2023-07-01T04:41:45Z

--- license: openrail language: - pt metrics: - character pipeline_tag: audio-to-audio tags: - music --- ![rei-ayanami-doing-side-eye-zgajjszogzasaz6p.gif](https://cdn-uploads.huggingface.co/production/uploads/649fad444812ea6b2d3447a0/Ml7zvJOHryeA51x1uRL_B.gif) ⠀ ⠀ ⠀ ⠀ # **ReiRei e ReiReiV2** • ReiRei é IA de voz da Rei Ayanami Brasileira feita no RVC ambas na V2, mais especificamente no EasyGui. Existem dois modelos que eu fiz, cada um apresentou uma vantagem para coisas diferentes, ReiRei é o Medelo mais velho enquanto ReiReiV2 é o modelo mais recente. • A dublagem escolhida foi a da Priscilla Concepcion feita pela netiflix em 2019 ⠀ ⠀ ⠀ ⠀ ⠀ ⠀ ![☆.jpg](https://cdn-uploads.huggingface.co/production/uploads/649fad444812ea6b2d3447a0/QgTNhZRv-eOJiXsMTm5pn.jpeg) # **ReiRei** • A primeira versão consegue cantar muito melhor do que sua sucessora, Isso acontece devido dela ter sido treinada apenas com um único áudio de Rei de duração de mais ou menos 1:28. • Sua dubladora costuma muito sussurrar a voz de Rei por conta da personalidade da personagem, e o áudio que ela foi treinada foi baseado na Voz da Rei mais solta e um timbre mais agudo e alto. • Ela foi treinada cerca de 540 vezes, um número bem alto, acho que eu não devia ter treinado ela tanto assim. # **ReiReiv2** • A segunda versão consegue ser muito mais fiel em questão de fala da Rei Ayanami, a mesma foi treinada com 3 áudios de Rei, 01:28m, 01:23m e 02:08. Porém essa versão não fica tão bem cantando que sua voz fica em um tom mais grave e baixo do sua antecessora. • Acredito que a baixa compatibilidade da versão ReiReiv2 vem dela ser baseado em muitos outros áudio onde Rei sussurra e respira muito, porém é bem mais fiel na hora de falar frases que a Rei falaria, o modelo combina bem mais se você está pensando em dublar algo usando o modelo. • Ela foi treinada bem menos que ReiRei, cerca 320x, mas ainda é um número alto, tendo em vista que não precisa de muito por ter 6 minutos de áudio da Rei falando constantemente. # **ReiRei.pht + ReiReiV2.index** • Fiz alguns teste, e impressionantemente, ReiRei.pht com o treinamento da ReiReiv2.index melhora em muito alguns aspectos, principalmente na hora de cantar que sua pronuncia melhora e alguns tons e em linguas estrangeiras, porem o inverso não funciona. ![75a.gif](https://cdn-uploads.huggingface.co/production/uploads/649fad444812ea6b2d3447a0/n3U9eJqNg8o_0HER37rSy.gif) # **Modelos pra download em Zip** • Os modelos estão prontos para serem usados no RVC do EasyGui • Os modelos também podem ser usando no RCV original, é só colocar os arquivos .pht e .index nas suas respectivas pastas que eles são detectados automaticamente ao dar **Refresh** # **Demonstração** [Rei Ayanami Cantando Ligia - anna (Lofi Remix) IA COVER](https://youtu.be/E-dxn2Mj1lQ)

Bellaaazzzzz/model_wireframe

Bellaaazzzzz

2023-07-25T23:38:46Z

1

3

diffusers

[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "controlnet", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-07-12T21:32:45Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet inference: true --- # controlnet-Bellaaazzzzz/model_wireframe These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images below. Validation result of 1 round. ![images_0_0)](./images_0_0.png) Validation result of 2 round. ![images_1_0)](./images_1_0.png) Validation result of 3 round. ![images_2_0)](./images_2_0.png)

ashercn97/awesome-prompts-merged

ashercn97

2023-07-25T23:17:53Z

10

0

transformers

[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-25T22:07:38Z

--- pipeline_tag: text2text-generation --- This is a merged version of my previous chatgpt prompt generator model, but this time it isnt in LoRA form so you can use it easier (and so i can make a space for it)

ChairWorm/ppo-Huggy

ChairWorm

2023-07-25T22:57:16Z

4

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2023-07-25T22:57:04Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** 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: ChairWorm/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

CordwainerSmith/distilhubert-finetuned-gtzan-v3

CordwainerSmith

2023-07-25T22:55:17Z

159

0

transformers

[ "transformers", "pytorch", "tensorboard", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-25T21:37:49Z

--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan-v3 results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.87 ---  # distilhubert-finetuned-gtzan-v3 This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5053 - Accuracy: 0.87 ## 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9855 | 1.0 | 113 | 1.7934 | 0.52 | | 1.3551 | 2.0 | 226 | 1.2638 | 0.68 | | 1.0094 | 3.0 | 339 | 0.9340 | 0.76 | | 0.9176 | 4.0 | 452 | 0.7845 | 0.78 | | 0.6402 | 5.0 | 565 | 0.6458 | 0.81 | | 0.3626 | 6.0 | 678 | 0.5620 | 0.85 | | 0.4944 | 7.0 | 791 | 0.5078 | 0.82 | | 0.1754 | 8.0 | 904 | 0.4793 | 0.81 | | 0.2203 | 9.0 | 1017 | 0.4875 | 0.84 | | 0.1121 | 10.0 | 1130 | 0.5053 | 0.87 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

bhenrym14/airophin-13b-pntk-16k-fp16

bhenrym14

2023-07-25T22:40:27Z

1,390

4

transformers

[ "transformers", "safetensors", "llama", "text-generation", "dataset:jondurbin/airoboros-gpt4-1.4.1", "dataset:ehartford/dolphin", "arxiv:2306.15595", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-25T22:16:03Z

--- datasets: - jondurbin/airoboros-gpt4-1.4.1 - ehartford/dolphin --- # Airophin: A NTK-by-Parts RoPE Scaled QLoRA Fine-tune of Llama-2-13b (fp16 weights)  GPTQ weights can be found here: https://huggingface.co/bhenrym14/airophin-13b-pntk-16k-GPTQ ## Overview This is a finetune of Llama-2-13b, intended to extend the useful context window to 16384 tokens. There are two training phases: 1. It is first trained on a long-context (7000-8192 tokens) subset of [dolphin](https://huggingface.co/datasets/ehartford/dolphin), an orca-like dataset (GPT4 split only). This amounts to roughly 110mm tokens. Airoboros-like training prompt was used instead of the dolphin system prompt. Training was done with partial NTK scaling applied (scale factor of 4). This took ~20 hours. 2. The model was then finetuned on [Jon Durbin's Airoboros GPT4 1.4.1](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1), with same scaling approach, for 2 epochs. This took ~15 hours. **This is a QLoRA fine-tune (rank 64)**. All training was performed with 1x RTX 6000 Ada. **For the 4096 context length model using airoboros-gpt4-1.4.1 see: [Jon Durbin's airoboros-l2-13b-gpt4-1.4.1](https://huggingface.co/jondurbin/airoboros-l2-13b-gpt4-1.4.1)** ## How to Use This model employs [Partial NTK Rope Scaling](https://github.com/jquesnelle/scaled-rope/pull/1). This methodology is not yet implemented natively in Transformers or Exllama (as of 7/21). There are three options to run this. 1. Transformers (use bnb for quantization). Use [fp16 weights](https://huggingface.co/bhenrym14/airophin-13b-pntk-16k-fp16). This will require replacing the `LlamaEmbedding` with `LlamaPartNTKScaledRotaryEmbedding`, with `max_position_embeddings=16384` and `original_max_position_embeddings=4096`. A monkeypatch can be found [here](https://github.com/bhenrym14/qlora-airoboros-longcontext/blob/main/scaledllama/llama_pntk_monkey_patch.py). 2. Autogptq/GPTQ-for-Llama. See the [GPTQ weights](https://huggingface.co/bhenrym14/airophin-13b-pntk-16k-GPTQ) 3. Use ExLLama, see the [GPTQ weights](https://huggingface.co/bhenrym14/airophin-13b-pntk-16k-GPTQ) Please comment with any questions. This hasn't been extensively tested. ## Motivation Methods of extending the useful context window of LLM's have gained significant traction. Several methods requiring little to no finetuning/retraining have emerged. Among these is linear position interpolation [kaiokendev](https://kaiokendev.github.io/til#extending-context-to-8k) and [meta AI)](https://arxiv.org/abs/2306.15595)) and [NTK aware scaling](https://github.com/jquesnelle/scaled-rope). My prior experiments demonstrate significant performance improvements both from finetuning with these scaling adjustments implemented **and** with longer sequences. Unfortunately it has also been shown that LLM's frequently struggle to attend to salient information in the middle of the context window. Attending to nearby tokens is essential to producing syntactically correct and semantically coherent sentences. Essential context is also most commonly found at the beginning of a context window. With this in mind, it is unsurprising LLMs often attend more strongly to these areas. Does this learned model behavior result in an "extrapolated deemphasis" when such embeddings are scaled? This hypothesis may be supported by the material improvements in perplexity achieved by training on long sequences (not just including the RoPE scaling during the fine-tune). Here I explore whether training on long sequences that have clear conceptual dependencies residing in the middle of the context helps attenuate the difficulties in attending to middle-context tokens. When/if I have time, I hope to perform a more rigorous assessment of the peformance with respect to this specific issue. ## Relative Performance (perplexity) | Context (tokens) | bhenrym14/airophin-13b-pntk-16k-fp16| bhenrym14/airoboros-13b-gpt4-1.4.1-PI-8192-fp16 |bhenrym14/airoboros-33b-gpt4-1.4.1-lxctx-PI-16384-fp16 | jondurbin/airoboros-l2-13b-gpt4-1.4.1 | | ---| ----- | -----| ------| --- | | 512 | 7.62 | 8.24 | 7.90 | **7.23** | | 1024 | 6.20 | 6.71 | 6.17 | **5.85** | | 2048 | 5.38 | 5.87 | 5.23 | **5.07** | | 4096 | 5.08 | 5.50 | 4.91 | **4.77** | | 8192 | **4.90** | 5.32 | Not Tested | 57.1 | | 12000 | **4.82** | 56.1 | Not Tested | Not Tested | - This model is very competitive with the Llama-1 33b extended context variants. In particular, at 512 tokens it has lower perplexity. This is probably an improvement imparted (in part) by the NTK by parts scaling method. - Not presented here, but this model outperforms the base llama-2-13b on MMLU-fs with a score of 54.9. While perhaps an insignificant difference, the fact there isn't a clear performance regression despite the context extension is notable. - Perplexity continues to decline to 12000 tokens, the longest context length I tested due to VRAM constraints. - Feedback regarding real-world performance is appreciated. I don't know if the first dolphin training phase really contributed much beyond what pile did for the 33b-lxctx model; many relevant modeling components changed here, so it's difficult to make any specific attributions. The base model improvement may very well be the most dominant change. ## Prompting: airoboros-like prompting remains. See the following from one of Jon Durbin's airoboros model cards: ### Context obedient question answering By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations. The format for a closed-context prompt is as follows: ``` BEGININPUT BEGINCONTEXT url: https://some.web.site/123 date: 2023-06-01 ... other metdata ... ENDCONTEXT [insert your text blocks here] ENDINPUT [add as many other blocks, in the exact same format] BEGININSTRUCTION [insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.] ENDINSTRUCTION ``` It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up. *The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!* I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it. - `BEGININPUT` - denotes a new input block - `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block - `ENDCONTEXT` - denotes the end of the metadata block for the current input - [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context. - `ENDINPUT` - denotes the end of the current input block - [repeat as many input blocks in this format as you want] - `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above. - [instruction(s)] - `ENDINSTRUCTION` - denotes the end of instruction set It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to. Here's a trivial, but important example to prove the point: ``` BEGININPUT BEGINCONTEXT date: 2021-01-01 url: https://web.site/123 ENDCONTEXT In a shocking turn of events, blueberries are now green, but will be sticking with the same name. ENDINPUT BEGININSTRUCTION What color are bluberries? Source? ENDINSTRUCTION ``` And the response: ``` Blueberries are now green. Source: date: 2021-01-01 url: https://web.site/123 ``` The prompt itself should be wrapped in the vicuna1.1 template if you aren't using fastchat with the conv-template vicuna_v1.1 as described: ``` USER: BEGININPUT BEGINCONTEXT date: 2021-01-01 url: https://web.site/123 ENDCONTEXT In a shocking turn of events, blueberries are now green, but will be sticking with the same name. ENDINPUT BEGININSTRUCTION What color are bluberries? Source? ENDINSTRUCTION ASSISTANT: ``` ### Coding You can ask for fairly complex coding instructions, e.g. (be sure to wrap with vicuna style prompt): ``` Create a python application with the following requirements: - Asyncio FastAPI webserver - ping endpoint that returns the current date in JSON format - file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate ``` Or: ``` Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values. ``` You can optionally add a single space and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.: ``` Write a websocket application in node.js. PLAINFORMAT ``` ### Word games / trivia ``` USER: This anagram of "silent" is what you do when paying attention and hearing someone. ASSISTANT: listen ``` ``` USER: Give me a list of 7 words that start with EN ASSISTANT: 1. Endlessly 2. Enthusiastically 3. Enormously 4. Enveloping 5. Enduring 6. Enlightening 7. Encouraging ``` ### Multiple choice ``` USER: Which of the following astronomical objects is composed primarily of ice and rock, and is located in the Kuiper Belt? A) Mercury B) Venus C) Pluto D) Jupiter ASSISTANT: C, Pluto ``` ### Writing <details> <summary>Write a resignation letter in the style of a pirate captain, in which you resign from your position as janitor.</summary> Response: ``` ASSISTANT: Captain Jack Sparrow here, as ye be knowin', has been forced to abandon me post as Janitor o' th' Festering Cesspool, which be a filthy hole o' water that we be callin' home. Ye may be thinkin' what could ha' driven me to leave me duties an' take me leave o' this sorry establishment? Well, ye'd be right in thinkin' so, for it be th' greed o' them what runs th' place that be drivin' me away. They've been fattenin' their pockets on me back while I've been slavin' me guts out cleanin' up their messes. No more, me hearties! Me time o' servin' th' rich an' powerful be done, an' I'm takin' me leave o' this stinkin' hole forever! Me destiny lies elsewhere, an' it be time I pursued it. Ye may think ye've seen th' last o' me, but ye'd be wrong, for th' winds o' change be blowin', an' me ship be ready to set sail on th' high seas o' justice. So, farewell ye scurvy dogs, ye who've made me life a living hell. I leave ye with me favorite saying: "ARRRGGGHHH, ye scurvy dogs!" An' remember, ye ain't never gonna see me comin', for me shadow be castin' long an' dark on th' horizon</summary> ``` </details> ### Jokes ``` USER: Tell me a joke about bananas. ASSISTANT: What did the banana say when it got hit by a bus? R.I.P.E. ``` ### Riddles (not great, but slightly better) ``` USER: What has a neck but no head? ASSISTANT: A bottle. ``` ### Multi-character conversations ``` USER: Characters: - Yoda, the ever clever jedi master. - Homer Simpson, you know, the goofy yellow guy. - USER/Jon - don't generate any speech or actions on behalf of Jon Rules: - be sure to use the manerisms and speaking style of the characters correctly - surround character actions with asterisks - always put quotes around words spoken by the characters - use a new line for each character's actions or speech - always prefix actions/speech with the character's name and colon - only generate text and actions on behalf of Yoda or Homer, and never anyone else Conversation will revolve around the grapes, in a local cafe with delicious coffee. Generate a single interaction between Yoda and Homer to start, with one sentence each. ASSISTANT: Yoda: "Mmm, these grapes are quite delicious, Master Homer." Homer: "Oh yeah, Yoda! They're the best grapes I've ever had!" USER: *enters the cafe* Sorry I'm late guys! ASSISTANT: Yoda: "No need to apologize, Jon. We were just enjoying these delightful grapes." Homer: "Yeah, man! It's not every day you get to eat grapes with a real-life Jedi Master!" *Yoda raises an eyebrow* ```

Adel-Elwan/msmarco-bert-base-dot-v5-fine-tuned-AI

Adel-Elwan

2023-07-25T22:16:26Z

69

0

sentence-transformers

[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "semantic-search", "sentence-similarity", "transformers", "artificial-intelligence", "computer-science", "question-answering", "en", "dataset:Adel-Elwan/Artificial-intelligence-dataset-for-IR-systems", "model-index", "region:us" ]

question-answering

2023-07-24T16:29:06Z

--- pipeline_tag: question-answering tags: - semantic-search - sentence-similarity - sentence-transformers - transformers - artificial-intelligence - computer-science language: - en metrics: - accuracy datasets: - Adel-Elwan/Artificial-intelligence-dataset-for-IR-systems model-index: - name: Adel-Elwan/msmarco-bert-base-dot-v5-fine-tuned-AI results: - task: type: semantic-search # Required. Example: automatic-speech-recognition name: Semantic Search # Optional. Example: Speech Recognition dataset: type: Adel-Elwan/Artificial-intelligence-dataset-for-IR-systems name: Artificial intelligence dataset for IR systems split: test # Optional. Example: test metrics: - type: accuracy value: 83.45% name: Accuracy@5 - type: accuracy value: 87.78% name: Accuracy@10 - type: precision value: 16.69% name: Precision@5 - type: recall value: 83.45% name: Recall@5 - type: recall value: 87.78% name: Recall@10 - type: mean_reciprocal_rank value: 0.7327 name: MRR@10 verified: true --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 6563 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'dot_score'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 5000, "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 656, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

WhoTookMyAmogusNickname/WizardLM-13B-V1.2-GGML

WhoTookMyAmogusNickname

2023-07-25T22:04:16Z

0

1

null

[ "region:us" ]

null

2023-07-25T16:51:07Z

GGML quantizations of [WizardLM-13B-V1.2](https://huggingface.co/WizardLM/WizardLM-13B-V1.2) Prompt template: ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT: ```

FashionAI4Wholesale/segformer-b2-finetuned-segments-dresses-071123

FashionAI4Wholesale

2023-07-25T21:58:17Z

197

0

transformers

[ "transformers", "pytorch", "tensorboard", "segformer", "image-segmentation", "en", "dataset:Latiste", "arxiv:1910.09700", "license:openrail", "endpoints_compatible", "region:us" ]

image-segmentation

2023-07-11T17:24:31Z

--- language: - en tags: - image-segmentation license: "openrail" datasets: - Latiste metrics: - iou --- # Model Card for Model ID  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 A SegFormer-b2 model fine tuned with private dress on mannequin datasets  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Habana/swin

Habana

2023-07-25T21:36:24Z

2,088

0

null

[ "optimum_habana", "license:apache-2.0", "region:us" ]

null

2022-08-23T08:10:57Z

--- license: apache-2.0 --- [Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU). It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks. Learn more about how to take advantage of the power of Habana HPUs to train and deploy Transformers and Diffusers models at [hf.co/hardware/habana](https://huggingface.co/hardware/habana). ## Swin Transformer model HPU configuration This model only contains the `GaudiConfig` file for running the [Swin Transformer](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) model on Habana's Gaudi processors (HPU). **This model contains no model weights, only a GaudiConfig.** This enables to specify: - `use_fused_adam`: whether to use Habana's custom AdamW implementation - `use_fused_clip_norm`: whether to use Habana's fused gradient norm clipping operator - `use_torch_autocast`: whether to use Torch Autocast for managing mixed precision ## Usage The model is instantiated the same way as in the Transformers library. The only difference is that there are a few new training arguments specific to HPUs.\ It is strongly recommended to train this model doing bf16 mixed-precision training for optimal performance and accuracy. [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/image-classification/run_image_classification.py) is an image classification example script to fine-tune a model. You can run it with Swin with the following command: ```bash python run_image_classification.py \ --model_name_or_path microsoft/swin-base-patch4-window7-224-in22k \ --dataset_name cifar10 \ --output_dir /tmp/outputs/ \ --remove_unused_columns False \ --do_train \ --do_eval \ --learning_rate 3e-5 \ --num_train_epochs 5 \ --per_device_train_batch_size 64 \ --per_device_eval_batch_size 64 \ --evaluation_strategy epoch \ --save_strategy epoch \ --load_best_model_at_end True \ --save_total_limit 3 \ --seed 1337 \ --use_habana \ --use_lazy_mode \ --gaudi_config_name Habana/swin \ --throughput_warmup_steps 3 \ --ignore_mismatched_sizes \ --bf16 ``` Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.

zpattdev/Reinforce-cartpoleV1

zpattdev

2023-07-25T21:31:30Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-07-25T21:31:20Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpoleV1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

grace-pro/no_repeats

grace-pro

2023-07-25T20:55:53Z

107

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:Davlan/afro-xlmr-base", "base_model:finetune:Davlan/afro-xlmr-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-25T19:38:27Z

--- license: mit base_model: Davlan/afro-xlmr-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: no_repeats results: [] ---  # no_repeats This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1658 - Precision: 0.7350 - Recall: 0.5701 - F1: 0.6421 - Accuracy: 0.9596 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1578 | 1.0 | 1283 | 0.1410 | 0.7141 | 0.4748 | 0.5704 | 0.9540 | | 0.1189 | 2.0 | 2566 | 0.1336 | 0.7023 | 0.5501 | 0.6170 | 0.9568 | | 0.0929 | 3.0 | 3849 | 0.1406 | 0.7380 | 0.5433 | 0.6259 | 0.9584 | | 0.0725 | 4.0 | 5132 | 0.1512 | 0.7283 | 0.5751 | 0.6427 | 0.9591 | | 0.057 | 5.0 | 6415 | 0.1658 | 0.7350 | 0.5701 | 0.6421 | 0.9596 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

hcrystalwang/bloom-3b-lora-Step10

hcrystalwang

2023-07-25T20:38:41Z

0

peft

[ "peft", "region:us" ]

null

2023-07-24T19:36:08Z

--- 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.dev0

wyx-ucl/bart-EDGAR-CORPUS

wyx-ucl

2023-07-25T20:28:25Z

104

1

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large-cnn", "base_model:finetune:facebook/bart-large-cnn", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-25T19:29:49Z

--- license: mit base_model: facebook/bart-large-cnn tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-EDGAR-CORPUS results: [] ---  # bart-EDGAR-CORPUS This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4876 - Rouge1: 0.5298 - Rouge2: 0.3439 - Rougel: 0.4251 - Rougelsum: 0.5210 ## 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: 3 - eval_batch_size: 3 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 0.9531 | 1.0 | 6 | 0.4876 | 0.5298 | 0.3439 | 0.4251 | 0.5210 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

ATrapenard/Discord-Impersonation-Bot

ATrapenard

2023-07-25T20:09:51Z

157

1

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "quite rude on purpose", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-25T19:13:46Z

--- tags: - conversational - quite rude on purpose --- #Specifically created for use in attempting to clone the speech consistencies of a particular person

cwiz/llama-saiga-7b-gofman

cwiz

2023-07-25T20:09:21Z

0

peft

[ "peft", "region:us" ]

null

2023-07-10T04:27:35Z

--- library_name: peft --- # LLaMa-Saiga-7b-Gofman [llama-7b-saiga-merged](https://huggingface.co/cwiz/llama-7b-saiga-merged) trained on [Igor Gofman](https://github.com/Shoe-Eye/gofman-digital-oracle) dataset.

dariowsz/dqn-SpaceInvadersNoFrameskip-v4

dariowsz

2023-07-25T20:03:49Z

11

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-25T20:03:13Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 702.00 +/- 172.35 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dariowsz -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dariowsz -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga dariowsz ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

jordyvl/cdip-tiny_rvl_cdip-NK1000_kd_test

jordyvl

2023-07-25T20:00:20Z

166

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-25T13:58:28Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: cdip-tiny_rvl_cdip-NK1000_kd_test results: [] ---  # cdip-tiny_rvl_cdip-NK1000_kd_test This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4588 - Accuracy: 0.8227 - Brier Loss: 0.2552 - Nll: 1.9117 - F1 Micro: 0.8227 - F1 Macro: 0.8239 - Ece: 0.0507 - Aurc: 0.0420 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 125 | 1.3371 | 0.5423 | 0.5838 | 2.6217 | 0.5423 | 0.5341 | 0.0583 | 0.2219 | | No log | 2.0 | 250 | 0.9983 | 0.646 | 0.4725 | 2.3143 | 0.646 | 0.6407 | 0.0490 | 0.1432 | | No log | 3.0 | 375 | 0.8094 | 0.7085 | 0.3977 | 2.2571 | 0.7085 | 0.7034 | 0.0500 | 0.1017 | | 1.2477 | 4.0 | 500 | 0.7633 | 0.7215 | 0.3806 | 2.2013 | 0.7215 | 0.7275 | 0.0447 | 0.0926 | | 1.2477 | 5.0 | 625 | 0.7295 | 0.7505 | 0.3565 | 2.1741 | 0.7505 | 0.7417 | 0.0631 | 0.0775 | | 1.2477 | 6.0 | 750 | 0.6706 | 0.7692 | 0.3321 | 2.1869 | 0.7692 | 0.7669 | 0.0614 | 0.0690 | | 1.2477 | 7.0 | 875 | 0.6933 | 0.767 | 0.3344 | 2.1685 | 0.767 | 0.7650 | 0.0811 | 0.0676 | | 0.3434 | 8.0 | 1000 | 0.6640 | 0.7778 | 0.3251 | 2.1666 | 0.7778 | 0.7795 | 0.0681 | 0.0650 | | 0.3434 | 9.0 | 1125 | 0.6874 | 0.774 | 0.3328 | 2.1401 | 0.774 | 0.7739 | 0.0863 | 0.0660 | | 0.3434 | 10.0 | 1250 | 0.6639 | 0.7795 | 0.3194 | 2.1335 | 0.7795 | 0.7800 | 0.0800 | 0.0618 | | 0.3434 | 11.0 | 1375 | 0.6827 | 0.7728 | 0.3332 | 2.1140 | 0.7728 | 0.7771 | 0.0891 | 0.0650 | | 0.1507 | 12.0 | 1500 | 0.6197 | 0.786 | 0.3106 | 2.1052 | 0.786 | 0.7873 | 0.0716 | 0.0586 | | 0.1507 | 13.0 | 1625 | 0.6264 | 0.7823 | 0.3133 | 2.1077 | 0.7823 | 0.7834 | 0.0784 | 0.0595 | | 0.1507 | 14.0 | 1750 | 0.5822 | 0.796 | 0.2964 | 2.0567 | 0.796 | 0.7983 | 0.0676 | 0.0549 | | 0.1507 | 15.0 | 1875 | 0.5900 | 0.7923 | 0.3016 | 2.0704 | 0.7923 | 0.7936 | 0.0724 | 0.0541 | | 0.107 | 16.0 | 2000 | 0.6044 | 0.7855 | 0.3099 | 2.0625 | 0.7855 | 0.7901 | 0.0730 | 0.0617 | | 0.107 | 17.0 | 2125 | 0.5692 | 0.7973 | 0.2930 | 2.0627 | 0.7973 | 0.7990 | 0.0676 | 0.0528 | | 0.107 | 18.0 | 2250 | 0.5836 | 0.7907 | 0.2984 | 2.0575 | 0.7907 | 0.7922 | 0.0749 | 0.0554 | | 0.107 | 19.0 | 2375 | 0.5469 | 0.806 | 0.2835 | 2.0754 | 0.806 | 0.8060 | 0.0576 | 0.0498 | | 0.0879 | 20.0 | 2500 | 0.5427 | 0.804 | 0.2892 | 2.0655 | 0.804 | 0.8089 | 0.0593 | 0.0528 | | 0.0879 | 21.0 | 2625 | 0.5305 | 0.806 | 0.2777 | 2.0213 | 0.806 | 0.8070 | 0.0604 | 0.0495 | | 0.0879 | 22.0 | 2750 | 0.5146 | 0.8113 | 0.2741 | 2.0127 | 0.8113 | 0.8121 | 0.0534 | 0.0480 | | 0.0879 | 23.0 | 2875 | 0.5196 | 0.8107 | 0.2750 | 2.0261 | 0.8108 | 0.8117 | 0.0541 | 0.0489 | | 0.0755 | 24.0 | 3000 | 0.5169 | 0.8123 | 0.2743 | 1.9561 | 0.8123 | 0.8127 | 0.0595 | 0.0478 | | 0.0755 | 25.0 | 3125 | 0.5129 | 0.8073 | 0.2777 | 2.0020 | 0.8073 | 0.8089 | 0.0552 | 0.0491 | | 0.0755 | 26.0 | 3250 | 0.4898 | 0.8177 | 0.2649 | 1.9710 | 0.8178 | 0.8177 | 0.0474 | 0.0451 | | 0.0755 | 27.0 | 3375 | 0.4966 | 0.8155 | 0.2682 | 2.0075 | 0.8155 | 0.8163 | 0.0514 | 0.0458 | | 0.0652 | 28.0 | 3500 | 0.4883 | 0.813 | 0.2690 | 1.9655 | 0.813 | 0.8141 | 0.0557 | 0.0465 | | 0.0652 | 29.0 | 3625 | 0.4860 | 0.8185 | 0.2659 | 1.9593 | 0.8185 | 0.8194 | 0.0481 | 0.0456 | | 0.0652 | 30.0 | 3750 | 0.4760 | 0.818 | 0.2600 | 1.9517 | 0.818 | 0.8194 | 0.0505 | 0.0441 | | 0.0652 | 31.0 | 3875 | 0.4755 | 0.8195 | 0.2611 | 1.9593 | 0.8195 | 0.8196 | 0.0507 | 0.0440 | | 0.0568 | 32.0 | 4000 | 0.4763 | 0.8155 | 0.2628 | 1.9508 | 0.8155 | 0.8161 | 0.0484 | 0.0451 | | 0.0568 | 33.0 | 4125 | 0.4675 | 0.8225 | 0.2574 | 1.9474 | 0.8225 | 0.8238 | 0.0477 | 0.0433 | | 0.0568 | 34.0 | 4250 | 0.4664 | 0.8207 | 0.2579 | 1.9478 | 0.8207 | 0.8220 | 0.0498 | 0.0431 | | 0.0568 | 35.0 | 4375 | 0.4635 | 0.8213 | 0.2567 | 1.9233 | 0.8213 | 0.8219 | 0.0481 | 0.0427 | | 0.0514 | 36.0 | 4500 | 0.4584 | 0.8245 | 0.2551 | 1.9196 | 0.8245 | 0.8260 | 0.0461 | 0.0424 | | 0.0514 | 37.0 | 4625 | 0.4627 | 0.825 | 0.2557 | 1.9274 | 0.825 | 0.8256 | 0.0454 | 0.0424 | | 0.0514 | 38.0 | 4750 | 0.4603 | 0.8213 | 0.2552 | 1.9319 | 0.8213 | 0.8221 | 0.0478 | 0.0425 | | 0.0514 | 39.0 | 4875 | 0.4610 | 0.8245 | 0.2560 | 1.9337 | 0.8245 | 0.8252 | 0.0476 | 0.0424 | | 0.0483 | 40.0 | 5000 | 0.4603 | 0.825 | 0.2559 | 1.9319 | 0.825 | 0.8262 | 0.0460 | 0.0421 | | 0.0483 | 41.0 | 5125 | 0.4589 | 0.8253 | 0.2545 | 1.9317 | 0.8253 | 0.8260 | 0.0459 | 0.0421 | | 0.0483 | 42.0 | 5250 | 0.4586 | 0.8245 | 0.2552 | 1.9192 | 0.8245 | 0.8260 | 0.0524 | 0.0420 | | 0.0483 | 43.0 | 5375 | 0.4581 | 0.825 | 0.2552 | 1.9179 | 0.825 | 0.8263 | 0.0477 | 0.0421 | | 0.0465 | 44.0 | 5500 | 0.4573 | 0.8245 | 0.2543 | 1.9187 | 0.8245 | 0.8257 | 0.0457 | 0.0417 | | 0.0465 | 45.0 | 5625 | 0.4589 | 0.8225 | 0.2554 | 1.9184 | 0.8225 | 0.8235 | 0.0549 | 0.0421 | | 0.0465 | 46.0 | 5750 | 0.4582 | 0.823 | 0.2547 | 1.9128 | 0.823 | 0.8242 | 0.0512 | 0.0420 | | 0.0465 | 47.0 | 5875 | 0.4587 | 0.823 | 0.2551 | 1.9135 | 0.823 | 0.8241 | 0.0484 | 0.0420 | | 0.0458 | 48.0 | 6000 | 0.4585 | 0.8235 | 0.2550 | 1.9127 | 0.8235 | 0.8246 | 0.0479 | 0.0420 | | 0.0458 | 49.0 | 6125 | 0.4589 | 0.8227 | 0.2553 | 1.9117 | 0.8227 | 0.8238 | 0.0490 | 0.0421 | | 0.0458 | 50.0 | 6250 | 0.4588 | 0.8227 | 0.2552 | 1.9117 | 0.8227 | 0.8239 | 0.0507 | 0.0420 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

reginaboateng/pfeiffer_Bioasq_adapter

reginaboateng

2023-07-25T19:58:04Z

1

0

adapter-transformers

[ "adapter-transformers", "adapterhub:biaoasq", "bert", "dataset:bioasq7b", "region:us" ]

null

2023-07-25T19:58:01Z

--- tags: - adapter-transformers - adapterhub:biaoasq - bert datasets: - bioasq7b --- # Adapter `reginaboateng/pfeiffer_Bioasq_adapter` for allenai/scibert_scivocab_uncased An [adapter](https://adapterhub.ml) for the `allenai/scibert_scivocab_uncased` model that was trained on the [biaoasq](https://adapterhub.ml/explore/biaoasq/) dataset. This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. ## Usage First, install `adapter-transformers`: ``` pip install -U adapter-transformers ``` _Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_ Now, the adapter can be loaded and activated like this: ```python from transformers import AutoAdapterModel model = AutoAdapterModel.from_pretrained("allenai/scibert_scivocab_uncased") adapter_name = model.load_adapter("reginaboateng/pfeiffer_Bioasq_adapter", source="hf", set_active=True) ``` ## Architecture & Training  ## Evaluation results  ## Citation

CordwainerSmith/distilhubert-finetuned-gtzan-v2

CordwainerSmith

2023-07-25T19:57:06Z

162

0

transformers

[ "transformers", "pytorch", "tensorboard", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-25T18:16:05Z

--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.83 ---  # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5786 - Accuracy: 0.83 ## 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9933 | 1.0 | 113 | 1.8547 | 0.55 | | 1.2871 | 2.0 | 226 | 1.2257 | 0.65 | | 0.9868 | 3.0 | 339 | 0.9143 | 0.73 | | 0.9468 | 4.0 | 452 | 0.7964 | 0.76 | | 0.6634 | 5.0 | 565 | 0.6592 | 0.82 | | 0.3877 | 6.0 | 678 | 0.6870 | 0.77 | | 0.426 | 7.0 | 791 | 0.5259 | 0.85 | | 0.1165 | 8.0 | 904 | 0.5274 | 0.86 | | 0.2397 | 9.0 | 1017 | 0.5487 | 0.84 | | 0.1039 | 10.0 | 1130 | 0.5786 | 0.83 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

BrianS15/prot_bert-finetuned-tchard

BrianS15

2023-07-25T19:52:26Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "base_model:Rostlab/prot_bert", "base_model:finetune:Rostlab/prot_bert", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-07-25T15:35:43Z

--- base_model: Rostlab/prot_bert tags: - generated_from_trainer model-index: - name: prot_bert-finetuned-tchard results: [] ---  # prot_bert-finetuned-tchard This model is a fine-tuned version of [Rostlab/prot_bert](https://huggingface.co/Rostlab/prot_bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0390 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 800 | 0.0454 | | 0.0667 | 2.0 | 1600 | 0.0376 | | 0.0394 | 3.0 | 2400 | 0.0390 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

annazhong/vit-base-patch16-224-finetuned-feature-map-v2

annazhong

2023-07-25T19:52:21Z

166

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-25T08:03:24Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-patch16-224-finetuned-feature-map-v2 results: [] ---  # vit-base-patch16-224-finetuned-feature-map-v2 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9026 - Accuracy: 0.22 ## 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: 150 - eval_batch_size: 150 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 600 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 2.1272 | 0.21 | | No log | 2.0 | 3 | 1.9026 | 0.22 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

Nerozud/ppo-LunarLander-v2

Nerozud

2023-07-25T19:45:00Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-25T19:44:40Z

--- 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: 270.79 +/- 18.07 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 ... ```

Za88yes/Icis

Za88yes

2023-07-25T19:44:52Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-25T19:42:24Z

--- license: creativeml-openrail-m ---

alvarote26/llama2-qlora-finetunined-french

alvarote26

2023-07-25T19:37:10Z

0

peft

[ "peft", "region:us" ]

null

2023-07-25T19:37:06Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

ShekDass/donut-base-cord-smart-43

ShekDass

2023-07-25T19:37:07Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base-finetuned-cord-v2", "base_model:finetune:naver-clova-ix/donut-base-finetuned-cord-v2", "license:mit", "endpoints_compatible", "region:us" ]

image-text-to-text

2023-07-25T18:30:50Z

--- license: mit base_model: naver-clova-ix/donut-base-finetuned-cord-v2 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-base-cord-smart-43 results: [] ---  # donut-base-cord-smart-43 This model is a fine-tuned version of [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2) on the imagefolder 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: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

NasimB/aochildes-rarity

NasimB

2023-07-25T19:24:03Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-25T15:06:23Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: aochildes-rarity results: [] ---  # aochildes-rarity This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.0570 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.3684 | 0.29 | 500 | 5.3161 | | 5.0527 | 0.58 | 1000 | 4.8912 | | 4.716 | 0.87 | 1500 | 4.6588 | | 4.4472 | 1.16 | 2000 | 4.5116 | | 4.3056 | 1.46 | 2500 | 4.3936 | | 4.2065 | 1.75 | 3000 | 4.3008 | | 4.0872 | 2.04 | 3500 | 4.2252 | | 3.8993 | 2.33 | 4000 | 4.1825 | | 3.8715 | 2.62 | 4500 | 4.1240 | | 3.8388 | 2.91 | 5000 | 4.0762 | | 3.6552 | 3.2 | 5500 | 4.0680 | | 3.592 | 3.49 | 6000 | 4.0385 | | 3.5701 | 3.79 | 6500 | 4.0111 | | 3.4953 | 4.08 | 7000 | 4.0047 | | 3.3186 | 4.37 | 7500 | 4.0017 | | 3.3174 | 4.66 | 8000 | 3.9888 | | 3.3059 | 4.95 | 8500 | 3.9759 | | 3.1594 | 5.24 | 9000 | 3.9886 | | 3.1356 | 5.53 | 9500 | 3.9881 | | 3.1353 | 5.82 | 10000 | 3.9870 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

zacdennis/dqn-SpaceInvadersNoFrameskip-v4

zacdennis

2023-07-25T19:09:29Z

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-25T19:08:57Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 379.00 +/- 138.00 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga zacdennis -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga zacdennis -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga zacdennis ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 10000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 10000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 100), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

NasimB/aochildes-log-rarity

NasimB

2023-07-25T19:07:06Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-25T14:48:27Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: aochildes-log-rarity results: [] ---  # aochildes-log-rarity This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.0784 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.3735 | 0.29 | 500 | 5.3132 | | 5.0467 | 0.58 | 1000 | 4.8995 | | 4.7113 | 0.87 | 1500 | 4.6669 | | 4.4637 | 1.16 | 2000 | 4.5216 | | 4.3059 | 1.46 | 2500 | 4.4059 | | 4.2053 | 1.75 | 3000 | 4.3042 | | 4.092 | 2.04 | 3500 | 4.2347 | | 3.9015 | 2.33 | 4000 | 4.1896 | | 3.8711 | 2.62 | 4500 | 4.1381 | | 3.8373 | 2.91 | 5000 | 4.0894 | | 3.6562 | 3.2 | 5500 | 4.0843 | | 3.5884 | 3.49 | 6000 | 4.0512 | | 3.5745 | 3.79 | 6500 | 4.0281 | | 3.494 | 4.08 | 7000 | 4.0312 | | 3.3149 | 4.37 | 7500 | 4.0223 | | 3.3155 | 4.66 | 8000 | 4.0143 | | 3.3071 | 4.95 | 8500 | 3.9997 | | 3.1576 | 5.24 | 9000 | 4.0174 | | 3.138 | 5.53 | 9500 | 4.0160 | | 3.1384 | 5.82 | 10000 | 4.0158 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

Envoid/MindFlay-22B

Envoid

2023-07-25T18:37:50Z

15

2

transformers

[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-25T16:15:19Z

This is the original FP16 result of the model created using chargoddard's frankenllama script so that others interested in further experimentation with the results may do so. # WARNING: this model is very unpredictable. This model is an experiment using the frankenstein script from https://huggingface.co/chargoddard/llama2-22b Except I decided to use it with two models that have already been extensively finetuned. With https://huggingface.co/TheBloke/Llama-2-13B-Chat-fp16 as the base model and https://huggingface.co/Aeala/Enterredaas-33b as the donor model. The resulting model is surprisingly coherent and still responds well to the llama2chat prompt format ```[INST]<<SYS>><</SYS>>[/INST]``` and still has most of llama2chat's bubbly/giddy personality but more gritty and visceral. It makes occasional "typos" along with some other quirks so it was not completely unscathed by the frankensteining process. I plan to massage it over with a LoRA in the near future to bring it into more harmony but in the meantime it is available now for your enjoyment. Use cases: Chat/RP not much else.

rossellison/kpop-face-generator

rossellison

2023-07-25T18:35:54Z

0

2

null

[ "kpop", "face-generation", "gan", "stylegan3", "en", "license:apache-2.0", "region:us" ]

null

2023-07-20T23:41:47Z

--- language: - en thumbnail: "https://huggingface.co/rossellison/kpop-face-generator/raw/main/finalthumb2-01.jpg" tags: - kpop - face-generation - gan - stylegan3 license: "apache-2.0" datasets: - https://www.kaggle.com/datasets/rossellison/kpop-idol-faces --- # Kpop Face Generator Welcome to the Kpop Face Generator! Generate new, unique faces. The model `kpopGG.pkl` file is trained using Stylegan. ## Dataset Available on Kaggle: [Kpop Idol Faces Dataset](https://www.kaggle.com/datasets/rossellison/kpop-idol-faces) ## Code Code to generate the data and notebooks: [Face Data Creation Repository](https://github.com/rossellison/face-data-creation) ## Try it Out! Hugging Face Space: [Kpop Face Generator](https://huggingface.co/spaces/rossellison/kpop-face-generator)

Adi0010/ppo-sb-LunarLander-v2

Adi0010

2023-07-25T18:12:30Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-25T18:12:09Z

--- 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: 243.64 +/- 20.51 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 ... ```

GeneralShan/fwap_models_20230725

GeneralShan

2023-07-25T18:06:58Z

0

null

[ "onnx", "region:us" ]

null

2023-07-25T17:59:17Z

Model for FWAP project, - includes INSIGHTFACE model - CODEFORMER conversion TO ONNX - also uses OPENNSFW to prevent abuse

naveenkarakavalasa/t5-small-finetunesmallT5

naveenkarakavalasa

2023-07-25T18:06:09Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-25T17:50:43Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetunesmallT5 results: [] ---  # t5-small-finetunesmallT5 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 22.8504 - Rouge1: 80.2116 - Rouge2: 70.3704 - Rougel: 80.2116 - Rougelsum: 80.2116 - Gen Len: 4.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 4 | 27.3213 | 80.2116 | 70.3704 | 80.2116 | 80.2116 | 4.0 | | No log | 2.0 | 8 | 25.8240 | 80.2116 | 70.3704 | 80.2116 | 80.2116 | 4.0 | | No log | 3.0 | 12 | 24.2754 | 80.2116 | 70.3704 | 80.2116 | 80.2116 | 4.0 | | No log | 4.0 | 16 | 23.4084 | 80.2116 | 70.3704 | 80.2116 | 80.2116 | 4.0 | | No log | 5.0 | 20 | 22.8504 | 80.2116 | 70.3704 | 80.2116 | 80.2116 | 4.0 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

admin-dev/Llama2-7b-hf-resume-formatter-modgen

admin-dev

2023-07-25T17:57:46Z

2

0

peft

[ "peft", "region:us" ]

null

2023-07-25T17:57:38Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0

brunoboat/dqn-SpaceInvadersNoFrameskip

brunoboat

2023-07-25T17:53:44Z

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-25T17:53:06Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 550.00 +/- 153.70 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga brunoboat -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga brunoboat -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga brunoboat ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 600000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

gaioNL/Pixelcopter-PLE-v2-r3

gaioNL

2023-07-25T17:38:52Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-07-25T17:38:44Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v2-r3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 97.80 +/- 70.54 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

jakariamd/opp_115_privacy_contact_information

jakariamd

2023-07-25T17:12:31Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-25T17:05:06Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: opp_115_privacy_contact_information results: [] ---  # opp_115_privacy_contact_information This model is a fine-tuned version of [mukund/privbert](https://huggingface.co/mukund/privbert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0744 - Accuracy: 0.9788 ## 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: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 165 | 0.0795 | 0.9788 | | No log | 2.0 | 330 | 0.0744 | 0.9788 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

Za88yes/Iccis

Za88yes

2023-07-25T17:11:51Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-07-25T17:09:46Z

--- license: creativeml-openrail-m ---