ftopal/huggingface-models-raw · Datasets at Hugging Face

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null	peft	<!-- 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. --> # GUE_prom_prom_core_all-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.4181 - F1 Score: 0.8048 - Accuracy: 0.8049 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5559 \| 0.54 \| 200 \| 0.4704 \| 0.7803 \| 0.7804 \| \| 0.4687 \| 1.08 \| 400 \| 0.4646 \| 0.7875 \| 0.7878 \| \| 0.4525 \| 1.62 \| 600 \| 0.4497 \| 0.7929 \| 0.7931 \| \| 0.4437 \| 2.16 \| 800 \| 0.4473 \| 0.7944 \| 0.7944 \| \| 0.4405 \| 2.7 \| 1000 \| 0.4449 \| 0.7921 \| 0.7922 \| \| 0.4363 \| 3.24 \| 1200 \| 0.4399 \| 0.7961 \| 0.7963 \| \| 0.4331 \| 3.78 \| 1400 \| 0.4419 \| 0.7909 \| 0.7912 \| \| 0.4313 \| 4.32 \| 1600 \| 0.4438 \| 0.7967 \| 0.7968 \| \| 0.4309 \| 4.86 \| 1800 \| 0.4419 \| 0.7937 \| 0.7941 \| \| 0.4266 \| 5.41 \| 2000 \| 0.4388 \| 0.7927 \| 0.7929 \| \| 0.4243 \| 5.95 \| 2200 \| 0.4391 \| 0.7981 \| 0.7981 \| \| 0.4279 \| 6.49 \| 2400 \| 0.4341 \| 0.7965 \| 0.7965 \| \| 0.4206 \| 7.03 \| 2600 \| 0.4416 \| 0.7977 \| 0.7981 \| \| 0.4231 \| 7.57 \| 2800 \| 0.4348 \| 0.7976 \| 0.7976 \| \| 0.4171 \| 8.11 \| 3000 \| 0.4362 \| 0.7944 \| 0.7946 \| \| 0.419 \| 8.65 \| 3200 \| 0.4297 \| 0.8017 \| 0.8017 \| \| 0.4207 \| 9.19 \| 3400 \| 0.4331 \| 0.7992 \| 0.7992 \| \| 0.418 \| 9.73 \| 3600 \| 0.4378 \| 0.7949 \| 0.7954 \| \| 0.4182 \| 10.27 \| 3800 \| 0.4330 \| 0.7982 \| 0.7983 \| \| 0.4164 \| 10.81 \| 4000 \| 0.4360 \| 0.7977 \| 0.7978 \| \| 0.414 \| 11.35 \| 4200 \| 0.4330 \| 0.7973 \| 0.7975 \| \| 0.4143 \| 11.89 \| 4400 \| 0.4336 \| 0.7964 \| 0.7966 \| \| 0.4115 \| 12.43 \| 4600 \| 0.4335 \| 0.8025 \| 0.8025 \| \| 0.4108 \| 12.97 \| 4800 \| 0.4331 \| 0.7990 \| 0.7992 \| \| 0.4133 \| 13.51 \| 5000 \| 0.4407 \| 0.7934 \| 0.7943 \| \| 0.4114 \| 14.05 \| 5200 \| 0.4303 \| 0.8029 \| 0.8029 \| \| 0.4085 \| 14.59 \| 5400 \| 0.4288 \| 0.8022 \| 0.8022 \| \| 0.4081 \| 15.14 \| 5600 \| 0.4326 \| 0.8021 \| 0.8022 \| \| 0.4096 \| 15.68 \| 5800 \| 0.4334 \| 0.7985 \| 0.7988 \| \| 0.4037 \| 16.22 \| 6000 \| 0.4312 \| 0.8023 \| 0.8025 \| \| 0.4114 \| 16.76 \| 6200 \| 0.4254 \| 0.8015 \| 0.8015 \| \| 0.4119 \| 17.3 \| 6400 \| 0.4278 \| 0.8046 \| 0.8047 \| \| 0.4072 \| 17.84 \| 6600 \| 0.4294 \| 0.8014 \| 0.8015 \| \| 0.4035 \| 18.38 \| 6800 \| 0.4337 \| 0.7972 \| 0.7978 \| \| 0.4047 \| 18.92 \| 7000 \| 0.4277 \| 0.8021 \| 0.8022 \| \| 0.4011 \| 19.46 \| 7200 \| 0.4286 \| 0.8035 \| 0.8035 \| \| 0.4118 \| 20.0 \| 7400 \| 0.4264 \| 0.8045 \| 0.8046 \| \| 0.4066 \| 20.54 \| 7600 \| 0.4286 \| 0.8025 \| 0.8027 \| \| 0.4031 \| 21.08 \| 7800 \| 0.4275 \| 0.8038 \| 0.8039 \| \| 0.4044 \| 21.62 \| 8000 \| 0.4255 \| 0.8037 \| 0.8037 \| \| 0.402 \| 22.16 \| 8200 \| 0.4259 \| 0.8040 \| 0.8041 \| \| 0.4101 \| 22.7 \| 8400 \| 0.4265 \| 0.8027 \| 0.8029 \| \| 0.4006 \| 23.24 \| 8600 \| 0.4249 \| 0.8047 \| 0.8047 \| \| 0.4005 \| 23.78 \| 8800 \| 0.4271 \| 0.8038 \| 0.8039 \| \| 0.3983 \| 24.32 \| 9000 \| 0.4269 \| 0.8045 \| 0.8046 \| \| 0.4017 \| 24.86 \| 9200 \| 0.4259 \| 0.8038 \| 0.8039 \| \| 0.4117 \| 25.41 \| 9400 \| 0.4257 \| 0.8043 \| 0.8044 \| \| 0.3956 \| 25.95 \| 9600 \| 0.4271 \| 0.8048 \| 0.8049 \| \| 0.4029 \| 26.49 \| 9800 \| 0.4272 \| 0.8050 \| 0.8051 \| \| 0.4004 \| 27.03 \| 10000 \| 0.4271 \| 0.8046 \| 0.8047 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_all-seqsight_4096_512_15M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_all-seqsight_4096_512_15M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:22:07+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_core_notata-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.3823 - F1 Score: 0.8291 - Accuracy: 0.8291 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5574 \| 0.6 \| 200 \| 0.4140 \| 0.8126 \| 0.8127 \| \| 0.4313 \| 1.2 \| 400 \| 0.3888 \| 0.8259 \| 0.8259 \| \| 0.4159 \| 1.81 \| 600 \| 0.3816 \| 0.8268 \| 0.8268 \| \| 0.4102 \| 2.41 \| 800 \| 0.3759 \| 0.8310 \| 0.8310 \| \| 0.4006 \| 3.01 \| 1000 \| 0.3744 \| 0.8299 \| 0.8298 \| \| 0.3973 \| 3.61 \| 1200 \| 0.3701 \| 0.8379 \| 0.8379 \| \| 0.3992 \| 4.22 \| 1400 \| 0.3703 \| 0.8355 \| 0.8355 \| \| 0.396 \| 4.82 \| 1600 \| 0.3687 \| 0.8381 \| 0.8381 \| \| 0.3877 \| 5.42 \| 1800 \| 0.3757 \| 0.8292 \| 0.8293 \| \| 0.3935 \| 6.02 \| 2000 \| 0.3690 \| 0.8383 \| 0.8383 \| \| 0.3911 \| 6.63 \| 2200 \| 0.3674 \| 0.8381 \| 0.8381 \| \| 0.3879 \| 7.23 \| 2400 \| 0.3679 \| 0.8380 \| 0.8381 \| \| 0.3887 \| 7.83 \| 2600 \| 0.3662 \| 0.8396 \| 0.8396 \| \| 0.3825 \| 8.43 \| 2800 \| 0.3721 \| 0.8344 \| 0.8349 \| \| 0.3879 \| 9.04 \| 3000 \| 0.3663 \| 0.8402 \| 0.8402 \| \| 0.3812 \| 9.64 \| 3200 \| 0.3637 \| 0.8397 \| 0.8396 \| \| 0.3823 \| 10.24 \| 3400 \| 0.3647 \| 0.8406 \| 0.8406 \| \| 0.383 \| 10.84 \| 3600 \| 0.3643 \| 0.8400 \| 0.8400 \| \| 0.3815 \| 11.45 \| 3800 \| 0.3640 \| 0.8382 \| 0.8381 \| \| 0.3804 \| 12.05 \| 4000 \| 0.3629 \| 0.8381 \| 0.8381 \| \| 0.3746 \| 12.65 \| 4200 \| 0.3634 \| 0.8382 \| 0.8383 \| \| 0.3799 \| 13.25 \| 4400 \| 0.3635 \| 0.8376 \| 0.8376 \| \| 0.378 \| 13.86 \| 4600 \| 0.3636 \| 0.8400 \| 0.8400 \| \| 0.3771 \| 14.46 \| 4800 \| 0.3633 \| 0.8415 \| 0.8415 \| \| 0.3741 \| 15.06 \| 5000 \| 0.3615 \| 0.8415 \| 0.8415 \| \| 0.371 \| 15.66 \| 5200 \| 0.3612 \| 0.8412 \| 0.8412 \| \| 0.3728 \| 16.27 \| 5400 \| 0.3642 \| 0.8400 \| 0.8400 \| \| 0.3718 \| 16.87 \| 5600 \| 0.3679 \| 0.8361 \| 0.8364 \| \| 0.3698 \| 17.47 \| 5800 \| 0.3664 \| 0.8369 \| 0.8372 \| \| 0.3758 \| 18.07 \| 6000 \| 0.3624 \| 0.8393 \| 0.8395 \| \| 0.3725 \| 18.67 \| 6200 \| 0.3605 \| 0.8412 \| 0.8413 \| \| 0.3716 \| 19.28 \| 6400 \| 0.3618 \| 0.8408 \| 0.8408 \| \| 0.3703 \| 19.88 \| 6600 \| 0.3613 \| 0.8388 \| 0.8389 \| \| 0.3658 \| 20.48 \| 6800 \| 0.3606 \| 0.8409 \| 0.8410 \| \| 0.3759 \| 21.08 \| 7000 \| 0.3640 \| 0.8363 \| 0.8366 \| \| 0.3748 \| 21.69 \| 7200 \| 0.3612 \| 0.8415 \| 0.8415 \| \| 0.3651 \| 22.29 \| 7400 \| 0.3610 \| 0.8399 \| 0.8400 \| \| 0.3673 \| 22.89 \| 7600 \| 0.3609 \| 0.8424 \| 0.8425 \| \| 0.3681 \| 23.49 \| 7800 \| 0.3622 \| 0.8380 \| 0.8381 \| \| 0.3688 \| 24.1 \| 8000 \| 0.3629 \| 0.8393 \| 0.8395 \| \| 0.3692 \| 24.7 \| 8200 \| 0.3639 \| 0.8388 \| 0.8391 \| \| 0.3645 \| 25.3 \| 8400 \| 0.3642 \| 0.8396 \| 0.8398 \| \| 0.3692 \| 25.9 \| 8600 \| 0.3609 \| 0.8422 \| 0.8423 \| \| 0.3687 \| 26.51 \| 8800 \| 0.3615 \| 0.8415 \| 0.8415 \| \| 0.3671 \| 27.11 \| 9000 \| 0.3610 \| 0.8409 \| 0.8410 \| \| 0.3726 \| 27.71 \| 9200 \| 0.3617 \| 0.8399 \| 0.8400 \| \| 0.3626 \| 28.31 \| 9400 \| 0.3631 \| 0.8387 \| 0.8389 \| \| 0.3658 \| 28.92 \| 9600 \| 0.3618 \| 0.8396 \| 0.8396 \| \| 0.3724 \| 29.52 \| 9800 \| 0.3614 \| 0.8392 \| 0.8393 \| \| 0.3612 \| 30.12 \| 10000 \| 0.3615 \| 0.8395 \| 0.8396 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_4096_512_15M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_4096_512_15M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:22:07+00:00
null	null		{}	joebamnasoda69/CheckardsArrVeeSeeStuff2	null	[ "region:us" ]	null	2024-05-03T17:22:22+00:00
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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 Dataset 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 Dataset 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]	{"library_name": "transformers", "tags": []}	hrangel/Mistral_7B_qlora_CoT_Matematicals	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-03T17:23:34+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_core_notata-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.3910 - F1 Score: 0.8233 - Accuracy: 0.8233 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6131 \| 0.6 \| 200 \| 0.4693 \| 0.7777 \| 0.7777 \| \| 0.4721 \| 1.2 \| 400 \| 0.4169 \| 0.8106 \| 0.8106 \| \| 0.4443 \| 1.81 \| 600 \| 0.4046 \| 0.8150 \| 0.8150 \| \| 0.4403 \| 2.41 \| 800 \| 0.3968 \| 0.8210 \| 0.8210 \| \| 0.4255 \| 3.01 \| 1000 \| 0.3953 \| 0.8213 \| 0.8214 \| \| 0.4233 \| 3.61 \| 1200 \| 0.3889 \| 0.8217 \| 0.8217 \| \| 0.4223 \| 4.22 \| 1400 \| 0.3869 \| 0.8225 \| 0.8225 \| \| 0.4197 \| 4.82 \| 1600 \| 0.3844 \| 0.8223 \| 0.8223 \| \| 0.4106 \| 5.42 \| 1800 \| 0.3869 \| 0.8248 \| 0.8249 \| \| 0.4124 \| 6.02 \| 2000 \| 0.3819 \| 0.8262 \| 0.8263 \| \| 0.4112 \| 6.63 \| 2200 \| 0.3791 \| 0.8285 \| 0.8285 \| \| 0.407 \| 7.23 \| 2400 \| 0.3801 \| 0.8313 \| 0.8314 \| \| 0.4063 \| 7.83 \| 2600 \| 0.3787 \| 0.8288 \| 0.8289 \| \| 0.4012 \| 8.43 \| 2800 \| 0.3808 \| 0.8296 \| 0.8298 \| \| 0.406 \| 9.04 \| 3000 \| 0.3761 \| 0.8322 \| 0.8323 \| \| 0.3994 \| 9.64 \| 3200 \| 0.3734 \| 0.8312 \| 0.8312 \| \| 0.4003 \| 10.24 \| 3400 \| 0.3750 \| 0.8323 \| 0.8323 \| \| 0.4008 \| 10.84 \| 3600 \| 0.3741 \| 0.8336 \| 0.8336 \| \| 0.3994 \| 11.45 \| 3800 \| 0.3736 \| 0.8327 \| 0.8327 \| \| 0.3982 \| 12.05 \| 4000 \| 0.3729 \| 0.8340 \| 0.8340 \| \| 0.3933 \| 12.65 \| 4200 \| 0.3739 \| 0.8342 \| 0.8342 \| \| 0.3995 \| 13.25 \| 4400 \| 0.3707 \| 0.8349 \| 0.8349 \| \| 0.3967 \| 13.86 \| 4600 \| 0.3721 \| 0.8355 \| 0.8355 \| \| 0.3951 \| 14.46 \| 4800 \| 0.3723 \| 0.8351 \| 0.8351 \| \| 0.3916 \| 15.06 \| 5000 \| 0.3705 \| 0.8336 \| 0.8336 \| \| 0.3907 \| 15.66 \| 5200 \| 0.3703 \| 0.8376 \| 0.8376 \| \| 0.3905 \| 16.27 \| 5400 \| 0.3728 \| 0.8355 \| 0.8355 \| \| 0.3917 \| 16.87 \| 5600 \| 0.3738 \| 0.8364 \| 0.8366 \| \| 0.39 \| 17.47 \| 5800 \| 0.3720 \| 0.8365 \| 0.8366 \| \| 0.3961 \| 18.07 \| 6000 \| 0.3706 \| 0.8377 \| 0.8378 \| \| 0.3917 \| 18.67 \| 6200 \| 0.3694 \| 0.8379 \| 0.8379 \| \| 0.3923 \| 19.28 \| 6400 \| 0.3711 \| 0.8374 \| 0.8374 \| \| 0.389 \| 19.88 \| 6600 \| 0.3690 \| 0.8377 \| 0.8378 \| \| 0.3847 \| 20.48 \| 6800 \| 0.3701 \| 0.8371 \| 0.8372 \| \| 0.3949 \| 21.08 \| 7000 \| 0.3710 \| 0.8359 \| 0.8361 \| \| 0.3961 \| 21.69 \| 7200 \| 0.3680 \| 0.8379 \| 0.8379 \| \| 0.386 \| 22.29 \| 7400 \| 0.3684 \| 0.8393 \| 0.8393 \| \| 0.387 \| 22.89 \| 7600 \| 0.3698 \| 0.8378 \| 0.8378 \| \| 0.388 \| 23.49 \| 7800 \| 0.3683 \| 0.8391 \| 0.8391 \| \| 0.3887 \| 24.1 \| 8000 \| 0.3689 \| 0.8381 \| 0.8381 \| \| 0.3889 \| 24.7 \| 8200 \| 0.3693 \| 0.8360 \| 0.8361 \| \| 0.3844 \| 25.3 \| 8400 \| 0.3699 \| 0.8389 \| 0.8389 \| \| 0.3902 \| 25.9 \| 8600 \| 0.3678 \| 0.8398 \| 0.8398 \| \| 0.3906 \| 26.51 \| 8800 \| 0.3681 \| 0.8383 \| 0.8383 \| \| 0.3874 \| 27.11 \| 9000 \| 0.3682 \| 0.8389 \| 0.8389 \| \| 0.3929 \| 27.71 \| 9200 \| 0.3682 \| 0.8393 \| 0.8393 \| \| 0.3847 \| 28.31 \| 9400 \| 0.3689 \| 0.8396 \| 0.8396 \| \| 0.3874 \| 28.92 \| 9600 \| 0.3684 \| 0.8393 \| 0.8393 \| \| 0.3929 \| 29.52 \| 9800 \| 0.3680 \| 0.8391 \| 0.8391 \| \| 0.3819 \| 30.12 \| 10000 \| 0.3682 \| 0.8391 \| 0.8391 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_4096_512_15M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_4096_512_15M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:24:03+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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 Dataset 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 Dataset 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]	{"library_name": "transformers", "tags": []}	OwOpeepeepoopoo/herewegoagain18	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T17:24:38+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_core_notata-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.3783 - F1 Score: 0.8328 - Accuracy: 0.8329 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5299 \| 0.6 \| 200 \| 0.4006 \| 0.8167 \| 0.8168 \| \| 0.4157 \| 1.2 \| 400 \| 0.3794 \| 0.8336 \| 0.8336 \| \| 0.4031 \| 1.81 \| 600 \| 0.3786 \| 0.8307 \| 0.8308 \| \| 0.399 \| 2.41 \| 800 \| 0.3696 \| 0.8336 \| 0.8336 \| \| 0.3916 \| 3.01 \| 1000 \| 0.3681 \| 0.8352 \| 0.8353 \| \| 0.387 \| 3.61 \| 1200 \| 0.3638 \| 0.8390 \| 0.8391 \| \| 0.3907 \| 4.22 \| 1400 \| 0.3662 \| 0.8395 \| 0.8395 \| \| 0.386 \| 4.82 \| 1600 \| 0.3624 \| 0.8418 \| 0.8419 \| \| 0.377 \| 5.42 \| 1800 \| 0.3715 \| 0.8339 \| 0.8340 \| \| 0.3833 \| 6.02 \| 2000 \| 0.3665 \| 0.8392 \| 0.8393 \| \| 0.3794 \| 6.63 \| 2200 \| 0.3616 \| 0.8398 \| 0.8398 \| \| 0.3765 \| 7.23 \| 2400 \| 0.3654 \| 0.8416 \| 0.8417 \| \| 0.3776 \| 7.83 \| 2600 \| 0.3619 \| 0.8391 \| 0.8391 \| \| 0.3695 \| 8.43 \| 2800 \| 0.3655 \| 0.8373 \| 0.8378 \| \| 0.3752 \| 9.04 \| 3000 \| 0.3597 \| 0.8442 \| 0.8442 \| \| 0.368 \| 9.64 \| 3200 \| 0.3595 \| 0.8425 \| 0.8425 \| \| 0.3675 \| 10.24 \| 3400 \| 0.3602 \| 0.8417 \| 0.8417 \| \| 0.3692 \| 10.84 \| 3600 \| 0.3594 \| 0.8407 \| 0.8408 \| \| 0.3657 \| 11.45 \| 3800 \| 0.3580 \| 0.8440 \| 0.8440 \| \| 0.3651 \| 12.05 \| 4000 \| 0.3583 \| 0.8419 \| 0.8419 \| \| 0.3594 \| 12.65 \| 4200 \| 0.3580 \| 0.8431 \| 0.8432 \| \| 0.3633 \| 13.25 \| 4400 \| 0.3588 \| 0.8428 \| 0.8428 \| \| 0.361 \| 13.86 \| 4600 \| 0.3606 \| 0.8413 \| 0.8413 \| \| 0.359 \| 14.46 \| 4800 \| 0.3588 \| 0.8434 \| 0.8434 \| \| 0.3573 \| 15.06 \| 5000 \| 0.3560 \| 0.8452 \| 0.8453 \| \| 0.3505 \| 15.66 \| 5200 \| 0.3603 \| 0.8428 \| 0.8428 \| \| 0.3549 \| 16.27 \| 5400 \| 0.3618 \| 0.8434 \| 0.8434 \| \| 0.3528 \| 16.87 \| 5600 \| 0.3677 \| 0.8386 \| 0.8391 \| \| 0.3501 \| 17.47 \| 5800 \| 0.3639 \| 0.8427 \| 0.8430 \| \| 0.3573 \| 18.07 \| 6000 \| 0.3615 \| 0.8446 \| 0.8447 \| \| 0.3517 \| 18.67 \| 6200 \| 0.3582 \| 0.8442 \| 0.8444 \| \| 0.3509 \| 19.28 \| 6400 \| 0.3615 \| 0.8432 \| 0.8432 \| \| 0.3489 \| 19.88 \| 6600 \| 0.3584 \| 0.8425 \| 0.8427 \| \| 0.3444 \| 20.48 \| 6800 \| 0.3580 \| 0.8447 \| 0.8447 \| \| 0.3544 \| 21.08 \| 7000 \| 0.3644 \| 0.8404 \| 0.8408 \| \| 0.3525 \| 21.69 \| 7200 \| 0.3604 \| 0.8423 \| 0.8423 \| \| 0.3441 \| 22.29 \| 7400 \| 0.3598 \| 0.8448 \| 0.8449 \| \| 0.346 \| 22.89 \| 7600 \| 0.3610 \| 0.8424 \| 0.8425 \| \| 0.346 \| 23.49 \| 7800 \| 0.3613 \| 0.8412 \| 0.8413 \| \| 0.347 \| 24.1 \| 8000 \| 0.3645 \| 0.8417 \| 0.8419 \| \| 0.3462 \| 24.7 \| 8200 \| 0.3650 \| 0.8416 \| 0.8419 \| \| 0.3401 \| 25.3 \| 8400 \| 0.3669 \| 0.8421 \| 0.8423 \| \| 0.3471 \| 25.9 \| 8600 \| 0.3612 \| 0.8428 \| 0.8428 \| \| 0.3451 \| 26.51 \| 8800 \| 0.3618 \| 0.8432 \| 0.8432 \| \| 0.3456 \| 27.11 \| 9000 \| 0.3604 \| 0.8432 \| 0.8432 \| \| 0.3485 \| 27.71 \| 9200 \| 0.3626 \| 0.8425 \| 0.8427 \| \| 0.3388 \| 28.31 \| 9400 \| 0.3632 \| 0.8442 \| 0.8444 \| \| 0.3412 \| 28.92 \| 9600 \| 0.3632 \| 0.8420 \| 0.8421 \| \| 0.3492 \| 29.52 \| 9800 \| 0.3614 \| 0.8422 \| 0.8423 \| \| 0.3355 \| 30.12 \| 10000 \| 0.3620 \| 0.8431 \| 0.8432 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_4096_512_15M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_4096_512_15M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:24:39+00:00
text-classification	transformers		{"license": "mit"}	wantuta/roberta_classifier_ancient2	null	[ "transformers", "safetensors", "roberta", "text-classification", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T17:24:42+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_core_tata-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.4439 - F1 Score: 0.8286 - Accuracy: 0.8287 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.629 \| 5.13 \| 200 \| 0.5857 \| 0.7072 \| 0.7080 \| \| 0.5743 \| 10.26 \| 400 \| 0.5824 \| 0.6910 \| 0.6949 \| \| 0.5505 \| 15.38 \| 600 \| 0.5729 \| 0.7040 \| 0.7096 \| \| 0.53 \| 20.51 \| 800 \| 0.5425 \| 0.7238 \| 0.7243 \| \| 0.5137 \| 25.64 \| 1000 \| 0.5271 \| 0.7352 \| 0.7357 \| \| 0.4924 \| 30.77 \| 1200 \| 0.4966 \| 0.7730 \| 0.7732 \| \| 0.466 \| 35.9 \| 1400 \| 0.4742 \| 0.7879 \| 0.7879 \| \| 0.4452 \| 41.03 \| 1600 \| 0.4655 \| 0.7808 \| 0.7814 \| \| 0.4341 \| 46.15 \| 1800 \| 0.4457 \| 0.8010 \| 0.8010 \| \| 0.4182 \| 51.28 \| 2000 \| 0.4385 \| 0.8042 \| 0.8042 \| \| 0.4107 \| 56.41 \| 2200 \| 0.4363 \| 0.8075 \| 0.8075 \| \| 0.4042 \| 61.54 \| 2400 \| 0.4199 \| 0.8074 \| 0.8075 \| \| 0.3981 \| 66.67 \| 2600 \| 0.4153 \| 0.8108 \| 0.8108 \| \| 0.3883 \| 71.79 \| 2800 \| 0.4141 \| 0.8075 \| 0.8075 \| \| 0.383 \| 76.92 \| 3000 \| 0.4142 \| 0.8140 \| 0.8140 \| \| 0.3755 \| 82.05 \| 3200 \| 0.4044 \| 0.8205 \| 0.8206 \| \| 0.3734 \| 87.18 \| 3400 \| 0.4064 \| 0.8222 \| 0.8222 \| \| 0.3695 \| 92.31 \| 3600 \| 0.4026 \| 0.8238 \| 0.8238 \| \| 0.3625 \| 97.44 \| 3800 \| 0.3999 \| 0.8352 \| 0.8352 \| \| 0.3664 \| 102.56 \| 4000 \| 0.3976 \| 0.8303 \| 0.8303 \| \| 0.3595 \| 107.69 \| 4200 \| 0.3992 \| 0.8303 \| 0.8303 \| \| 0.352 \| 112.82 \| 4400 \| 0.3970 \| 0.8303 \| 0.8303 \| \| 0.347 \| 117.95 \| 4600 \| 0.3906 \| 0.8303 \| 0.8303 \| \| 0.3497 \| 123.08 \| 4800 \| 0.3944 \| 0.8351 \| 0.8352 \| \| 0.3398 \| 128.21 \| 5000 \| 0.3941 \| 0.8352 \| 0.8352 \| \| 0.3432 \| 133.33 \| 5200 \| 0.3897 \| 0.8352 \| 0.8352 \| \| 0.3371 \| 138.46 \| 5400 \| 0.3878 \| 0.8369 \| 0.8369 \| \| 0.3331 \| 143.59 \| 5600 \| 0.3882 \| 0.8352 \| 0.8352 \| \| 0.3377 \| 148.72 \| 5800 \| 0.3883 \| 0.8352 \| 0.8352 \| \| 0.3288 \| 153.85 \| 6000 \| 0.3889 \| 0.8352 \| 0.8352 \| \| 0.3261 \| 158.97 \| 6200 \| 0.3843 \| 0.8401 \| 0.8401 \| \| 0.3284 \| 164.1 \| 6400 \| 0.3902 \| 0.8335 \| 0.8336 \| \| 0.3293 \| 169.23 \| 6600 \| 0.3837 \| 0.8384 \| 0.8385 \| \| 0.3242 \| 174.36 \| 6800 \| 0.3899 \| 0.8385 \| 0.8385 \| \| 0.3263 \| 179.49 \| 7000 \| 0.3861 \| 0.8352 \| 0.8352 \| \| 0.3193 \| 184.62 \| 7200 \| 0.3874 \| 0.8434 \| 0.8434 \| \| 0.3187 \| 189.74 \| 7400 \| 0.3903 \| 0.8385 \| 0.8385 \| \| 0.3201 \| 194.87 \| 7600 \| 0.3908 \| 0.8385 \| 0.8385 \| \| 0.3194 \| 200.0 \| 7800 \| 0.3860 \| 0.8466 \| 0.8467 \| \| 0.3187 \| 205.13 \| 8000 \| 0.3869 \| 0.8449 \| 0.8450 \| \| 0.3163 \| 210.26 \| 8200 \| 0.3877 \| 0.8401 \| 0.8401 \| \| 0.313 \| 215.38 \| 8400 \| 0.3892 \| 0.8417 \| 0.8418 \| \| 0.316 \| 220.51 \| 8600 \| 0.3888 \| 0.8385 \| 0.8385 \| \| 0.3144 \| 225.64 \| 8800 \| 0.3886 \| 0.8417 \| 0.8418 \| \| 0.3124 \| 230.77 \| 9000 \| 0.3866 \| 0.8449 \| 0.8450 \| \| 0.3119 \| 235.9 \| 9200 \| 0.3874 \| 0.8417 \| 0.8418 \| \| 0.3125 \| 241.03 \| 9400 \| 0.3884 \| 0.8450 \| 0.8450 \| \| 0.3151 \| 246.15 \| 9600 \| 0.3868 \| 0.8417 \| 0.8418 \| \| 0.3084 \| 251.28 \| 9800 \| 0.3879 \| 0.8417 \| 0.8418 \| \| 0.3116 \| 256.41 \| 10000 \| 0.3878 \| 0.8450 \| 0.8450 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_4096_512_15M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_4096_512_15M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:25:18+00:00
image-segmentation	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b0-finetuned-raw_img_ready2train_patches This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the raw_img_ready2train_patches dataset. It achieves the following results on the evaluation set: - Loss: 0.6829 - Mean Iou: 0.4110 - Mean Accuracy: 0.7629 - Overall Accuracy: 0.7631 - Accuracy Unlabeled: nan - Accuracy Eczema: 0.7673 - Accuracy Background: 0.7585 - Iou Unlabeled: 0.0 - Iou Eczema: 0.6284 - Iou Background: 0.6047 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Eczema \| Accuracy Background \| Iou Unlabeled \| Iou Eczema \| Iou Background \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:---------------:\|:-------------------:\|:-------------:\|:----------:\|:--------------:\| \| 1.0753 \| 0.0312 \| 5 \| 1.0925 \| 0.2358 \| 0.4682 \| 0.4698 \| nan \| 0.5042 \| 0.4322 \| 0.0 \| 0.3705 \| 0.3367 \| \| 0.9863 \| 0.0625 \| 10 \| 1.0697 \| 0.2994 \| 0.6182 \| 0.6306 \| nan \| 0.8979 \| 0.3385 \| 0.0 \| 0.5784 \| 0.3198 \| \| 1.0056 \| 0.0938 \| 15 \| 1.0377 \| 0.3303 \| 0.6678 \| 0.6792 \| nan \| 0.9236 \| 0.4121 \| 0.0 \| 0.6064 \| 0.3844 \| \| 1.0133 \| 0.125 \| 20 \| 1.0006 \| 0.3478 \| 0.6869 \| 0.6950 \| nan \| 0.8710 \| 0.5027 \| 0.0 \| 0.6008 \| 0.4425 \| \| 0.9748 \| 0.1562 \| 25 \| 0.9689 \| 0.3543 \| 0.6947 \| 0.7022 \| nan \| 0.8647 \| 0.5246 \| 0.0 \| 0.6043 \| 0.4586 \| \| 0.9367 \| 0.1875 \| 30 \| 0.9417 \| 0.3566 \| 0.6950 \| 0.6965 \| nan \| 0.7290 \| 0.6610 \| 0.0 \| 0.5583 \| 0.5114 \| \| 0.8363 \| 0.2188 \| 35 \| 0.9118 \| 0.3557 \| 0.6940 \| 0.6959 \| nan \| 0.7366 \| 0.6514 \| 0.0 \| 0.5600 \| 0.5069 \| \| 1.1431 \| 0.25 \| 40 \| 0.8830 \| 0.3575 \| 0.6963 \| 0.6989 \| nan \| 0.7556 \| 0.6370 \| 0.0 \| 0.5686 \| 0.5039 \| \| 0.7312 \| 0.2812 \| 45 \| 0.8592 \| 0.3680 \| 0.7098 \| 0.7133 \| nan \| 0.7888 \| 0.6307 \| 0.0 \| 0.5907 \| 0.5133 \| \| 0.8135 \| 0.3125 \| 50 \| 0.8268 \| 0.3559 \| 0.6994 \| 0.7083 \| nan \| 0.8992 \| 0.4997 \| 0.0 \| 0.6173 \| 0.4505 \| \| 0.7528 \| 0.3438 \| 55 \| 0.8110 \| 0.3525 \| 0.6960 \| 0.7053 \| nan \| 0.9055 \| 0.4866 \| 0.0 \| 0.6162 \| 0.4412 \| \| 0.8405 \| 0.375 \| 60 \| 0.7967 \| 0.3518 \| 0.6950 \| 0.7041 \| nan \| 0.9008 \| 0.4893 \| 0.0 \| 0.6140 \| 0.4415 \| \| 0.7865 \| 0.4062 \| 65 \| 0.7791 \| 0.3561 \| 0.6992 \| 0.7075 \| nan \| 0.8869 \| 0.5116 \| 0.0 \| 0.6130 \| 0.4553 \| \| 0.8309 \| 0.4375 \| 70 \| 0.7650 \| 0.3652 \| 0.7083 \| 0.7147 \| nan \| 0.8512 \| 0.5655 \| 0.0 \| 0.6090 \| 0.4864 \| \| 0.6775 \| 0.4688 \| 75 \| 0.7615 \| 0.3613 \| 0.7044 \| 0.7115 \| nan \| 0.8651 \| 0.5437 \| 0.0 \| 0.6102 \| 0.4738 \| \| 0.7033 \| 0.5 \| 80 \| 0.7498 \| 0.3737 \| 0.7179 \| 0.7227 \| nan \| 0.8260 \| 0.6099 \| 0.0 \| 0.6087 \| 0.5125 \| \| 0.8377 \| 0.5312 \| 85 \| 0.7443 \| 0.3790 \| 0.7243 \| 0.7290 \| nan \| 0.8303 \| 0.6184 \| 0.0 \| 0.6154 \| 0.5217 \| \| 0.825 \| 0.5625 \| 90 \| 0.7547 \| 0.3676 \| 0.7125 \| 0.7201 \| nan \| 0.8840 \| 0.5411 \| 0.0 \| 0.6225 \| 0.4802 \| \| 0.7408 \| 0.5938 \| 95 \| 0.7415 \| 0.3767 \| 0.7228 \| 0.7295 \| nan \| 0.8747 \| 0.5708 \| 0.0 \| 0.6281 \| 0.5021 \| \| 0.8087 \| 0.625 \| 100 \| 0.7201 \| 0.3926 \| 0.7404 \| 0.7445 \| nan \| 0.8318 \| 0.6491 \| 0.0 \| 0.6296 \| 0.5483 \| \| 0.7146 \| 0.6562 \| 105 \| 0.7096 \| 0.4002 \| 0.7493 \| 0.7520 \| nan \| 0.8109 \| 0.6877 \| 0.0 \| 0.6307 \| 0.5699 \| \| 0.6875 \| 0.6875 \| 110 \| 0.7047 \| 0.4010 \| 0.7502 \| 0.7541 \| nan \| 0.8398 \| 0.6606 \| 0.0 \| 0.6407 \| 0.5621 \| \| 0.6382 \| 0.7188 \| 115 \| 0.7031 \| 0.3982 \| 0.7471 \| 0.7519 \| nan \| 0.8543 \| 0.6400 \| 0.0 \| 0.6426 \| 0.5521 \| \| 0.6551 \| 0.75 \| 120 \| 0.6953 \| 0.4018 \| 0.7512 \| 0.7553 \| nan \| 0.8450 \| 0.6573 \| 0.0 \| 0.6433 \| 0.5621 \| \| 0.7074 \| 0.7812 \| 125 \| 0.6912 \| 0.4054 \| 0.7553 \| 0.7583 \| nan \| 0.8236 \| 0.6871 \| 0.0 \| 0.6402 \| 0.5760 \| \| 0.768 \| 0.8125 \| 130 \| 0.6866 \| 0.4048 \| 0.7546 \| 0.7579 \| nan \| 0.8278 \| 0.6814 \| 0.0 \| 0.6410 \| 0.5736 \| \| 0.7543 \| 0.8438 \| 135 \| 0.6851 \| 0.4031 \| 0.7526 \| 0.7564 \| nan \| 0.8374 \| 0.6679 \| 0.0 \| 0.6422 \| 0.5671 \| \| 0.7107 \| 0.875 \| 140 \| 0.6803 \| 0.6122 \| 0.7586 \| 0.7608 \| nan \| 0.8071 \| 0.7101 \| nan \| 0.6379 \| 0.5865 \| \| 0.7054 \| 0.9062 \| 145 \| 0.6799 \| 0.4098 \| 0.7608 \| 0.7622 \| nan \| 0.7924 \| 0.7292 \| 0.0 \| 0.6350 \| 0.5943 \| \| 1.1302 \| 0.9375 \| 150 \| 0.6801 \| 0.4103 \| 0.7616 \| 0.7626 \| nan \| 0.7840 \| 0.7393 \| 0.0 \| 0.6330 \| 0.5981 \| \| 0.6037 \| 0.9688 \| 155 \| 0.6827 \| 0.4111 \| 0.7628 \| 0.7632 \| nan \| 0.7721 \| 0.7534 \| 0.0 \| 0.6300 \| 0.6032 \| \| 0.8577 \| 1.0 \| 160 \| 0.6829 \| 0.4110 \| 0.7629 \| 0.7631 \| nan \| 0.7673 \| 0.7585 \| 0.0 \| 0.6284 \| 0.6047 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "base_model": "nvidia/mit-b0", "model-index": [{"name": "segformer-b0-finetuned-raw_img_ready2train_patches", "results": []}]}	ruisusanofi/segformer-b0-finetuned-raw_img_ready2train_patches	null	[ "transformers", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]	null	2024-05-03T17:27:04+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_core_tata-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.4594 - F1 Score: 0.8271 - Accuracy: 0.8271 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6032 \| 5.13 \| 200 \| 0.5634 \| 0.7185 \| 0.7194 \| \| 0.5278 \| 10.26 \| 400 \| 0.5256 \| 0.7344 \| 0.7357 \| \| 0.4699 \| 15.38 \| 600 \| 0.4605 \| 0.7856 \| 0.7863 \| \| 0.4245 \| 20.51 \| 800 \| 0.4272 \| 0.7976 \| 0.7977 \| \| 0.3889 \| 25.64 \| 1000 \| 0.4069 \| 0.8155 \| 0.8157 \| \| 0.3665 \| 30.77 \| 1200 \| 0.3898 \| 0.8236 \| 0.8238 \| \| 0.3478 \| 35.9 \| 1400 \| 0.3919 \| 0.8320 \| 0.8320 \| \| 0.3314 \| 41.03 \| 1600 \| 0.3985 \| 0.8265 \| 0.8271 \| \| 0.3178 \| 46.15 \| 1800 \| 0.3865 \| 0.8352 \| 0.8352 \| \| 0.3045 \| 51.28 \| 2000 \| 0.3880 \| 0.8319 \| 0.8320 \| \| 0.2962 \| 56.41 \| 2200 \| 0.3923 \| 0.8434 \| 0.8434 \| \| 0.2901 \| 61.54 \| 2400 \| 0.3825 \| 0.8401 \| 0.8401 \| \| 0.2789 \| 66.67 \| 2600 \| 0.3828 \| 0.8352 \| 0.8352 \| \| 0.2688 \| 71.79 \| 2800 \| 0.3823 \| 0.8367 \| 0.8369 \| \| 0.2668 \| 76.92 \| 3000 \| 0.3948 \| 0.8352 \| 0.8352 \| \| 0.2553 \| 82.05 \| 3200 \| 0.3873 \| 0.8385 \| 0.8385 \| \| 0.25 \| 87.18 \| 3400 \| 0.3933 \| 0.8385 \| 0.8385 \| \| 0.2466 \| 92.31 \| 3600 \| 0.3986 \| 0.8466 \| 0.8467 \| \| 0.2419 \| 97.44 \| 3800 \| 0.3981 \| 0.8465 \| 0.8467 \| \| 0.2396 \| 102.56 \| 4000 \| 0.3904 \| 0.8596 \| 0.8597 \| \| 0.2347 \| 107.69 \| 4200 \| 0.4066 \| 0.8548 \| 0.8548 \| \| 0.2237 \| 112.82 \| 4400 \| 0.4169 \| 0.8548 \| 0.8548 \| \| 0.2197 \| 117.95 \| 4600 \| 0.4028 \| 0.8613 \| 0.8613 \| \| 0.2178 \| 123.08 \| 4800 \| 0.4289 \| 0.8483 \| 0.8483 \| \| 0.2117 \| 128.21 \| 5000 \| 0.4253 \| 0.8499 \| 0.8499 \| \| 0.2147 \| 133.33 \| 5200 \| 0.4187 \| 0.8596 \| 0.8597 \| \| 0.2068 \| 138.46 \| 5400 \| 0.4218 \| 0.8611 \| 0.8613 \| \| 0.2019 \| 143.59 \| 5600 \| 0.4296 \| 0.8466 \| 0.8467 \| \| 0.2023 \| 148.72 \| 5800 \| 0.4374 \| 0.8548 \| 0.8548 \| \| 0.1959 \| 153.85 \| 6000 \| 0.4354 \| 0.8515 \| 0.8515 \| \| 0.1974 \| 158.97 \| 6200 \| 0.4282 \| 0.8564 \| 0.8564 \| \| 0.1983 \| 164.1 \| 6400 \| 0.4305 \| 0.8515 \| 0.8515 \| \| 0.1928 \| 169.23 \| 6600 \| 0.4352 \| 0.8581 \| 0.8581 \| \| 0.1889 \| 174.36 \| 6800 \| 0.4507 \| 0.8532 \| 0.8532 \| \| 0.1909 \| 179.49 \| 7000 \| 0.4417 \| 0.8450 \| 0.8450 \| \| 0.1855 \| 184.62 \| 7200 \| 0.4481 \| 0.8548 \| 0.8548 \| \| 0.1824 \| 189.74 \| 7400 \| 0.4513 \| 0.8564 \| 0.8564 \| \| 0.1837 \| 194.87 \| 7600 \| 0.4567 \| 0.8515 \| 0.8515 \| \| 0.1841 \| 200.0 \| 7800 \| 0.4383 \| 0.8630 \| 0.8630 \| \| 0.1819 \| 205.13 \| 8000 \| 0.4506 \| 0.8532 \| 0.8532 \| \| 0.1809 \| 210.26 \| 8200 \| 0.4516 \| 0.8499 \| 0.8499 \| \| 0.1753 \| 215.38 \| 8400 \| 0.4639 \| 0.8467 \| 0.8467 \| \| 0.1771 \| 220.51 \| 8600 \| 0.4612 \| 0.8548 \| 0.8548 \| \| 0.1777 \| 225.64 \| 8800 \| 0.4593 \| 0.8483 \| 0.8483 \| \| 0.1723 \| 230.77 \| 9000 \| 0.4591 \| 0.8499 \| 0.8499 \| \| 0.1727 \| 235.9 \| 9200 \| 0.4602 \| 0.8467 \| 0.8467 \| \| 0.1714 \| 241.03 \| 9400 \| 0.4662 \| 0.8548 \| 0.8548 \| \| 0.1739 \| 246.15 \| 9600 \| 0.4643 \| 0.8450 \| 0.8450 \| \| 0.1721 \| 251.28 \| 9800 \| 0.4632 \| 0.8532 \| 0.8532 \| \| 0.1689 \| 256.41 \| 10000 \| 0.4628 \| 0.8532 \| 0.8532 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_4096_512_15M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_4096_512_15M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:28:17+00:00
null	null		{}	HarshitSheoran/solafune_fields	null	[ "region:us" ]	null	2024-05-03T17:28:18+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_core_tata-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.4640 - F1 Score: 0.8320 - Accuracy: 0.8320 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5782 \| 5.13 \| 200 \| 0.5315 \| 0.7406 \| 0.7406 \| \| 0.4665 \| 10.26 \| 400 \| 0.4535 \| 0.7895 \| 0.7896 \| \| 0.3912 \| 15.38 \| 600 \| 0.3940 \| 0.8189 \| 0.8189 \| \| 0.3406 \| 20.51 \| 800 \| 0.3676 \| 0.8482 \| 0.8483 \| \| 0.301 \| 25.64 \| 1000 \| 0.3680 \| 0.8676 \| 0.8679 \| \| 0.2781 \| 30.77 \| 1200 \| 0.3465 \| 0.8596 \| 0.8597 \| \| 0.2586 \| 35.9 \| 1400 \| 0.3497 \| 0.8662 \| 0.8662 \| \| 0.2365 \| 41.03 \| 1600 \| 0.3888 \| 0.8575 \| 0.8581 \| \| 0.2231 \| 46.15 \| 1800 \| 0.3801 \| 0.8547 \| 0.8548 \| \| 0.2111 \| 51.28 \| 2000 \| 0.3956 \| 0.8612 \| 0.8613 \| \| 0.1949 \| 56.41 \| 2200 \| 0.4369 \| 0.8532 \| 0.8532 \| \| 0.1843 \| 61.54 \| 2400 \| 0.4161 \| 0.8611 \| 0.8613 \| \| 0.1706 \| 66.67 \| 2600 \| 0.4586 \| 0.8659 \| 0.8662 \| \| 0.1597 \| 71.79 \| 2800 \| 0.4525 \| 0.8679 \| 0.8679 \| \| 0.1529 \| 76.92 \| 3000 \| 0.4764 \| 0.8449 \| 0.8450 \| \| 0.1405 \| 82.05 \| 3200 \| 0.5161 \| 0.8547 \| 0.8548 \| \| 0.1323 \| 87.18 \| 3400 \| 0.5201 \| 0.8662 \| 0.8662 \| \| 0.1275 \| 92.31 \| 3600 \| 0.5121 \| 0.8628 \| 0.8630 \| \| 0.1212 \| 97.44 \| 3800 \| 0.5360 \| 0.8645 \| 0.8646 \| \| 0.1135 \| 102.56 \| 4000 \| 0.5797 \| 0.8595 \| 0.8597 \| \| 0.11 \| 107.69 \| 4200 \| 0.5665 \| 0.8613 \| 0.8613 \| \| 0.1041 \| 112.82 \| 4400 \| 0.5754 \| 0.8597 \| 0.8597 \| \| 0.1008 \| 117.95 \| 4600 \| 0.5795 \| 0.8547 \| 0.8548 \| \| 0.093 \| 123.08 \| 4800 \| 0.6056 \| 0.8630 \| 0.8630 \| \| 0.0896 \| 128.21 \| 5000 \| 0.6137 \| 0.8564 \| 0.8564 \| \| 0.0883 \| 133.33 \| 5200 \| 0.6119 \| 0.8564 \| 0.8564 \| \| 0.0813 \| 138.46 \| 5400 \| 0.6257 \| 0.8629 \| 0.8630 \| \| 0.0794 \| 143.59 \| 5600 \| 0.6374 \| 0.8630 \| 0.8630 \| \| 0.0781 \| 148.72 \| 5800 \| 0.6801 \| 0.8597 \| 0.8597 \| \| 0.0753 \| 153.85 \| 6000 \| 0.6478 \| 0.8580 \| 0.8581 \| \| 0.0709 \| 158.97 \| 6200 \| 0.6664 \| 0.8630 \| 0.8630 \| \| 0.0725 \| 164.1 \| 6400 \| 0.6262 \| 0.8564 \| 0.8564 \| \| 0.067 \| 169.23 \| 6600 \| 0.6659 \| 0.8581 \| 0.8581 \| \| 0.0632 \| 174.36 \| 6800 \| 0.6947 \| 0.8564 \| 0.8564 \| \| 0.067 \| 179.49 \| 7000 \| 0.6948 \| 0.8564 \| 0.8564 \| \| 0.0627 \| 184.62 \| 7200 \| 0.7080 \| 0.8564 \| 0.8564 \| \| 0.0611 \| 189.74 \| 7400 \| 0.7102 \| 0.8548 \| 0.8548 \| \| 0.0595 \| 194.87 \| 7600 \| 0.7069 \| 0.8629 \| 0.8630 \| \| 0.062 \| 200.0 \| 7800 \| 0.6852 \| 0.8646 \| 0.8646 \| \| 0.0554 \| 205.13 \| 8000 \| 0.7127 \| 0.8613 \| 0.8613 \| \| 0.0596 \| 210.26 \| 8200 \| 0.6846 \| 0.8548 \| 0.8548 \| \| 0.0534 \| 215.38 \| 8400 \| 0.7266 \| 0.8597 \| 0.8597 \| \| 0.0561 \| 220.51 \| 8600 \| 0.7142 \| 0.8532 \| 0.8532 \| \| 0.0517 \| 225.64 \| 8800 \| 0.7146 \| 0.8532 \| 0.8532 \| \| 0.0512 \| 230.77 \| 9000 \| 0.7151 \| 0.8564 \| 0.8564 \| \| 0.0523 \| 235.9 \| 9200 \| 0.6998 \| 0.8581 \| 0.8581 \| \| 0.053 \| 241.03 \| 9400 \| 0.7092 \| 0.8662 \| 0.8662 \| \| 0.0495 \| 246.15 \| 9600 \| 0.7234 \| 0.8613 \| 0.8613 \| \| 0.0514 \| 251.28 \| 9800 \| 0.7236 \| 0.8613 \| 0.8613 \| \| 0.0514 \| 256.41 \| 10000 \| 0.7248 \| 0.8597 \| 0.8597 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_4096_512_15M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_4096_512_15M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:28:21+00:00
null	null		{}	Aryan0310/bart-small-finetuned-xsum	null	[ "region:us" ]	null	2024-05-03T17:28:40+00:00
text-classification	transformers	<!-- 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. --> # question_classifier This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0621 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 15 \| 0.1063 \| 1.0 \| \| No log \| 2.0 \| 30 \| 0.0621 \| 1.0 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.0 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert/distilbert-base-uncased", "model-index": [{"name": "question_classifier", "results": []}]}	philgrey/question_classifier	null	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T17:29:08+00:00
null	null		{}	PauloSeze/teste	null	[ "region:us" ]	null	2024-05-03T17:29:18+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_300_all-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.2156 - F1 Score: 0.9135 - Accuracy: 0.9135 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.4293 \| 0.54 \| 200 \| 0.2948 \| 0.8828 \| 0.8828 \| \| 0.3066 \| 1.08 \| 400 \| 0.2688 \| 0.8919 \| 0.8919 \| \| 0.2856 \| 1.62 \| 600 \| 0.2528 \| 0.8953 \| 0.8953 \| \| 0.2612 \| 2.16 \| 800 \| 0.2449 \| 0.9021 \| 0.9022 \| \| 0.2536 \| 2.7 \| 1000 \| 0.2343 \| 0.9061 \| 0.9061 \| \| 0.2466 \| 3.24 \| 1200 \| 0.2309 \| 0.9101 \| 0.9101 \| \| 0.2442 \| 3.78 \| 1400 \| 0.2255 \| 0.9123 \| 0.9123 \| \| 0.2415 \| 4.32 \| 1600 \| 0.2236 \| 0.9142 \| 0.9142 \| \| 0.2313 \| 4.86 \| 1800 \| 0.2214 \| 0.9160 \| 0.9160 \| \| 0.232 \| 5.41 \| 2000 \| 0.2196 \| 0.9165 \| 0.9166 \| \| 0.2312 \| 5.95 \| 2200 \| 0.2174 \| 0.9179 \| 0.9179 \| \| 0.2288 \| 6.49 \| 2400 \| 0.2151 \| 0.9184 \| 0.9184 \| \| 0.2271 \| 7.03 \| 2600 \| 0.2132 \| 0.9179 \| 0.9179 \| \| 0.2222 \| 7.57 \| 2800 \| 0.2103 \| 0.9199 \| 0.9199 \| \| 0.2241 \| 8.11 \| 3000 \| 0.2105 \| 0.9206 \| 0.9206 \| \| 0.2221 \| 8.65 \| 3200 \| 0.2076 \| 0.9218 \| 0.9218 \| \| 0.2162 \| 9.19 \| 3400 \| 0.2091 \| 0.9213 \| 0.9213 \| \| 0.2148 \| 9.73 \| 3600 \| 0.2041 \| 0.9235 \| 0.9235 \| \| 0.2211 \| 10.27 \| 3800 \| 0.2025 \| 0.9233 \| 0.9233 \| \| 0.2149 \| 10.81 \| 4000 \| 0.2022 \| 0.9243 \| 0.9243 \| \| 0.2168 \| 11.35 \| 4200 \| 0.2010 \| 0.9241 \| 0.9242 \| \| 0.2128 \| 11.89 \| 4400 \| 0.2016 \| 0.9270 \| 0.9270 \| \| 0.2117 \| 12.43 \| 4600 \| 0.1994 \| 0.9223 \| 0.9223 \| \| 0.2135 \| 12.97 \| 4800 \| 0.1967 \| 0.9280 \| 0.9280 \| \| 0.2084 \| 13.51 \| 5000 \| 0.1976 \| 0.9262 \| 0.9262 \| \| 0.2139 \| 14.05 \| 5200 \| 0.1957 \| 0.9265 \| 0.9265 \| \| 0.2089 \| 14.59 \| 5400 \| 0.1966 \| 0.9260 \| 0.9260 \| \| 0.2067 \| 15.14 \| 5600 \| 0.1960 \| 0.9255 \| 0.9255 \| \| 0.2062 \| 15.68 \| 5800 \| 0.1948 \| 0.9284 \| 0.9284 \| \| 0.2084 \| 16.22 \| 6000 \| 0.1950 \| 0.9253 \| 0.9253 \| \| 0.2052 \| 16.76 \| 6200 \| 0.1935 \| 0.9285 \| 0.9285 \| \| 0.2056 \| 17.3 \| 6400 \| 0.1949 \| 0.9260 \| 0.9260 \| \| 0.2074 \| 17.84 \| 6600 \| 0.1934 \| 0.9258 \| 0.9258 \| \| 0.2021 \| 18.38 \| 6800 \| 0.1926 \| 0.9277 \| 0.9277 \| \| 0.2082 \| 18.92 \| 7000 \| 0.1913 \| 0.9284 \| 0.9284 \| \| 0.2074 \| 19.46 \| 7200 \| 0.1923 \| 0.9282 \| 0.9282 \| \| 0.2013 \| 20.0 \| 7400 \| 0.1917 \| 0.9282 \| 0.9282 \| \| 0.2033 \| 20.54 \| 7600 \| 0.1910 \| 0.9284 \| 0.9284 \| \| 0.2014 \| 21.08 \| 7800 \| 0.1903 \| 0.9294 \| 0.9294 \| \| 0.2051 \| 21.62 \| 8000 \| 0.1904 \| 0.9287 \| 0.9287 \| \| 0.2025 \| 22.16 \| 8200 \| 0.1903 \| 0.9291 \| 0.9291 \| \| 0.1986 \| 22.7 \| 8400 \| 0.1903 \| 0.9282 \| 0.9282 \| \| 0.2057 \| 23.24 \| 8600 \| 0.1898 \| 0.9289 \| 0.9289 \| \| 0.2012 \| 23.78 \| 8800 \| 0.1893 \| 0.9289 \| 0.9289 \| \| 0.2033 \| 24.32 \| 9000 \| 0.1896 \| 0.9294 \| 0.9294 \| \| 0.2009 \| 24.86 \| 9200 \| 0.1898 \| 0.9291 \| 0.9291 \| \| 0.2009 \| 25.41 \| 9400 \| 0.1902 \| 0.9291 \| 0.9291 \| \| 0.1996 \| 25.95 \| 9600 \| 0.1899 \| 0.9289 \| 0.9289 \| \| 0.2019 \| 26.49 \| 9800 \| 0.1894 \| 0.9296 \| 0.9296 \| \| 0.2001 \| 27.03 \| 10000 \| 0.1895 \| 0.9287 \| 0.9287 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_4096_512_15M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_4096_512_15M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:29:51+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_300_all-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.1974 - F1 Score: 0.9209 - Accuracy: 0.9209 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.3768 \| 0.54 \| 200 \| 0.2589 \| 0.8973 \| 0.8973 \| \| 0.2631 \| 1.08 \| 400 \| 0.2351 \| 0.9071 \| 0.9071 \| \| 0.2483 \| 1.62 \| 600 \| 0.2179 \| 0.9130 \| 0.9130 \| \| 0.2279 \| 2.16 \| 800 \| 0.2134 \| 0.9146 \| 0.9147 \| \| 0.2247 \| 2.7 \| 1000 \| 0.2063 \| 0.9203 \| 0.9203 \| \| 0.2187 \| 3.24 \| 1200 \| 0.2048 \| 0.9182 \| 0.9182 \| \| 0.2199 \| 3.78 \| 1400 \| 0.1983 \| 0.9216 \| 0.9216 \| \| 0.2136 \| 4.32 \| 1600 \| 0.1935 \| 0.9238 \| 0.9238 \| \| 0.2055 \| 4.86 \| 1800 \| 0.1926 \| 0.9250 \| 0.925 \| \| 0.2062 \| 5.41 \| 2000 \| 0.1902 \| 0.9292 \| 0.9292 \| \| 0.2048 \| 5.95 \| 2200 \| 0.1900 \| 0.9240 \| 0.9240 \| \| 0.2034 \| 6.49 \| 2400 \| 0.1868 \| 0.9270 \| 0.9270 \| \| 0.2024 \| 7.03 \| 2600 \| 0.1869 \| 0.9284 \| 0.9284 \| \| 0.194 \| 7.57 \| 2800 \| 0.1862 \| 0.9287 \| 0.9287 \| \| 0.2 \| 8.11 \| 3000 \| 0.1853 \| 0.9302 \| 0.9302 \| \| 0.1959 \| 8.65 \| 3200 \| 0.1851 \| 0.9292 \| 0.9292 \| \| 0.1885 \| 9.19 \| 3400 \| 0.1864 \| 0.9296 \| 0.9296 \| \| 0.1888 \| 9.73 \| 3600 \| 0.1827 \| 0.9280 \| 0.9280 \| \| 0.1944 \| 10.27 \| 3800 \| 0.1824 \| 0.9292 \| 0.9292 \| \| 0.1895 \| 10.81 \| 4000 \| 0.1819 \| 0.9304 \| 0.9304 \| \| 0.1917 \| 11.35 \| 4200 \| 0.1797 \| 0.9306 \| 0.9306 \| \| 0.1854 \| 11.89 \| 4400 \| 0.1828 \| 0.9307 \| 0.9307 \| \| 0.1873 \| 12.43 \| 4600 \| 0.1790 \| 0.9296 \| 0.9296 \| \| 0.1861 \| 12.97 \| 4800 \| 0.1771 \| 0.9314 \| 0.9314 \| \| 0.1823 \| 13.51 \| 5000 \| 0.1789 \| 0.9289 \| 0.9289 \| \| 0.187 \| 14.05 \| 5200 \| 0.1809 \| 0.9280 \| 0.9280 \| \| 0.1817 \| 14.59 \| 5400 \| 0.1778 \| 0.9323 \| 0.9323 \| \| 0.1801 \| 15.14 \| 5600 \| 0.1776 \| 0.9316 \| 0.9316 \| \| 0.1801 \| 15.68 \| 5800 \| 0.1781 \| 0.9304 \| 0.9304 \| \| 0.179 \| 16.22 \| 6000 \| 0.1787 \| 0.9316 \| 0.9316 \| \| 0.1784 \| 16.76 \| 6200 \| 0.1779 \| 0.9296 \| 0.9296 \| \| 0.1787 \| 17.3 \| 6400 \| 0.1792 \| 0.9277 \| 0.9277 \| \| 0.1794 \| 17.84 \| 6600 \| 0.1755 \| 0.9328 \| 0.9328 \| \| 0.1748 \| 18.38 \| 6800 \| 0.1776 \| 0.9294 \| 0.9294 \| \| 0.1804 \| 18.92 \| 7000 \| 0.1763 \| 0.9292 \| 0.9292 \| \| 0.1802 \| 19.46 \| 7200 \| 0.1765 \| 0.9316 \| 0.9316 \| \| 0.1741 \| 20.0 \| 7400 \| 0.1755 \| 0.9326 \| 0.9326 \| \| 0.1767 \| 20.54 \| 7600 \| 0.1752 \| 0.9309 \| 0.9309 \| \| 0.1739 \| 21.08 \| 7800 \| 0.1747 \| 0.9312 \| 0.9313 \| \| 0.1747 \| 21.62 \| 8000 \| 0.1748 \| 0.9311 \| 0.9311 \| \| 0.1758 \| 22.16 \| 8200 \| 0.1758 \| 0.9319 \| 0.9319 \| \| 0.1724 \| 22.7 \| 8400 \| 0.1738 \| 0.9336 \| 0.9336 \| \| 0.1762 \| 23.24 \| 8600 \| 0.1753 \| 0.9306 \| 0.9306 \| \| 0.1759 \| 23.78 \| 8800 \| 0.1744 \| 0.9312 \| 0.9313 \| \| 0.1751 \| 24.32 \| 9000 \| 0.1756 \| 0.9307 \| 0.9307 \| \| 0.1727 \| 24.86 \| 9200 \| 0.1742 \| 0.9318 \| 0.9318 \| \| 0.1718 \| 25.41 \| 9400 \| 0.1766 \| 0.9309 \| 0.9309 \| \| 0.1719 \| 25.95 \| 9600 \| 0.1750 \| 0.9321 \| 0.9321 \| \| 0.173 \| 26.49 \| 9800 \| 0.1745 \| 0.9311 \| 0.9311 \| \| 0.1729 \| 27.03 \| 10000 \| 0.1746 \| 0.9311 \| 0.9311 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_4096_512_15M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_4096_512_15M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:30:16+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_300_all-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.1996 - F1 Score: 0.9221 - Accuracy: 0.9221 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.3384 \| 0.54 \| 200 \| 0.2347 \| 0.9054 \| 0.9054 \| \| 0.238 \| 1.08 \| 400 \| 0.2090 \| 0.9182 \| 0.9182 \| \| 0.2278 \| 1.62 \| 600 \| 0.1982 \| 0.9226 \| 0.9226 \| \| 0.2116 \| 2.16 \| 800 \| 0.1958 \| 0.9222 \| 0.9223 \| \| 0.2086 \| 2.7 \| 1000 \| 0.1936 \| 0.9238 \| 0.9238 \| \| 0.2038 \| 3.24 \| 1200 \| 0.1907 \| 0.9248 \| 0.9248 \| \| 0.2055 \| 3.78 \| 1400 \| 0.1871 \| 0.9264 \| 0.9264 \| \| 0.1993 \| 4.32 \| 1600 \| 0.1862 \| 0.9258 \| 0.9258 \| \| 0.1938 \| 4.86 \| 1800 \| 0.1810 \| 0.9292 \| 0.9292 \| \| 0.1914 \| 5.41 \| 2000 \| 0.1831 \| 0.9319 \| 0.9319 \| \| 0.1908 \| 5.95 \| 2200 \| 0.1822 \| 0.9272 \| 0.9272 \| \| 0.1883 \| 6.49 \| 2400 \| 0.1779 \| 0.9301 \| 0.9301 \| \| 0.1878 \| 7.03 \| 2600 \| 0.1818 \| 0.9321 \| 0.9321 \| \| 0.1787 \| 7.57 \| 2800 \| 0.1776 \| 0.9321 \| 0.9321 \| \| 0.1846 \| 8.11 \| 3000 \| 0.1798 \| 0.9304 \| 0.9304 \| \| 0.1792 \| 8.65 \| 3200 \| 0.1748 \| 0.9321 \| 0.9321 \| \| 0.1713 \| 9.19 \| 3400 \| 0.1808 \| 0.9311 \| 0.9311 \| \| 0.1723 \| 9.73 \| 3600 \| 0.1742 \| 0.9307 \| 0.9307 \| \| 0.1774 \| 10.27 \| 3800 \| 0.1742 \| 0.9311 \| 0.9311 \| \| 0.1732 \| 10.81 \| 4000 \| 0.1763 \| 0.9346 \| 0.9346 \| \| 0.1724 \| 11.35 \| 4200 \| 0.1725 \| 0.9345 \| 0.9345 \| \| 0.167 \| 11.89 \| 4400 \| 0.1760 \| 0.9346 \| 0.9346 \| \| 0.1691 \| 12.43 \| 4600 \| 0.1716 \| 0.9333 \| 0.9333 \| \| 0.1638 \| 12.97 \| 4800 \| 0.1699 \| 0.9311 \| 0.9311 \| \| 0.1619 \| 13.51 \| 5000 \| 0.1736 \| 0.9302 \| 0.9302 \| \| 0.1661 \| 14.05 \| 5200 \| 0.1766 \| 0.9273 \| 0.9274 \| \| 0.16 \| 14.59 \| 5400 \| 0.1720 \| 0.9309 \| 0.9309 \| \| 0.1591 \| 15.14 \| 5600 \| 0.1725 \| 0.9323 \| 0.9323 \| \| 0.1584 \| 15.68 \| 5800 \| 0.1710 \| 0.9318 \| 0.9318 \| \| 0.1562 \| 16.22 \| 6000 \| 0.1739 \| 0.9309 \| 0.9309 \| \| 0.1552 \| 16.76 \| 6200 \| 0.1748 \| 0.9321 \| 0.9321 \| \| 0.1551 \| 17.3 \| 6400 \| 0.1751 \| 0.9309 \| 0.9309 \| \| 0.1566 \| 17.84 \| 6600 \| 0.1718 \| 0.9331 \| 0.9331 \| \| 0.1509 \| 18.38 \| 6800 \| 0.1730 \| 0.9314 \| 0.9314 \| \| 0.1546 \| 18.92 \| 7000 \| 0.1714 \| 0.9331 \| 0.9331 \| \| 0.1538 \| 19.46 \| 7200 \| 0.1716 \| 0.9334 \| 0.9334 \| \| 0.15 \| 20.0 \| 7400 \| 0.1728 \| 0.9339 \| 0.9340 \| \| 0.1513 \| 20.54 \| 7600 \| 0.1715 \| 0.9328 \| 0.9328 \| \| 0.1485 \| 21.08 \| 7800 \| 0.1698 \| 0.9326 \| 0.9326 \| \| 0.1484 \| 21.62 \| 8000 \| 0.1706 \| 0.9326 \| 0.9326 \| \| 0.1494 \| 22.16 \| 8200 \| 0.1711 \| 0.9331 \| 0.9331 \| \| 0.1448 \| 22.7 \| 8400 \| 0.1689 \| 0.9333 \| 0.9333 \| \| 0.1468 \| 23.24 \| 8600 \| 0.1715 \| 0.9323 \| 0.9323 \| \| 0.1478 \| 23.78 \| 8800 \| 0.1719 \| 0.9317 \| 0.9318 \| \| 0.1448 \| 24.32 \| 9000 \| 0.1721 \| 0.9317 \| 0.9318 \| \| 0.1454 \| 24.86 \| 9200 \| 0.1707 \| 0.9331 \| 0.9331 \| \| 0.1432 \| 25.41 \| 9400 \| 0.1746 \| 0.9328 \| 0.9328 \| \| 0.1437 \| 25.95 \| 9600 \| 0.1727 \| 0.9326 \| 0.9326 \| \| 0.1448 \| 26.49 \| 9800 \| 0.1718 \| 0.9328 \| 0.9328 \| \| 0.1434 \| 27.03 \| 10000 \| 0.1715 \| 0.9331 \| 0.9331 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_4096_512_15M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_4096_512_15M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:30:56+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K14ac-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.5179 - F1 Score: 0.7401 - Accuracy: 0.7398 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.621 \| 0.97 \| 200 \| 0.5813 \| 0.7034 \| 0.7014 \| \| 0.5829 \| 1.93 \| 400 \| 0.5649 \| 0.7244 \| 0.7234 \| \| 0.5699 \| 2.9 \| 600 \| 0.5881 \| 0.7000 \| 0.6989 \| \| 0.5629 \| 3.86 \| 800 \| 0.5478 \| 0.7259 \| 0.7277 \| \| 0.5513 \| 4.83 \| 1000 \| 0.5659 \| 0.7205 \| 0.7189 \| \| 0.5467 \| 5.8 \| 1200 \| 0.5586 \| 0.7257 \| 0.7241 \| \| 0.5421 \| 6.76 \| 1400 \| 0.5415 \| 0.7344 \| 0.7325 \| \| 0.54 \| 7.73 \| 1600 \| 0.5395 \| 0.7347 \| 0.7328 \| \| 0.5343 \| 8.7 \| 1800 \| 0.5408 \| 0.7313 \| 0.7295 \| \| 0.5335 \| 9.66 \| 2000 \| 0.5432 \| 0.7340 \| 0.7322 \| \| 0.5333 \| 10.63 \| 2200 \| 0.5558 \| 0.7252 \| 0.7241 \| \| 0.5269 \| 11.59 \| 2400 \| 0.5283 \| 0.7408 \| 0.7392 \| \| 0.5308 \| 12.56 \| 2600 \| 0.5436 \| 0.7342 \| 0.7325 \| \| 0.5281 \| 13.53 \| 2800 \| 0.5438 \| 0.7280 \| 0.7265 \| \| 0.5271 \| 14.49 \| 3000 \| 0.5531 \| 0.7231 \| 0.7222 \| \| 0.5225 \| 15.46 \| 3200 \| 0.5235 \| 0.7473 \| 0.7461 \| \| 0.5232 \| 16.43 \| 3400 \| 0.5536 \| 0.7240 \| 0.7231 \| \| 0.5238 \| 17.39 \| 3600 \| 0.5289 \| 0.7389 \| 0.7371 \| \| 0.52 \| 18.36 \| 3800 \| 0.5192 \| 0.7531 \| 0.7525 \| \| 0.5196 \| 19.32 \| 4000 \| 0.5257 \| 0.7443 \| 0.7425 \| \| 0.5165 \| 20.29 \| 4200 \| 0.5332 \| 0.7413 \| 0.7395 \| \| 0.5193 \| 21.26 \| 4400 \| 0.5360 \| 0.7372 \| 0.7356 \| \| 0.5184 \| 22.22 \| 4600 \| 0.5446 \| 0.7270 \| 0.7259 \| \| 0.5189 \| 23.19 \| 4800 \| 0.5232 \| 0.7500 \| 0.7483 \| \| 0.5167 \| 24.15 \| 5000 \| 0.5251 \| 0.7461 \| 0.7443 \| \| 0.5142 \| 25.12 \| 5200 \| 0.5545 \| 0.7270 \| 0.7262 \| \| 0.5155 \| 26.09 \| 5400 \| 0.5322 \| 0.7387 \| 0.7371 \| \| 0.5159 \| 27.05 \| 5600 \| 0.5536 \| 0.7217 \| 0.7213 \| \| 0.5137 \| 28.02 \| 5800 \| 0.5214 \| 0.7500 \| 0.7483 \| \| 0.514 \| 28.99 \| 6000 \| 0.5382 \| 0.7318 \| 0.7304 \| \| 0.5121 \| 29.95 \| 6200 \| 0.5395 \| 0.7333 \| 0.7319 \| \| 0.5146 \| 30.92 \| 6400 \| 0.5213 \| 0.7512 \| 0.7495 \| \| 0.5135 \| 31.88 \| 6600 \| 0.5305 \| 0.7396 \| 0.7380 \| \| 0.509 \| 32.85 \| 6800 \| 0.5327 \| 0.7377 \| 0.7362 \| \| 0.5134 \| 33.82 \| 7000 \| 0.5423 \| 0.7309 \| 0.7298 \| \| 0.51 \| 34.78 \| 7200 \| 0.5412 \| 0.7326 \| 0.7313 \| \| 0.5122 \| 35.75 \| 7400 \| 0.5335 \| 0.7362 \| 0.7346 \| \| 0.508 \| 36.71 \| 7600 \| 0.5288 \| 0.7417 \| 0.7401 \| \| 0.509 \| 37.68 \| 7800 \| 0.5311 \| 0.7423 \| 0.7407 \| \| 0.5105 \| 38.65 \| 8000 \| 0.5237 \| 0.7482 \| 0.7464 \| \| 0.5139 \| 39.61 \| 8200 \| 0.5312 \| 0.7398 \| 0.7383 \| \| 0.5052 \| 40.58 \| 8400 \| 0.5363 \| 0.7345 \| 0.7331 \| \| 0.5068 \| 41.55 \| 8600 \| 0.5293 \| 0.7438 \| 0.7422 \| \| 0.5084 \| 42.51 \| 8800 \| 0.5338 \| 0.7380 \| 0.7365 \| \| 0.5113 \| 43.48 \| 9000 \| 0.5397 \| 0.7341 \| 0.7328 \| \| 0.5068 \| 44.44 \| 9200 \| 0.5338 \| 0.7383 \| 0.7368 \| \| 0.5112 \| 45.41 \| 9400 \| 0.5303 \| 0.7402 \| 0.7386 \| \| 0.504 \| 46.38 \| 9600 \| 0.5351 \| 0.7373 \| 0.7359 \| \| 0.5109 \| 47.34 \| 9800 \| 0.5327 \| 0.7380 \| 0.7365 \| \| 0.5066 \| 48.31 \| 10000 \| 0.5302 \| 0.7408 \| 0.7392 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_4096_512_15M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_4096_512_15M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:30:56+00:00
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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 Dataset 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 Dataset 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]	{"library_name": "transformers", "tags": []}	amuseix/w2v-bert-2.0-bulgarian-CV17.0-FLEURS	null	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-03T17:31:10+00:00
text-generation	transformers		{}	RyotaKadoya1993/Llama-3-JPN-MoE2	null	[ "transformers", "safetensors", "mixtral", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T17:31:12+00:00
null	null		{}	Ketansomewhere/sdxl-pokemon-model	null	[ "region:us" ]	null	2024-05-03T17:31:18+00:00
null	transformers	# Uploaded model - Developed by: eugeniosegala - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	eugeniosegala/model	null	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-03T17:33:07+00:00
null	null		{}	Anoop03031988/Code-Llama-2-7B-instruct-text2sql-GGUF	null	[ "gguf", "region:us" ]	null	2024-05-03T17:33:17+00:00
null	null		{}	d-d-o-s/g1	null	[ "region:us" ]	null	2024-05-03T17:33:37+00:00
null	null		{}	d-d-o-s/g2	null	[ "region:us" ]	null	2024-05-03T17:33:45+00:00
text-generation	transformers	<br/><br/> 8bpw/h8 exl2 quantization of [NeverSleep/Llama-3-Lumimaid-8B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) using default exllamav2 calibration dataset. --- ORIGINAL CARD: ## Lumimaid 0.1 <center><div style="width: 100%;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/630dfb008df86f1e5becadc3/d3QMaxy3peFTpSlWdWF-k.png" style="display: block; margin: auto;"> </div></center> This model uses the Llama3 prompting format Llama3 trained on our RP datasets, we tried to have a balance between the ERP and the RP, not too horny, but just enough. We also added some non-RP dataset, making the model less dumb overall. It should look like a 40%/60% ratio for Non-RP/RP+ERP data. This model includes the new Luminae dataset from Ikari. If you consider trying this model please give us some feedback either on the Community tab on hf or on our [Discord Server](https://discord.gg/MtCVRWTZXY). ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of Lumimaid-8B-v0.1. Switch: [8B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) - [70B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1) - [70B-alt](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt) ## Training data used: - [Aesir datasets](https://huggingface.co/MinervaAI) - [NoRobots](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) - [limarp](https://huggingface.co/datasets/lemonilia/LimaRP) - 8k ctx - [toxic-dpo-v0.1-sharegpt](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt) - [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) - Luminae-i1 (70B/70B-alt) (i2 was not existing when the 70b started training) \| Luminae-i2 (8B) (this one gave better results on the 8b) - Ikari's Dataset - [Squish42/bluemoon-fandom-1-1-rp-cleaned](https://huggingface.co/datasets/Squish42/bluemoon-fandom-1-1-rp-cleaned) - 50% (randomly) - [NobodyExistsOnTheInternet/PIPPAsharegptv2test](https://huggingface.co/datasets/NobodyExistsOnTheInternet/PIPPAsharegptv2test) - 5% (randomly) - [cgato/SlimOrcaDedupCleaned](https://huggingface.co/datasets/cgato/SlimOrcaDedupCleaned) - 5% (randomly) - Airoboros (reduced) - [Capybara](https://huggingface.co/datasets/Undi95/Capybara-ShareGPT/) (reduced) ## Models used (only for 8B) - Initial LumiMaid 8B Finetune - Undi95/Llama-3-Unholy-8B-e4 - Undi95/Llama-3-LewdPlay-8B ## Prompt template: Llama3 ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> {system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {input}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> {output}<\|eot_id\|> ``` ## Others Undi: If you want to support us, you can [here](https://ko-fi.com/undiai). IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek	{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}	JayhC/Llama-3-Lumimaid-8B-v0.1-8bpw-h8-exl2	null	[ "transformers", "safetensors", "llama", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-05-03T17:33:49+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K14ac-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.5143 - F1 Score: 0.7518 - Accuracy: 0.7507 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6069 \| 0.97 \| 200 \| 0.5639 \| 0.7202 \| 0.7189 \| \| 0.5659 \| 1.93 \| 400 \| 0.5476 \| 0.7285 \| 0.7271 \| \| 0.5461 \| 2.9 \| 600 \| 0.5649 \| 0.7130 \| 0.7123 \| \| 0.5404 \| 3.86 \| 800 \| 0.5287 \| 0.7451 \| 0.7440 \| \| 0.5311 \| 4.83 \| 1000 \| 0.5559 \| 0.7271 \| 0.7259 \| \| 0.5299 \| 5.8 \| 1200 \| 0.5429 \| 0.7335 \| 0.7319 \| \| 0.5247 \| 6.76 \| 1400 \| 0.5236 \| 0.7494 \| 0.7477 \| \| 0.5209 \| 7.73 \| 1600 \| 0.5307 \| 0.7500 \| 0.7483 \| \| 0.5164 \| 8.7 \| 1800 \| 0.5279 \| 0.7429 \| 0.7413 \| \| 0.5144 \| 9.66 \| 2000 \| 0.5352 \| 0.7374 \| 0.7359 \| \| 0.5133 \| 10.63 \| 2200 \| 0.5353 \| 0.7373 \| 0.7359 \| \| 0.5063 \| 11.59 \| 2400 \| 0.5131 \| 0.7591 \| 0.7576 \| \| 0.5103 \| 12.56 \| 2600 \| 0.5332 \| 0.7430 \| 0.7416 \| \| 0.5067 \| 13.53 \| 2800 \| 0.5274 \| 0.7448 \| 0.7434 \| \| 0.5053 \| 14.49 \| 3000 \| 0.5314 \| 0.7414 \| 0.7401 \| \| 0.4984 \| 15.46 \| 3200 \| 0.5152 \| 0.7564 \| 0.7549 \| \| 0.5017 \| 16.43 \| 3400 \| 0.5355 \| 0.7398 \| 0.7386 \| \| 0.5011 \| 17.39 \| 3600 \| 0.5153 \| 0.7557 \| 0.7540 \| \| 0.4956 \| 18.36 \| 3800 \| 0.5074 \| 0.7642 \| 0.7634 \| \| 0.4947 \| 19.32 \| 4000 \| 0.5103 \| 0.7619 \| 0.7604 \| \| 0.4904 \| 20.29 \| 4200 \| 0.5248 \| 0.7575 \| 0.7558 \| \| 0.4948 \| 21.26 \| 4400 \| 0.5249 \| 0.7508 \| 0.7492 \| \| 0.4924 \| 22.22 \| 4600 \| 0.5366 \| 0.7369 \| 0.7359 \| \| 0.4933 \| 23.19 \| 4800 \| 0.5116 \| 0.7598 \| 0.7582 \| \| 0.4892 \| 24.15 \| 5000 \| 0.5158 \| 0.7530 \| 0.7513 \| \| 0.4868 \| 25.12 \| 5200 \| 0.5430 \| 0.7402 \| 0.7392 \| \| 0.4865 \| 26.09 \| 5400 \| 0.5305 \| 0.7469 \| 0.7455 \| \| 0.4888 \| 27.05 \| 5600 \| 0.5468 \| 0.7348 \| 0.7340 \| \| 0.4838 \| 28.02 \| 5800 \| 0.5166 \| 0.7548 \| 0.7531 \| \| 0.4852 \| 28.99 \| 6000 \| 0.5230 \| 0.7511 \| 0.7495 \| \| 0.4821 \| 29.95 \| 6200 \| 0.5328 \| 0.7448 \| 0.7434 \| \| 0.4827 \| 30.92 \| 6400 \| 0.5079 \| 0.7651 \| 0.7637 \| \| 0.4839 \| 31.88 \| 6600 \| 0.5158 \| 0.7536 \| 0.7519 \| \| 0.4765 \| 32.85 \| 6800 \| 0.5259 \| 0.7498 \| 0.7483 \| \| 0.4826 \| 33.82 \| 7000 \| 0.5297 \| 0.7448 \| 0.7434 \| \| 0.4768 \| 34.78 \| 7200 \| 0.5302 \| 0.7472 \| 0.7458 \| \| 0.481 \| 35.75 \| 7400 \| 0.5245 \| 0.7505 \| 0.7489 \| \| 0.4745 \| 36.71 \| 7600 \| 0.5234 \| 0.7523 \| 0.7507 \| \| 0.4762 \| 37.68 \| 7800 \| 0.5197 \| 0.7526 \| 0.7510 \| \| 0.4771 \| 38.65 \| 8000 \| 0.5158 \| 0.7521 \| 0.7504 \| \| 0.4792 \| 39.61 \| 8200 \| 0.5203 \| 0.7526 \| 0.7510 \| \| 0.4711 \| 40.58 \| 8400 \| 0.5316 \| 0.7458 \| 0.7443 \| \| 0.4719 \| 41.55 \| 8600 \| 0.5230 \| 0.7523 \| 0.7507 \| \| 0.4748 \| 42.51 \| 8800 \| 0.5263 \| 0.7511 \| 0.7495 \| \| 0.4772 \| 43.48 \| 9000 \| 0.5299 \| 0.7468 \| 0.7452 \| \| 0.4734 \| 44.44 \| 9200 \| 0.5273 \| 0.7502 \| 0.7486 \| \| 0.478 \| 45.41 \| 9400 \| 0.5242 \| 0.7502 \| 0.7486 \| \| 0.4673 \| 46.38 \| 9600 \| 0.5285 \| 0.7480 \| 0.7464 \| \| 0.4758 \| 47.34 \| 9800 \| 0.5244 \| 0.7505 \| 0.7489 \| \| 0.4699 \| 48.31 \| 10000 \| 0.5224 \| 0.7511 \| 0.7495 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_4096_512_15M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_4096_512_15M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:33:59+00:00
text-generation	transformers	![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/pf4d6FA7DriRtVq5HCkxd.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/VcZWbW_eZkJAZZ5ricL4B.png) # Llama-3-Giraffe-70B-Instruct Abacus.AI presents our longer-necked variant of Llama 3 70B - now with the instruct variant! This model has an effective context length of approximately 128k. We have currently trained on ~1.5B tokens. There are our Needle-in-a-Haystack heatmap results. We are conducting further evals of model efficacy and will update our model card as these come in: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/Z4uUhcjgf1P7EPGQyRLkW.png) ## Training Methodology The methodology for training uses [PoSE](https://arxiv.org/abs/2309.10400) and dynamic-NTK interpolation. ### NTK-scaling The scale factor for NTK is 4. Note that we also tried theta-scaling but this did not work as well as NTK scaling in our experiments. ### PoSE We utilise Positional Skip-wise Training (PoSE) with the following parameters: - Number of Chunks: 5 - Max position ID: 32768 ### Data We use on average ~8K long samples from [RedPajama](https://github.com/togethercomputer/RedPajama-Data). ### Hardware We train on 8xH100 GPUs with Deepspeed Zero Stage 3. ## Evaluation Methodology We use the [EasyContext](https://github.com/abacusai/EasyContext/blob/eval_runs/eval_needle.py) implementation of Needle-in-a-Haystack to evaluate Llama-3-Giraffe-70B. We evaluate with the following parameters: - Min context length: 2000 - Max context length: 128000 - Context interval: 4000 - Depth interval: 0.1 - Num samples: 2 - Rnd number digits: 7 - Haystack dir: PaulGrahamEssays ### Adapter Transfer We apply the above techniques first to Llama-3-70B-Base, using LoRA on the Q and K weights only. This adapter is then applied to Llama-3-70B-Instruct, and we release the merged version here.	{"language": ["en"], "license": "llama3", "tags": ["meta", "llama-3"], "pipeline_tag": "text-generation"}	abacusai/Llama-3-Giraffe-70B-Instruct	null	[ "transformers", "safetensors", "llama", "text-generation", "meta", "llama-3", "conversational", "en", "arxiv:2309.10400", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T17:34:02+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me2-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.6021 - F1 Score: 0.6656 - Accuracy: 0.6667 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6627 \| 1.04 \| 200 \| 0.6377 \| 0.5711 \| 0.6331 \| \| 0.6293 \| 2.08 \| 400 \| 0.6279 \| 0.6475 \| 0.6471 \| \| 0.6201 \| 3.12 \| 600 \| 0.6184 \| 0.6407 \| 0.6641 \| \| 0.6192 \| 4.17 \| 800 \| 0.6200 \| 0.6465 \| 0.6536 \| \| 0.6176 \| 5.21 \| 1000 \| 0.6203 \| 0.6515 \| 0.6540 \| \| 0.6144 \| 6.25 \| 1200 \| 0.6163 \| 0.6488 \| 0.6543 \| \| 0.609 \| 7.29 \| 1400 \| 0.6247 \| 0.6541 \| 0.6533 \| \| 0.6101 \| 8.33 \| 1600 \| 0.6215 \| 0.6542 \| 0.6543 \| \| 0.6095 \| 9.38 \| 1800 \| 0.6298 \| 0.6494 \| 0.6471 \| \| 0.6088 \| 10.42 \| 2000 \| 0.6181 \| 0.6592 \| 0.6598 \| \| 0.6097 \| 11.46 \| 2200 \| 0.6094 \| 0.6533 \| 0.6628 \| \| 0.6028 \| 12.5 \| 2400 \| 0.6121 \| 0.6578 \| 0.6621 \| \| 0.6016 \| 13.54 \| 2600 \| 0.6112 \| 0.6534 \| 0.6611 \| \| 0.6037 \| 14.58 \| 2800 \| 0.6100 \| 0.6536 \| 0.6605 \| \| 0.6058 \| 15.62 \| 3000 \| 0.6102 \| 0.6558 \| 0.6621 \| \| 0.6011 \| 16.67 \| 3200 \| 0.6148 \| 0.6607 \| 0.6621 \| \| 0.6004 \| 17.71 \| 3400 \| 0.6086 \| 0.6574 \| 0.6644 \| \| 0.6031 \| 18.75 \| 3600 \| 0.6099 \| 0.6617 \| 0.6660 \| \| 0.6016 \| 19.79 \| 3800 \| 0.6130 \| 0.6658 \| 0.6680 \| \| 0.5948 \| 20.83 \| 4000 \| 0.6156 \| 0.6632 \| 0.6637 \| \| 0.6 \| 21.88 \| 4200 \| 0.6166 \| 0.6623 \| 0.6631 \| \| 0.5969 \| 22.92 \| 4400 \| 0.6148 \| 0.6644 \| 0.6657 \| \| 0.5979 \| 23.96 \| 4600 \| 0.6176 \| 0.6650 \| 0.6650 \| \| 0.5961 \| 25.0 \| 4800 \| 0.6084 \| 0.6649 \| 0.6699 \| \| 0.594 \| 26.04 \| 5000 \| 0.6150 \| 0.6680 \| 0.6689 \| \| 0.5947 \| 27.08 \| 5200 \| 0.6137 \| 0.6665 \| 0.6676 \| \| 0.5937 \| 28.12 \| 5400 \| 0.6101 \| 0.6647 \| 0.6676 \| \| 0.5947 \| 29.17 \| 5600 \| 0.6156 \| 0.6682 \| 0.6683 \| \| 0.5904 \| 30.21 \| 5800 \| 0.6164 \| 0.6698 \| 0.6699 \| \| 0.5929 \| 31.25 \| 6000 \| 0.6136 \| 0.6693 \| 0.6699 \| \| 0.5924 \| 32.29 \| 6200 \| 0.6135 \| 0.6682 \| 0.6689 \| \| 0.5925 \| 33.33 \| 6400 \| 0.6170 \| 0.6693 \| 0.6693 \| \| 0.5933 \| 34.38 \| 6600 \| 0.6090 \| 0.6683 \| 0.6719 \| \| 0.5905 \| 35.42 \| 6800 \| 0.6095 \| 0.6691 \| 0.6722 \| \| 0.5904 \| 36.46 \| 7000 \| 0.6083 \| 0.6705 \| 0.6742 \| \| 0.5866 \| 37.5 \| 7200 \| 0.6134 \| 0.6711 \| 0.6719 \| \| 0.5887 \| 38.54 \| 7400 \| 0.6110 \| 0.6729 \| 0.6748 \| \| 0.5927 \| 39.58 \| 7600 \| 0.6105 \| 0.6705 \| 0.6725 \| \| 0.5898 \| 40.62 \| 7800 \| 0.6198 \| 0.6666 \| 0.6654 \| \| 0.5882 \| 41.67 \| 8000 \| 0.6124 \| 0.6703 \| 0.6709 \| \| 0.5878 \| 42.71 \| 8200 \| 0.6088 \| 0.6686 \| 0.6729 \| \| 0.5902 \| 43.75 \| 8400 \| 0.6109 \| 0.6714 \| 0.6729 \| \| 0.5885 \| 44.79 \| 8600 \| 0.6156 \| 0.6702 \| 0.6699 \| \| 0.5862 \| 45.83 \| 8800 \| 0.6122 \| 0.6709 \| 0.6722 \| \| 0.5905 \| 46.88 \| 9000 \| 0.6144 \| 0.6695 \| 0.6696 \| \| 0.5869 \| 47.92 \| 9200 \| 0.6138 \| 0.6689 \| 0.6693 \| \| 0.5888 \| 48.96 \| 9400 \| 0.6124 \| 0.6695 \| 0.6706 \| \| 0.5884 \| 50.0 \| 9600 \| 0.6128 \| 0.6682 \| 0.6689 \| \| 0.5867 \| 51.04 \| 9800 \| 0.6131 \| 0.6699 \| 0.6706 \| \| 0.5862 \| 52.08 \| 10000 \| 0.6128 \| 0.6678 \| 0.6686 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_4096_512_15M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_4096_512_15M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:34:02+00:00
null	null		{}	Aryan0310/bert-small-finetuned-xsum	null	[ "region:us" ]	null	2024-05-03T17:34:09+00:00
text-generation	transformers		{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "llama"], "base_model": "meta-llama/Meta-Llama-3-70B"}	predibase/Meta-Llama-3-70B-dequantized	null	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "en", "base_model:meta-llama/Meta-Llama-3-70B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T17:35:02+00:00
multiple-choice	transformers	<!-- 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. --> # fine_tuned_copa_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6931 - Accuracy: 0.51 - F1: 0.4857 ## 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: 0.003 - 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 - training_steps: 400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.7257 \| 1.0 \| 50 \| 0.6931 \| 0.49 \| 0.4838 \| \| 0.7001 \| 2.0 \| 100 \| 0.6931 \| 0.48 \| 0.48 \| \| 0.7196 \| 3.0 \| 150 \| 0.6931 \| 0.52 \| 0.4603 \| \| 0.6895 \| 4.0 \| 200 \| 0.6931 \| 0.5 \| 0.4926 \| \| 0.745 \| 5.0 \| 250 \| 0.6931 \| 0.46 \| 0.4244 \| \| 0.7102 \| 6.0 \| 300 \| 0.6931 \| 0.5 \| 0.4861 \| \| 0.7245 \| 7.0 \| 350 \| 0.6931 \| 0.55 \| 0.5391 \| \| 0.7283 \| 8.0 \| 400 \| 0.6931 \| 0.51 \| 0.4857 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "fine_tuned_copa_croslo", "results": []}]}	lenatr99/fine_tuned_copa_croslo	null	[ "transformers", "safetensors", "bert", "multiple-choice", "generated_from_trainer", "base_model:EMBEDDIA/crosloengual-bert", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	null	2024-05-03T17:35:12+00:00
null	null	<!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Steelskull/L3-Arcania-4x8b	{}	mradermacher/L3-Arcania-4x8b-GGUF	null	[ "gguf", "region:us" ]	null	2024-05-03T17:36:03+00:00
null	null		{}	d-d-o-s/g3	null	[ "region:us" ]	null	2024-05-03T17:36:50+00:00
null	peft	<!-- 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. --> # loha_fine_tuned_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6890 - Accuracy: 0.52 - F1: 0.5212 ## 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 - training_steps: 400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.7072 \| 1.0 \| 50 \| 0.6896 \| 0.52 \| 0.5212 \| \| 0.6973 \| 2.0 \| 100 \| 0.6894 \| 0.53 \| 0.5312 \| \| 0.6988 \| 3.0 \| 150 \| 0.6892 \| 0.54 \| 0.5411 \| \| 0.7016 \| 4.0 \| 200 \| 0.6891 \| 0.53 \| 0.5312 \| \| 0.7034 \| 5.0 \| 250 \| 0.6890 \| 0.52 \| 0.5212 \| \| 0.6978 \| 6.0 \| 300 \| 0.6890 \| 0.51 \| 0.5112 \| \| 0.6965 \| 7.0 \| 350 \| 0.6890 \| 0.51 \| 0.5112 \| \| 0.6907 \| 8.0 \| 400 \| 0.6890 \| 0.52 \| 0.5212 \| ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "cc-by-4.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "loha_fine_tuned_croslo", "results": []}]}	lenatr99/loha_fine_tuned_croslo	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:EMBEDDIA/crosloengual-bert", "license:cc-by-4.0", "region:us" ]	null	2024-05-03T17:36:50+00:00
robotics	null	# Introduction This model is currently being tested, further details to be added in future for now see [robot_learning_baselines](https://github.com/peterdavidfagan/robot_learning_baselines).	{"license": "apache-2.0", "datasets": ["peterdavidfagan/transporter_networks"], "pipeline_tag": "robotics"}	peterdavidfagan/transporter_networks	null	[ "tflite", "robotics", "dataset:peterdavidfagan/transporter_networks", "license:apache-2.0", "region:us" ]	null	2024-05-03T17:38:29+00:00
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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 Dataset 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 Dataset 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]	{"library_name": "transformers", "tags": ["unsloth"]}	sparvekar/critique_lora_model	null	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-03T17:38:51+00:00
null	null		{"license": "openrail"}	enescakircali/Melis-Fis	null	[ "license:openrail", "region:us" ]	null	2024-05-03T17:39:01+00:00
text-generation	transformers	# PULI LlumiX 32K instruct (6.74B billion parameter) Intruct finetuned version of NYTK/PULI-LlumiX-32K. ## Training platform [Lightning AI Studio](https://lightning.ai/studios) L4 GPU ## Hyper parameters - Epoch: 3 - LoRA rank (r): 16 - LoRA alpha: 16 - Lr: 2e-4 - Lr scheduler: cosine - Optimizer: adamw_8bit - Weight decay: 0.01 ## Dataset boapps/szurkemarha In total ~30k instructions were selected. ## Prompt template: ChatML ``` <\|im_start\|>system Az alábbiakban egy feladatot leíró utasítás található. Írjál olyan választ, amely megfelelően teljesíti a kérést.<\|im_end\|> <\|im_start\|>user Ki a legerősebb szuperhős?<\|im_end\|> <\|im_start\|>assistant A legerősebb szuperhős a Marvel univerzumában Hulk.<\|im_end\|> ``` ## Base model - Trained with OpenChatKit [github](https://github.com/togethercomputer/OpenChatKit) - The [LLaMA-2-7B-32K](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K) model were continuously pretrained on Hungarian dataset - The model has been extended to a context length of 32K with position interpolation - Checkpoint: 100 000 steps ## Dataset for continued pretraining - Hungarian: 7.9 billion words, documents (763K) that exceed 5000 words in length - English: Long Context QA (2 billion words), BookSum (78 million words) ## Limitations - max_seq_length = 32 768 - float16 - vocab size: 32 000	{"language": ["hu", "en"], "license": "llama2", "tags": ["puli", "text-generation-inference", "transformers", "unsloth", "llama", "trl", "finetuned"], "datasets": ["boapps/szurkemarha"], "base_model": "NYTK/PULI-LlumiX-32K", "pipeline_tag": "text-generation"}	ariel-ml/PULI-LlumiX-32K-instruct-lora	null	[ "transformers", "safetensors", "puli", "text-generation-inference", "unsloth", "llama", "trl", "finetuned", "text-generation", "conversational", "hu", "en", "dataset:boapps/szurkemarha", "base_model:NYTK/PULI-LlumiX-32K", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-05-03T17:39:54+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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 Dataset 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 Dataset 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]	{"library_name": "transformers", "tags": []}	golf2248/g2rr5al	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T17:40:20+00:00
text-generation	transformers	# PULI LlumiX 32K instruct (6.74B billion parameter) Intruct finetuned version of NYTK/PULI-LlumiX-32K. ## Training platform [Lightning AI Studio](https://lightning.ai/studios) L4 GPU ## Hyper parameters - Epoch: 3 - LoRA rank (r): 16 - LoRA alpha: 16 - Lr: 2e-4 - Lr scheduler: cosine - Optimizer: adamw_8bit - Weight decay: 0.01 ## Dataset boapps/szurkemarha In total ~30k instructions were selected. ## Prompt template: ChatML ``` <\|im_start\|>system Az alábbiakban egy feladatot leíró utasítás található. Írjál olyan választ, amely megfelelően teljesíti a kérést.<\|im_end\|> <\|im_start\|>user Ki a legerősebb szuperhős?<\|im_end\|> <\|im_start\|>assistant A legerősebb szuperhős a Marvel univerzumában Hulk.<\|im_end\|> ``` ## Base model - Trained with OpenChatKit [github](https://github.com/togethercomputer/OpenChatKit) - The [LLaMA-2-7B-32K](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K) model were continuously pretrained on Hungarian dataset - The model has been extended to a context length of 32K with position interpolation - Checkpoint: 100 000 steps ## Dataset for continued pretraining - Hungarian: 7.9 billion words, documents (763K) that exceed 5000 words in length - English: Long Context QA (2 billion words), BookSum (78 million words) ## Limitations - max_seq_length = 32 768 - float16 - vocab size: 32 000	{"language": ["hu", "en"], "license": "llama2", "tags": ["puli", "text-generation-inference", "transformers", "unsloth", "llama", "trl", "finetuned"], "datasets": ["boapps/szurkemarha"], "base_model": "NYTK/PULI-LlumiX-32K", "pipeline_tag": "text-generation"}	ariel-ml/PULI-LlumiX-32K-instruct-f16	null	[ "transformers", "safetensors", "llama", "text-generation", "puli", "text-generation-inference", "unsloth", "trl", "finetuned", "conversational", "custom_code", "hu", "en", "dataset:boapps/szurkemarha", "base_model:NYTK/PULI-LlumiX-32K", "license:llama2", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T17:40:22+00:00
text-generation	transformers	# merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Jayant9928/orpo_med_v3](https://huggingface.co/Jayant9928/orpo_med_v3) as a base. ### Models Merged The following models were included in the merge: * [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Jayant9928/orpo_med_v3 parameters: density: 0.53 weight: 0.4 - model: meta-llama/Meta-Llama-3-8B-Instruct parameters: density: 0.53 weight: 0.3 merge_method: dare_ties base_model: Jayant9928/orpo_med_v3 tokenizer_source: union parameters: int8_mask: true dtype: float16 ```	{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Jayant9928/orpo_med_v3", "meta-llama/Meta-Llama-3-8B-Instruct"]}	Muhammad2003/Dmitry69	null	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Jayant9928/orpo_med_v3", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T17:40:29+00:00
text-generation	transformers		{}	dgonier/Llama-3-8B-ORPO-Debate-Card-Cutting	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T17:41:36+00:00
null	null		{}	terry69/llama2-poison-50p	null	[ "tensorboard", "safetensors", "region:us" ]	null	2024-05-03T17:42:35+00:00
null	null		{}	bwjc117/motomura	null	[ "region:us" ]	null	2024-05-03T17:43:32+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K14ac-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.5137 - F1 Score: 0.7465 - Accuracy: 0.7452 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5962 \| 0.97 \| 200 \| 0.5550 \| 0.7252 \| 0.7241 \| \| 0.5522 \| 1.93 \| 400 \| 0.5384 \| 0.7392 \| 0.7377 \| \| 0.5347 \| 2.9 \| 600 \| 0.5523 \| 0.7224 \| 0.7213 \| \| 0.5295 \| 3.86 \| 800 \| 0.5225 \| 0.7510 \| 0.7495 \| \| 0.5188 \| 4.83 \| 1000 \| 0.5540 \| 0.7280 \| 0.7271 \| \| 0.5171 \| 5.8 \| 1200 \| 0.5321 \| 0.7383 \| 0.7368 \| \| 0.5098 \| 6.76 \| 1400 \| 0.5174 \| 0.7512 \| 0.7495 \| \| 0.5041 \| 7.73 \| 1600 \| 0.5242 \| 0.7454 \| 0.7437 \| \| 0.499 \| 8.7 \| 1800 \| 0.5289 \| 0.7457 \| 0.7443 \| \| 0.4945 \| 9.66 \| 2000 \| 0.5280 \| 0.7488 \| 0.7474 \| \| 0.4928 \| 10.63 \| 2200 \| 0.5247 \| 0.7495 \| 0.7480 \| \| 0.4843 \| 11.59 \| 2400 \| 0.5053 \| 0.7654 \| 0.7640 \| \| 0.4847 \| 12.56 \| 2600 \| 0.5261 \| 0.7461 \| 0.7446 \| \| 0.4807 \| 13.53 \| 2800 \| 0.5256 \| 0.7504 \| 0.7489 \| \| 0.478 \| 14.49 \| 3000 \| 0.5253 \| 0.7434 \| 0.7419 \| \| 0.4683 \| 15.46 \| 3200 \| 0.5126 \| 0.7638 \| 0.7622 \| \| 0.4695 \| 16.43 \| 3400 \| 0.5248 \| 0.7485 \| 0.7470 \| \| 0.4665 \| 17.39 \| 3600 \| 0.5196 \| 0.7578 \| 0.7561 \| \| 0.4595 \| 18.36 \| 3800 \| 0.5050 \| 0.7626 \| 0.7619 \| \| 0.4571 \| 19.32 \| 4000 \| 0.5115 \| 0.7579 \| 0.7564 \| \| 0.4522 \| 20.29 \| 4200 \| 0.5346 \| 0.7557 \| 0.7540 \| \| 0.4557 \| 21.26 \| 4400 \| 0.5250 \| 0.7566 \| 0.7549 \| \| 0.449 \| 22.22 \| 4600 \| 0.5417 \| 0.7443 \| 0.7431 \| \| 0.4484 \| 23.19 \| 4800 \| 0.5210 \| 0.7545 \| 0.7528 \| \| 0.4437 \| 24.15 \| 5000 \| 0.5327 \| 0.7544 \| 0.7528 \| \| 0.4398 \| 25.12 \| 5200 \| 0.5487 \| 0.7435 \| 0.7425 \| \| 0.4388 \| 26.09 \| 5400 \| 0.5419 \| 0.7453 \| 0.7440 \| \| 0.4372 \| 27.05 \| 5600 \| 0.5656 \| 0.7427 \| 0.7416 \| \| 0.4307 \| 28.02 \| 5800 \| 0.5400 \| 0.7533 \| 0.7516 \| \| 0.429 \| 28.99 \| 6000 \| 0.5285 \| 0.7539 \| 0.7522 \| \| 0.4243 \| 29.95 \| 6200 \| 0.5554 \| 0.7452 \| 0.7437 \| \| 0.4249 \| 30.92 \| 6400 \| 0.5254 \| 0.7546 \| 0.7534 \| \| 0.426 \| 31.88 \| 6600 \| 0.5293 \| 0.7494 \| 0.7477 \| \| 0.4144 \| 32.85 \| 6800 \| 0.5486 \| 0.7502 \| 0.7486 \| \| 0.4206 \| 33.82 \| 7000 \| 0.5444 \| 0.7498 \| 0.7483 \| \| 0.4113 \| 34.78 \| 7200 \| 0.5544 \| 0.7529 \| 0.7513 \| \| 0.4185 \| 35.75 \| 7400 \| 0.5436 \| 0.7481 \| 0.7464 \| \| 0.4096 \| 36.71 \| 7600 \| 0.5489 \| 0.7499 \| 0.7483 \| \| 0.4124 \| 37.68 \| 7800 \| 0.5416 \| 0.7554 \| 0.7537 \| \| 0.4109 \| 38.65 \| 8000 \| 0.5439 \| 0.7488 \| 0.7470 \| \| 0.4081 \| 39.61 \| 8200 \| 0.5420 \| 0.7506 \| 0.7489 \| \| 0.4018 \| 40.58 \| 8400 \| 0.5606 \| 0.7492 \| 0.7477 \| \| 0.4028 \| 41.55 \| 8600 \| 0.5520 \| 0.7524 \| 0.7507 \| \| 0.4059 \| 42.51 \| 8800 \| 0.5511 \| 0.7539 \| 0.7522 \| \| 0.4061 \| 43.48 \| 9000 \| 0.5581 \| 0.7514 \| 0.7498 \| \| 0.4036 \| 44.44 \| 9200 \| 0.5532 \| 0.7521 \| 0.7504 \| \| 0.408 \| 45.41 \| 9400 \| 0.5504 \| 0.7551 \| 0.7534 \| \| 0.3953 \| 46.38 \| 9600 \| 0.5564 \| 0.7517 \| 0.7501 \| \| 0.4054 \| 47.34 \| 9800 \| 0.5496 \| 0.7512 \| 0.7495 \| \| 0.3949 \| 48.31 \| 10000 \| 0.5515 \| 0.7527 \| 0.7510 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_4096_512_15M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_4096_512_15M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:44:41+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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 Dataset 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 Dataset 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]	{"library_name": "transformers", "tags": []}	BotCuddles/gemma-2b-it-ft-mental	null	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T17:44:46+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me2-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.5973 - F1 Score: 0.6642 - Accuracy: 0.6693 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6513 \| 1.04 \| 200 \| 0.6213 \| 0.6317 \| 0.6549 \| \| 0.6199 \| 2.08 \| 400 \| 0.6293 \| 0.6443 \| 0.6426 \| \| 0.6123 \| 3.12 \| 600 \| 0.6108 \| 0.6493 \| 0.6680 \| \| 0.6106 \| 4.17 \| 800 \| 0.6176 \| 0.6585 \| 0.6588 \| \| 0.6064 \| 5.21 \| 1000 \| 0.6064 \| 0.6673 \| 0.6758 \| \| 0.6033 \| 6.25 \| 1200 \| 0.6056 \| 0.6679 \| 0.6738 \| \| 0.5957 \| 7.29 \| 1400 \| 0.6180 \| 0.6640 \| 0.6624 \| \| 0.5959 \| 8.33 \| 1600 \| 0.6186 \| 0.6685 \| 0.6667 \| \| 0.5932 \| 9.38 \| 1800 \| 0.6305 \| 0.6574 \| 0.6549 \| \| 0.5919 \| 10.42 \| 2000 \| 0.6082 \| 0.6766 \| 0.6774 \| \| 0.593 \| 11.46 \| 2200 \| 0.6020 \| 0.6772 \| 0.6826 \| \| 0.5848 \| 12.5 \| 2400 \| 0.6121 \| 0.6768 \| 0.6758 \| \| 0.5841 \| 13.54 \| 2600 \| 0.6098 \| 0.6725 \| 0.6729 \| \| 0.5834 \| 14.58 \| 2800 \| 0.6081 \| 0.6715 \| 0.6719 \| \| 0.5864 \| 15.62 \| 3000 \| 0.6126 \| 0.6760 \| 0.6751 \| \| 0.5818 \| 16.67 \| 3200 \| 0.6155 \| 0.6718 \| 0.6699 \| \| 0.5802 \| 17.71 \| 3400 \| 0.6068 \| 0.6744 \| 0.6751 \| \| 0.5828 \| 18.75 \| 3600 \| 0.6077 \| 0.6713 \| 0.6719 \| \| 0.5803 \| 19.79 \| 3800 \| 0.6130 \| 0.6742 \| 0.6735 \| \| 0.5743 \| 20.83 \| 4000 \| 0.6197 \| 0.6699 \| 0.6680 \| \| 0.5769 \| 21.88 \| 4200 \| 0.6318 \| 0.6626 \| 0.6601 \| \| 0.5746 \| 22.92 \| 4400 \| 0.6185 \| 0.6679 \| 0.6663 \| \| 0.5741 \| 23.96 \| 4600 \| 0.6256 \| 0.6661 \| 0.6637 \| \| 0.5728 \| 25.0 \| 4800 \| 0.6091 \| 0.6691 \| 0.6693 \| \| 0.5694 \| 26.04 \| 5000 \| 0.6206 \| 0.6678 \| 0.6660 \| \| 0.5706 \| 27.08 \| 5200 \| 0.6181 \| 0.6659 \| 0.6644 \| \| 0.5682 \| 28.12 \| 5400 \| 0.6203 \| 0.6699 \| 0.6680 \| \| 0.5684 \| 29.17 \| 5600 \| 0.6188 \| 0.6727 \| 0.6716 \| \| 0.5626 \| 30.21 \| 5800 \| 0.6244 \| 0.6680 \| 0.6663 \| \| 0.5659 \| 31.25 \| 6000 \| 0.6298 \| 0.6645 \| 0.6621 \| \| 0.5652 \| 32.29 \| 6200 \| 0.6119 \| 0.6672 \| 0.6667 \| \| 0.565 \| 33.33 \| 6400 \| 0.6228 \| 0.6646 \| 0.6628 \| \| 0.5636 \| 34.38 \| 6600 \| 0.6187 \| 0.6672 \| 0.6663 \| \| 0.5624 \| 35.42 \| 6800 \| 0.6183 \| 0.6671 \| 0.6660 \| \| 0.5631 \| 36.46 \| 7000 \| 0.6131 \| 0.6729 \| 0.6729 \| \| 0.5575 \| 37.5 \| 7200 \| 0.6277 \| 0.6620 \| 0.6601 \| \| 0.5588 \| 38.54 \| 7400 \| 0.6218 \| 0.6689 \| 0.6680 \| \| 0.5624 \| 39.58 \| 7600 \| 0.6139 \| 0.6722 \| 0.6722 \| \| 0.56 \| 40.62 \| 7800 \| 0.6328 \| 0.6586 \| 0.6562 \| \| 0.5583 \| 41.67 \| 8000 \| 0.6191 \| 0.6650 \| 0.6634 \| \| 0.5563 \| 42.71 \| 8200 \| 0.6189 \| 0.6708 \| 0.6706 \| \| 0.5599 \| 43.75 \| 8400 \| 0.6180 \| 0.6674 \| 0.6663 \| \| 0.5572 \| 44.79 \| 8600 \| 0.6239 \| 0.6643 \| 0.6624 \| \| 0.5543 \| 45.83 \| 8800 \| 0.6204 \| 0.6676 \| 0.6670 \| \| 0.5576 \| 46.88 \| 9000 \| 0.6294 \| 0.6597 \| 0.6575 \| \| 0.5544 \| 47.92 \| 9200 \| 0.6281 \| 0.6599 \| 0.6579 \| \| 0.555 \| 48.96 \| 9400 \| 0.6271 \| 0.6637 \| 0.6621 \| \| 0.555 \| 50.0 \| 9600 \| 0.6273 \| 0.6652 \| 0.6634 \| \| 0.5544 \| 51.04 \| 9800 \| 0.6263 \| 0.6641 \| 0.6624 \| \| 0.5523 \| 52.08 \| 10000 \| 0.6264 \| 0.6642 \| 0.6624 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_4096_512_15M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_4096_512_15M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:44:48+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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 Dataset 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 Dataset 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]	{"library_name": "transformers", "tags": []}	golf2248/ox9od86	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T17:44:59+00:00
text-generation	transformers	<!-- 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. --> # sft-fsi-masked-loss This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3788 ## 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 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 17.1473 \| 0.5333 \| 1 \| 18.7762 \| \| 17.1473 \| 1.6 \| 3 \| 9.0847 \| \| 11.7791 \| 2.6667 \| 5 \| 3.0550 \| \| 11.7791 \| 3.7333 \| 7 \| 0.6706 \| \| 11.7791 \| 4.8 \| 9 \| 0.6697 \| \| 1.4045 \| 5.8667 \| 11 \| 0.5653 \| \| 1.4045 \| 6.9333 \| 13 \| 0.4982 \| \| 0.6622 \| 8.0 \| 15 \| 0.4756 \| \| 0.6622 \| 8.5333 \| 16 \| 0.4777 \| \| 0.6622 \| 9.6 \| 18 \| 0.4338 \| \| 0.5586 \| 10.6667 \| 20 \| 0.3788 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"tags": ["trl", "sft", "generated_from_trainer"], "model-index": [{"name": "sft-fsi-masked-loss", "results": []}]}	jamesoneill12/sft-fsi-masked-loss	null	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T17:45:22+00:00
text2text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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 Dataset 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 Dataset 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]	{"library_name": "transformers", "tags": []}	Ragab167/m2m_translation_v2	null	[ "transformers", "safetensors", "m2m_100", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T17:45:31+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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 Dataset 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 Dataset 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]	{"library_name": "transformers", "tags": []}	cilantro9246/vlrcskl	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T17:45:32+00:00
text-classification	transformers		{}	philgrey/question	null	[ "transformers", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T17:45:40+00:00
text-generation	transformers	<br/><br/> 3bpw/h6 exl2 quantization of [NeverSleep/Llama-3-Lumimaid-70B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1) using default exllamav2 calibration dataset. --- ORIGINAL CARD: ## Lumimaid 0.1 <center><div style="width: 100%;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/630dfb008df86f1e5becadc3/d3QMaxy3peFTpSlWdWF-k.png" style="display: block; margin: auto;"> </div></center> This model uses the Llama3 prompting format Llama3 trained on our RP datasets, we tried to have a balance between the ERP and the RP, not too horny, but just enough. We also added some non-RP dataset, making the model less dumb overall. It should look like a 40%/60% ratio for Non-RP/RP+ERP data. This model includes the new Luminae dataset from Ikari. If you consider trying this model please give us some feedback either on the Community tab on hf or on our [Discord Server](https://discord.gg/MtCVRWTZXY). ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of Lumimaid-70B-v0.1. Switch: [8B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) - [70B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1) - [70B-alt](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt) ## Training data used: - [Aesir datasets](https://huggingface.co/MinervaAI) - [NoRobots](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) - [limarp](https://huggingface.co/datasets/lemonilia/LimaRP) - 8k ctx - [toxic-dpo-v0.1-sharegpt](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt) - [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) - Luminae-i1 (70B/70B-alt) (i2 was not existing when the 70b started training) \| Luminae-i2 (8B) (this one gave better results on the 8b) - Ikari's Dataset - [Squish42/bluemoon-fandom-1-1-rp-cleaned](https://huggingface.co/datasets/Squish42/bluemoon-fandom-1-1-rp-cleaned) - 50% (randomly) - [NobodyExistsOnTheInternet/PIPPAsharegptv2test](https://huggingface.co/datasets/NobodyExistsOnTheInternet/PIPPAsharegptv2test) - 5% (randomly) - [cgato/SlimOrcaDedupCleaned](https://huggingface.co/datasets/cgato/SlimOrcaDedupCleaned) - 5% (randomly) - Airoboros (reduced) - [Capybara](https://huggingface.co/datasets/Undi95/Capybara-ShareGPT/) (reduced) ## Models used (only for 8B) - Initial LumiMaid 8B Finetune - Undi95/Llama-3-Unholy-8B-e4 - Undi95/Llama-3-LewdPlay-8B ## Prompt template: Llama3 ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> {system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {input}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> {output}<\|eot_id\|> ``` ## Others Undi: If you want to support us, you can [here](https://ko-fi.com/undiai). IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek	{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}	JayhC/Llama-3-Lumimaid-70B-v0.1-3bpw-h6-exl2	null	[ "transformers", "safetensors", "llama", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "3-bit", "region:us" ]	null	2024-05-03T17:45:41+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me2-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.6006 - F1 Score: 0.6727 - Accuracy: 0.6748 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6452 \| 1.04 \| 200 \| 0.6178 \| 0.6311 \| 0.6566 \| \| 0.6153 \| 2.08 \| 400 \| 0.6301 \| 0.6434 \| 0.6409 \| \| 0.6065 \| 3.12 \| 600 \| 0.6049 \| 0.6694 \| 0.6804 \| \| 0.603 \| 4.17 \| 800 \| 0.6251 \| 0.6584 \| 0.6559 \| \| 0.5966 \| 5.21 \| 1000 \| 0.6064 \| 0.6686 \| 0.6755 \| \| 0.5941 \| 6.25 \| 1200 \| 0.6047 \| 0.6722 \| 0.6745 \| \| 0.5852 \| 7.29 \| 1400 \| 0.6158 \| 0.6688 \| 0.6673 \| \| 0.5845 \| 8.33 \| 1600 \| 0.6197 \| 0.6646 \| 0.6624 \| \| 0.5814 \| 9.38 \| 1800 \| 0.6251 \| 0.6585 \| 0.6559 \| \| 0.5764 \| 10.42 \| 2000 \| 0.6045 \| 0.6777 \| 0.6804 \| \| 0.5778 \| 11.46 \| 2200 \| 0.6041 \| 0.6711 \| 0.6758 \| \| 0.5668 \| 12.5 \| 2400 \| 0.6185 \| 0.6726 \| 0.6722 \| \| 0.5644 \| 13.54 \| 2600 \| 0.6260 \| 0.6671 \| 0.6657 \| \| 0.5642 \| 14.58 \| 2800 \| 0.6139 \| 0.6665 \| 0.6670 \| \| 0.5637 \| 15.62 \| 3000 \| 0.6193 \| 0.6636 \| 0.6631 \| \| 0.5576 \| 16.67 \| 3200 \| 0.6239 \| 0.6590 \| 0.6579 \| \| 0.5523 \| 17.71 \| 3400 \| 0.6274 \| 0.6560 \| 0.6546 \| \| 0.556 \| 18.75 \| 3600 \| 0.6327 \| 0.6570 \| 0.6553 \| \| 0.5516 \| 19.79 \| 3800 \| 0.6394 \| 0.6645 \| 0.6628 \| \| 0.5438 \| 20.83 \| 4000 \| 0.6292 \| 0.6633 \| 0.6621 \| \| 0.5438 \| 21.88 \| 4200 \| 0.6535 \| 0.6475 \| 0.6448 \| \| 0.5386 \| 22.92 \| 4400 \| 0.6413 \| 0.6594 \| 0.6579 \| \| 0.5357 \| 23.96 \| 4600 \| 0.6465 \| 0.6519 \| 0.6497 \| \| 0.5325 \| 25.0 \| 4800 \| 0.6459 \| 0.6539 \| 0.6517 \| \| 0.5274 \| 26.04 \| 5000 \| 0.6459 \| 0.6504 \| 0.6484 \| \| 0.526 \| 27.08 \| 5200 \| 0.6466 \| 0.6535 \| 0.6520 \| \| 0.523 \| 28.12 \| 5400 \| 0.6561 \| 0.6495 \| 0.6471 \| \| 0.5191 \| 29.17 \| 5600 \| 0.6623 \| 0.6535 \| 0.6514 \| \| 0.5115 \| 30.21 \| 5800 \| 0.6637 \| 0.6552 \| 0.6533 \| \| 0.5137 \| 31.25 \| 6000 \| 0.6703 \| 0.6423 \| 0.6396 \| \| 0.5119 \| 32.29 \| 6200 \| 0.6508 \| 0.6502 \| 0.6487 \| \| 0.5088 \| 33.33 \| 6400 \| 0.6721 \| 0.6439 \| 0.6413 \| \| 0.5057 \| 34.38 \| 6600 \| 0.6668 \| 0.6495 \| 0.6491 \| \| 0.5043 \| 35.42 \| 6800 \| 0.6701 \| 0.6503 \| 0.6481 \| \| 0.506 \| 36.46 \| 7000 \| 0.6517 \| 0.6510 \| 0.6497 \| \| 0.4961 \| 37.5 \| 7200 \| 0.6784 \| 0.6473 \| 0.6452 \| \| 0.4929 \| 38.54 \| 7400 \| 0.6843 \| 0.6489 \| 0.6471 \| \| 0.4942 \| 39.58 \| 7600 \| 0.6631 \| 0.6505 \| 0.6510 \| \| 0.4938 \| 40.62 \| 7800 \| 0.6954 \| 0.6413 \| 0.6386 \| \| 0.4898 \| 41.67 \| 8000 \| 0.6708 \| 0.6492 \| 0.6474 \| \| 0.4866 \| 42.71 \| 8200 \| 0.6798 \| 0.6518 \| 0.6504 \| \| 0.4901 \| 43.75 \| 8400 \| 0.6709 \| 0.6427 \| 0.6413 \| \| 0.4866 \| 44.79 \| 8600 \| 0.6799 \| 0.6513 \| 0.6494 \| \| 0.4819 \| 45.83 \| 8800 \| 0.6798 \| 0.6502 \| 0.6494 \| \| 0.4847 \| 46.88 \| 9000 \| 0.6948 \| 0.6396 \| 0.6370 \| \| 0.4809 \| 47.92 \| 9200 \| 0.6960 \| 0.6417 \| 0.6393 \| \| 0.4816 \| 48.96 \| 9400 \| 0.6919 \| 0.6486 \| 0.6468 \| \| 0.482 \| 50.0 \| 9600 \| 0.6903 \| 0.6467 \| 0.6445 \| \| 0.4792 \| 51.04 \| 9800 \| 0.6937 \| 0.6468 \| 0.6445 \| \| 0.4762 \| 52.08 \| 10000 \| 0.6930 \| 0.6458 \| 0.6435 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_4096_512_15M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_4096_512_15M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:45:43+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K9ac-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4994 - F1 Score: 0.7632 - Accuracy: 0.7625 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6178 \| 1.15 \| 200 \| 0.5812 \| 0.7080 \| 0.7074 \| \| 0.5678 \| 2.3 \| 400 \| 0.6382 \| 0.6436 \| 0.6582 \| \| 0.5459 \| 3.45 \| 600 \| 0.5881 \| 0.7031 \| 0.7071 \| \| 0.5401 \| 4.6 \| 800 \| 0.5820 \| 0.7005 \| 0.7046 \| \| 0.5342 \| 5.75 \| 1000 \| 0.5525 \| 0.7202 \| 0.7200 \| \| 0.5258 \| 6.9 \| 1200 \| 0.5593 \| 0.7173 \| 0.7179 \| \| 0.5225 \| 8.05 \| 1400 \| 0.5496 \| 0.7250 \| 0.7247 \| \| 0.5197 \| 9.2 \| 1600 \| 0.5916 \| 0.6860 \| 0.6923 \| \| 0.5155 \| 10.34 \| 1800 \| 0.5553 \| 0.7197 \| 0.7200 \| \| 0.5146 \| 11.49 \| 2000 \| 0.5560 \| 0.7191 \| 0.7200 \| \| 0.508 \| 12.64 \| 2200 \| 0.5824 \| 0.7011 \| 0.7053 \| \| 0.5132 \| 13.79 \| 2400 \| 0.5530 \| 0.7193 \| 0.7211 \| \| 0.506 \| 14.94 \| 2600 \| 0.5556 \| 0.7127 \| 0.7143 \| \| 0.504 \| 16.09 \| 2800 \| 0.5451 \| 0.7312 \| 0.7316 \| \| 0.503 \| 17.24 \| 3000 \| 0.5652 \| 0.7205 \| 0.7222 \| \| 0.4994 \| 18.39 \| 3200 \| 0.5591 \| 0.7246 \| 0.7262 \| \| 0.5011 \| 19.54 \| 3400 \| 0.5456 \| 0.7289 \| 0.7298 \| \| 0.497 \| 20.69 \| 3600 \| 0.5430 \| 0.7267 \| 0.7269 \| \| 0.4967 \| 21.84 \| 3800 \| 0.5407 \| 0.7314 \| 0.7319 \| \| 0.4947 \| 22.99 \| 4000 \| 0.5471 \| 0.7285 \| 0.7290 \| \| 0.4959 \| 24.14 \| 4200 \| 0.5297 \| 0.7354 \| 0.7352 \| \| 0.4894 \| 25.29 \| 4400 \| 0.5519 \| 0.7314 \| 0.7319 \| \| 0.4965 \| 26.44 \| 4600 \| 0.5460 \| 0.7324 \| 0.7326 \| \| 0.4902 \| 27.59 \| 4800 \| 0.5525 \| 0.7269 \| 0.7280 \| \| 0.487 \| 28.74 \| 5000 \| 0.5480 \| 0.7240 \| 0.7251 \| \| 0.4945 \| 29.89 \| 5200 \| 0.5410 \| 0.7337 \| 0.7341 \| \| 0.4869 \| 31.03 \| 5400 \| 0.5507 \| 0.7291 \| 0.7301 \| \| 0.4896 \| 32.18 \| 5600 \| 0.5256 \| 0.7396 \| 0.7391 \| \| 0.4832 \| 33.33 \| 5800 \| 0.5439 \| 0.7342 \| 0.7344 \| \| 0.4921 \| 34.48 \| 6000 \| 0.5405 \| 0.7330 \| 0.7337 \| \| 0.4814 \| 35.63 \| 6200 \| 0.5309 \| 0.7376 \| 0.7373 \| \| 0.4888 \| 36.78 \| 6400 \| 0.5390 \| 0.7330 \| 0.7334 \| \| 0.4837 \| 37.93 \| 6600 \| 0.5416 \| 0.7329 \| 0.7330 \| \| 0.4815 \| 39.08 \| 6800 \| 0.5345 \| 0.7384 \| 0.7384 \| \| 0.4833 \| 40.23 \| 7000 \| 0.5349 \| 0.7385 \| 0.7384 \| \| 0.486 \| 41.38 \| 7200 \| 0.5310 \| 0.7382 \| 0.7380 \| \| 0.483 \| 42.53 \| 7400 \| 0.5359 \| 0.7330 \| 0.7334 \| \| 0.4805 \| 43.68 \| 7600 \| 0.5332 \| 0.7385 \| 0.7384 \| \| 0.4801 \| 44.83 \| 7800 \| 0.5450 \| 0.7309 \| 0.7316 \| \| 0.4821 \| 45.98 \| 8000 \| 0.5359 \| 0.7349 \| 0.7352 \| \| 0.4806 \| 47.13 \| 8200 \| 0.5407 \| 0.7325 \| 0.7330 \| \| 0.4819 \| 48.28 \| 8400 \| 0.5387 \| 0.7352 \| 0.7355 \| \| 0.4829 \| 49.43 \| 8600 \| 0.5323 \| 0.7389 \| 0.7388 \| \| 0.4819 \| 50.57 \| 8800 \| 0.5356 \| 0.7366 \| 0.7366 \| \| 0.48 \| 51.72 \| 9000 \| 0.5423 \| 0.7321 \| 0.7326 \| \| 0.4766 \| 52.87 \| 9200 \| 0.5446 \| 0.7321 \| 0.7326 \| \| 0.4815 \| 54.02 \| 9400 \| 0.5420 \| 0.7328 \| 0.7334 \| \| 0.48 \| 55.17 \| 9600 \| 0.5405 \| 0.7329 \| 0.7334 \| \| 0.476 \| 56.32 \| 9800 \| 0.5379 \| 0.7349 \| 0.7352 \| \| 0.4808 \| 57.47 \| 10000 \| 0.5386 \| 0.7342 \| 0.7344 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_4096_512_15M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_4096_512_15M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:45:55+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K9ac-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4829 - F1 Score: 0.7825 - Accuracy: 0.7819 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5985 \| 1.15 \| 200 \| 0.5713 \| 0.7153 \| 0.7154 \| \| 0.5469 \| 2.3 \| 400 \| 0.6351 \| 0.6395 \| 0.6564 \| \| 0.5227 \| 3.45 \| 600 \| 0.5475 \| 0.7292 \| 0.7294 \| \| 0.5141 \| 4.6 \| 800 \| 0.5387 \| 0.7369 \| 0.7373 \| \| 0.5053 \| 5.75 \| 1000 \| 0.5242 \| 0.7497 \| 0.7492 \| \| 0.4998 \| 6.9 \| 1200 \| 0.5305 \| 0.7395 \| 0.7391 \| \| 0.4963 \| 8.05 \| 1400 \| 0.5248 \| 0.7393 \| 0.7388 \| \| 0.4931 \| 9.2 \| 1600 \| 0.5473 \| 0.7262 \| 0.7283 \| \| 0.4876 \| 10.34 \| 1800 \| 0.5194 \| 0.7439 \| 0.7434 \| \| 0.4872 \| 11.49 \| 2000 \| 0.5172 \| 0.7510 \| 0.7506 \| \| 0.4807 \| 12.64 \| 2200 \| 0.5475 \| 0.7303 \| 0.7319 \| \| 0.4855 \| 13.79 \| 2400 \| 0.5089 \| 0.7587 \| 0.7582 \| \| 0.4776 \| 14.94 \| 2600 \| 0.5157 \| 0.7514 \| 0.7510 \| \| 0.4752 \| 16.09 \| 2800 \| 0.5177 \| 0.7500 \| 0.7499 \| \| 0.4758 \| 17.24 \| 3000 \| 0.5201 \| 0.7531 \| 0.7528 \| \| 0.4699 \| 18.39 \| 3200 \| 0.5240 \| 0.7504 \| 0.7503 \| \| 0.4728 \| 19.54 \| 3400 \| 0.5102 \| 0.7498 \| 0.7496 \| \| 0.4662 \| 20.69 \| 3600 \| 0.5063 \| 0.7545 \| 0.7542 \| \| 0.4668 \| 21.84 \| 3800 \| 0.5230 \| 0.7458 \| 0.7460 \| \| 0.4627 \| 22.99 \| 4000 \| 0.5297 \| 0.7412 \| 0.7416 \| \| 0.4655 \| 24.14 \| 4200 \| 0.5121 \| 0.7515 \| 0.7510 \| \| 0.4565 \| 25.29 \| 4400 \| 0.5336 \| 0.7506 \| 0.7506 \| \| 0.463 \| 26.44 \| 4600 \| 0.5167 \| 0.7540 \| 0.7535 \| \| 0.4583 \| 27.59 \| 4800 \| 0.5223 \| 0.7470 \| 0.7474 \| \| 0.4553 \| 28.74 \| 5000 \| 0.5166 \| 0.7515 \| 0.7513 \| \| 0.4595 \| 29.89 \| 5200 \| 0.5159 \| 0.7546 \| 0.7542 \| \| 0.4532 \| 31.03 \| 5400 \| 0.5204 \| 0.7508 \| 0.7506 \| \| 0.4546 \| 32.18 \| 5600 \| 0.5063 \| 0.7537 \| 0.7531 \| \| 0.4474 \| 33.33 \| 5800 \| 0.5128 \| 0.7562 \| 0.7557 \| \| 0.4565 \| 34.48 \| 6000 \| 0.5174 \| 0.7511 \| 0.7506 \| \| 0.4419 \| 35.63 \| 6200 \| 0.5137 \| 0.7540 \| 0.7535 \| \| 0.4492 \| 36.78 \| 6400 \| 0.5112 \| 0.7576 \| 0.7571 \| \| 0.4456 \| 37.93 \| 6600 \| 0.5413 \| 0.7403 \| 0.7402 \| \| 0.4434 \| 39.08 \| 6800 \| 0.5180 \| 0.7519 \| 0.7513 \| \| 0.4448 \| 40.23 \| 7000 \| 0.5249 \| 0.7538 \| 0.7535 \| \| 0.4468 \| 41.38 \| 7200 \| 0.5210 \| 0.7503 \| 0.7499 \| \| 0.444 \| 42.53 \| 7400 \| 0.5156 \| 0.7479 \| 0.7474 \| \| 0.4406 \| 43.68 \| 7600 \| 0.5162 \| 0.7490 \| 0.7485 \| \| 0.4386 \| 44.83 \| 7800 \| 0.5258 \| 0.7495 \| 0.7492 \| \| 0.443 \| 45.98 \| 8000 \| 0.5153 \| 0.7486 \| 0.7481 \| \| 0.4409 \| 47.13 \| 8200 \| 0.5243 \| 0.7488 \| 0.7485 \| \| 0.4412 \| 48.28 \| 8400 \| 0.5204 \| 0.7493 \| 0.7488 \| \| 0.4385 \| 49.43 \| 8600 \| 0.5198 \| 0.7501 \| 0.7496 \| \| 0.4406 \| 50.57 \| 8800 \| 0.5227 \| 0.7521 \| 0.7517 \| \| 0.4391 \| 51.72 \| 9000 \| 0.5283 \| 0.7511 \| 0.7510 \| \| 0.4376 \| 52.87 \| 9200 \| 0.5288 \| 0.7484 \| 0.7481 \| \| 0.4383 \| 54.02 \| 9400 \| 0.5270 \| 0.7479 \| 0.7478 \| \| 0.4378 \| 55.17 \| 9600 \| 0.5240 \| 0.7488 \| 0.7485 \| \| 0.4332 \| 56.32 \| 9800 \| 0.5228 \| 0.7514 \| 0.7510 \| \| 0.4382 \| 57.47 \| 10000 \| 0.5228 \| 0.7503 \| 0.7499 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_4096_512_15M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_4096_512_15M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:46:02+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K9ac-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4683 - F1 Score: 0.7846 - Accuracy: 0.7841 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5805 \| 1.15 \| 200 \| 0.5699 \| 0.7096 \| 0.7121 \| \| 0.5311 \| 2.3 \| 400 \| 0.6196 \| 0.6574 \| 0.6711 \| \| 0.5054 \| 3.45 \| 600 \| 0.5329 \| 0.7385 \| 0.7384 \| \| 0.4987 \| 4.6 \| 800 \| 0.5223 \| 0.7407 \| 0.7406 \| \| 0.4901 \| 5.75 \| 1000 \| 0.5191 \| 0.7511 \| 0.7506 \| \| 0.4858 \| 6.9 \| 1200 \| 0.5215 \| 0.7479 \| 0.7478 \| \| 0.4814 \| 8.05 \| 1400 \| 0.5293 \| 0.7434 \| 0.7431 \| \| 0.4752 \| 9.2 \| 1600 \| 0.5324 \| 0.7414 \| 0.7427 \| \| 0.4677 \| 10.34 \| 1800 \| 0.5228 \| 0.7472 \| 0.7467 \| \| 0.4686 \| 11.49 \| 2000 \| 0.5185 \| 0.7565 \| 0.7560 \| \| 0.4585 \| 12.64 \| 2200 \| 0.5343 \| 0.7408 \| 0.7409 \| \| 0.4611 \| 13.79 \| 2400 \| 0.5132 \| 0.7546 \| 0.7542 \| \| 0.4551 \| 14.94 \| 2600 \| 0.5177 \| 0.7486 \| 0.7481 \| \| 0.45 \| 16.09 \| 2800 \| 0.5290 \| 0.7467 \| 0.7470 \| \| 0.4485 \| 17.24 \| 3000 \| 0.5097 \| 0.7583 \| 0.7578 \| \| 0.4414 \| 18.39 \| 3200 \| 0.5293 \| 0.7483 \| 0.7481 \| \| 0.4412 \| 19.54 \| 3400 \| 0.5122 \| 0.7461 \| 0.7456 \| \| 0.4354 \| 20.69 \| 3600 \| 0.5108 \| 0.7502 \| 0.7499 \| \| 0.4326 \| 21.84 \| 3800 \| 0.5305 \| 0.7444 \| 0.7445 \| \| 0.4262 \| 22.99 \| 4000 \| 0.5570 \| 0.7396 \| 0.7406 \| \| 0.4284 \| 24.14 \| 4200 \| 0.5263 \| 0.7511 \| 0.7506 \| \| 0.4186 \| 25.29 \| 4400 \| 0.5468 \| 0.7512 \| 0.7510 \| \| 0.4232 \| 26.44 \| 4600 \| 0.5302 \| 0.7490 \| 0.7485 \| \| 0.4159 \| 27.59 \| 4800 \| 0.5412 \| 0.7507 \| 0.7506 \| \| 0.4109 \| 28.74 \| 5000 \| 0.5274 \| 0.7464 \| 0.7460 \| \| 0.4147 \| 29.89 \| 5200 \| 0.5354 \| 0.7479 \| 0.7481 \| \| 0.4047 \| 31.03 \| 5400 \| 0.5491 \| 0.7428 \| 0.7427 \| \| 0.4047 \| 32.18 \| 5600 \| 0.5310 \| 0.7433 \| 0.7427 \| \| 0.3938 \| 33.33 \| 5800 \| 0.5478 \| 0.7511 \| 0.7506 \| \| 0.4018 \| 34.48 \| 6000 \| 0.5339 \| 0.7508 \| 0.7503 \| \| 0.3872 \| 35.63 \| 6200 \| 0.5474 \| 0.7439 \| 0.7434 \| \| 0.3911 \| 36.78 \| 6400 \| 0.5366 \| 0.7428 \| 0.7424 \| \| 0.3877 \| 37.93 \| 6600 \| 0.5748 \| 0.7417 \| 0.7413 \| \| 0.3853 \| 39.08 \| 6800 \| 0.5557 \| 0.7392 \| 0.7388 \| \| 0.3846 \| 40.23 \| 7000 \| 0.5654 \| 0.7439 \| 0.7434 \| \| 0.3872 \| 41.38 \| 7200 \| 0.5705 \| 0.7375 \| 0.7373 \| \| 0.3829 \| 42.53 \| 7400 \| 0.5605 \| 0.7393 \| 0.7388 \| \| 0.3754 \| 43.68 \| 7600 \| 0.5542 \| 0.7450 \| 0.7445 \| \| 0.3755 \| 44.83 \| 7800 \| 0.5678 \| 0.7403 \| 0.7398 \| \| 0.3758 \| 45.98 \| 8000 \| 0.5571 \| 0.7418 \| 0.7413 \| \| 0.3735 \| 47.13 \| 8200 \| 0.5867 \| 0.7398 \| 0.7395 \| \| 0.3728 \| 48.28 \| 8400 \| 0.5711 \| 0.7382 \| 0.7377 \| \| 0.371 \| 49.43 \| 8600 \| 0.5742 \| 0.7407 \| 0.7402 \| \| 0.3695 \| 50.57 \| 8800 \| 0.5821 \| 0.7402 \| 0.7398 \| \| 0.368 \| 51.72 \| 9000 \| 0.5897 \| 0.7393 \| 0.7391 \| \| 0.3675 \| 52.87 \| 9200 \| 0.5823 \| 0.7362 \| 0.7359 \| \| 0.3668 \| 54.02 \| 9400 \| 0.5857 \| 0.7365 \| 0.7362 \| \| 0.3671 \| 55.17 \| 9600 \| 0.5799 \| 0.7396 \| 0.7391 \| \| 0.3637 \| 56.32 \| 9800 \| 0.5779 \| 0.7410 \| 0.7406 \| \| 0.3655 \| 57.47 \| 10000 \| 0.5769 \| 0.7406 \| 0.7402 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_4096_512_15M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_4096_512_15M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:46:07+00:00
text-classification	transformers		{}	philgrey/question_classification	null	[ "transformers", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T17:46:22+00:00
null	null		{"license": "apache-2.0"}	MarkLogic/marklogic-connect	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-03T17:48:04+00:00
null	null		{}	LongDaHo/finetuned-gemma-2b	null	[ "region:us" ]	null	2024-05-03T17:48:14+00:00
null	peft	<!-- 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. --> # prompt_fine_tuned_CB_bert This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1636 - Accuracy: 0.3182 - F1: 0.1536 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "google-bert/bert-base-uncased", "model-index": [{"name": "prompt_fine_tuned_CB_bert", "results": []}]}	lenatr99/prompt_fine_tuned_CB_bert	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "license:apache-2.0", "region:us" ]	null	2024-05-03T17:48:19+00:00
null	null		{}	vinven7/Llama2-ft-BandGap	null	[ "region:us" ]	null	2024-05-03T17:49:11+00:00
null	null	<!-- 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. --> # Phi0503B1 This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0800 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.5697 \| 0.09 \| 10 \| 0.7185 \| \| 0.345 \| 0.18 \| 20 \| 0.1655 \| \| 0.1552 \| 0.27 \| 30 \| 0.1343 \| \| 0.1345 \| 0.36 \| 40 \| 0.1175 \| \| 0.121 \| 0.45 \| 50 \| 0.1152 \| \| 0.1088 \| 0.54 \| 60 \| 0.0861 \| \| 0.0923 \| 0.63 \| 70 \| 0.0942 \| \| 0.0773 \| 0.73 \| 80 \| 0.0681 \| \| 0.0606 \| 0.82 \| 90 \| 0.0686 \| \| 0.0647 \| 0.91 \| 100 \| 0.0624 \| \| 0.062 \| 1.0 \| 110 \| 0.0663 \| \| 0.0434 \| 1.09 \| 120 \| 0.0687 \| \| 0.042 \| 1.18 \| 130 \| 0.0675 \| \| 0.0503 \| 1.27 \| 140 \| 0.0681 \| \| 0.0445 \| 1.36 \| 150 \| 0.0654 \| \| 0.0511 \| 1.45 \| 160 \| 0.0593 \| \| 0.0462 \| 1.54 \| 170 \| 0.0687 \| \| 0.0498 \| 1.63 \| 180 \| 0.0651 \| \| 0.0448 \| 1.72 \| 190 \| 0.0640 \| \| 0.043 \| 1.81 \| 200 \| 0.0636 \| \| 0.04 \| 1.9 \| 210 \| 0.0617 \| \| 0.043 \| 1.99 \| 220 \| 0.0613 \| \| 0.0226 \| 2.08 \| 230 \| 0.0657 \| \| 0.0165 \| 2.18 \| 240 \| 0.0788 \| \| 0.011 \| 2.27 \| 250 \| 0.0943 \| \| 0.0097 \| 2.36 \| 260 \| 0.0946 \| \| 0.0167 \| 2.45 \| 270 \| 0.0864 \| \| 0.0105 \| 2.54 \| 280 \| 0.0827 \| \| 0.0118 \| 2.63 \| 290 \| 0.0819 \| \| 0.0156 \| 2.72 \| 300 \| 0.0802 \| \| 0.0137 \| 2.81 \| 310 \| 0.0800 \| \| 0.013 \| 2.9 \| 320 \| 0.0800 \| \| 0.0098 \| 2.99 \| 330 \| 0.0800 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1	{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/Phi-3-mini-4k-instruct", "model-index": [{"name": "Phi0503B1", "results": []}]}	Litzy619/Phi0503B1	null	[ "safetensors", "generated_from_trainer", "base_model:microsoft/Phi-3-mini-4k-instruct", "license:mit", "region:us" ]	null	2024-05-03T17:49:24+00:00
null	null	<!-- 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. --> # Phi0503B2 This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0690 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 5.4837 \| 0.09 \| 10 \| 5.4342 \| \| 5.4537 \| 0.18 \| 20 \| 5.2266 \| \| 4.774 \| 0.27 \| 30 \| 3.6419 \| \| 2.4745 \| 0.36 \| 40 \| 1.0488 \| \| 0.5621 \| 0.45 \| 50 \| 0.2015 \| \| 0.1739 \| 0.54 \| 60 \| 0.1465 \| \| 0.1373 \| 0.63 \| 70 \| 0.1350 \| \| 0.1328 \| 0.73 \| 80 \| 0.1258 \| \| 0.1091 \| 0.82 \| 90 \| 0.1152 \| \| 0.1142 \| 0.91 \| 100 \| 0.0968 \| \| 0.0918 \| 1.0 \| 110 \| 0.1021 \| \| 0.0773 \| 1.09 \| 120 \| 0.0807 \| \| 0.0711 \| 1.18 \| 130 \| 0.0793 \| \| 0.0751 \| 1.27 \| 140 \| 0.0661 \| \| 0.06 \| 1.36 \| 150 \| 0.0651 \| \| 0.0647 \| 1.45 \| 160 \| 0.0658 \| \| 0.0577 \| 1.54 \| 170 \| 0.0657 \| \| 0.0575 \| 1.63 \| 180 \| 0.0644 \| \| 0.0534 \| 1.72 \| 190 \| 0.0661 \| \| 0.0594 \| 1.81 \| 200 \| 0.0622 \| \| 0.0473 \| 1.9 \| 210 \| 0.0628 \| \| 0.0522 \| 1.99 \| 220 \| 0.0643 \| \| 0.0402 \| 2.08 \| 230 \| 0.0644 \| \| 0.0436 \| 2.18 \| 240 \| 0.0674 \| \| 0.0343 \| 2.27 \| 250 \| 0.0708 \| \| 0.0358 \| 2.36 \| 260 \| 0.0724 \| \| 0.0411 \| 2.45 \| 270 \| 0.0720 \| \| 0.0359 \| 2.54 \| 280 \| 0.0706 \| \| 0.0366 \| 2.63 \| 290 \| 0.0702 \| \| 0.0397 \| 2.72 \| 300 \| 0.0697 \| \| 0.044 \| 2.81 \| 310 \| 0.0692 \| \| 0.0415 \| 2.9 \| 320 \| 0.0688 \| \| 0.037 \| 2.99 \| 330 \| 0.0690 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1	{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/Phi-3-mini-4k-instruct", "model-index": [{"name": "Phi0503B2", "results": []}]}	Litzy619/Phi0503B2	null	[ "safetensors", "generated_from_trainer", "base_model:microsoft/Phi-3-mini-4k-instruct", "license:mit", "region:us" ]	null	2024-05-03T17:49:39+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me3-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.6104 - F1 Score: 0.6754 - Accuracy: 0.6755 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6748 \| 0.87 \| 200 \| 0.6571 \| 0.6173 \| 0.6188 \| \| 0.6536 \| 1.74 \| 400 \| 0.6488 \| 0.6324 \| 0.6323 \| \| 0.6479 \| 2.61 \| 600 \| 0.6438 \| 0.6414 \| 0.6410 \| \| 0.6365 \| 3.48 \| 800 \| 0.6347 \| 0.6385 \| 0.6397 \| \| 0.6346 \| 4.35 \| 1000 \| 0.6307 \| 0.6387 \| 0.6416 \| \| 0.6286 \| 5.22 \| 1200 \| 0.6395 \| 0.6339 \| 0.6391 \| \| 0.6242 \| 6.09 \| 1400 \| 0.6266 \| 0.6497 \| 0.6511 \| \| 0.6186 \| 6.96 \| 1600 \| 0.6242 \| 0.6600 \| 0.6606 \| \| 0.6171 \| 7.83 \| 1800 \| 0.6186 \| 0.6623 \| 0.6630 \| \| 0.6159 \| 8.7 \| 2000 \| 0.6171 \| 0.6646 \| 0.6644 \| \| 0.6094 \| 9.57 \| 2200 \| 0.6146 \| 0.6613 \| 0.6611 \| \| 0.6144 \| 10.43 \| 2400 \| 0.6139 \| 0.6648 \| 0.6647 \| \| 0.6105 \| 11.3 \| 2600 \| 0.6175 \| 0.6571 \| 0.6584 \| \| 0.6118 \| 12.17 \| 2800 \| 0.6119 \| 0.6676 \| 0.6674 \| \| 0.6086 \| 13.04 \| 3000 \| 0.6103 \| 0.6679 \| 0.6677 \| \| 0.6053 \| 13.91 \| 3200 \| 0.6114 \| 0.6620 \| 0.6625 \| \| 0.6039 \| 14.78 \| 3400 \| 0.6115 \| 0.6615 \| 0.6628 \| \| 0.606 \| 15.65 \| 3600 \| 0.6125 \| 0.6653 \| 0.6660 \| \| 0.6002 \| 16.52 \| 3800 \| 0.6121 \| 0.6665 \| 0.6668 \| \| 0.6016 \| 17.39 \| 4000 \| 0.6084 \| 0.6693 \| 0.6696 \| \| 0.603 \| 18.26 \| 4200 \| 0.6086 \| 0.6690 \| 0.6690 \| \| 0.597 \| 19.13 \| 4400 \| 0.6072 \| 0.6692 \| 0.6693 \| \| 0.5983 \| 20.0 \| 4600 \| 0.6074 \| 0.6661 \| 0.6666 \| \| 0.5986 \| 20.87 \| 4800 \| 0.6091 \| 0.6645 \| 0.6649 \| \| 0.5976 \| 21.74 \| 5000 \| 0.6116 \| 0.6619 \| 0.6630 \| \| 0.5976 \| 22.61 \| 5200 \| 0.6068 \| 0.6666 \| 0.6677 \| \| 0.5978 \| 23.48 \| 5400 \| 0.6129 \| 0.6573 \| 0.6611 \| \| 0.5943 \| 24.35 \| 5600 \| 0.6047 \| 0.6673 \| 0.6674 \| \| 0.5966 \| 25.22 \| 5800 \| 0.6116 \| 0.6578 \| 0.6617 \| \| 0.5934 \| 26.09 \| 6000 \| 0.6113 \| 0.6585 \| 0.6614 \| \| 0.5951 \| 26.96 \| 6200 \| 0.6116 \| 0.6622 \| 0.6652 \| \| 0.5948 \| 27.83 \| 6400 \| 0.6180 \| 0.6534 \| 0.6592 \| \| 0.5914 \| 28.7 \| 6600 \| 0.6068 \| 0.6609 \| 0.6628 \| \| 0.5915 \| 29.57 \| 6800 \| 0.6048 \| 0.6677 \| 0.6690 \| \| 0.5893 \| 30.43 \| 7000 \| 0.6109 \| 0.6600 \| 0.6633 \| \| 0.5974 \| 31.3 \| 7200 \| 0.6085 \| 0.6625 \| 0.6652 \| \| 0.5923 \| 32.17 \| 7400 \| 0.6108 \| 0.6596 \| 0.6639 \| \| 0.5891 \| 33.04 \| 7600 \| 0.6036 \| 0.6659 \| 0.6671 \| \| 0.5919 \| 33.91 \| 7800 \| 0.6048 \| 0.6618 \| 0.6633 \| \| 0.5906 \| 34.78 \| 8000 \| 0.6055 \| 0.6651 \| 0.6666 \| \| 0.5927 \| 35.65 \| 8200 \| 0.6027 \| 0.6657 \| 0.6668 \| \| 0.5891 \| 36.52 \| 8400 \| 0.6069 \| 0.6614 \| 0.6639 \| \| 0.5908 \| 37.39 \| 8600 \| 0.6063 \| 0.6635 \| 0.6655 \| \| 0.5857 \| 38.26 \| 8800 \| 0.6095 \| 0.6630 \| 0.6660 \| \| 0.5921 \| 39.13 \| 9000 \| 0.6070 \| 0.6622 \| 0.6649 \| \| 0.5895 \| 40.0 \| 9200 \| 0.6047 \| 0.6643 \| 0.6660 \| \| 0.5884 \| 40.87 \| 9400 \| 0.6029 \| 0.6672 \| 0.6679 \| \| 0.5909 \| 41.74 \| 9600 \| 0.6040 \| 0.6656 \| 0.6668 \| \| 0.5906 \| 42.61 \| 9800 \| 0.6042 \| 0.6650 \| 0.6666 \| \| 0.5892 \| 43.48 \| 10000 \| 0.6047 \| 0.6640 \| 0.6658 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_4096_512_15M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_4096_512_15M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:51:35+00:00
text-generation	transformers		{}	PB7-DUT-2023/finetuned_Bloomz_1b1_v5	null	[ "transformers", "pytorch", "bloom", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T17:51:49+00:00
null	null		{}	UC3M-LCPM/Bart_large_mnli_task1a_en_normal	null	[ "region:us" ]	null	2024-05-03T17:52:50+00:00
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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 Dataset 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 Dataset 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]	{"library_name": "transformers", "tags": []}	ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_64_0.05_2_5e-05	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-03T17:53:04+00:00
null	null	# LlamaJarvis-7B LlamaJarvis-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) ## 🧩 Configuration ```yaml models: - model: NousResearch/Meta-Llama-3-8B # No parameters necessary for base model - model: NousResearch/Meta-Llama-3-8B-Instruct parameters: density: 0.6 weight: 0.5 - model: mlabonne/OrpoLlama-3-8B parameters: density: 0.55 weight: 0.05 merge_method: dare_ties base_model: NousResearch/Meta-Llama-3-8B parameters: int8_mask: true dtype: float16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/LlamaJarvis-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```	{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"], "base_model": ["NousResearch/Meta-Llama-3-8B-Instruct", "mlabonne/OrpoLlama-3-8B"]}	automerger/LlamaJarvis-7B	null	[ "merge", "mergekit", "lazymergekit", "automerger", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:mlabonne/OrpoLlama-3-8B", "license:apache-2.0", "region:us" ]	null	2024-05-03T17:53:49+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me3-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.6053 - F1 Score: 0.6811 - Accuracy: 0.6823 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6682 \| 0.87 \| 200 \| 0.6471 \| 0.6349 \| 0.6359 \| \| 0.6393 \| 1.74 \| 400 \| 0.6363 \| 0.6456 \| 0.6457 \| \| 0.6281 \| 2.61 \| 600 \| 0.6212 \| 0.6612 \| 0.6609 \| \| 0.6181 \| 3.48 \| 800 \| 0.6148 \| 0.6642 \| 0.6641 \| \| 0.6148 \| 4.35 \| 1000 \| 0.6139 \| 0.6601 \| 0.6603 \| \| 0.6098 \| 5.22 \| 1200 \| 0.6154 \| 0.6526 \| 0.6552 \| \| 0.6042 \| 6.09 \| 1400 \| 0.6202 \| 0.6480 \| 0.6527 \| \| 0.5989 \| 6.96 \| 1600 \| 0.6141 \| 0.6615 \| 0.6639 \| \| 0.5962 \| 7.83 \| 1800 \| 0.6078 \| 0.6712 \| 0.6709 \| \| 0.5953 \| 8.7 \| 2000 \| 0.6030 \| 0.6731 \| 0.6731 \| \| 0.5864 \| 9.57 \| 2200 \| 0.5979 \| 0.6767 \| 0.6766 \| \| 0.5919 \| 10.43 \| 2400 \| 0.6012 \| 0.6721 \| 0.6723 \| \| 0.5862 \| 11.3 \| 2600 \| 0.6009 \| 0.6692 \| 0.6715 \| \| 0.5893 \| 12.17 \| 2800 \| 0.5981 \| 0.6709 \| 0.6717 \| \| 0.5824 \| 13.04 \| 3000 \| 0.5966 \| 0.6752 \| 0.6758 \| \| 0.5807 \| 13.91 \| 3200 \| 0.5975 \| 0.6735 \| 0.6747 \| \| 0.5772 \| 14.78 \| 3400 \| 0.6008 \| 0.6742 \| 0.6766 \| \| 0.5799 \| 15.65 \| 3600 \| 0.6016 \| 0.6730 \| 0.6758 \| \| 0.5746 \| 16.52 \| 3800 \| 0.5983 \| 0.6759 \| 0.6764 \| \| 0.5731 \| 17.39 \| 4000 \| 0.5999 \| 0.6770 \| 0.6777 \| \| 0.5756 \| 18.26 \| 4200 \| 0.5986 \| 0.6797 \| 0.6815 \| \| 0.5684 \| 19.13 \| 4400 \| 0.5978 \| 0.6775 \| 0.6780 \| \| 0.5707 \| 20.0 \| 4600 \| 0.5995 \| 0.6755 \| 0.6769 \| \| 0.5702 \| 20.87 \| 4800 \| 0.5974 \| 0.6778 \| 0.6791 \| \| 0.5675 \| 21.74 \| 5000 \| 0.6075 \| 0.6707 \| 0.6720 \| \| 0.569 \| 22.61 \| 5200 \| 0.5955 \| 0.6776 \| 0.6785 \| \| 0.5645 \| 23.48 \| 5400 \| 0.6137 \| 0.6672 \| 0.6723 \| \| 0.5628 \| 24.35 \| 5600 \| 0.6011 \| 0.6756 \| 0.6769 \| \| 0.5664 \| 25.22 \| 5800 \| 0.6027 \| 0.6728 \| 0.6764 \| \| 0.5609 \| 26.09 \| 6000 \| 0.6073 \| 0.6746 \| 0.6772 \| \| 0.5618 \| 26.96 \| 6200 \| 0.6067 \| 0.6739 \| 0.6769 \| \| 0.5603 \| 27.83 \| 6400 \| 0.6151 \| 0.6679 \| 0.6728 \| \| 0.5578 \| 28.7 \| 6600 \| 0.5997 \| 0.6778 \| 0.6796 \| \| 0.559 \| 29.57 \| 6800 \| 0.5980 \| 0.6795 \| 0.6807 \| \| 0.5551 \| 30.43 \| 7000 \| 0.6067 \| 0.6740 \| 0.6772 \| \| 0.5636 \| 31.3 \| 7200 \| 0.6002 \| 0.6794 \| 0.6810 \| \| 0.5549 \| 32.17 \| 7400 \| 0.6016 \| 0.6790 \| 0.6807 \| \| 0.5543 \| 33.04 \| 7600 \| 0.5994 \| 0.6770 \| 0.6783 \| \| 0.5558 \| 33.91 \| 7800 \| 0.5993 \| 0.6776 \| 0.6793 \| \| 0.5546 \| 34.78 \| 8000 \| 0.6022 \| 0.6781 \| 0.6793 \| \| 0.5567 \| 35.65 \| 8200 \| 0.5980 \| 0.6793 \| 0.6807 \| \| 0.553 \| 36.52 \| 8400 \| 0.6025 \| 0.6756 \| 0.6783 \| \| 0.5553 \| 37.39 \| 8600 \| 0.6016 \| 0.6774 \| 0.6788 \| \| 0.5478 \| 38.26 \| 8800 \| 0.6096 \| 0.6733 \| 0.6764 \| \| 0.5536 \| 39.13 \| 9000 \| 0.6045 \| 0.6756 \| 0.6777 \| \| 0.5508 \| 40.0 \| 9200 \| 0.6035 \| 0.6800 \| 0.6818 \| \| 0.5521 \| 40.87 \| 9400 \| 0.6018 \| 0.6760 \| 0.6769 \| \| 0.5512 \| 41.74 \| 9600 \| 0.6028 \| 0.6758 \| 0.6772 \| \| 0.552 \| 42.61 \| 9800 \| 0.6021 \| 0.6789 \| 0.6802 \| \| 0.5521 \| 43.48 \| 10000 \| 0.6031 \| 0.6783 \| 0.6799 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_4096_512_15M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_4096_512_15M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:54:40+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me3-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.6214 - F1 Score: 0.6871 - Accuracy: 0.6872 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6604 \| 0.87 \| 200 \| 0.6356 \| 0.6419 \| 0.6446 \| \| 0.6283 \| 1.74 \| 400 \| 0.6323 \| 0.6420 \| 0.6451 \| \| 0.6159 \| 2.61 \| 600 \| 0.6089 \| 0.6698 \| 0.6696 \| \| 0.6064 \| 3.48 \| 800 \| 0.6044 \| 0.6734 \| 0.6731 \| \| 0.6011 \| 4.35 \| 1000 \| 0.6067 \| 0.6681 \| 0.6679 \| \| 0.5945 \| 5.22 \| 1200 \| 0.6015 \| 0.6739 \| 0.6742 \| \| 0.5889 \| 6.09 \| 1400 \| 0.6102 \| 0.6588 \| 0.6630 \| \| 0.5839 \| 6.96 \| 1600 \| 0.6055 \| 0.6744 \| 0.6764 \| \| 0.5777 \| 7.83 \| 1800 \| 0.6038 \| 0.6778 \| 0.6777 \| \| 0.5768 \| 8.7 \| 2000 \| 0.6082 \| 0.6716 \| 0.6717 \| \| 0.5664 \| 9.57 \| 2200 \| 0.5990 \| 0.6784 \| 0.6785 \| \| 0.5693 \| 10.43 \| 2400 \| 0.6050 \| 0.6708 \| 0.6726 \| \| 0.5635 \| 11.3 \| 2600 \| 0.5996 \| 0.6714 \| 0.675 \| \| 0.566 \| 12.17 \| 2800 \| 0.5940 \| 0.6735 \| 0.6747 \| \| 0.556 \| 13.04 \| 3000 \| 0.5968 \| 0.6770 \| 0.6780 \| \| 0.553 \| 13.91 \| 3200 \| 0.6026 \| 0.6703 \| 0.6720 \| \| 0.5486 \| 14.78 \| 3400 \| 0.6150 \| 0.6675 \| 0.6709 \| \| 0.5497 \| 15.65 \| 3600 \| 0.6032 \| 0.6709 \| 0.6731 \| \| 0.5432 \| 16.52 \| 3800 \| 0.6059 \| 0.6764 \| 0.6766 \| \| 0.5393 \| 17.39 \| 4000 \| 0.6131 \| 0.6752 \| 0.6772 \| \| 0.5427 \| 18.26 \| 4200 \| 0.6093 \| 0.6747 \| 0.6785 \| \| 0.5304 \| 19.13 \| 4400 \| 0.6131 \| 0.6716 \| 0.6739 \| \| 0.5329 \| 20.0 \| 4600 \| 0.6077 \| 0.6777 \| 0.6793 \| \| 0.531 \| 20.87 \| 4800 \| 0.6070 \| 0.6769 \| 0.6783 \| \| 0.5239 \| 21.74 \| 5000 \| 0.6174 \| 0.6708 \| 0.6723 \| \| 0.5272 \| 22.61 \| 5200 \| 0.6096 \| 0.6799 \| 0.6813 \| \| 0.5188 \| 23.48 \| 5400 \| 0.6364 \| 0.6696 \| 0.6731 \| \| 0.5177 \| 24.35 \| 5600 \| 0.6255 \| 0.6697 \| 0.6736 \| \| 0.5185 \| 25.22 \| 5800 \| 0.6251 \| 0.6740 \| 0.6777 \| \| 0.513 \| 26.09 \| 6000 \| 0.6339 \| 0.6707 \| 0.6742 \| \| 0.5119 \| 26.96 \| 6200 \| 0.6245 \| 0.6742 \| 0.6777 \| \| 0.5078 \| 27.83 \| 6400 \| 0.6367 \| 0.6723 \| 0.6766 \| \| 0.504 \| 28.7 \| 6600 \| 0.6171 \| 0.6765 \| 0.6772 \| \| 0.5056 \| 29.57 \| 6800 \| 0.6165 \| 0.6755 \| 0.6769 \| \| 0.5021 \| 30.43 \| 7000 \| 0.6280 \| 0.6777 \| 0.6804 \| \| 0.5093 \| 31.3 \| 7200 \| 0.6212 \| 0.6818 \| 0.6826 \| \| 0.4991 \| 32.17 \| 7400 \| 0.6257 \| 0.6770 \| 0.6783 \| \| 0.4968 \| 33.04 \| 7600 \| 0.6238 \| 0.6776 \| 0.6791 \| \| 0.4957 \| 33.91 \| 7800 \| 0.6232 \| 0.6764 \| 0.6785 \| \| 0.4945 \| 34.78 \| 8000 \| 0.6249 \| 0.6765 \| 0.6780 \| \| 0.4986 \| 35.65 \| 8200 \| 0.6241 \| 0.6784 \| 0.6802 \| \| 0.4907 \| 36.52 \| 8400 \| 0.6303 \| 0.6738 \| 0.6761 \| \| 0.495 \| 37.39 \| 8600 \| 0.6312 \| 0.6758 \| 0.6769 \| \| 0.4868 \| 38.26 \| 8800 \| 0.6352 \| 0.6774 \| 0.6793 \| \| 0.4894 \| 39.13 \| 9000 \| 0.6343 \| 0.6773 \| 0.6791 \| \| 0.4875 \| 40.0 \| 9200 \| 0.6298 \| 0.6787 \| 0.6802 \| \| 0.4871 \| 40.87 \| 9400 \| 0.6313 \| 0.6760 \| 0.6769 \| \| 0.4861 \| 41.74 \| 9600 \| 0.6330 \| 0.6773 \| 0.6791 \| \| 0.4892 \| 42.61 \| 9800 \| 0.6306 \| 0.6777 \| 0.6791 \| \| 0.4891 \| 43.48 \| 10000 \| 0.6317 \| 0.6775 \| 0.6791 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_4096_512_15M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_4096_512_15M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T17:55:10+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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 Dataset 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 Dataset 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]	{"library_name": "transformers", "tags": []}	LongDHo/finetuned-gemma-2b	null	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T17:55:44+00:00
null	peft	<!-- 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. --> # cosmosDPO_CodeTest2 This model is a fine-tuned version of [ytu-ce-cosmos/turkish-gpt2-large-750m-instruct-v0.1](https://huggingface.co/ytu-ce-cosmos/turkish-gpt2-large-750m-instruct-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5356 - Rewards/chosen: -1.3639 - Rewards/rejected: -3.6411 - Rewards/accuracies: 0.2640 - Rewards/margins: 2.2772 - Logps/rejected: -477.7171 - Logps/chosen: -224.9044 - Logits/rejected: -4.1447 - Logits/chosen: -3.7114 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6915 \| 0.0524 \| 8 \| 0.6855 \| -0.0225 \| -0.0387 \| 0.2171 \| 0.0162 \| -117.4774 \| -90.7611 \| -2.7135 \| -2.4688 \| \| 0.6639 \| 0.1047 \| 16 \| 0.6480 \| -0.2509 \| -0.4015 \| 0.2189 \| 0.1506 \| -153.7584 \| -113.6010 \| -3.2208 \| -2.9343 \| \| 0.6251 \| 0.1571 \| 24 \| 0.6436 \| -0.7453 \| -1.1570 \| 0.2217 \| 0.4117 \| -229.3032 \| -163.0413 \| -3.8702 \| -3.5589 \| \| 0.6238 \| 0.2095 \| 32 \| 0.6047 \| -0.7237 \| -1.2597 \| 0.2355 \| 0.5360 \| -239.5777 \| -160.8856 \| -3.7913 \| -3.4555 \| \| 0.5586 \| 0.2619 \| 40 \| 0.5789 \| -1.0590 \| -1.9755 \| 0.2474 \| 0.9164 \| -311.1551 \| -194.4169 \| -3.9560 \| -3.5940 \| \| 0.5389 \| 0.3142 \| 48 \| 0.5577 \| -1.0922 \| -2.3486 \| 0.2548 \| 1.2564 \| -348.4677 \| -197.7312 \| -3.9027 \| -3.5251 \| \| 0.5102 \| 0.3666 \| 56 \| 0.5606 \| -1.4904 \| -3.3229 \| 0.2548 \| 1.8325 \| -445.8979 \| -237.5522 \| -4.0088 \| -3.6310 \| \| 0.5506 \| 0.4190 \| 64 \| 0.5529 \| -1.4084 \| -3.4076 \| 0.2585 \| 1.9992 \| -454.3663 \| -229.3532 \| -3.9314 \| -3.5543 \| \| 0.5696 \| 0.4714 \| 72 \| 0.5365 \| -0.7411 \| -2.1788 \| 0.2621 \| 1.4377 \| -331.4860 \| -162.6252 \| -3.6733 \| -3.2798 \| \| 0.5265 \| 0.5237 \| 80 \| 0.5355 \| -0.8770 \| -2.4950 \| 0.2612 \| 1.6180 \| -363.1028 \| -176.2112 \| -3.7304 \| -3.3452 \| \| 0.5199 \| 0.5761 \| 88 \| 0.5482 \| -1.5559 \| -3.7745 \| 0.2585 \| 2.2186 \| -491.0597 \| -244.1054 \| -3.9633 \| -3.5958 \| \| 0.5163 \| 0.6285 \| 96 \| 0.5464 \| -1.5899 \| -3.8545 \| 0.2594 \| 2.2646 \| -499.0518 \| -247.5011 \| -4.0472 \| -3.6688 \| \| 0.5421 \| 0.6809 \| 104 \| 0.5408 \| -1.4973 \| -3.8002 \| 0.2631 \| 2.3029 \| -493.6231 \| -238.2402 \| -4.1221 \| -3.7151 \| \| 0.5416 \| 0.7332 \| 112 \| 0.5356 \| -1.2811 \| -3.4299 \| 0.2640 \| 2.1488 \| -456.5994 \| -216.6231 \| -4.0861 \| -3.6611 \| \| 0.4967 \| 0.7856 \| 120 \| 0.5347 \| -1.2626 \| -3.4278 \| 0.2640 \| 2.1653 \| -456.3912 \| -214.7687 \| -4.1048 \| -3.6705 \| \| 0.4783 \| 0.8380 \| 128 \| 0.5345 \| -1.2666 \| -3.4477 \| 0.2640 \| 2.1811 \| -458.3748 \| -215.1744 \| -4.1066 \| -3.6704 \| \| 0.508 \| 0.8903 \| 136 \| 0.5352 \| -1.3287 \| -3.5746 \| 0.2640 \| 2.2459 \| -471.0667 \| -221.3868 \| -4.1311 \| -3.6966 \| \| 0.5417 \| 0.9427 \| 144 \| 0.5356 \| -1.3619 \| -3.6366 \| 0.2640 \| 2.2746 \| -477.2621 \| -224.7045 \| -4.1435 \| -3.7103 \| \| 0.5414 \| 0.9951 \| 152 \| 0.5356 \| -1.3639 \| -3.6411 \| 0.2640 \| 2.2772 \| -477.7171 \| -224.9044 \| -4.1447 \| -3.7114 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "mit", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "ytu-ce-cosmos/turkish-gpt2-large-750m-instruct-v0.1", "model-index": [{"name": "cosmosDPO_CodeTest2", "results": []}]}	meguzn/cosmosDPO_CodeTest2	null	[ "peft", "tensorboard", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:ytu-ce-cosmos/turkish-gpt2-large-750m-instruct-v0.1", "license:mit", "region:us" ]	null	2024-05-03T17:55:54+00:00
null	null		{}	lanzv/model_output_dir	null	[ "region:us" ]	null	2024-05-03T17:56:00+00:00
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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 Dataset 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 Dataset 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]	{"library_name": "transformers", "tags": ["unsloth", "trl", "sft"]}	MohamedSaeed-dev/gemma7b-unsloth	null	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-03T17:56:25+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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 Dataset 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 Dataset 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]	{"library_name": "transformers", "tags": ["unsloth", "trl", "sft"]}	MohamedSaeed-dev/phi-unsloth	null	[ "transformers", "pytorch", "mistral", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T17:56:38+00:00
null	null		{}	UC3M-LCPM/Bart_large_task1a_en_normal	null	[ "region:us" ]	null	2024-05-03T17:56:40+00:00
null	peft	<!-- 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. --> # loha_fine_tuned_cb_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2436 - Accuracy: 0.3182 - F1: 0.1536 ## 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 - training_steps: 400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.9874 \| 3.5714 \| 50 \| 1.1351 \| 0.3182 \| 0.1591 \| \| 0.8665 \| 7.1429 \| 100 \| 1.1589 \| 0.3182 \| 0.1536 \| \| 0.8359 \| 10.7143 \| 150 \| 1.1890 \| 0.3182 \| 0.1536 \| \| 0.7662 \| 14.2857 \| 200 \| 1.2116 \| 0.3182 \| 0.1536 \| \| 0.769 \| 17.8571 \| 250 \| 1.2287 \| 0.3182 \| 0.1536 \| \| 0.7534 \| 21.4286 \| 300 \| 1.2380 \| 0.3182 \| 0.1536 \| \| 0.7359 \| 25.0 \| 350 \| 1.2421 \| 0.3182 \| 0.1536 \| \| 0.7449 \| 28.5714 \| 400 \| 1.2436 \| 0.3182 \| 0.1536 \| ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "cc-by-4.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "loha_fine_tuned_cb_croslo", "results": []}]}	lenatr99/loha_fine_tuned_cb_croslo	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:EMBEDDIA/crosloengual-bert", "license:cc-by-4.0", "region:us" ]	null	2024-05-03T17:56:45+00:00
null	peft	<!-- 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. --> # lora_fine_tuned_cb_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3172 - Accuracy: 0.3182 - F1: 0.1536 ## 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 - training_steps: 400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\|:------:\| \| 1.0956 \| 3.5714 \| 50 \| 1.1024 \| 0.3636 \| 0.3027 \| \| 0.8669 \| 7.1429 \| 100 \| 1.1540 \| 0.3182 \| 0.1536 \| \| 0.7634 \| 10.7143 \| 150 \| 1.2351 \| 0.3182 \| 0.1536 \| \| 0.7 \| 14.2857 \| 200 \| 1.2885 \| 0.3182 \| 0.1536 \| \| 0.6951 \| 17.8571 \| 250 \| 1.3121 \| 0.3182 \| 0.1536 \| \| 0.7047 \| 21.4286 \| 300 \| 1.3145 \| 0.3182 \| 0.1536 \| \| 0.6769 \| 25.0 \| 350 \| 1.3154 \| 0.3182 \| 0.1536 \| \| 0.6886 \| 28.5714 \| 400 \| 1.3172 \| 0.3182 \| 0.1536 \| ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "cc-by-4.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "lora_fine_tuned_cb_croslo", "results": []}]}	lenatr99/lora_fine_tuned_cb_croslo	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:EMBEDDIA/crosloengual-bert", "license:cc-by-4.0", "region:us" ]	null	2024-05-03T17:56:45+00:00
null	peft	<!-- 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. --> # prompt_fine_tuned_CB_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2046 - Accuracy: 0.3182 - F1: 0.1536 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\| \| 1.0278 \| 0.4545 \| 50 \| 1.1158 \| 0.3182 \| 0.2306 \| \| 0.9865 \| 0.9091 \| 100 \| 1.1195 \| 0.3636 \| 0.2430 \| \| 0.8601 \| 1.3636 \| 150 \| 1.1357 \| 0.3182 \| 0.1536 \| \| 0.8769 \| 1.8182 \| 200 \| 1.1595 \| 0.3182 \| 0.1536 \| \| 0.9026 \| 2.2727 \| 250 \| 1.1733 \| 0.3182 \| 0.1536 \| \| 0.8002 \| 2.7273 \| 300 \| 1.1885 \| 0.3182 \| 0.1536 \| \| 0.8093 \| 3.1818 \| 350 \| 1.1996 \| 0.3182 \| 0.1536 \| \| 0.7259 \| 3.6364 \| 400 \| 1.2046 \| 0.3182 \| 0.1536 \| ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "cc-by-4.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "prompt_fine_tuned_CB_croslo", "results": []}]}	lenatr99/prompt_fine_tuned_CB_croslo	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:EMBEDDIA/crosloengual-bert", "license:cc-by-4.0", "region:us" ]	null	2024-05-03T17:57:06+00:00
null	null		{}	mvera/my-awesome-model	null	[ "region:us" ]	null	2024-05-03T17:57:27+00:00
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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 Dataset 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 Dataset 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]	{"library_name": "transformers", "tags": []}	ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_64_0.05_2_0.0002	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-03T17:59:42+00:00
null	null		{}	Aryan0310/bart-large-cnn-finetuned-xsum	null	[ "region:us" ]	null	2024-05-03T17:59:49+00:00
null	null		{}	GeronimoYeah/Harvester_v2	null	[ "region:us" ]	null	2024-05-03T18:00:20+00:00
null	null		{"license": "mit"}	omarques/testgb	null	[ "license:mit", "region:us" ]	null	2024-05-03T18:02:07+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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 Dataset 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 Dataset 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]	{"library_name": "transformers", "tags": []}	Pruz0/Len2GPT	null	[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T18:02:18+00:00
null	diffusers		{"license": "wtfpl"}	Poomz/loreitup	null	[ "diffusers", "safetensors", "license:wtfpl", "diffusers:DiTPipeline", "region:us" ]	null	2024-05-03T18:02:37+00:00
null	null		{}	HarshitSheoran/birdclef24_comp_data	null	[ "region:us" ]	null	2024-05-03T18:03:31+00:00
text-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base_bedtype This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0771 - Accuracy: 0.7143 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 27 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.6124 \| 1.67 \| 10 \| 1.6887 \| 0.2857 \| \| 1.1698 \| 3.33 \| 20 \| 1.6470 \| 0.2571 \| \| 0.9559 \| 5.0 \| 30 \| 1.3456 \| 0.5429 \| \| 0.765 \| 6.67 \| 40 \| 1.3860 \| 0.4571 \| \| 0.5258 \| 8.33 \| 50 \| 1.0184 \| 0.5714 \| \| 0.2822 \| 10.0 \| 60 \| 0.7994 \| 0.6571 \| \| 0.1576 \| 11.67 \| 70 \| 0.8624 \| 0.7429 \| \| 0.0648 \| 13.33 \| 80 \| 0.6489 \| 0.8 \| \| 0.0483 \| 15.0 \| 90 \| 0.7762 \| 0.7429 \| \| 0.0148 \| 16.67 \| 100 \| 0.7915 \| 0.7714 \| \| 0.0086 \| 18.33 \| 110 \| 1.0589 \| 0.7429 \| \| 0.0058 \| 20.0 \| 120 \| 0.8353 \| 0.8 \| \| 0.005 \| 21.67 \| 130 \| 0.8960 \| 0.8 \| \| 0.0042 \| 23.33 \| 140 \| 0.9228 \| 0.7429 \| \| 0.0036 \| 25.0 \| 150 \| 0.9461 \| 0.7429 \| \| 0.0032 \| 26.67 \| 160 \| 0.9904 \| 0.7714 \| \| 0.003 \| 28.33 \| 170 \| 0.9968 \| 0.7429 \| \| 0.0029 \| 30.0 \| 180 \| 0.9978 \| 0.7429 \| \| 0.0026 \| 31.67 \| 190 \| 1.0043 \| 0.7429 \| \| 0.0029 \| 33.33 \| 200 \| 1.0712 \| 0.7143 \| \| 0.0423 \| 35.0 \| 210 \| 1.0914 \| 0.7429 \| \| 0.0187 \| 36.67 \| 220 \| 0.9988 \| 0.7714 \| \| 0.0025 \| 38.33 \| 230 \| 1.0863 \| 0.7143 \| \| 0.0023 \| 40.0 \| 240 \| 1.1078 \| 0.7143 \| \| 0.0022 \| 41.67 \| 250 \| 1.1058 \| 0.7143 \| \| 0.0023 \| 43.33 \| 260 \| 1.0903 \| 0.7143 \| \| 0.0021 \| 45.0 \| 270 \| 1.0833 \| 0.7143 \| \| 0.0021 \| 46.67 \| 280 \| 1.0805 \| 0.7143 \| \| 0.0021 \| 48.33 \| 290 \| 1.0777 \| 0.7143 \| \| 0.0021 \| 50.0 \| 300 \| 1.0771 \| 0.7143 \| ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base_bedtype", "results": []}]}	JBhug/roberta-base_bedtype	null	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T18:03:58+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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 Dataset 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 Dataset 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]	{"library_name": "transformers", "tags": []}	golf2248/qkuzzuz	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-03T18:04:11+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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 Dataset 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 Dataset 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]	{"library_name": "transformers", "tags": []}	abc88767/model57	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-03T18:05:08+00:00
null	peft	<!-- 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. --> # GUE_EMP_H4-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2686 - F1 Score: 0.8975 - Accuracy: 0.8973 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.4376 \| 2.17 \| 200 \| 0.3131 \| 0.8777 \| 0.8775 \| \| 0.3136 \| 4.35 \| 400 \| 0.3095 \| 0.8718 \| 0.8713 \| \| 0.2965 \| 6.52 \| 600 \| 0.2957 \| 0.8853 \| 0.8850 \| \| 0.2931 \| 8.7 \| 800 \| 0.2967 \| 0.8853 \| 0.8850 \| \| 0.2855 \| 10.87 \| 1000 \| 0.2915 \| 0.8921 \| 0.8919 \| \| 0.279 \| 13.04 \| 1200 \| 0.2921 \| 0.8921 \| 0.8919 \| \| 0.2774 \| 15.22 \| 1400 \| 0.2821 \| 0.8941 \| 0.8939 \| \| 0.2731 \| 17.39 \| 1600 \| 0.2847 \| 0.8935 \| 0.8932 \| \| 0.2726 \| 19.57 \| 1800 \| 0.2780 \| 0.8891 \| 0.8891 \| \| 0.2711 \| 21.74 \| 2000 \| 0.2868 \| 0.8908 \| 0.8905 \| \| 0.2622 \| 23.91 \| 2200 \| 0.2883 \| 0.8949 \| 0.8946 \| \| 0.2659 \| 26.09 \| 2400 \| 0.2896 \| 0.8908 \| 0.8905 \| \| 0.2646 \| 28.26 \| 2600 \| 0.2777 \| 0.8928 \| 0.8925 \| \| 0.2609 \| 30.43 \| 2800 \| 0.2813 \| 0.8942 \| 0.8939 \| \| 0.2605 \| 32.61 \| 3000 \| 0.2823 \| 0.8963 \| 0.8960 \| \| 0.2589 \| 34.78 \| 3200 \| 0.2771 \| 0.8990 \| 0.8987 \| \| 0.2556 \| 36.96 \| 3400 \| 0.2850 \| 0.8942 \| 0.8939 \| \| 0.2551 \| 39.13 \| 3600 \| 0.2833 \| 0.8970 \| 0.8966 \| \| 0.2518 \| 41.3 \| 3800 \| 0.2831 \| 0.8963 \| 0.8960 \| \| 0.2515 \| 43.48 \| 4000 \| 0.2820 \| 0.8963 \| 0.8960 \| \| 0.255 \| 45.65 \| 4200 \| 0.2756 \| 0.8969 \| 0.8966 \| \| 0.248 \| 47.83 \| 4400 \| 0.2804 \| 0.8956 \| 0.8953 \| \| 0.2508 \| 50.0 \| 4600 \| 0.2736 \| 0.8969 \| 0.8966 \| \| 0.2465 \| 52.17 \| 4800 \| 0.2755 \| 0.8942 \| 0.8939 \| \| 0.2485 \| 54.35 \| 5000 \| 0.2929 \| 0.8855 \| 0.8850 \| \| 0.2436 \| 56.52 \| 5200 \| 0.2922 \| 0.8855 \| 0.8850 \| \| 0.2435 \| 58.7 \| 5400 \| 0.2806 \| 0.8881 \| 0.8877 \| \| 0.2428 \| 60.87 \| 5600 \| 0.2852 \| 0.8888 \| 0.8884 \| \| 0.2452 \| 63.04 \| 5800 \| 0.2816 \| 0.8881 \| 0.8877 \| \| 0.2428 \| 65.22 \| 6000 \| 0.2777 \| 0.8915 \| 0.8912 \| \| 0.242 \| 67.39 \| 6200 \| 0.2873 \| 0.8841 \| 0.8836 \| \| 0.2387 \| 69.57 \| 6400 \| 0.2821 \| 0.8861 \| 0.8857 \| \| 0.2393 \| 71.74 \| 6600 \| 0.2894 \| 0.8854 \| 0.8850 \| \| 0.2409 \| 73.91 \| 6800 \| 0.2829 \| 0.8888 \| 0.8884 \| \| 0.236 \| 76.09 \| 7000 \| 0.2819 \| 0.8881 \| 0.8877 \| \| 0.2364 \| 78.26 \| 7200 \| 0.2800 \| 0.8908 \| 0.8905 \| \| 0.2363 \| 80.43 \| 7400 \| 0.2764 \| 0.8915 \| 0.8912 \| \| 0.2361 \| 82.61 \| 7600 \| 0.2743 \| 0.8935 \| 0.8932 \| \| 0.2391 \| 84.78 \| 7800 \| 0.2879 \| 0.8875 \| 0.8871 \| \| 0.2357 \| 86.96 \| 8000 \| 0.2767 \| 0.8922 \| 0.8919 \| \| 0.237 \| 89.13 \| 8200 \| 0.2769 \| 0.8915 \| 0.8912 \| \| 0.2348 \| 91.3 \| 8400 \| 0.2780 \| 0.8915 \| 0.8912 \| \| 0.2338 \| 93.48 \| 8600 \| 0.2758 \| 0.8935 \| 0.8932 \| \| 0.2339 \| 95.65 \| 8800 \| 0.2753 \| 0.8935 \| 0.8932 \| \| 0.2358 \| 97.83 \| 9000 \| 0.2763 \| 0.8929 \| 0.8925 \| \| 0.2353 \| 100.0 \| 9200 \| 0.2829 \| 0.8888 \| 0.8884 \| \| 0.2349 \| 102.17 \| 9400 \| 0.2784 \| 0.8915 \| 0.8912 \| \| 0.2346 \| 104.35 \| 9600 \| 0.2794 \| 0.8908 \| 0.8905 \| \| 0.2327 \| 106.52 \| 9800 \| 0.2779 \| 0.8929 \| 0.8925 \| \| 0.2332 \| 108.7 \| 10000 \| 0.2783 \| 0.8929 \| 0.8925 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H4-seqsight_4096_512_15M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H4-seqsight_4096_512_15M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T18:05:36+00:00
null	peft	<!-- 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. --> # GUE_EMP_H4-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2787 - F1 Score: 0.9048 - Accuracy: 0.9049 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.3874 \| 2.17 \| 200 \| 0.2947 \| 0.8811 \| 0.8809 \| \| 0.2921 \| 4.35 \| 400 \| 0.2955 \| 0.8888 \| 0.8884 \| \| 0.2805 \| 6.52 \| 600 \| 0.2811 \| 0.8927 \| 0.8925 \| \| 0.2752 \| 8.7 \| 800 \| 0.2907 \| 0.8888 \| 0.8884 \| \| 0.2682 \| 10.87 \| 1000 \| 0.2885 \| 0.8874 \| 0.8871 \| \| 0.2612 \| 13.04 \| 1200 \| 0.2793 \| 0.8962 \| 0.8960 \| \| 0.2565 \| 15.22 \| 1400 \| 0.2839 \| 0.8902 \| 0.8898 \| \| 0.2495 \| 17.39 \| 1600 \| 0.2838 \| 0.8949 \| 0.8946 \| \| 0.2489 \| 19.57 \| 1800 \| 0.2698 \| 0.9030 \| 0.9028 \| \| 0.2432 \| 21.74 \| 2000 \| 0.2757 \| 0.8942 \| 0.8939 \| \| 0.2346 \| 23.91 \| 2200 \| 0.2817 \| 0.8929 \| 0.8925 \| \| 0.234 \| 26.09 \| 2400 \| 0.2747 \| 0.8935 \| 0.8932 \| \| 0.2307 \| 28.26 \| 2600 \| 0.2722 \| 0.9043 \| 0.9042 \| \| 0.2248 \| 30.43 \| 2800 \| 0.2782 \| 0.8874 \| 0.8871 \| \| 0.224 \| 32.61 \| 3000 \| 0.2796 \| 0.8989 \| 0.8987 \| \| 0.2198 \| 34.78 \| 3200 \| 0.2762 \| 0.9017 \| 0.9014 \| \| 0.2167 \| 36.96 \| 3400 \| 0.2808 \| 0.8983 \| 0.8980 \| \| 0.2139 \| 39.13 \| 3600 \| 0.2760 \| 0.8982 \| 0.8980 \| \| 0.2062 \| 41.3 \| 3800 \| 0.2803 \| 0.8983 \| 0.8980 \| \| 0.2082 \| 43.48 \| 4000 \| 0.2833 \| 0.8976 \| 0.8973 \| \| 0.2089 \| 45.65 \| 4200 \| 0.2781 \| 0.9030 \| 0.9028 \| \| 0.2016 \| 47.83 \| 4400 \| 0.2828 \| 0.9056 \| 0.9055 \| \| 0.2043 \| 50.0 \| 4600 \| 0.2781 \| 0.8962 \| 0.8960 \| \| 0.1996 \| 52.17 \| 4800 \| 0.2825 \| 0.8969 \| 0.8966 \| \| 0.1967 \| 54.35 \| 5000 \| 0.2907 \| 0.8977 \| 0.8973 \| \| 0.1945 \| 56.52 \| 5200 \| 0.3027 \| 0.8841 \| 0.8836 \| \| 0.1929 \| 58.7 \| 5400 \| 0.2808 \| 0.8990 \| 0.8987 \| \| 0.1909 \| 60.87 \| 5600 \| 0.2814 \| 0.8990 \| 0.8987 \| \| 0.1904 \| 63.04 \| 5800 \| 0.2880 \| 0.8915 \| 0.8912 \| \| 0.19 \| 65.22 \| 6000 \| 0.2885 \| 0.8970 \| 0.8966 \| \| 0.187 \| 67.39 \| 6200 \| 0.2886 \| 0.8963 \| 0.8960 \| \| 0.1834 \| 69.57 \| 6400 \| 0.2960 \| 0.8922 \| 0.8919 \| \| 0.1855 \| 71.74 \| 6600 \| 0.2953 \| 0.8983 \| 0.8980 \| \| 0.1826 \| 73.91 \| 6800 \| 0.2914 \| 0.8956 \| 0.8953 \| \| 0.1796 \| 76.09 \| 7000 \| 0.2983 \| 0.8929 \| 0.8925 \| \| 0.1791 \| 78.26 \| 7200 \| 0.2885 \| 0.9010 \| 0.9008 \| \| 0.1798 \| 80.43 \| 7400 \| 0.2929 \| 0.8963 \| 0.8960 \| \| 0.178 \| 82.61 \| 7600 \| 0.2925 \| 0.9044 \| 0.9042 \| \| 0.1767 \| 84.78 \| 7800 \| 0.3021 \| 0.8909 \| 0.8905 \| \| 0.1783 \| 86.96 \| 8000 \| 0.2919 \| 0.8989 \| 0.8987 \| \| 0.1754 \| 89.13 \| 8200 \| 0.2978 \| 0.8949 \| 0.8946 \| \| 0.1753 \| 91.3 \| 8400 \| 0.2952 \| 0.8942 \| 0.8939 \| \| 0.1735 \| 93.48 \| 8600 \| 0.2969 \| 0.8996 \| 0.8994 \| \| 0.1745 \| 95.65 \| 8800 \| 0.2928 \| 0.9037 \| 0.9035 \| \| 0.1725 \| 97.83 \| 9000 \| 0.2929 \| 0.9030 \| 0.9028 \| \| 0.1732 \| 100.0 \| 9200 \| 0.3001 \| 0.8915 \| 0.8912 \| \| 0.1718 \| 102.17 \| 9400 \| 0.2951 \| 0.9003 \| 0.9001 \| \| 0.1729 \| 104.35 \| 9600 \| 0.2937 \| 0.8996 \| 0.8994 \| \| 0.1718 \| 106.52 \| 9800 \| 0.2948 \| 0.8996 \| 0.8994 \| \| 0.1713 \| 108.7 \| 10000 \| 0.2957 \| 0.8996 \| 0.8994 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H4-seqsight_4096_512_15M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H4-seqsight_4096_512_15M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T18:05:39+00:00
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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 Dataset 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 Dataset 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]	{"library_name": "transformers", "tags": []}	ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_64_0.05_4_5e-05	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-03T18:06:11+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset. It achieves the following results on the evaluation set: - Loss: 0.3157 - F1 Score: 0.8771 - Accuracy: 0.8771 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5141 \| 2.13 \| 200 \| 0.4359 \| 0.7886 \| 0.7902 \| \| 0.3998 \| 4.26 \| 400 \| 0.3945 \| 0.8303 \| 0.8303 \| \| 0.3759 \| 6.38 \| 600 \| 0.3907 \| 0.8321 \| 0.8323 \| \| 0.3625 \| 8.51 \| 800 \| 0.3761 \| 0.8377 \| 0.8377 \| \| 0.3542 \| 10.64 \| 1000 \| 0.3715 \| 0.8382 \| 0.8383 \| \| 0.3365 \| 12.77 \| 1200 \| 0.3685 \| 0.8462 \| 0.8464 \| \| 0.3269 \| 14.89 \| 1400 \| 0.3503 \| 0.8470 \| 0.8470 \| \| 0.3101 \| 17.02 \| 1600 \| 0.3486 \| 0.8503 \| 0.8504 \| \| 0.3011 \| 19.15 \| 1800 \| 0.3602 \| 0.8527 \| 0.8530 \| \| 0.2962 \| 21.28 \| 2000 \| 0.3519 \| 0.8593 \| 0.8597 \| \| 0.2926 \| 23.4 \| 2200 \| 0.3292 \| 0.8570 \| 0.8570 \| \| 0.284 \| 25.53 \| 2400 \| 0.3457 \| 0.8575 \| 0.8577 \| \| 0.2853 \| 27.66 \| 2600 \| 0.3263 \| 0.8590 \| 0.8591 \| \| 0.284 \| 29.79 \| 2800 \| 0.3354 \| 0.8582 \| 0.8584 \| \| 0.2801 \| 31.91 \| 3000 \| 0.3358 \| 0.8622 \| 0.8624 \| \| 0.2764 \| 34.04 \| 3200 \| 0.3264 \| 0.8656 \| 0.8657 \| \| 0.2755 \| 36.17 \| 3400 \| 0.3383 \| 0.8622 \| 0.8624 \| \| 0.2724 \| 38.3 \| 3600 \| 0.3252 \| 0.8663 \| 0.8664 \| \| 0.2728 \| 40.43 \| 3800 \| 0.3269 \| 0.8676 \| 0.8677 \| \| 0.2718 \| 42.55 \| 4000 \| 0.3340 \| 0.8676 \| 0.8677 \| \| 0.2695 \| 44.68 \| 4200 \| 0.3235 \| 0.8657 \| 0.8657 \| \| 0.2667 \| 46.81 \| 4400 \| 0.3381 \| 0.8655 \| 0.8657 \| \| 0.2664 \| 48.94 \| 4600 \| 0.3301 \| 0.8689 \| 0.8691 \| \| 0.2654 \| 51.06 \| 4800 \| 0.3266 \| 0.8697 \| 0.8697 \| \| 0.2641 \| 53.19 \| 5000 \| 0.3489 \| 0.8607 \| 0.8611 \| \| 0.2677 \| 55.32 \| 5200 \| 0.3200 \| 0.8677 \| 0.8677 \| \| 0.2607 \| 57.45 \| 5400 \| 0.3333 \| 0.8683 \| 0.8684 \| \| 0.2581 \| 59.57 \| 5600 \| 0.3247 \| 0.8690 \| 0.8691 \| \| 0.2612 \| 61.7 \| 5800 \| 0.3181 \| 0.8677 \| 0.8677 \| \| 0.2565 \| 63.83 \| 6000 \| 0.3443 \| 0.8682 \| 0.8684 \| \| 0.2626 \| 65.96 \| 6200 \| 0.3354 \| 0.8676 \| 0.8677 \| \| 0.2579 \| 68.09 \| 6400 \| 0.3338 \| 0.8663 \| 0.8664 \| \| 0.2546 \| 70.21 \| 6600 \| 0.3380 \| 0.8683 \| 0.8684 \| \| 0.2562 \| 72.34 \| 6800 \| 0.3305 \| 0.8683 \| 0.8684 \| \| 0.2545 \| 74.47 \| 7000 \| 0.3513 \| 0.8600 \| 0.8604 \| \| 0.2507 \| 76.6 \| 7200 \| 0.3392 \| 0.8670 \| 0.8671 \| \| 0.2568 \| 78.72 \| 7400 \| 0.3329 \| 0.8676 \| 0.8677 \| \| 0.2515 \| 80.85 \| 7600 \| 0.3307 \| 0.8670 \| 0.8671 \| \| 0.2494 \| 82.98 \| 7800 \| 0.3388 \| 0.8649 \| 0.8651 \| \| 0.2537 \| 85.11 \| 8000 \| 0.3287 \| 0.8663 \| 0.8664 \| \| 0.2535 \| 87.23 \| 8200 \| 0.3317 \| 0.8663 \| 0.8664 \| \| 0.25 \| 89.36 \| 8400 \| 0.3356 \| 0.8663 \| 0.8664 \| \| 0.2493 \| 91.49 \| 8600 \| 0.3397 \| 0.8649 \| 0.8651 \| \| 0.2504 \| 93.62 \| 8800 \| 0.3360 \| 0.8656 \| 0.8657 \| \| 0.2532 \| 95.74 \| 9000 \| 0.3389 \| 0.8649 \| 0.8651 \| \| 0.2502 \| 97.87 \| 9200 \| 0.3313 \| 0.8676 \| 0.8677 \| \| 0.2497 \| 100.0 \| 9400 \| 0.3302 \| 0.8683 \| 0.8684 \| \| 0.2516 \| 102.13 \| 9600 \| 0.3324 \| 0.8676 \| 0.8677 \| \| 0.2499 \| 104.26 \| 9800 \| 0.3338 \| 0.8663 \| 0.8664 \| \| 0.2503 \| 106.38 \| 10000 \| 0.3336 \| 0.8663 \| 0.8664 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3-seqsight_4096_512_15M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3-seqsight_4096_512_15M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T18:06:35+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset. It achieves the following results on the evaluation set: - Loss: 0.3240 - F1 Score: 0.8737 - Accuracy: 0.8737 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.4577 \| 2.13 \| 200 \| 0.3877 \| 0.8286 \| 0.8290 \| \| 0.3469 \| 4.26 \| 400 \| 0.3609 \| 0.8490 \| 0.8490 \| \| 0.3103 \| 6.38 \| 600 \| 0.3633 \| 0.8493 \| 0.8497 \| \| 0.2955 \| 8.51 \| 800 \| 0.3413 \| 0.8589 \| 0.8591 \| \| 0.2851 \| 10.64 \| 1000 \| 0.3289 \| 0.8663 \| 0.8664 \| \| 0.2766 \| 12.77 \| 1200 \| 0.3486 \| 0.8540 \| 0.8544 \| \| 0.2727 \| 14.89 \| 1400 \| 0.3322 \| 0.8649 \| 0.8651 \| \| 0.2675 \| 17.02 \| 1600 \| 0.3172 \| 0.8731 \| 0.8731 \| \| 0.2608 \| 19.15 \| 1800 \| 0.3377 \| 0.8601 \| 0.8604 \| \| 0.2563 \| 21.28 \| 2000 \| 0.3345 \| 0.8643 \| 0.8644 \| \| 0.2561 \| 23.4 \| 2200 \| 0.3228 \| 0.8751 \| 0.8751 \| \| 0.2447 \| 25.53 \| 2400 \| 0.3772 \| 0.8566 \| 0.8570 \| \| 0.2497 \| 27.66 \| 2600 \| 0.3324 \| 0.8663 \| 0.8664 \| \| 0.2466 \| 29.79 \| 2800 \| 0.3237 \| 0.8744 \| 0.8744 \| \| 0.2406 \| 31.91 \| 3000 \| 0.3300 \| 0.8730 \| 0.8731 \| \| 0.2368 \| 34.04 \| 3200 \| 0.3247 \| 0.8737 \| 0.8737 \| \| 0.2362 \| 36.17 \| 3400 \| 0.3424 \| 0.8696 \| 0.8697 \| \| 0.2321 \| 38.3 \| 3600 \| 0.3380 \| 0.8730 \| 0.8731 \| \| 0.2306 \| 40.43 \| 3800 \| 0.3357 \| 0.8750 \| 0.8751 \| \| 0.2297 \| 42.55 \| 4000 \| 0.3661 \| 0.8669 \| 0.8671 \| \| 0.2267 \| 44.68 \| 4200 \| 0.3540 \| 0.8730 \| 0.8731 \| \| 0.2239 \| 46.81 \| 4400 \| 0.3621 \| 0.8722 \| 0.8724 \| \| 0.2206 \| 48.94 \| 4600 \| 0.3667 \| 0.8668 \| 0.8671 \| \| 0.2184 \| 51.06 \| 4800 \| 0.3429 \| 0.8751 \| 0.8751 \| \| 0.2186 \| 53.19 \| 5000 \| 0.3682 \| 0.8695 \| 0.8697 \| \| 0.2168 \| 55.32 \| 5200 \| 0.3536 \| 0.8717 \| 0.8717 \| \| 0.2109 \| 57.45 \| 5400 \| 0.3656 \| 0.8696 \| 0.8697 \| \| 0.2103 \| 59.57 \| 5600 \| 0.3503 \| 0.8764 \| 0.8764 \| \| 0.2118 \| 61.7 \| 5800 \| 0.3421 \| 0.8784 \| 0.8784 \| \| 0.2059 \| 63.83 \| 6000 \| 0.3849 \| 0.8716 \| 0.8717 \| \| 0.2099 \| 65.96 \| 6200 \| 0.3856 \| 0.8654 \| 0.8657 \| \| 0.2062 \| 68.09 \| 6400 \| 0.3654 \| 0.8737 \| 0.8737 \| \| 0.2016 \| 70.21 \| 6600 \| 0.3694 \| 0.8730 \| 0.8731 \| \| 0.2023 \| 72.34 \| 6800 \| 0.3634 \| 0.8770 \| 0.8771 \| \| 0.2009 \| 74.47 \| 7000 \| 0.4164 \| 0.8619 \| 0.8624 \| \| 0.1965 \| 76.6 \| 7200 \| 0.3775 \| 0.8696 \| 0.8697 \| \| 0.2008 \| 78.72 \| 7400 \| 0.3728 \| 0.8730 \| 0.8731 \| \| 0.1949 \| 80.85 \| 7600 \| 0.3727 \| 0.8723 \| 0.8724 \| \| 0.1923 \| 82.98 \| 7800 \| 0.3806 \| 0.8722 \| 0.8724 \| \| 0.195 \| 85.11 \| 8000 \| 0.3777 \| 0.8702 \| 0.8704 \| \| 0.1949 \| 87.23 \| 8200 \| 0.3796 \| 0.8736 \| 0.8737 \| \| 0.1924 \| 89.36 \| 8400 \| 0.3854 \| 0.8709 \| 0.8711 \| \| 0.1899 \| 91.49 \| 8600 \| 0.4060 \| 0.8688 \| 0.8691 \| \| 0.1873 \| 93.62 \| 8800 \| 0.3931 \| 0.8722 \| 0.8724 \| \| 0.1945 \| 95.74 \| 9000 \| 0.3907 \| 0.8716 \| 0.8717 \| \| 0.1907 \| 97.87 \| 9200 \| 0.3828 \| 0.8716 \| 0.8717 \| \| 0.1898 \| 100.0 \| 9400 \| 0.3809 \| 0.8737 \| 0.8737 \| \| 0.1906 \| 102.13 \| 9600 \| 0.3861 \| 0.8729 \| 0.8731 \| \| 0.1884 \| 104.26 \| 9800 \| 0.3847 \| 0.8716 \| 0.8717 \| \| 0.1891 \| 106.38 \| 10000 \| 0.3846 \| 0.8710 \| 0.8711 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3-seqsight_4096_512_15M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3-seqsight_4096_512_15M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T18:06:45+00:00
null	peft	<!-- 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. --> # GUE_EMP_H4-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2642 - F1 Score: 0.9023 - Accuracy: 0.9021 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.3648 \| 2.17 \| 200 \| 0.2892 \| 0.8852 \| 0.8850 \| \| 0.2836 \| 4.35 \| 400 \| 0.2803 \| 0.8954 \| 0.8953 \| \| 0.2716 \| 6.52 \| 600 \| 0.2752 \| 0.9001 \| 0.9001 \| \| 0.2644 \| 8.7 \| 800 \| 0.2867 \| 0.8874 \| 0.8871 \| \| 0.2542 \| 10.87 \| 1000 \| 0.2844 \| 0.8936 \| 0.8932 \| \| 0.2455 \| 13.04 \| 1200 \| 0.2891 \| 0.8875 \| 0.8871 \| \| 0.2348 \| 15.22 \| 1400 \| 0.2901 \| 0.8889 \| 0.8884 \| \| 0.226 \| 17.39 \| 1600 \| 0.2846 \| 0.8949 \| 0.8946 \| \| 0.2204 \| 19.57 \| 1800 \| 0.2804 \| 0.9037 \| 0.9035 \| \| 0.2127 \| 21.74 \| 2000 \| 0.2843 \| 0.8963 \| 0.8960 \| \| 0.1998 \| 23.91 \| 2200 \| 0.2911 \| 0.8935 \| 0.8932 \| \| 0.1956 \| 26.09 \| 2400 \| 0.2842 \| 0.8982 \| 0.8980 \| \| 0.1909 \| 28.26 \| 2600 \| 0.2996 \| 0.9016 \| 0.9014 \| \| 0.1785 \| 30.43 \| 2800 \| 0.3167 \| 0.8847 \| 0.8843 \| \| 0.1761 \| 32.61 \| 3000 \| 0.3012 \| 0.8974 \| 0.8973 \| \| 0.1675 \| 34.78 \| 3200 \| 0.3298 \| 0.8820 \| 0.8816 \| \| 0.158 \| 36.96 \| 3400 \| 0.3107 \| 0.8934 \| 0.8932 \| \| 0.1546 \| 39.13 \| 3600 \| 0.3235 \| 0.8893 \| 0.8891 \| \| 0.1427 \| 41.3 \| 3800 \| 0.3383 \| 0.8827 \| 0.8823 \| \| 0.1421 \| 43.48 \| 4000 \| 0.3797 \| 0.8739 \| 0.8734 \| \| 0.1371 \| 45.65 \| 4200 \| 0.3425 \| 0.8907 \| 0.8905 \| \| 0.1291 \| 47.83 \| 4400 \| 0.3497 \| 0.8819 \| 0.8816 \| \| 0.1265 \| 50.0 \| 4600 \| 0.3615 \| 0.8866 \| 0.8864 \| \| 0.1208 \| 52.17 \| 4800 \| 0.3905 \| 0.8752 \| 0.8747 \| \| 0.1148 \| 54.35 \| 5000 \| 0.3784 \| 0.8839 \| 0.8836 \| \| 0.115 \| 56.52 \| 5200 \| 0.3857 \| 0.8732 \| 0.8727 \| \| 0.1046 \| 58.7 \| 5400 \| 0.3877 \| 0.8786 \| 0.8782 \| \| 0.1019 \| 60.87 \| 5600 \| 0.3844 \| 0.8772 \| 0.8768 \| \| 0.1023 \| 63.04 \| 5800 \| 0.3925 \| 0.8790 \| 0.8789 \| \| 0.0957 \| 65.22 \| 6000 \| 0.4220 \| 0.8797 \| 0.8795 \| \| 0.095 \| 67.39 \| 6200 \| 0.4142 \| 0.8765 \| 0.8761 \| \| 0.0902 \| 69.57 \| 6400 \| 0.4613 \| 0.8739 \| 0.8734 \| \| 0.0899 \| 71.74 \| 6600 \| 0.4272 \| 0.8812 \| 0.8809 \| \| 0.0829 \| 73.91 \| 6800 \| 0.4464 \| 0.8690 \| 0.8686 \| \| 0.0841 \| 76.09 \| 7000 \| 0.4438 \| 0.8764 \| 0.8761 \| \| 0.0817 \| 78.26 \| 7200 \| 0.4425 \| 0.8751 \| 0.8747 \| \| 0.0782 \| 80.43 \| 7400 \| 0.4576 \| 0.8785 \| 0.8782 \| \| 0.0765 \| 82.61 \| 7600 \| 0.4707 \| 0.8744 \| 0.8741 \| \| 0.0689 \| 84.78 \| 7800 \| 0.5113 \| 0.8704 \| 0.8700 \| \| 0.0742 \| 86.96 \| 8000 \| 0.4925 \| 0.8738 \| 0.8734 \| \| 0.0721 \| 89.13 \| 8200 \| 0.4897 \| 0.8758 \| 0.8754 \| \| 0.0746 \| 91.3 \| 8400 \| 0.4792 \| 0.8717 \| 0.8713 \| \| 0.0713 \| 93.48 \| 8600 \| 0.4866 \| 0.8738 \| 0.8734 \| \| 0.0695 \| 95.65 \| 8800 \| 0.4749 \| 0.8791 \| 0.8789 \| \| 0.0676 \| 97.83 \| 9000 \| 0.4816 \| 0.8757 \| 0.8754 \| \| 0.0684 \| 100.0 \| 9200 \| 0.5062 \| 0.8725 \| 0.8720 \| \| 0.0661 \| 102.17 \| 9400 \| 0.4917 \| 0.8730 \| 0.8727 \| \| 0.0642 \| 104.35 \| 9600 \| 0.4921 \| 0.8751 \| 0.8747 \| \| 0.0628 \| 106.52 \| 9800 \| 0.4966 \| 0.8751 \| 0.8747 \| \| 0.062 \| 108.7 \| 10000 \| 0.5035 \| 0.8744 \| 0.8741 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H4-seqsight_4096_512_15M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H4-seqsight_4096_512_15M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]	null	2024-05-03T18:06:50+00:00

null

peft

# GUE_prom_prom_core_all-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.4181 - F1 Score: 0.8048 - Accuracy: 0.8049 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5559 | 0.54 | 200 | 0.4704 | 0.7803 | 0.7804 | | 0.4687 | 1.08 | 400 | 0.4646 | 0.7875 | 0.7878 | | 0.4525 | 1.62 | 600 | 0.4497 | 0.7929 | 0.7931 | | 0.4437 | 2.16 | 800 | 0.4473 | 0.7944 | 0.7944 | | 0.4405 | 2.7 | 1000 | 0.4449 | 0.7921 | 0.7922 | | 0.4363 | 3.24 | 1200 | 0.4399 | 0.7961 | 0.7963 | | 0.4331 | 3.78 | 1400 | 0.4419 | 0.7909 | 0.7912 | | 0.4313 | 4.32 | 1600 | 0.4438 | 0.7967 | 0.7968 | | 0.4309 | 4.86 | 1800 | 0.4419 | 0.7937 | 0.7941 | | 0.4266 | 5.41 | 2000 | 0.4388 | 0.7927 | 0.7929 | | 0.4243 | 5.95 | 2200 | 0.4391 | 0.7981 | 0.7981 | | 0.4279 | 6.49 | 2400 | 0.4341 | 0.7965 | 0.7965 | | 0.4206 | 7.03 | 2600 | 0.4416 | 0.7977 | 0.7981 | | 0.4231 | 7.57 | 2800 | 0.4348 | 0.7976 | 0.7976 | | 0.4171 | 8.11 | 3000 | 0.4362 | 0.7944 | 0.7946 | | 0.419 | 8.65 | 3200 | 0.4297 | 0.8017 | 0.8017 | | 0.4207 | 9.19 | 3400 | 0.4331 | 0.7992 | 0.7992 | | 0.418 | 9.73 | 3600 | 0.4378 | 0.7949 | 0.7954 | | 0.4182 | 10.27 | 3800 | 0.4330 | 0.7982 | 0.7983 | | 0.4164 | 10.81 | 4000 | 0.4360 | 0.7977 | 0.7978 | | 0.414 | 11.35 | 4200 | 0.4330 | 0.7973 | 0.7975 | | 0.4143 | 11.89 | 4400 | 0.4336 | 0.7964 | 0.7966 | | 0.4115 | 12.43 | 4600 | 0.4335 | 0.8025 | 0.8025 | | 0.4108 | 12.97 | 4800 | 0.4331 | 0.7990 | 0.7992 | | 0.4133 | 13.51 | 5000 | 0.4407 | 0.7934 | 0.7943 | | 0.4114 | 14.05 | 5200 | 0.4303 | 0.8029 | 0.8029 | | 0.4085 | 14.59 | 5400 | 0.4288 | 0.8022 | 0.8022 | | 0.4081 | 15.14 | 5600 | 0.4326 | 0.8021 | 0.8022 | | 0.4096 | 15.68 | 5800 | 0.4334 | 0.7985 | 0.7988 | | 0.4037 | 16.22 | 6000 | 0.4312 | 0.8023 | 0.8025 | | 0.4114 | 16.76 | 6200 | 0.4254 | 0.8015 | 0.8015 | | 0.4119 | 17.3 | 6400 | 0.4278 | 0.8046 | 0.8047 | | 0.4072 | 17.84 | 6600 | 0.4294 | 0.8014 | 0.8015 | | 0.4035 | 18.38 | 6800 | 0.4337 | 0.7972 | 0.7978 | | 0.4047 | 18.92 | 7000 | 0.4277 | 0.8021 | 0.8022 | | 0.4011 | 19.46 | 7200 | 0.4286 | 0.8035 | 0.8035 | | 0.4118 | 20.0 | 7400 | 0.4264 | 0.8045 | 0.8046 | | 0.4066 | 20.54 | 7600 | 0.4286 | 0.8025 | 0.8027 | | 0.4031 | 21.08 | 7800 | 0.4275 | 0.8038 | 0.8039 | | 0.4044 | 21.62 | 8000 | 0.4255 | 0.8037 | 0.8037 | | 0.402 | 22.16 | 8200 | 0.4259 | 0.8040 | 0.8041 | | 0.4101 | 22.7 | 8400 | 0.4265 | 0.8027 | 0.8029 | | 0.4006 | 23.24 | 8600 | 0.4249 | 0.8047 | 0.8047 | | 0.4005 | 23.78 | 8800 | 0.4271 | 0.8038 | 0.8039 | | 0.3983 | 24.32 | 9000 | 0.4269 | 0.8045 | 0.8046 | | 0.4017 | 24.86 | 9200 | 0.4259 | 0.8038 | 0.8039 | | 0.4117 | 25.41 | 9400 | 0.4257 | 0.8043 | 0.8044 | | 0.3956 | 25.95 | 9600 | 0.4271 | 0.8048 | 0.8049 | | 0.4029 | 26.49 | 9800 | 0.4272 | 0.8050 | 0.8051 | | 0.4004 | 27.03 | 10000 | 0.4271 | 0.8046 | 0.8047 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_all-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_all-seqsight_4096_512_15M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:22:07+00:00

null

peft

# GUE_prom_prom_core_notata-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.3823 - F1 Score: 0.8291 - Accuracy: 0.8291 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5574 | 0.6 | 200 | 0.4140 | 0.8126 | 0.8127 | | 0.4313 | 1.2 | 400 | 0.3888 | 0.8259 | 0.8259 | | 0.4159 | 1.81 | 600 | 0.3816 | 0.8268 | 0.8268 | | 0.4102 | 2.41 | 800 | 0.3759 | 0.8310 | 0.8310 | | 0.4006 | 3.01 | 1000 | 0.3744 | 0.8299 | 0.8298 | | 0.3973 | 3.61 | 1200 | 0.3701 | 0.8379 | 0.8379 | | 0.3992 | 4.22 | 1400 | 0.3703 | 0.8355 | 0.8355 | | 0.396 | 4.82 | 1600 | 0.3687 | 0.8381 | 0.8381 | | 0.3877 | 5.42 | 1800 | 0.3757 | 0.8292 | 0.8293 | | 0.3935 | 6.02 | 2000 | 0.3690 | 0.8383 | 0.8383 | | 0.3911 | 6.63 | 2200 | 0.3674 | 0.8381 | 0.8381 | | 0.3879 | 7.23 | 2400 | 0.3679 | 0.8380 | 0.8381 | | 0.3887 | 7.83 | 2600 | 0.3662 | 0.8396 | 0.8396 | | 0.3825 | 8.43 | 2800 | 0.3721 | 0.8344 | 0.8349 | | 0.3879 | 9.04 | 3000 | 0.3663 | 0.8402 | 0.8402 | | 0.3812 | 9.64 | 3200 | 0.3637 | 0.8397 | 0.8396 | | 0.3823 | 10.24 | 3400 | 0.3647 | 0.8406 | 0.8406 | | 0.383 | 10.84 | 3600 | 0.3643 | 0.8400 | 0.8400 | | 0.3815 | 11.45 | 3800 | 0.3640 | 0.8382 | 0.8381 | | 0.3804 | 12.05 | 4000 | 0.3629 | 0.8381 | 0.8381 | | 0.3746 | 12.65 | 4200 | 0.3634 | 0.8382 | 0.8383 | | 0.3799 | 13.25 | 4400 | 0.3635 | 0.8376 | 0.8376 | | 0.378 | 13.86 | 4600 | 0.3636 | 0.8400 | 0.8400 | | 0.3771 | 14.46 | 4800 | 0.3633 | 0.8415 | 0.8415 | | 0.3741 | 15.06 | 5000 | 0.3615 | 0.8415 | 0.8415 | | 0.371 | 15.66 | 5200 | 0.3612 | 0.8412 | 0.8412 | | 0.3728 | 16.27 | 5400 | 0.3642 | 0.8400 | 0.8400 | | 0.3718 | 16.87 | 5600 | 0.3679 | 0.8361 | 0.8364 | | 0.3698 | 17.47 | 5800 | 0.3664 | 0.8369 | 0.8372 | | 0.3758 | 18.07 | 6000 | 0.3624 | 0.8393 | 0.8395 | | 0.3725 | 18.67 | 6200 | 0.3605 | 0.8412 | 0.8413 | | 0.3716 | 19.28 | 6400 | 0.3618 | 0.8408 | 0.8408 | | 0.3703 | 19.88 | 6600 | 0.3613 | 0.8388 | 0.8389 | | 0.3658 | 20.48 | 6800 | 0.3606 | 0.8409 | 0.8410 | | 0.3759 | 21.08 | 7000 | 0.3640 | 0.8363 | 0.8366 | | 0.3748 | 21.69 | 7200 | 0.3612 | 0.8415 | 0.8415 | | 0.3651 | 22.29 | 7400 | 0.3610 | 0.8399 | 0.8400 | | 0.3673 | 22.89 | 7600 | 0.3609 | 0.8424 | 0.8425 | | 0.3681 | 23.49 | 7800 | 0.3622 | 0.8380 | 0.8381 | | 0.3688 | 24.1 | 8000 | 0.3629 | 0.8393 | 0.8395 | | 0.3692 | 24.7 | 8200 | 0.3639 | 0.8388 | 0.8391 | | 0.3645 | 25.3 | 8400 | 0.3642 | 0.8396 | 0.8398 | | 0.3692 | 25.9 | 8600 | 0.3609 | 0.8422 | 0.8423 | | 0.3687 | 26.51 | 8800 | 0.3615 | 0.8415 | 0.8415 | | 0.3671 | 27.11 | 9000 | 0.3610 | 0.8409 | 0.8410 | | 0.3726 | 27.71 | 9200 | 0.3617 | 0.8399 | 0.8400 | | 0.3626 | 28.31 | 9400 | 0.3631 | 0.8387 | 0.8389 | | 0.3658 | 28.92 | 9600 | 0.3618 | 0.8396 | 0.8396 | | 0.3724 | 29.52 | 9800 | 0.3614 | 0.8392 | 0.8393 | | 0.3612 | 30.12 | 10000 | 0.3615 | 0.8395 | 0.8396 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_4096_512_15M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:22:07+00:00

null

{}

joebamnasoda69/CheckardsArrVeeSeeStuff2

null

[ "region:us" ]

null

2024-05-03T17:22:22+00:00

null

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

{"library_name": "transformers", "tags": []}

hrangel/Mistral_7B_qlora_CoT_Matematicals

null

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-03T17:23:34+00:00

null

peft

# GUE_prom_prom_core_notata-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.3910 - F1 Score: 0.8233 - Accuracy: 0.8233 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6131 | 0.6 | 200 | 0.4693 | 0.7777 | 0.7777 | | 0.4721 | 1.2 | 400 | 0.4169 | 0.8106 | 0.8106 | | 0.4443 | 1.81 | 600 | 0.4046 | 0.8150 | 0.8150 | | 0.4403 | 2.41 | 800 | 0.3968 | 0.8210 | 0.8210 | | 0.4255 | 3.01 | 1000 | 0.3953 | 0.8213 | 0.8214 | | 0.4233 | 3.61 | 1200 | 0.3889 | 0.8217 | 0.8217 | | 0.4223 | 4.22 | 1400 | 0.3869 | 0.8225 | 0.8225 | | 0.4197 | 4.82 | 1600 | 0.3844 | 0.8223 | 0.8223 | | 0.4106 | 5.42 | 1800 | 0.3869 | 0.8248 | 0.8249 | | 0.4124 | 6.02 | 2000 | 0.3819 | 0.8262 | 0.8263 | | 0.4112 | 6.63 | 2200 | 0.3791 | 0.8285 | 0.8285 | | 0.407 | 7.23 | 2400 | 0.3801 | 0.8313 | 0.8314 | | 0.4063 | 7.83 | 2600 | 0.3787 | 0.8288 | 0.8289 | | 0.4012 | 8.43 | 2800 | 0.3808 | 0.8296 | 0.8298 | | 0.406 | 9.04 | 3000 | 0.3761 | 0.8322 | 0.8323 | | 0.3994 | 9.64 | 3200 | 0.3734 | 0.8312 | 0.8312 | | 0.4003 | 10.24 | 3400 | 0.3750 | 0.8323 | 0.8323 | | 0.4008 | 10.84 | 3600 | 0.3741 | 0.8336 | 0.8336 | | 0.3994 | 11.45 | 3800 | 0.3736 | 0.8327 | 0.8327 | | 0.3982 | 12.05 | 4000 | 0.3729 | 0.8340 | 0.8340 | | 0.3933 | 12.65 | 4200 | 0.3739 | 0.8342 | 0.8342 | | 0.3995 | 13.25 | 4400 | 0.3707 | 0.8349 | 0.8349 | | 0.3967 | 13.86 | 4600 | 0.3721 | 0.8355 | 0.8355 | | 0.3951 | 14.46 | 4800 | 0.3723 | 0.8351 | 0.8351 | | 0.3916 | 15.06 | 5000 | 0.3705 | 0.8336 | 0.8336 | | 0.3907 | 15.66 | 5200 | 0.3703 | 0.8376 | 0.8376 | | 0.3905 | 16.27 | 5400 | 0.3728 | 0.8355 | 0.8355 | | 0.3917 | 16.87 | 5600 | 0.3738 | 0.8364 | 0.8366 | | 0.39 | 17.47 | 5800 | 0.3720 | 0.8365 | 0.8366 | | 0.3961 | 18.07 | 6000 | 0.3706 | 0.8377 | 0.8378 | | 0.3917 | 18.67 | 6200 | 0.3694 | 0.8379 | 0.8379 | | 0.3923 | 19.28 | 6400 | 0.3711 | 0.8374 | 0.8374 | | 0.389 | 19.88 | 6600 | 0.3690 | 0.8377 | 0.8378 | | 0.3847 | 20.48 | 6800 | 0.3701 | 0.8371 | 0.8372 | | 0.3949 | 21.08 | 7000 | 0.3710 | 0.8359 | 0.8361 | | 0.3961 | 21.69 | 7200 | 0.3680 | 0.8379 | 0.8379 | | 0.386 | 22.29 | 7400 | 0.3684 | 0.8393 | 0.8393 | | 0.387 | 22.89 | 7600 | 0.3698 | 0.8378 | 0.8378 | | 0.388 | 23.49 | 7800 | 0.3683 | 0.8391 | 0.8391 | | 0.3887 | 24.1 | 8000 | 0.3689 | 0.8381 | 0.8381 | | 0.3889 | 24.7 | 8200 | 0.3693 | 0.8360 | 0.8361 | | 0.3844 | 25.3 | 8400 | 0.3699 | 0.8389 | 0.8389 | | 0.3902 | 25.9 | 8600 | 0.3678 | 0.8398 | 0.8398 | | 0.3906 | 26.51 | 8800 | 0.3681 | 0.8383 | 0.8383 | | 0.3874 | 27.11 | 9000 | 0.3682 | 0.8389 | 0.8389 | | 0.3929 | 27.71 | 9200 | 0.3682 | 0.8393 | 0.8393 | | 0.3847 | 28.31 | 9400 | 0.3689 | 0.8396 | 0.8396 | | 0.3874 | 28.92 | 9600 | 0.3684 | 0.8393 | 0.8393 | | 0.3929 | 29.52 | 9800 | 0.3680 | 0.8391 | 0.8391 | | 0.3819 | 30.12 | 10000 | 0.3682 | 0.8391 | 0.8391 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_4096_512_15M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:24:03+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

{"library_name": "transformers", "tags": []}

OwOpeepeepoopoo/herewegoagain18

null

[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T17:24:38+00:00

null

peft

# GUE_prom_prom_core_notata-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.3783 - F1 Score: 0.8328 - Accuracy: 0.8329 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5299 | 0.6 | 200 | 0.4006 | 0.8167 | 0.8168 | | 0.4157 | 1.2 | 400 | 0.3794 | 0.8336 | 0.8336 | | 0.4031 | 1.81 | 600 | 0.3786 | 0.8307 | 0.8308 | | 0.399 | 2.41 | 800 | 0.3696 | 0.8336 | 0.8336 | | 0.3916 | 3.01 | 1000 | 0.3681 | 0.8352 | 0.8353 | | 0.387 | 3.61 | 1200 | 0.3638 | 0.8390 | 0.8391 | | 0.3907 | 4.22 | 1400 | 0.3662 | 0.8395 | 0.8395 | | 0.386 | 4.82 | 1600 | 0.3624 | 0.8418 | 0.8419 | | 0.377 | 5.42 | 1800 | 0.3715 | 0.8339 | 0.8340 | | 0.3833 | 6.02 | 2000 | 0.3665 | 0.8392 | 0.8393 | | 0.3794 | 6.63 | 2200 | 0.3616 | 0.8398 | 0.8398 | | 0.3765 | 7.23 | 2400 | 0.3654 | 0.8416 | 0.8417 | | 0.3776 | 7.83 | 2600 | 0.3619 | 0.8391 | 0.8391 | | 0.3695 | 8.43 | 2800 | 0.3655 | 0.8373 | 0.8378 | | 0.3752 | 9.04 | 3000 | 0.3597 | 0.8442 | 0.8442 | | 0.368 | 9.64 | 3200 | 0.3595 | 0.8425 | 0.8425 | | 0.3675 | 10.24 | 3400 | 0.3602 | 0.8417 | 0.8417 | | 0.3692 | 10.84 | 3600 | 0.3594 | 0.8407 | 0.8408 | | 0.3657 | 11.45 | 3800 | 0.3580 | 0.8440 | 0.8440 | | 0.3651 | 12.05 | 4000 | 0.3583 | 0.8419 | 0.8419 | | 0.3594 | 12.65 | 4200 | 0.3580 | 0.8431 | 0.8432 | | 0.3633 | 13.25 | 4400 | 0.3588 | 0.8428 | 0.8428 | | 0.361 | 13.86 | 4600 | 0.3606 | 0.8413 | 0.8413 | | 0.359 | 14.46 | 4800 | 0.3588 | 0.8434 | 0.8434 | | 0.3573 | 15.06 | 5000 | 0.3560 | 0.8452 | 0.8453 | | 0.3505 | 15.66 | 5200 | 0.3603 | 0.8428 | 0.8428 | | 0.3549 | 16.27 | 5400 | 0.3618 | 0.8434 | 0.8434 | | 0.3528 | 16.87 | 5600 | 0.3677 | 0.8386 | 0.8391 | | 0.3501 | 17.47 | 5800 | 0.3639 | 0.8427 | 0.8430 | | 0.3573 | 18.07 | 6000 | 0.3615 | 0.8446 | 0.8447 | | 0.3517 | 18.67 | 6200 | 0.3582 | 0.8442 | 0.8444 | | 0.3509 | 19.28 | 6400 | 0.3615 | 0.8432 | 0.8432 | | 0.3489 | 19.88 | 6600 | 0.3584 | 0.8425 | 0.8427 | | 0.3444 | 20.48 | 6800 | 0.3580 | 0.8447 | 0.8447 | | 0.3544 | 21.08 | 7000 | 0.3644 | 0.8404 | 0.8408 | | 0.3525 | 21.69 | 7200 | 0.3604 | 0.8423 | 0.8423 | | 0.3441 | 22.29 | 7400 | 0.3598 | 0.8448 | 0.8449 | | 0.346 | 22.89 | 7600 | 0.3610 | 0.8424 | 0.8425 | | 0.346 | 23.49 | 7800 | 0.3613 | 0.8412 | 0.8413 | | 0.347 | 24.1 | 8000 | 0.3645 | 0.8417 | 0.8419 | | 0.3462 | 24.7 | 8200 | 0.3650 | 0.8416 | 0.8419 | | 0.3401 | 25.3 | 8400 | 0.3669 | 0.8421 | 0.8423 | | 0.3471 | 25.9 | 8600 | 0.3612 | 0.8428 | 0.8428 | | 0.3451 | 26.51 | 8800 | 0.3618 | 0.8432 | 0.8432 | | 0.3456 | 27.11 | 9000 | 0.3604 | 0.8432 | 0.8432 | | 0.3485 | 27.71 | 9200 | 0.3626 | 0.8425 | 0.8427 | | 0.3388 | 28.31 | 9400 | 0.3632 | 0.8442 | 0.8444 | | 0.3412 | 28.92 | 9600 | 0.3632 | 0.8420 | 0.8421 | | 0.3492 | 29.52 | 9800 | 0.3614 | 0.8422 | 0.8423 | | 0.3355 | 30.12 | 10000 | 0.3620 | 0.8431 | 0.8432 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_4096_512_15M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:24:39+00:00

text-classification

transformers

{"license": "mit"}

wantuta/roberta_classifier_ancient2

null

[ "transformers", "safetensors", "roberta", "text-classification", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T17:24:42+00:00

null

peft

# GUE_prom_prom_core_tata-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.4439 - F1 Score: 0.8286 - Accuracy: 0.8287 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.629 | 5.13 | 200 | 0.5857 | 0.7072 | 0.7080 | | 0.5743 | 10.26 | 400 | 0.5824 | 0.6910 | 0.6949 | | 0.5505 | 15.38 | 600 | 0.5729 | 0.7040 | 0.7096 | | 0.53 | 20.51 | 800 | 0.5425 | 0.7238 | 0.7243 | | 0.5137 | 25.64 | 1000 | 0.5271 | 0.7352 | 0.7357 | | 0.4924 | 30.77 | 1200 | 0.4966 | 0.7730 | 0.7732 | | 0.466 | 35.9 | 1400 | 0.4742 | 0.7879 | 0.7879 | | 0.4452 | 41.03 | 1600 | 0.4655 | 0.7808 | 0.7814 | | 0.4341 | 46.15 | 1800 | 0.4457 | 0.8010 | 0.8010 | | 0.4182 | 51.28 | 2000 | 0.4385 | 0.8042 | 0.8042 | | 0.4107 | 56.41 | 2200 | 0.4363 | 0.8075 | 0.8075 | | 0.4042 | 61.54 | 2400 | 0.4199 | 0.8074 | 0.8075 | | 0.3981 | 66.67 | 2600 | 0.4153 | 0.8108 | 0.8108 | | 0.3883 | 71.79 | 2800 | 0.4141 | 0.8075 | 0.8075 | | 0.383 | 76.92 | 3000 | 0.4142 | 0.8140 | 0.8140 | | 0.3755 | 82.05 | 3200 | 0.4044 | 0.8205 | 0.8206 | | 0.3734 | 87.18 | 3400 | 0.4064 | 0.8222 | 0.8222 | | 0.3695 | 92.31 | 3600 | 0.4026 | 0.8238 | 0.8238 | | 0.3625 | 97.44 | 3800 | 0.3999 | 0.8352 | 0.8352 | | 0.3664 | 102.56 | 4000 | 0.3976 | 0.8303 | 0.8303 | | 0.3595 | 107.69 | 4200 | 0.3992 | 0.8303 | 0.8303 | | 0.352 | 112.82 | 4400 | 0.3970 | 0.8303 | 0.8303 | | 0.347 | 117.95 | 4600 | 0.3906 | 0.8303 | 0.8303 | | 0.3497 | 123.08 | 4800 | 0.3944 | 0.8351 | 0.8352 | | 0.3398 | 128.21 | 5000 | 0.3941 | 0.8352 | 0.8352 | | 0.3432 | 133.33 | 5200 | 0.3897 | 0.8352 | 0.8352 | | 0.3371 | 138.46 | 5400 | 0.3878 | 0.8369 | 0.8369 | | 0.3331 | 143.59 | 5600 | 0.3882 | 0.8352 | 0.8352 | | 0.3377 | 148.72 | 5800 | 0.3883 | 0.8352 | 0.8352 | | 0.3288 | 153.85 | 6000 | 0.3889 | 0.8352 | 0.8352 | | 0.3261 | 158.97 | 6200 | 0.3843 | 0.8401 | 0.8401 | | 0.3284 | 164.1 | 6400 | 0.3902 | 0.8335 | 0.8336 | | 0.3293 | 169.23 | 6600 | 0.3837 | 0.8384 | 0.8385 | | 0.3242 | 174.36 | 6800 | 0.3899 | 0.8385 | 0.8385 | | 0.3263 | 179.49 | 7000 | 0.3861 | 0.8352 | 0.8352 | | 0.3193 | 184.62 | 7200 | 0.3874 | 0.8434 | 0.8434 | | 0.3187 | 189.74 | 7400 | 0.3903 | 0.8385 | 0.8385 | | 0.3201 | 194.87 | 7600 | 0.3908 | 0.8385 | 0.8385 | | 0.3194 | 200.0 | 7800 | 0.3860 | 0.8466 | 0.8467 | | 0.3187 | 205.13 | 8000 | 0.3869 | 0.8449 | 0.8450 | | 0.3163 | 210.26 | 8200 | 0.3877 | 0.8401 | 0.8401 | | 0.313 | 215.38 | 8400 | 0.3892 | 0.8417 | 0.8418 | | 0.316 | 220.51 | 8600 | 0.3888 | 0.8385 | 0.8385 | | 0.3144 | 225.64 | 8800 | 0.3886 | 0.8417 | 0.8418 | | 0.3124 | 230.77 | 9000 | 0.3866 | 0.8449 | 0.8450 | | 0.3119 | 235.9 | 9200 | 0.3874 | 0.8417 | 0.8418 | | 0.3125 | 241.03 | 9400 | 0.3884 | 0.8450 | 0.8450 | | 0.3151 | 246.15 | 9600 | 0.3868 | 0.8417 | 0.8418 | | 0.3084 | 251.28 | 9800 | 0.3879 | 0.8417 | 0.8418 | | 0.3116 | 256.41 | 10000 | 0.3878 | 0.8450 | 0.8450 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_4096_512_15M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:25:18+00:00

image-segmentation

transformers

# segformer-b0-finetuned-raw_img_ready2train_patches This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the raw_img_ready2train_patches dataset. It achieves the following results on the evaluation set: - Loss: 0.6829 - Mean Iou: 0.4110 - Mean Accuracy: 0.7629 - Overall Accuracy: 0.7631 - Accuracy Unlabeled: nan - Accuracy Eczema: 0.7673 - Accuracy Background: 0.7585 - Iou Unlabeled: 0.0 - Iou Eczema: 0.6284 - Iou Background: 0.6047 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Eczema | Accuracy Background | Iou Unlabeled | Iou Eczema | Iou Background | |:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:---------------:|:-------------------:|:-------------:|:----------:|:--------------:| | 1.0753 | 0.0312 | 5 | 1.0925 | 0.2358 | 0.4682 | 0.4698 | nan | 0.5042 | 0.4322 | 0.0 | 0.3705 | 0.3367 | | 0.9863 | 0.0625 | 10 | 1.0697 | 0.2994 | 0.6182 | 0.6306 | nan | 0.8979 | 0.3385 | 0.0 | 0.5784 | 0.3198 | | 1.0056 | 0.0938 | 15 | 1.0377 | 0.3303 | 0.6678 | 0.6792 | nan | 0.9236 | 0.4121 | 0.0 | 0.6064 | 0.3844 | | 1.0133 | 0.125 | 20 | 1.0006 | 0.3478 | 0.6869 | 0.6950 | nan | 0.8710 | 0.5027 | 0.0 | 0.6008 | 0.4425 | | 0.9748 | 0.1562 | 25 | 0.9689 | 0.3543 | 0.6947 | 0.7022 | nan | 0.8647 | 0.5246 | 0.0 | 0.6043 | 0.4586 | | 0.9367 | 0.1875 | 30 | 0.9417 | 0.3566 | 0.6950 | 0.6965 | nan | 0.7290 | 0.6610 | 0.0 | 0.5583 | 0.5114 | | 0.8363 | 0.2188 | 35 | 0.9118 | 0.3557 | 0.6940 | 0.6959 | nan | 0.7366 | 0.6514 | 0.0 | 0.5600 | 0.5069 | | 1.1431 | 0.25 | 40 | 0.8830 | 0.3575 | 0.6963 | 0.6989 | nan | 0.7556 | 0.6370 | 0.0 | 0.5686 | 0.5039 | | 0.7312 | 0.2812 | 45 | 0.8592 | 0.3680 | 0.7098 | 0.7133 | nan | 0.7888 | 0.6307 | 0.0 | 0.5907 | 0.5133 | | 0.8135 | 0.3125 | 50 | 0.8268 | 0.3559 | 0.6994 | 0.7083 | nan | 0.8992 | 0.4997 | 0.0 | 0.6173 | 0.4505 | | 0.7528 | 0.3438 | 55 | 0.8110 | 0.3525 | 0.6960 | 0.7053 | nan | 0.9055 | 0.4866 | 0.0 | 0.6162 | 0.4412 | | 0.8405 | 0.375 | 60 | 0.7967 | 0.3518 | 0.6950 | 0.7041 | nan | 0.9008 | 0.4893 | 0.0 | 0.6140 | 0.4415 | | 0.7865 | 0.4062 | 65 | 0.7791 | 0.3561 | 0.6992 | 0.7075 | nan | 0.8869 | 0.5116 | 0.0 | 0.6130 | 0.4553 | | 0.8309 | 0.4375 | 70 | 0.7650 | 0.3652 | 0.7083 | 0.7147 | nan | 0.8512 | 0.5655 | 0.0 | 0.6090 | 0.4864 | | 0.6775 | 0.4688 | 75 | 0.7615 | 0.3613 | 0.7044 | 0.7115 | nan | 0.8651 | 0.5437 | 0.0 | 0.6102 | 0.4738 | | 0.7033 | 0.5 | 80 | 0.7498 | 0.3737 | 0.7179 | 0.7227 | nan | 0.8260 | 0.6099 | 0.0 | 0.6087 | 0.5125 | | 0.8377 | 0.5312 | 85 | 0.7443 | 0.3790 | 0.7243 | 0.7290 | nan | 0.8303 | 0.6184 | 0.0 | 0.6154 | 0.5217 | | 0.825 | 0.5625 | 90 | 0.7547 | 0.3676 | 0.7125 | 0.7201 | nan | 0.8840 | 0.5411 | 0.0 | 0.6225 | 0.4802 | | 0.7408 | 0.5938 | 95 | 0.7415 | 0.3767 | 0.7228 | 0.7295 | nan | 0.8747 | 0.5708 | 0.0 | 0.6281 | 0.5021 | | 0.8087 | 0.625 | 100 | 0.7201 | 0.3926 | 0.7404 | 0.7445 | nan | 0.8318 | 0.6491 | 0.0 | 0.6296 | 0.5483 | | 0.7146 | 0.6562 | 105 | 0.7096 | 0.4002 | 0.7493 | 0.7520 | nan | 0.8109 | 0.6877 | 0.0 | 0.6307 | 0.5699 | | 0.6875 | 0.6875 | 110 | 0.7047 | 0.4010 | 0.7502 | 0.7541 | nan | 0.8398 | 0.6606 | 0.0 | 0.6407 | 0.5621 | | 0.6382 | 0.7188 | 115 | 0.7031 | 0.3982 | 0.7471 | 0.7519 | nan | 0.8543 | 0.6400 | 0.0 | 0.6426 | 0.5521 | | 0.6551 | 0.75 | 120 | 0.6953 | 0.4018 | 0.7512 | 0.7553 | nan | 0.8450 | 0.6573 | 0.0 | 0.6433 | 0.5621 | | 0.7074 | 0.7812 | 125 | 0.6912 | 0.4054 | 0.7553 | 0.7583 | nan | 0.8236 | 0.6871 | 0.0 | 0.6402 | 0.5760 | | 0.768 | 0.8125 | 130 | 0.6866 | 0.4048 | 0.7546 | 0.7579 | nan | 0.8278 | 0.6814 | 0.0 | 0.6410 | 0.5736 | | 0.7543 | 0.8438 | 135 | 0.6851 | 0.4031 | 0.7526 | 0.7564 | nan | 0.8374 | 0.6679 | 0.0 | 0.6422 | 0.5671 | | 0.7107 | 0.875 | 140 | 0.6803 | 0.6122 | 0.7586 | 0.7608 | nan | 0.8071 | 0.7101 | nan | 0.6379 | 0.5865 | | 0.7054 | 0.9062 | 145 | 0.6799 | 0.4098 | 0.7608 | 0.7622 | nan | 0.7924 | 0.7292 | 0.0 | 0.6350 | 0.5943 | | 1.1302 | 0.9375 | 150 | 0.6801 | 0.4103 | 0.7616 | 0.7626 | nan | 0.7840 | 0.7393 | 0.0 | 0.6330 | 0.5981 | | 0.6037 | 0.9688 | 155 | 0.6827 | 0.4111 | 0.7628 | 0.7632 | nan | 0.7721 | 0.7534 | 0.0 | 0.6300 | 0.6032 | | 0.8577 | 1.0 | 160 | 0.6829 | 0.4110 | 0.7629 | 0.7631 | nan | 0.7673 | 0.7585 | 0.0 | 0.6284 | 0.6047 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "base_model": "nvidia/mit-b0", "model-index": [{"name": "segformer-b0-finetuned-raw_img_ready2train_patches", "results": []}]}

ruisusanofi/segformer-b0-finetuned-raw_img_ready2train_patches

null

[ "transformers", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]

null

2024-05-03T17:27:04+00:00

null

peft

# GUE_prom_prom_core_tata-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.4594 - F1 Score: 0.8271 - Accuracy: 0.8271 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.6032 | 5.13 | 200 | 0.5634 | 0.7185 | 0.7194 | | 0.5278 | 10.26 | 400 | 0.5256 | 0.7344 | 0.7357 | | 0.4699 | 15.38 | 600 | 0.4605 | 0.7856 | 0.7863 | | 0.4245 | 20.51 | 800 | 0.4272 | 0.7976 | 0.7977 | | 0.3889 | 25.64 | 1000 | 0.4069 | 0.8155 | 0.8157 | | 0.3665 | 30.77 | 1200 | 0.3898 | 0.8236 | 0.8238 | | 0.3478 | 35.9 | 1400 | 0.3919 | 0.8320 | 0.8320 | | 0.3314 | 41.03 | 1600 | 0.3985 | 0.8265 | 0.8271 | | 0.3178 | 46.15 | 1800 | 0.3865 | 0.8352 | 0.8352 | | 0.3045 | 51.28 | 2000 | 0.3880 | 0.8319 | 0.8320 | | 0.2962 | 56.41 | 2200 | 0.3923 | 0.8434 | 0.8434 | | 0.2901 | 61.54 | 2400 | 0.3825 | 0.8401 | 0.8401 | | 0.2789 | 66.67 | 2600 | 0.3828 | 0.8352 | 0.8352 | | 0.2688 | 71.79 | 2800 | 0.3823 | 0.8367 | 0.8369 | | 0.2668 | 76.92 | 3000 | 0.3948 | 0.8352 | 0.8352 | | 0.2553 | 82.05 | 3200 | 0.3873 | 0.8385 | 0.8385 | | 0.25 | 87.18 | 3400 | 0.3933 | 0.8385 | 0.8385 | | 0.2466 | 92.31 | 3600 | 0.3986 | 0.8466 | 0.8467 | | 0.2419 | 97.44 | 3800 | 0.3981 | 0.8465 | 0.8467 | | 0.2396 | 102.56 | 4000 | 0.3904 | 0.8596 | 0.8597 | | 0.2347 | 107.69 | 4200 | 0.4066 | 0.8548 | 0.8548 | | 0.2237 | 112.82 | 4400 | 0.4169 | 0.8548 | 0.8548 | | 0.2197 | 117.95 | 4600 | 0.4028 | 0.8613 | 0.8613 | | 0.2178 | 123.08 | 4800 | 0.4289 | 0.8483 | 0.8483 | | 0.2117 | 128.21 | 5000 | 0.4253 | 0.8499 | 0.8499 | | 0.2147 | 133.33 | 5200 | 0.4187 | 0.8596 | 0.8597 | | 0.2068 | 138.46 | 5400 | 0.4218 | 0.8611 | 0.8613 | | 0.2019 | 143.59 | 5600 | 0.4296 | 0.8466 | 0.8467 | | 0.2023 | 148.72 | 5800 | 0.4374 | 0.8548 | 0.8548 | | 0.1959 | 153.85 | 6000 | 0.4354 | 0.8515 | 0.8515 | | 0.1974 | 158.97 | 6200 | 0.4282 | 0.8564 | 0.8564 | | 0.1983 | 164.1 | 6400 | 0.4305 | 0.8515 | 0.8515 | | 0.1928 | 169.23 | 6600 | 0.4352 | 0.8581 | 0.8581 | | 0.1889 | 174.36 | 6800 | 0.4507 | 0.8532 | 0.8532 | | 0.1909 | 179.49 | 7000 | 0.4417 | 0.8450 | 0.8450 | | 0.1855 | 184.62 | 7200 | 0.4481 | 0.8548 | 0.8548 | | 0.1824 | 189.74 | 7400 | 0.4513 | 0.8564 | 0.8564 | | 0.1837 | 194.87 | 7600 | 0.4567 | 0.8515 | 0.8515 | | 0.1841 | 200.0 | 7800 | 0.4383 | 0.8630 | 0.8630 | | 0.1819 | 205.13 | 8000 | 0.4506 | 0.8532 | 0.8532 | | 0.1809 | 210.26 | 8200 | 0.4516 | 0.8499 | 0.8499 | | 0.1753 | 215.38 | 8400 | 0.4639 | 0.8467 | 0.8467 | | 0.1771 | 220.51 | 8600 | 0.4612 | 0.8548 | 0.8548 | | 0.1777 | 225.64 | 8800 | 0.4593 | 0.8483 | 0.8483 | | 0.1723 | 230.77 | 9000 | 0.4591 | 0.8499 | 0.8499 | | 0.1727 | 235.9 | 9200 | 0.4602 | 0.8467 | 0.8467 | | 0.1714 | 241.03 | 9400 | 0.4662 | 0.8548 | 0.8548 | | 0.1739 | 246.15 | 9600 | 0.4643 | 0.8450 | 0.8450 | | 0.1721 | 251.28 | 9800 | 0.4632 | 0.8532 | 0.8532 | | 0.1689 | 256.41 | 10000 | 0.4628 | 0.8532 | 0.8532 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_4096_512_15M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:28:17+00:00

null

{}

HarshitSheoran/solafune_fields

null

[ "region:us" ]

null

2024-05-03T17:28:18+00:00

null

peft

# GUE_prom_prom_core_tata-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.4640 - F1 Score: 0.8320 - Accuracy: 0.8320 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.5782 | 5.13 | 200 | 0.5315 | 0.7406 | 0.7406 | | 0.4665 | 10.26 | 400 | 0.4535 | 0.7895 | 0.7896 | | 0.3912 | 15.38 | 600 | 0.3940 | 0.8189 | 0.8189 | | 0.3406 | 20.51 | 800 | 0.3676 | 0.8482 | 0.8483 | | 0.301 | 25.64 | 1000 | 0.3680 | 0.8676 | 0.8679 | | 0.2781 | 30.77 | 1200 | 0.3465 | 0.8596 | 0.8597 | | 0.2586 | 35.9 | 1400 | 0.3497 | 0.8662 | 0.8662 | | 0.2365 | 41.03 | 1600 | 0.3888 | 0.8575 | 0.8581 | | 0.2231 | 46.15 | 1800 | 0.3801 | 0.8547 | 0.8548 | | 0.2111 | 51.28 | 2000 | 0.3956 | 0.8612 | 0.8613 | | 0.1949 | 56.41 | 2200 | 0.4369 | 0.8532 | 0.8532 | | 0.1843 | 61.54 | 2400 | 0.4161 | 0.8611 | 0.8613 | | 0.1706 | 66.67 | 2600 | 0.4586 | 0.8659 | 0.8662 | | 0.1597 | 71.79 | 2800 | 0.4525 | 0.8679 | 0.8679 | | 0.1529 | 76.92 | 3000 | 0.4764 | 0.8449 | 0.8450 | | 0.1405 | 82.05 | 3200 | 0.5161 | 0.8547 | 0.8548 | | 0.1323 | 87.18 | 3400 | 0.5201 | 0.8662 | 0.8662 | | 0.1275 | 92.31 | 3600 | 0.5121 | 0.8628 | 0.8630 | | 0.1212 | 97.44 | 3800 | 0.5360 | 0.8645 | 0.8646 | | 0.1135 | 102.56 | 4000 | 0.5797 | 0.8595 | 0.8597 | | 0.11 | 107.69 | 4200 | 0.5665 | 0.8613 | 0.8613 | | 0.1041 | 112.82 | 4400 | 0.5754 | 0.8597 | 0.8597 | | 0.1008 | 117.95 | 4600 | 0.5795 | 0.8547 | 0.8548 | | 0.093 | 123.08 | 4800 | 0.6056 | 0.8630 | 0.8630 | | 0.0896 | 128.21 | 5000 | 0.6137 | 0.8564 | 0.8564 | | 0.0883 | 133.33 | 5200 | 0.6119 | 0.8564 | 0.8564 | | 0.0813 | 138.46 | 5400 | 0.6257 | 0.8629 | 0.8630 | | 0.0794 | 143.59 | 5600 | 0.6374 | 0.8630 | 0.8630 | | 0.0781 | 148.72 | 5800 | 0.6801 | 0.8597 | 0.8597 | | 0.0753 | 153.85 | 6000 | 0.6478 | 0.8580 | 0.8581 | | 0.0709 | 158.97 | 6200 | 0.6664 | 0.8630 | 0.8630 | | 0.0725 | 164.1 | 6400 | 0.6262 | 0.8564 | 0.8564 | | 0.067 | 169.23 | 6600 | 0.6659 | 0.8581 | 0.8581 | | 0.0632 | 174.36 | 6800 | 0.6947 | 0.8564 | 0.8564 | | 0.067 | 179.49 | 7000 | 0.6948 | 0.8564 | 0.8564 | | 0.0627 | 184.62 | 7200 | 0.7080 | 0.8564 | 0.8564 | | 0.0611 | 189.74 | 7400 | 0.7102 | 0.8548 | 0.8548 | | 0.0595 | 194.87 | 7600 | 0.7069 | 0.8629 | 0.8630 | | 0.062 | 200.0 | 7800 | 0.6852 | 0.8646 | 0.8646 | | 0.0554 | 205.13 | 8000 | 0.7127 | 0.8613 | 0.8613 | | 0.0596 | 210.26 | 8200 | 0.6846 | 0.8548 | 0.8548 | | 0.0534 | 215.38 | 8400 | 0.7266 | 0.8597 | 0.8597 | | 0.0561 | 220.51 | 8600 | 0.7142 | 0.8532 | 0.8532 | | 0.0517 | 225.64 | 8800 | 0.7146 | 0.8532 | 0.8532 | | 0.0512 | 230.77 | 9000 | 0.7151 | 0.8564 | 0.8564 | | 0.0523 | 235.9 | 9200 | 0.6998 | 0.8581 | 0.8581 | | 0.053 | 241.03 | 9400 | 0.7092 | 0.8662 | 0.8662 | | 0.0495 | 246.15 | 9600 | 0.7234 | 0.8613 | 0.8613 | | 0.0514 | 251.28 | 9800 | 0.7236 | 0.8613 | 0.8613 | | 0.0514 | 256.41 | 10000 | 0.7248 | 0.8597 | 0.8597 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_4096_512_15M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:28:21+00:00

null

{}

Aryan0310/bart-small-finetuned-xsum

null

[ "region:us" ]

null

2024-05-03T17:28:40+00:00

text-classification

transformers

# question_classifier This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0621 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 15 | 0.1063 | 1.0 | | No log | 2.0 | 30 | 0.0621 | 1.0 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert/distilbert-base-uncased", "model-index": [{"name": "question_classifier", "results": []}]}

philgrey/question_classifier

null

[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T17:29:08+00:00

null

{}

PauloSeze/teste

null

[ "region:us" ]

null

2024-05-03T17:29:18+00:00

null

peft

# GUE_prom_prom_300_all-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.2156 - F1 Score: 0.9135 - Accuracy: 0.9135 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.4293 | 0.54 | 200 | 0.2948 | 0.8828 | 0.8828 | | 0.3066 | 1.08 | 400 | 0.2688 | 0.8919 | 0.8919 | | 0.2856 | 1.62 | 600 | 0.2528 | 0.8953 | 0.8953 | | 0.2612 | 2.16 | 800 | 0.2449 | 0.9021 | 0.9022 | | 0.2536 | 2.7 | 1000 | 0.2343 | 0.9061 | 0.9061 | | 0.2466 | 3.24 | 1200 | 0.2309 | 0.9101 | 0.9101 | | 0.2442 | 3.78 | 1400 | 0.2255 | 0.9123 | 0.9123 | | 0.2415 | 4.32 | 1600 | 0.2236 | 0.9142 | 0.9142 | | 0.2313 | 4.86 | 1800 | 0.2214 | 0.9160 | 0.9160 | | 0.232 | 5.41 | 2000 | 0.2196 | 0.9165 | 0.9166 | | 0.2312 | 5.95 | 2200 | 0.2174 | 0.9179 | 0.9179 | | 0.2288 | 6.49 | 2400 | 0.2151 | 0.9184 | 0.9184 | | 0.2271 | 7.03 | 2600 | 0.2132 | 0.9179 | 0.9179 | | 0.2222 | 7.57 | 2800 | 0.2103 | 0.9199 | 0.9199 | | 0.2241 | 8.11 | 3000 | 0.2105 | 0.9206 | 0.9206 | | 0.2221 | 8.65 | 3200 | 0.2076 | 0.9218 | 0.9218 | | 0.2162 | 9.19 | 3400 | 0.2091 | 0.9213 | 0.9213 | | 0.2148 | 9.73 | 3600 | 0.2041 | 0.9235 | 0.9235 | | 0.2211 | 10.27 | 3800 | 0.2025 | 0.9233 | 0.9233 | | 0.2149 | 10.81 | 4000 | 0.2022 | 0.9243 | 0.9243 | | 0.2168 | 11.35 | 4200 | 0.2010 | 0.9241 | 0.9242 | | 0.2128 | 11.89 | 4400 | 0.2016 | 0.9270 | 0.9270 | | 0.2117 | 12.43 | 4600 | 0.1994 | 0.9223 | 0.9223 | | 0.2135 | 12.97 | 4800 | 0.1967 | 0.9280 | 0.9280 | | 0.2084 | 13.51 | 5000 | 0.1976 | 0.9262 | 0.9262 | | 0.2139 | 14.05 | 5200 | 0.1957 | 0.9265 | 0.9265 | | 0.2089 | 14.59 | 5400 | 0.1966 | 0.9260 | 0.9260 | | 0.2067 | 15.14 | 5600 | 0.1960 | 0.9255 | 0.9255 | | 0.2062 | 15.68 | 5800 | 0.1948 | 0.9284 | 0.9284 | | 0.2084 | 16.22 | 6000 | 0.1950 | 0.9253 | 0.9253 | | 0.2052 | 16.76 | 6200 | 0.1935 | 0.9285 | 0.9285 | | 0.2056 | 17.3 | 6400 | 0.1949 | 0.9260 | 0.9260 | | 0.2074 | 17.84 | 6600 | 0.1934 | 0.9258 | 0.9258 | | 0.2021 | 18.38 | 6800 | 0.1926 | 0.9277 | 0.9277 | | 0.2082 | 18.92 | 7000 | 0.1913 | 0.9284 | 0.9284 | | 0.2074 | 19.46 | 7200 | 0.1923 | 0.9282 | 0.9282 | | 0.2013 | 20.0 | 7400 | 0.1917 | 0.9282 | 0.9282 | | 0.2033 | 20.54 | 7600 | 0.1910 | 0.9284 | 0.9284 | | 0.2014 | 21.08 | 7800 | 0.1903 | 0.9294 | 0.9294 | | 0.2051 | 21.62 | 8000 | 0.1904 | 0.9287 | 0.9287 | | 0.2025 | 22.16 | 8200 | 0.1903 | 0.9291 | 0.9291 | | 0.1986 | 22.7 | 8400 | 0.1903 | 0.9282 | 0.9282 | | 0.2057 | 23.24 | 8600 | 0.1898 | 0.9289 | 0.9289 | | 0.2012 | 23.78 | 8800 | 0.1893 | 0.9289 | 0.9289 | | 0.2033 | 24.32 | 9000 | 0.1896 | 0.9294 | 0.9294 | | 0.2009 | 24.86 | 9200 | 0.1898 | 0.9291 | 0.9291 | | 0.2009 | 25.41 | 9400 | 0.1902 | 0.9291 | 0.9291 | | 0.1996 | 25.95 | 9600 | 0.1899 | 0.9289 | 0.9289 | | 0.2019 | 26.49 | 9800 | 0.1894 | 0.9296 | 0.9296 | | 0.2001 | 27.03 | 10000 | 0.1895 | 0.9287 | 0.9287 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_4096_512_15M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:29:51+00:00

null

peft

# GUE_prom_prom_300_all-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.1974 - F1 Score: 0.9209 - Accuracy: 0.9209 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.3768 | 0.54 | 200 | 0.2589 | 0.8973 | 0.8973 | | 0.2631 | 1.08 | 400 | 0.2351 | 0.9071 | 0.9071 | | 0.2483 | 1.62 | 600 | 0.2179 | 0.9130 | 0.9130 | | 0.2279 | 2.16 | 800 | 0.2134 | 0.9146 | 0.9147 | | 0.2247 | 2.7 | 1000 | 0.2063 | 0.9203 | 0.9203 | | 0.2187 | 3.24 | 1200 | 0.2048 | 0.9182 | 0.9182 | | 0.2199 | 3.78 | 1400 | 0.1983 | 0.9216 | 0.9216 | | 0.2136 | 4.32 | 1600 | 0.1935 | 0.9238 | 0.9238 | | 0.2055 | 4.86 | 1800 | 0.1926 | 0.9250 | 0.925 | | 0.2062 | 5.41 | 2000 | 0.1902 | 0.9292 | 0.9292 | | 0.2048 | 5.95 | 2200 | 0.1900 | 0.9240 | 0.9240 | | 0.2034 | 6.49 | 2400 | 0.1868 | 0.9270 | 0.9270 | | 0.2024 | 7.03 | 2600 | 0.1869 | 0.9284 | 0.9284 | | 0.194 | 7.57 | 2800 | 0.1862 | 0.9287 | 0.9287 | | 0.2 | 8.11 | 3000 | 0.1853 | 0.9302 | 0.9302 | | 0.1959 | 8.65 | 3200 | 0.1851 | 0.9292 | 0.9292 | | 0.1885 | 9.19 | 3400 | 0.1864 | 0.9296 | 0.9296 | | 0.1888 | 9.73 | 3600 | 0.1827 | 0.9280 | 0.9280 | | 0.1944 | 10.27 | 3800 | 0.1824 | 0.9292 | 0.9292 | | 0.1895 | 10.81 | 4000 | 0.1819 | 0.9304 | 0.9304 | | 0.1917 | 11.35 | 4200 | 0.1797 | 0.9306 | 0.9306 | | 0.1854 | 11.89 | 4400 | 0.1828 | 0.9307 | 0.9307 | | 0.1873 | 12.43 | 4600 | 0.1790 | 0.9296 | 0.9296 | | 0.1861 | 12.97 | 4800 | 0.1771 | 0.9314 | 0.9314 | | 0.1823 | 13.51 | 5000 | 0.1789 | 0.9289 | 0.9289 | | 0.187 | 14.05 | 5200 | 0.1809 | 0.9280 | 0.9280 | | 0.1817 | 14.59 | 5400 | 0.1778 | 0.9323 | 0.9323 | | 0.1801 | 15.14 | 5600 | 0.1776 | 0.9316 | 0.9316 | | 0.1801 | 15.68 | 5800 | 0.1781 | 0.9304 | 0.9304 | | 0.179 | 16.22 | 6000 | 0.1787 | 0.9316 | 0.9316 | | 0.1784 | 16.76 | 6200 | 0.1779 | 0.9296 | 0.9296 | | 0.1787 | 17.3 | 6400 | 0.1792 | 0.9277 | 0.9277 | | 0.1794 | 17.84 | 6600 | 0.1755 | 0.9328 | 0.9328 | | 0.1748 | 18.38 | 6800 | 0.1776 | 0.9294 | 0.9294 | | 0.1804 | 18.92 | 7000 | 0.1763 | 0.9292 | 0.9292 | | 0.1802 | 19.46 | 7200 | 0.1765 | 0.9316 | 0.9316 | | 0.1741 | 20.0 | 7400 | 0.1755 | 0.9326 | 0.9326 | | 0.1767 | 20.54 | 7600 | 0.1752 | 0.9309 | 0.9309 | | 0.1739 | 21.08 | 7800 | 0.1747 | 0.9312 | 0.9313 | | 0.1747 | 21.62 | 8000 | 0.1748 | 0.9311 | 0.9311 | | 0.1758 | 22.16 | 8200 | 0.1758 | 0.9319 | 0.9319 | | 0.1724 | 22.7 | 8400 | 0.1738 | 0.9336 | 0.9336 | | 0.1762 | 23.24 | 8600 | 0.1753 | 0.9306 | 0.9306 | | 0.1759 | 23.78 | 8800 | 0.1744 | 0.9312 | 0.9313 | | 0.1751 | 24.32 | 9000 | 0.1756 | 0.9307 | 0.9307 | | 0.1727 | 24.86 | 9200 | 0.1742 | 0.9318 | 0.9318 | | 0.1718 | 25.41 | 9400 | 0.1766 | 0.9309 | 0.9309 | | 0.1719 | 25.95 | 9600 | 0.1750 | 0.9321 | 0.9321 | | 0.173 | 26.49 | 9800 | 0.1745 | 0.9311 | 0.9311 | | 0.1729 | 27.03 | 10000 | 0.1746 | 0.9311 | 0.9311 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_4096_512_15M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:30:16+00:00

null

peft

# GUE_prom_prom_300_all-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.1996 - F1 Score: 0.9221 - Accuracy: 0.9221 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.3384 | 0.54 | 200 | 0.2347 | 0.9054 | 0.9054 | | 0.238 | 1.08 | 400 | 0.2090 | 0.9182 | 0.9182 | | 0.2278 | 1.62 | 600 | 0.1982 | 0.9226 | 0.9226 | | 0.2116 | 2.16 | 800 | 0.1958 | 0.9222 | 0.9223 | | 0.2086 | 2.7 | 1000 | 0.1936 | 0.9238 | 0.9238 | | 0.2038 | 3.24 | 1200 | 0.1907 | 0.9248 | 0.9248 | | 0.2055 | 3.78 | 1400 | 0.1871 | 0.9264 | 0.9264 | | 0.1993 | 4.32 | 1600 | 0.1862 | 0.9258 | 0.9258 | | 0.1938 | 4.86 | 1800 | 0.1810 | 0.9292 | 0.9292 | | 0.1914 | 5.41 | 2000 | 0.1831 | 0.9319 | 0.9319 | | 0.1908 | 5.95 | 2200 | 0.1822 | 0.9272 | 0.9272 | | 0.1883 | 6.49 | 2400 | 0.1779 | 0.9301 | 0.9301 | | 0.1878 | 7.03 | 2600 | 0.1818 | 0.9321 | 0.9321 | | 0.1787 | 7.57 | 2800 | 0.1776 | 0.9321 | 0.9321 | | 0.1846 | 8.11 | 3000 | 0.1798 | 0.9304 | 0.9304 | | 0.1792 | 8.65 | 3200 | 0.1748 | 0.9321 | 0.9321 | | 0.1713 | 9.19 | 3400 | 0.1808 | 0.9311 | 0.9311 | | 0.1723 | 9.73 | 3600 | 0.1742 | 0.9307 | 0.9307 | | 0.1774 | 10.27 | 3800 | 0.1742 | 0.9311 | 0.9311 | | 0.1732 | 10.81 | 4000 | 0.1763 | 0.9346 | 0.9346 | | 0.1724 | 11.35 | 4200 | 0.1725 | 0.9345 | 0.9345 | | 0.167 | 11.89 | 4400 | 0.1760 | 0.9346 | 0.9346 | | 0.1691 | 12.43 | 4600 | 0.1716 | 0.9333 | 0.9333 | | 0.1638 | 12.97 | 4800 | 0.1699 | 0.9311 | 0.9311 | | 0.1619 | 13.51 | 5000 | 0.1736 | 0.9302 | 0.9302 | | 0.1661 | 14.05 | 5200 | 0.1766 | 0.9273 | 0.9274 | | 0.16 | 14.59 | 5400 | 0.1720 | 0.9309 | 0.9309 | | 0.1591 | 15.14 | 5600 | 0.1725 | 0.9323 | 0.9323 | | 0.1584 | 15.68 | 5800 | 0.1710 | 0.9318 | 0.9318 | | 0.1562 | 16.22 | 6000 | 0.1739 | 0.9309 | 0.9309 | | 0.1552 | 16.76 | 6200 | 0.1748 | 0.9321 | 0.9321 | | 0.1551 | 17.3 | 6400 | 0.1751 | 0.9309 | 0.9309 | | 0.1566 | 17.84 | 6600 | 0.1718 | 0.9331 | 0.9331 | | 0.1509 | 18.38 | 6800 | 0.1730 | 0.9314 | 0.9314 | | 0.1546 | 18.92 | 7000 | 0.1714 | 0.9331 | 0.9331 | | 0.1538 | 19.46 | 7200 | 0.1716 | 0.9334 | 0.9334 | | 0.15 | 20.0 | 7400 | 0.1728 | 0.9339 | 0.9340 | | 0.1513 | 20.54 | 7600 | 0.1715 | 0.9328 | 0.9328 | | 0.1485 | 21.08 | 7800 | 0.1698 | 0.9326 | 0.9326 | | 0.1484 | 21.62 | 8000 | 0.1706 | 0.9326 | 0.9326 | | 0.1494 | 22.16 | 8200 | 0.1711 | 0.9331 | 0.9331 | | 0.1448 | 22.7 | 8400 | 0.1689 | 0.9333 | 0.9333 | | 0.1468 | 23.24 | 8600 | 0.1715 | 0.9323 | 0.9323 | | 0.1478 | 23.78 | 8800 | 0.1719 | 0.9317 | 0.9318 | | 0.1448 | 24.32 | 9000 | 0.1721 | 0.9317 | 0.9318 | | 0.1454 | 24.86 | 9200 | 0.1707 | 0.9331 | 0.9331 | | 0.1432 | 25.41 | 9400 | 0.1746 | 0.9328 | 0.9328 | | 0.1437 | 25.95 | 9600 | 0.1727 | 0.9326 | 0.9326 | | 0.1448 | 26.49 | 9800 | 0.1718 | 0.9328 | 0.9328 | | 0.1434 | 27.03 | 10000 | 0.1715 | 0.9331 | 0.9331 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_4096_512_15M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:30:56+00:00

null

peft

# GUE_EMP_H3K14ac-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.5179 - F1 Score: 0.7401 - Accuracy: 0.7398 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.621 | 0.97 | 200 | 0.5813 | 0.7034 | 0.7014 | | 0.5829 | 1.93 | 400 | 0.5649 | 0.7244 | 0.7234 | | 0.5699 | 2.9 | 600 | 0.5881 | 0.7000 | 0.6989 | | 0.5629 | 3.86 | 800 | 0.5478 | 0.7259 | 0.7277 | | 0.5513 | 4.83 | 1000 | 0.5659 | 0.7205 | 0.7189 | | 0.5467 | 5.8 | 1200 | 0.5586 | 0.7257 | 0.7241 | | 0.5421 | 6.76 | 1400 | 0.5415 | 0.7344 | 0.7325 | | 0.54 | 7.73 | 1600 | 0.5395 | 0.7347 | 0.7328 | | 0.5343 | 8.7 | 1800 | 0.5408 | 0.7313 | 0.7295 | | 0.5335 | 9.66 | 2000 | 0.5432 | 0.7340 | 0.7322 | | 0.5333 | 10.63 | 2200 | 0.5558 | 0.7252 | 0.7241 | | 0.5269 | 11.59 | 2400 | 0.5283 | 0.7408 | 0.7392 | | 0.5308 | 12.56 | 2600 | 0.5436 | 0.7342 | 0.7325 | | 0.5281 | 13.53 | 2800 | 0.5438 | 0.7280 | 0.7265 | | 0.5271 | 14.49 | 3000 | 0.5531 | 0.7231 | 0.7222 | | 0.5225 | 15.46 | 3200 | 0.5235 | 0.7473 | 0.7461 | | 0.5232 | 16.43 | 3400 | 0.5536 | 0.7240 | 0.7231 | | 0.5238 | 17.39 | 3600 | 0.5289 | 0.7389 | 0.7371 | | 0.52 | 18.36 | 3800 | 0.5192 | 0.7531 | 0.7525 | | 0.5196 | 19.32 | 4000 | 0.5257 | 0.7443 | 0.7425 | | 0.5165 | 20.29 | 4200 | 0.5332 | 0.7413 | 0.7395 | | 0.5193 | 21.26 | 4400 | 0.5360 | 0.7372 | 0.7356 | | 0.5184 | 22.22 | 4600 | 0.5446 | 0.7270 | 0.7259 | | 0.5189 | 23.19 | 4800 | 0.5232 | 0.7500 | 0.7483 | | 0.5167 | 24.15 | 5000 | 0.5251 | 0.7461 | 0.7443 | | 0.5142 | 25.12 | 5200 | 0.5545 | 0.7270 | 0.7262 | | 0.5155 | 26.09 | 5400 | 0.5322 | 0.7387 | 0.7371 | | 0.5159 | 27.05 | 5600 | 0.5536 | 0.7217 | 0.7213 | | 0.5137 | 28.02 | 5800 | 0.5214 | 0.7500 | 0.7483 | | 0.514 | 28.99 | 6000 | 0.5382 | 0.7318 | 0.7304 | | 0.5121 | 29.95 | 6200 | 0.5395 | 0.7333 | 0.7319 | | 0.5146 | 30.92 | 6400 | 0.5213 | 0.7512 | 0.7495 | | 0.5135 | 31.88 | 6600 | 0.5305 | 0.7396 | 0.7380 | | 0.509 | 32.85 | 6800 | 0.5327 | 0.7377 | 0.7362 | | 0.5134 | 33.82 | 7000 | 0.5423 | 0.7309 | 0.7298 | | 0.51 | 34.78 | 7200 | 0.5412 | 0.7326 | 0.7313 | | 0.5122 | 35.75 | 7400 | 0.5335 | 0.7362 | 0.7346 | | 0.508 | 36.71 | 7600 | 0.5288 | 0.7417 | 0.7401 | | 0.509 | 37.68 | 7800 | 0.5311 | 0.7423 | 0.7407 | | 0.5105 | 38.65 | 8000 | 0.5237 | 0.7482 | 0.7464 | | 0.5139 | 39.61 | 8200 | 0.5312 | 0.7398 | 0.7383 | | 0.5052 | 40.58 | 8400 | 0.5363 | 0.7345 | 0.7331 | | 0.5068 | 41.55 | 8600 | 0.5293 | 0.7438 | 0.7422 | | 0.5084 | 42.51 | 8800 | 0.5338 | 0.7380 | 0.7365 | | 0.5113 | 43.48 | 9000 | 0.5397 | 0.7341 | 0.7328 | | 0.5068 | 44.44 | 9200 | 0.5338 | 0.7383 | 0.7368 | | 0.5112 | 45.41 | 9400 | 0.5303 | 0.7402 | 0.7386 | | 0.504 | 46.38 | 9600 | 0.5351 | 0.7373 | 0.7359 | | 0.5109 | 47.34 | 9800 | 0.5327 | 0.7380 | 0.7365 | | 0.5066 | 48.31 | 10000 | 0.5302 | 0.7408 | 0.7392 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_4096_512_15M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:30:56+00:00

null

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

{"library_name": "transformers", "tags": []}

amuseix/w2v-bert-2.0-bulgarian-CV17.0-FLEURS

null

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-03T17:31:10+00:00

text-generation

transformers

{}

RyotaKadoya1993/Llama-3-JPN-MoE2

null

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

null

2024-05-03T17:31:12+00:00

null

{}

Ketansomewhere/sdxl-pokemon-model

null

[ "region:us" ]

null

2024-05-03T17:31:18+00:00

null

transformers

# Uploaded model - **Developed by:** eugeniosegala - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}

eugeniosegala/model

null

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-03T17:33:07+00:00

null

{}

Anoop03031988/Code-Llama-2-7B-instruct-text2sql-GGUF

null

[ "gguf", "region:us" ]

null

2024-05-03T17:33:17+00:00

null

{}

d-d-o-s/g1

null

[ "region:us" ]

null

2024-05-03T17:33:37+00:00

null

{}

d-d-o-s/g2

null

[ "region:us" ]

null

2024-05-03T17:33:45+00:00

text-generation

transformers

<br/><br/> 8bpw/h8 exl2 quantization of [NeverSleep/Llama-3-Lumimaid-8B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) using default exllamav2 calibration dataset. --- **ORIGINAL CARD:** ## Lumimaid 0.1 <center><div style="width: 100%;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/630dfb008df86f1e5becadc3/d3QMaxy3peFTpSlWdWF-k.png" style="display: block; margin: auto;"> </div></center> This model uses the Llama3 **prompting format** Llama3 trained on our RP datasets, we tried to have a balance between the ERP and the RP, not too horny, but just enough. We also added some non-RP dataset, making the model less dumb overall. It should look like a 40%/60% ratio for Non-RP/RP+ERP data. This model includes the new Luminae dataset from Ikari. If you consider trying this model please give us some feedback either on the Community tab on hf or on our [Discord Server](https://discord.gg/MtCVRWTZXY). ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of Lumimaid-8B-v0.1. Switch: [8B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) - [70B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1) - [70B-alt](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt) ## Training data used: - [Aesir datasets](https://huggingface.co/MinervaAI) - [NoRobots](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) - [limarp](https://huggingface.co/datasets/lemonilia/LimaRP) - 8k ctx - [toxic-dpo-v0.1-sharegpt](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt) - [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) - Luminae-i1 (70B/70B-alt) (i2 was not existing when the 70b started training) | Luminae-i2 (8B) (this one gave better results on the 8b) - Ikari's Dataset - [Squish42/bluemoon-fandom-1-1-rp-cleaned](https://huggingface.co/datasets/Squish42/bluemoon-fandom-1-1-rp-cleaned) - 50% (randomly) - [NobodyExistsOnTheInternet/PIPPAsharegptv2test](https://huggingface.co/datasets/NobodyExistsOnTheInternet/PIPPAsharegptv2test) - 5% (randomly) - [cgato/SlimOrcaDedupCleaned](https://huggingface.co/datasets/cgato/SlimOrcaDedupCleaned) - 5% (randomly) - Airoboros (reduced) - [Capybara](https://huggingface.co/datasets/Undi95/Capybara-ShareGPT/) (reduced) ## Models used (only for 8B) - Initial LumiMaid 8B Finetune - Undi95/Llama-3-Unholy-8B-e4 - Undi95/Llama-3-LewdPlay-8B ## Prompt template: Llama3 ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {input}<|eot_id|><|start_header_id|>assistant<|end_header_id|> {output}<|eot_id|> ``` ## Others Undi: If you want to support us, you can [here](https://ko-fi.com/undiai). IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek

{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}

JayhC/Llama-3-Lumimaid-8B-v0.1-8bpw-h8-exl2

null

[ "transformers", "safetensors", "llama", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]

null

2024-05-03T17:33:49+00:00

null

peft

# GUE_EMP_H3K14ac-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.5143 - F1 Score: 0.7518 - Accuracy: 0.7507 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6069 | 0.97 | 200 | 0.5639 | 0.7202 | 0.7189 | | 0.5659 | 1.93 | 400 | 0.5476 | 0.7285 | 0.7271 | | 0.5461 | 2.9 | 600 | 0.5649 | 0.7130 | 0.7123 | | 0.5404 | 3.86 | 800 | 0.5287 | 0.7451 | 0.7440 | | 0.5311 | 4.83 | 1000 | 0.5559 | 0.7271 | 0.7259 | | 0.5299 | 5.8 | 1200 | 0.5429 | 0.7335 | 0.7319 | | 0.5247 | 6.76 | 1400 | 0.5236 | 0.7494 | 0.7477 | | 0.5209 | 7.73 | 1600 | 0.5307 | 0.7500 | 0.7483 | | 0.5164 | 8.7 | 1800 | 0.5279 | 0.7429 | 0.7413 | | 0.5144 | 9.66 | 2000 | 0.5352 | 0.7374 | 0.7359 | | 0.5133 | 10.63 | 2200 | 0.5353 | 0.7373 | 0.7359 | | 0.5063 | 11.59 | 2400 | 0.5131 | 0.7591 | 0.7576 | | 0.5103 | 12.56 | 2600 | 0.5332 | 0.7430 | 0.7416 | | 0.5067 | 13.53 | 2800 | 0.5274 | 0.7448 | 0.7434 | | 0.5053 | 14.49 | 3000 | 0.5314 | 0.7414 | 0.7401 | | 0.4984 | 15.46 | 3200 | 0.5152 | 0.7564 | 0.7549 | | 0.5017 | 16.43 | 3400 | 0.5355 | 0.7398 | 0.7386 | | 0.5011 | 17.39 | 3600 | 0.5153 | 0.7557 | 0.7540 | | 0.4956 | 18.36 | 3800 | 0.5074 | 0.7642 | 0.7634 | | 0.4947 | 19.32 | 4000 | 0.5103 | 0.7619 | 0.7604 | | 0.4904 | 20.29 | 4200 | 0.5248 | 0.7575 | 0.7558 | | 0.4948 | 21.26 | 4400 | 0.5249 | 0.7508 | 0.7492 | | 0.4924 | 22.22 | 4600 | 0.5366 | 0.7369 | 0.7359 | | 0.4933 | 23.19 | 4800 | 0.5116 | 0.7598 | 0.7582 | | 0.4892 | 24.15 | 5000 | 0.5158 | 0.7530 | 0.7513 | | 0.4868 | 25.12 | 5200 | 0.5430 | 0.7402 | 0.7392 | | 0.4865 | 26.09 | 5400 | 0.5305 | 0.7469 | 0.7455 | | 0.4888 | 27.05 | 5600 | 0.5468 | 0.7348 | 0.7340 | | 0.4838 | 28.02 | 5800 | 0.5166 | 0.7548 | 0.7531 | | 0.4852 | 28.99 | 6000 | 0.5230 | 0.7511 | 0.7495 | | 0.4821 | 29.95 | 6200 | 0.5328 | 0.7448 | 0.7434 | | 0.4827 | 30.92 | 6400 | 0.5079 | 0.7651 | 0.7637 | | 0.4839 | 31.88 | 6600 | 0.5158 | 0.7536 | 0.7519 | | 0.4765 | 32.85 | 6800 | 0.5259 | 0.7498 | 0.7483 | | 0.4826 | 33.82 | 7000 | 0.5297 | 0.7448 | 0.7434 | | 0.4768 | 34.78 | 7200 | 0.5302 | 0.7472 | 0.7458 | | 0.481 | 35.75 | 7400 | 0.5245 | 0.7505 | 0.7489 | | 0.4745 | 36.71 | 7600 | 0.5234 | 0.7523 | 0.7507 | | 0.4762 | 37.68 | 7800 | 0.5197 | 0.7526 | 0.7510 | | 0.4771 | 38.65 | 8000 | 0.5158 | 0.7521 | 0.7504 | | 0.4792 | 39.61 | 8200 | 0.5203 | 0.7526 | 0.7510 | | 0.4711 | 40.58 | 8400 | 0.5316 | 0.7458 | 0.7443 | | 0.4719 | 41.55 | 8600 | 0.5230 | 0.7523 | 0.7507 | | 0.4748 | 42.51 | 8800 | 0.5263 | 0.7511 | 0.7495 | | 0.4772 | 43.48 | 9000 | 0.5299 | 0.7468 | 0.7452 | | 0.4734 | 44.44 | 9200 | 0.5273 | 0.7502 | 0.7486 | | 0.478 | 45.41 | 9400 | 0.5242 | 0.7502 | 0.7486 | | 0.4673 | 46.38 | 9600 | 0.5285 | 0.7480 | 0.7464 | | 0.4758 | 47.34 | 9800 | 0.5244 | 0.7505 | 0.7489 | | 0.4699 | 48.31 | 10000 | 0.5224 | 0.7511 | 0.7495 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_4096_512_15M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:33:59+00:00

text-generation

transformers

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/pf4d6FA7DriRtVq5HCkxd.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/VcZWbW_eZkJAZZ5ricL4B.png) # Llama-3-Giraffe-70B-Instruct Abacus.AI presents our longer-necked variant of Llama 3 70B - now with the instruct variant! This model has an effective context length of approximately 128k. We have currently trained on ~1.5B tokens. There are our Needle-in-a-Haystack heatmap results. We are conducting further evals of model efficacy and will update our model card as these come in: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/Z4uUhcjgf1P7EPGQyRLkW.png) ## Training Methodology The methodology for training uses [PoSE](https://arxiv.org/abs/2309.10400) and dynamic-NTK interpolation. ### NTK-scaling The scale factor for NTK is 4. Note that we also tried theta-scaling but this did not work as well as NTK scaling in our experiments. ### PoSE We utilise Positional Skip-wise Training (PoSE) with the following parameters: - **Number of Chunks**: 5 - **Max position ID**: 32768 ### Data We use on average ~8K long samples from [RedPajama](https://github.com/togethercomputer/RedPajama-Data). ### Hardware We train on 8xH100 GPUs with Deepspeed Zero Stage 3. ## Evaluation Methodology We use the [EasyContext](https://github.com/abacusai/EasyContext/blob/eval_runs/eval_needle.py) implementation of Needle-in-a-Haystack to evaluate Llama-3-Giraffe-70B. We evaluate with the following parameters: - **Min context length**: 2000 - **Max context length**: 128000 - **Context interval**: 4000 - **Depth interval**: 0.1 - **Num samples**: 2 - **Rnd number digits**: 7 - **Haystack dir**: PaulGrahamEssays ### Adapter Transfer We apply the above techniques first to Llama-3-70B-Base, using LoRA on the Q and K weights only. This adapter is then applied to Llama-3-70B-Instruct, and we release the merged version here.

{"language": ["en"], "license": "llama3", "tags": ["meta", "llama-3"], "pipeline_tag": "text-generation"}

abacusai/Llama-3-Giraffe-70B-Instruct

null

[ "transformers", "safetensors", "llama", "text-generation", "meta", "llama-3", "conversational", "en", "arxiv:2309.10400", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T17:34:02+00:00

null

peft

# GUE_EMP_H3K4me2-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.6021 - F1 Score: 0.6656 - Accuracy: 0.6667 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6627 | 1.04 | 200 | 0.6377 | 0.5711 | 0.6331 | | 0.6293 | 2.08 | 400 | 0.6279 | 0.6475 | 0.6471 | | 0.6201 | 3.12 | 600 | 0.6184 | 0.6407 | 0.6641 | | 0.6192 | 4.17 | 800 | 0.6200 | 0.6465 | 0.6536 | | 0.6176 | 5.21 | 1000 | 0.6203 | 0.6515 | 0.6540 | | 0.6144 | 6.25 | 1200 | 0.6163 | 0.6488 | 0.6543 | | 0.609 | 7.29 | 1400 | 0.6247 | 0.6541 | 0.6533 | | 0.6101 | 8.33 | 1600 | 0.6215 | 0.6542 | 0.6543 | | 0.6095 | 9.38 | 1800 | 0.6298 | 0.6494 | 0.6471 | | 0.6088 | 10.42 | 2000 | 0.6181 | 0.6592 | 0.6598 | | 0.6097 | 11.46 | 2200 | 0.6094 | 0.6533 | 0.6628 | | 0.6028 | 12.5 | 2400 | 0.6121 | 0.6578 | 0.6621 | | 0.6016 | 13.54 | 2600 | 0.6112 | 0.6534 | 0.6611 | | 0.6037 | 14.58 | 2800 | 0.6100 | 0.6536 | 0.6605 | | 0.6058 | 15.62 | 3000 | 0.6102 | 0.6558 | 0.6621 | | 0.6011 | 16.67 | 3200 | 0.6148 | 0.6607 | 0.6621 | | 0.6004 | 17.71 | 3400 | 0.6086 | 0.6574 | 0.6644 | | 0.6031 | 18.75 | 3600 | 0.6099 | 0.6617 | 0.6660 | | 0.6016 | 19.79 | 3800 | 0.6130 | 0.6658 | 0.6680 | | 0.5948 | 20.83 | 4000 | 0.6156 | 0.6632 | 0.6637 | | 0.6 | 21.88 | 4200 | 0.6166 | 0.6623 | 0.6631 | | 0.5969 | 22.92 | 4400 | 0.6148 | 0.6644 | 0.6657 | | 0.5979 | 23.96 | 4600 | 0.6176 | 0.6650 | 0.6650 | | 0.5961 | 25.0 | 4800 | 0.6084 | 0.6649 | 0.6699 | | 0.594 | 26.04 | 5000 | 0.6150 | 0.6680 | 0.6689 | | 0.5947 | 27.08 | 5200 | 0.6137 | 0.6665 | 0.6676 | | 0.5937 | 28.12 | 5400 | 0.6101 | 0.6647 | 0.6676 | | 0.5947 | 29.17 | 5600 | 0.6156 | 0.6682 | 0.6683 | | 0.5904 | 30.21 | 5800 | 0.6164 | 0.6698 | 0.6699 | | 0.5929 | 31.25 | 6000 | 0.6136 | 0.6693 | 0.6699 | | 0.5924 | 32.29 | 6200 | 0.6135 | 0.6682 | 0.6689 | | 0.5925 | 33.33 | 6400 | 0.6170 | 0.6693 | 0.6693 | | 0.5933 | 34.38 | 6600 | 0.6090 | 0.6683 | 0.6719 | | 0.5905 | 35.42 | 6800 | 0.6095 | 0.6691 | 0.6722 | | 0.5904 | 36.46 | 7000 | 0.6083 | 0.6705 | 0.6742 | | 0.5866 | 37.5 | 7200 | 0.6134 | 0.6711 | 0.6719 | | 0.5887 | 38.54 | 7400 | 0.6110 | 0.6729 | 0.6748 | | 0.5927 | 39.58 | 7600 | 0.6105 | 0.6705 | 0.6725 | | 0.5898 | 40.62 | 7800 | 0.6198 | 0.6666 | 0.6654 | | 0.5882 | 41.67 | 8000 | 0.6124 | 0.6703 | 0.6709 | | 0.5878 | 42.71 | 8200 | 0.6088 | 0.6686 | 0.6729 | | 0.5902 | 43.75 | 8400 | 0.6109 | 0.6714 | 0.6729 | | 0.5885 | 44.79 | 8600 | 0.6156 | 0.6702 | 0.6699 | | 0.5862 | 45.83 | 8800 | 0.6122 | 0.6709 | 0.6722 | | 0.5905 | 46.88 | 9000 | 0.6144 | 0.6695 | 0.6696 | | 0.5869 | 47.92 | 9200 | 0.6138 | 0.6689 | 0.6693 | | 0.5888 | 48.96 | 9400 | 0.6124 | 0.6695 | 0.6706 | | 0.5884 | 50.0 | 9600 | 0.6128 | 0.6682 | 0.6689 | | 0.5867 | 51.04 | 9800 | 0.6131 | 0.6699 | 0.6706 | | 0.5862 | 52.08 | 10000 | 0.6128 | 0.6678 | 0.6686 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_4096_512_15M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:34:02+00:00

null

{}

Aryan0310/bert-small-finetuned-xsum

null

[ "region:us" ]

null

2024-05-03T17:34:09+00:00

text-generation

transformers

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "llama"], "base_model": "meta-llama/Meta-Llama-3-70B"}

predibase/Meta-Llama-3-70B-dequantized

null

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "en", "base_model:meta-llama/Meta-Llama-3-70B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T17:35:02+00:00

multiple-choice

transformers

# fine_tuned_copa_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6931 - Accuracy: 0.51 - F1: 0.4857 ## 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: 0.003 - 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 - training_steps: 400 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7257 | 1.0 | 50 | 0.6931 | 0.49 | 0.4838 | | 0.7001 | 2.0 | 100 | 0.6931 | 0.48 | 0.48 | | 0.7196 | 3.0 | 150 | 0.6931 | 0.52 | 0.4603 | | 0.6895 | 4.0 | 200 | 0.6931 | 0.5 | 0.4926 | | 0.745 | 5.0 | 250 | 0.6931 | 0.46 | 0.4244 | | 0.7102 | 6.0 | 300 | 0.6931 | 0.5 | 0.4861 | | 0.7245 | 7.0 | 350 | 0.6931 | 0.55 | 0.5391 | | 0.7283 | 8.0 | 400 | 0.6931 | 0.51 | 0.4857 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "fine_tuned_copa_croslo", "results": []}]}

lenatr99/fine_tuned_copa_croslo

null

[ "transformers", "safetensors", "bert", "multiple-choice", "generated_from_trainer", "base_model:EMBEDDIA/crosloengual-bert", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

null

2024-05-03T17:35:12+00:00

null

static quants of https://huggingface.co/Steelskull/L3-Arcania-4x8b

{}

mradermacher/L3-Arcania-4x8b-GGUF

null

[ "gguf", "region:us" ]

null

2024-05-03T17:36:03+00:00

null

{}

d-d-o-s/g3

null

[ "region:us" ]

null

2024-05-03T17:36:50+00:00

null

peft

# loha_fine_tuned_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6890 - Accuracy: 0.52 - F1: 0.5212 ## 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 - training_steps: 400 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7072 | 1.0 | 50 | 0.6896 | 0.52 | 0.5212 | | 0.6973 | 2.0 | 100 | 0.6894 | 0.53 | 0.5312 | | 0.6988 | 3.0 | 150 | 0.6892 | 0.54 | 0.5411 | | 0.7016 | 4.0 | 200 | 0.6891 | 0.53 | 0.5312 | | 0.7034 | 5.0 | 250 | 0.6890 | 0.52 | 0.5212 | | 0.6978 | 6.0 | 300 | 0.6890 | 0.51 | 0.5112 | | 0.6965 | 7.0 | 350 | 0.6890 | 0.51 | 0.5112 | | 0.6907 | 8.0 | 400 | 0.6890 | 0.52 | 0.5212 | ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "cc-by-4.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "loha_fine_tuned_croslo", "results": []}]}

lenatr99/loha_fine_tuned_croslo

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:EMBEDDIA/crosloengual-bert", "license:cc-by-4.0", "region:us" ]

null

2024-05-03T17:36:50+00:00

robotics

null

# Introduction This model is currently being tested, further details to be added in future for now see [robot_learning_baselines](https://github.com/peterdavidfagan/robot_learning_baselines).

{"license": "apache-2.0", "datasets": ["peterdavidfagan/transporter_networks"], "pipeline_tag": "robotics"}

peterdavidfagan/transporter_networks

null

[ "tflite", "robotics", "dataset:peterdavidfagan/transporter_networks", "license:apache-2.0", "region:us" ]

null

2024-05-03T17:38:29+00:00

null

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

{"library_name": "transformers", "tags": ["unsloth"]}

sparvekar/critique_lora_model

null

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-03T17:38:51+00:00

null

{"license": "openrail"}

enescakircali/Melis-Fis

null

[ "license:openrail", "region:us" ]

null

2024-05-03T17:39:01+00:00

text-generation

transformers

# PULI LlumiX 32K instruct (6.74B billion parameter) Intruct finetuned version of NYTK/PULI-LlumiX-32K. ## Training platform [Lightning AI Studio](https://lightning.ai/studios) L4 GPU ## Hyper parameters - Epoch: 3 - LoRA rank (r): 16 - LoRA alpha: 16 - Lr: 2e-4 - Lr scheduler: cosine - Optimizer: adamw_8bit - Weight decay: 0.01 ## Dataset boapps/szurkemarha In total ~30k instructions were selected. ## Prompt template: ChatML ``` <|im_start|>system Az alábbiakban egy feladatot leíró utasítás található. Írjál olyan választ, amely megfelelően teljesíti a kérést.<|im_end|> <|im_start|>user Ki a legerősebb szuperhős?<|im_end|> <|im_start|>assistant A legerősebb szuperhős a Marvel univerzumában Hulk.<|im_end|> ``` ## Base model - Trained with OpenChatKit [github](https://github.com/togethercomputer/OpenChatKit) - The [LLaMA-2-7B-32K](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K) model were continuously pretrained on Hungarian dataset - The model has been extended to a context length of 32K with position interpolation - Checkpoint: 100 000 steps ## Dataset for continued pretraining - Hungarian: 7.9 billion words, documents (763K) that exceed 5000 words in length - English: Long Context QA (2 billion words), BookSum (78 million words) ## Limitations - max_seq_length = 32 768 - float16 - vocab size: 32 000

{"language": ["hu", "en"], "license": "llama2", "tags": ["puli", "text-generation-inference", "transformers", "unsloth", "llama", "trl", "finetuned"], "datasets": ["boapps/szurkemarha"], "base_model": "NYTK/PULI-LlumiX-32K", "pipeline_tag": "text-generation"}

ariel-ml/PULI-LlumiX-32K-instruct-lora

null

[ "transformers", "safetensors", "puli", "text-generation-inference", "unsloth", "llama", "trl", "finetuned", "text-generation", "conversational", "hu", "en", "dataset:boapps/szurkemarha", "base_model:NYTK/PULI-LlumiX-32K", "license:llama2", "endpoints_compatible", "region:us" ]

null

2024-05-03T17:39:54+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

{"library_name": "transformers", "tags": []}

golf2248/g2rr5al

null

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T17:40:20+00:00

text-generation

transformers

# PULI LlumiX 32K instruct (6.74B billion parameter) Intruct finetuned version of NYTK/PULI-LlumiX-32K. ## Training platform [Lightning AI Studio](https://lightning.ai/studios) L4 GPU ## Hyper parameters - Epoch: 3 - LoRA rank (r): 16 - LoRA alpha: 16 - Lr: 2e-4 - Lr scheduler: cosine - Optimizer: adamw_8bit - Weight decay: 0.01 ## Dataset boapps/szurkemarha In total ~30k instructions were selected. ## Prompt template: ChatML ``` <|im_start|>system Az alábbiakban egy feladatot leíró utasítás található. Írjál olyan választ, amely megfelelően teljesíti a kérést.<|im_end|> <|im_start|>user Ki a legerősebb szuperhős?<|im_end|> <|im_start|>assistant A legerősebb szuperhős a Marvel univerzumában Hulk.<|im_end|> ``` ## Base model - Trained with OpenChatKit [github](https://github.com/togethercomputer/OpenChatKit) - The [LLaMA-2-7B-32K](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K) model were continuously pretrained on Hungarian dataset - The model has been extended to a context length of 32K with position interpolation - Checkpoint: 100 000 steps ## Dataset for continued pretraining - Hungarian: 7.9 billion words, documents (763K) that exceed 5000 words in length - English: Long Context QA (2 billion words), BookSum (78 million words) ## Limitations - max_seq_length = 32 768 - float16 - vocab size: 32 000

{"language": ["hu", "en"], "license": "llama2", "tags": ["puli", "text-generation-inference", "transformers", "unsloth", "llama", "trl", "finetuned"], "datasets": ["boapps/szurkemarha"], "base_model": "NYTK/PULI-LlumiX-32K", "pipeline_tag": "text-generation"}

ariel-ml/PULI-LlumiX-32K-instruct-f16

null

[ "transformers", "safetensors", "llama", "text-generation", "puli", "text-generation-inference", "unsloth", "trl", "finetuned", "conversational", "custom_code", "hu", "en", "dataset:boapps/szurkemarha", "base_model:NYTK/PULI-LlumiX-32K", "license:llama2", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T17:40:22+00:00

text-generation

transformers

# merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Jayant9928/orpo_med_v3](https://huggingface.co/Jayant9928/orpo_med_v3) as a base. ### Models Merged The following models were included in the merge: * [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Jayant9928/orpo_med_v3 parameters: density: 0.53 weight: 0.4 - model: meta-llama/Meta-Llama-3-8B-Instruct parameters: density: 0.53 weight: 0.3 merge_method: dare_ties base_model: Jayant9928/orpo_med_v3 tokenizer_source: union parameters: int8_mask: true dtype: float16 ```

{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Jayant9928/orpo_med_v3", "meta-llama/Meta-Llama-3-8B-Instruct"]}

Muhammad2003/Dmitry69

null

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Jayant9928/orpo_med_v3", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T17:40:29+00:00

text-generation

transformers

{}

dgonier/Llama-3-8B-ORPO-Debate-Card-Cutting

null

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

null

2024-05-03T17:41:36+00:00

null

{}

terry69/llama2-poison-50p

null

[ "tensorboard", "safetensors", "region:us" ]

null

2024-05-03T17:42:35+00:00

null

{}

bwjc117/motomura

null

[ "region:us" ]

null

2024-05-03T17:43:32+00:00

null

peft

# GUE_EMP_H3K14ac-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.5137 - F1 Score: 0.7465 - Accuracy: 0.7452 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5962 | 0.97 | 200 | 0.5550 | 0.7252 | 0.7241 | | 0.5522 | 1.93 | 400 | 0.5384 | 0.7392 | 0.7377 | | 0.5347 | 2.9 | 600 | 0.5523 | 0.7224 | 0.7213 | | 0.5295 | 3.86 | 800 | 0.5225 | 0.7510 | 0.7495 | | 0.5188 | 4.83 | 1000 | 0.5540 | 0.7280 | 0.7271 | | 0.5171 | 5.8 | 1200 | 0.5321 | 0.7383 | 0.7368 | | 0.5098 | 6.76 | 1400 | 0.5174 | 0.7512 | 0.7495 | | 0.5041 | 7.73 | 1600 | 0.5242 | 0.7454 | 0.7437 | | 0.499 | 8.7 | 1800 | 0.5289 | 0.7457 | 0.7443 | | 0.4945 | 9.66 | 2000 | 0.5280 | 0.7488 | 0.7474 | | 0.4928 | 10.63 | 2200 | 0.5247 | 0.7495 | 0.7480 | | 0.4843 | 11.59 | 2400 | 0.5053 | 0.7654 | 0.7640 | | 0.4847 | 12.56 | 2600 | 0.5261 | 0.7461 | 0.7446 | | 0.4807 | 13.53 | 2800 | 0.5256 | 0.7504 | 0.7489 | | 0.478 | 14.49 | 3000 | 0.5253 | 0.7434 | 0.7419 | | 0.4683 | 15.46 | 3200 | 0.5126 | 0.7638 | 0.7622 | | 0.4695 | 16.43 | 3400 | 0.5248 | 0.7485 | 0.7470 | | 0.4665 | 17.39 | 3600 | 0.5196 | 0.7578 | 0.7561 | | 0.4595 | 18.36 | 3800 | 0.5050 | 0.7626 | 0.7619 | | 0.4571 | 19.32 | 4000 | 0.5115 | 0.7579 | 0.7564 | | 0.4522 | 20.29 | 4200 | 0.5346 | 0.7557 | 0.7540 | | 0.4557 | 21.26 | 4400 | 0.5250 | 0.7566 | 0.7549 | | 0.449 | 22.22 | 4600 | 0.5417 | 0.7443 | 0.7431 | | 0.4484 | 23.19 | 4800 | 0.5210 | 0.7545 | 0.7528 | | 0.4437 | 24.15 | 5000 | 0.5327 | 0.7544 | 0.7528 | | 0.4398 | 25.12 | 5200 | 0.5487 | 0.7435 | 0.7425 | | 0.4388 | 26.09 | 5400 | 0.5419 | 0.7453 | 0.7440 | | 0.4372 | 27.05 | 5600 | 0.5656 | 0.7427 | 0.7416 | | 0.4307 | 28.02 | 5800 | 0.5400 | 0.7533 | 0.7516 | | 0.429 | 28.99 | 6000 | 0.5285 | 0.7539 | 0.7522 | | 0.4243 | 29.95 | 6200 | 0.5554 | 0.7452 | 0.7437 | | 0.4249 | 30.92 | 6400 | 0.5254 | 0.7546 | 0.7534 | | 0.426 | 31.88 | 6600 | 0.5293 | 0.7494 | 0.7477 | | 0.4144 | 32.85 | 6800 | 0.5486 | 0.7502 | 0.7486 | | 0.4206 | 33.82 | 7000 | 0.5444 | 0.7498 | 0.7483 | | 0.4113 | 34.78 | 7200 | 0.5544 | 0.7529 | 0.7513 | | 0.4185 | 35.75 | 7400 | 0.5436 | 0.7481 | 0.7464 | | 0.4096 | 36.71 | 7600 | 0.5489 | 0.7499 | 0.7483 | | 0.4124 | 37.68 | 7800 | 0.5416 | 0.7554 | 0.7537 | | 0.4109 | 38.65 | 8000 | 0.5439 | 0.7488 | 0.7470 | | 0.4081 | 39.61 | 8200 | 0.5420 | 0.7506 | 0.7489 | | 0.4018 | 40.58 | 8400 | 0.5606 | 0.7492 | 0.7477 | | 0.4028 | 41.55 | 8600 | 0.5520 | 0.7524 | 0.7507 | | 0.4059 | 42.51 | 8800 | 0.5511 | 0.7539 | 0.7522 | | 0.4061 | 43.48 | 9000 | 0.5581 | 0.7514 | 0.7498 | | 0.4036 | 44.44 | 9200 | 0.5532 | 0.7521 | 0.7504 | | 0.408 | 45.41 | 9400 | 0.5504 | 0.7551 | 0.7534 | | 0.3953 | 46.38 | 9600 | 0.5564 | 0.7517 | 0.7501 | | 0.4054 | 47.34 | 9800 | 0.5496 | 0.7512 | 0.7495 | | 0.3949 | 48.31 | 10000 | 0.5515 | 0.7527 | 0.7510 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_4096_512_15M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:44:41+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

{"library_name": "transformers", "tags": []}

BotCuddles/gemma-2b-it-ft-mental

null

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T17:44:46+00:00

null

peft

# GUE_EMP_H3K4me2-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.5973 - F1 Score: 0.6642 - Accuracy: 0.6693 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6513 | 1.04 | 200 | 0.6213 | 0.6317 | 0.6549 | | 0.6199 | 2.08 | 400 | 0.6293 | 0.6443 | 0.6426 | | 0.6123 | 3.12 | 600 | 0.6108 | 0.6493 | 0.6680 | | 0.6106 | 4.17 | 800 | 0.6176 | 0.6585 | 0.6588 | | 0.6064 | 5.21 | 1000 | 0.6064 | 0.6673 | 0.6758 | | 0.6033 | 6.25 | 1200 | 0.6056 | 0.6679 | 0.6738 | | 0.5957 | 7.29 | 1400 | 0.6180 | 0.6640 | 0.6624 | | 0.5959 | 8.33 | 1600 | 0.6186 | 0.6685 | 0.6667 | | 0.5932 | 9.38 | 1800 | 0.6305 | 0.6574 | 0.6549 | | 0.5919 | 10.42 | 2000 | 0.6082 | 0.6766 | 0.6774 | | 0.593 | 11.46 | 2200 | 0.6020 | 0.6772 | 0.6826 | | 0.5848 | 12.5 | 2400 | 0.6121 | 0.6768 | 0.6758 | | 0.5841 | 13.54 | 2600 | 0.6098 | 0.6725 | 0.6729 | | 0.5834 | 14.58 | 2800 | 0.6081 | 0.6715 | 0.6719 | | 0.5864 | 15.62 | 3000 | 0.6126 | 0.6760 | 0.6751 | | 0.5818 | 16.67 | 3200 | 0.6155 | 0.6718 | 0.6699 | | 0.5802 | 17.71 | 3400 | 0.6068 | 0.6744 | 0.6751 | | 0.5828 | 18.75 | 3600 | 0.6077 | 0.6713 | 0.6719 | | 0.5803 | 19.79 | 3800 | 0.6130 | 0.6742 | 0.6735 | | 0.5743 | 20.83 | 4000 | 0.6197 | 0.6699 | 0.6680 | | 0.5769 | 21.88 | 4200 | 0.6318 | 0.6626 | 0.6601 | | 0.5746 | 22.92 | 4400 | 0.6185 | 0.6679 | 0.6663 | | 0.5741 | 23.96 | 4600 | 0.6256 | 0.6661 | 0.6637 | | 0.5728 | 25.0 | 4800 | 0.6091 | 0.6691 | 0.6693 | | 0.5694 | 26.04 | 5000 | 0.6206 | 0.6678 | 0.6660 | | 0.5706 | 27.08 | 5200 | 0.6181 | 0.6659 | 0.6644 | | 0.5682 | 28.12 | 5400 | 0.6203 | 0.6699 | 0.6680 | | 0.5684 | 29.17 | 5600 | 0.6188 | 0.6727 | 0.6716 | | 0.5626 | 30.21 | 5800 | 0.6244 | 0.6680 | 0.6663 | | 0.5659 | 31.25 | 6000 | 0.6298 | 0.6645 | 0.6621 | | 0.5652 | 32.29 | 6200 | 0.6119 | 0.6672 | 0.6667 | | 0.565 | 33.33 | 6400 | 0.6228 | 0.6646 | 0.6628 | | 0.5636 | 34.38 | 6600 | 0.6187 | 0.6672 | 0.6663 | | 0.5624 | 35.42 | 6800 | 0.6183 | 0.6671 | 0.6660 | | 0.5631 | 36.46 | 7000 | 0.6131 | 0.6729 | 0.6729 | | 0.5575 | 37.5 | 7200 | 0.6277 | 0.6620 | 0.6601 | | 0.5588 | 38.54 | 7400 | 0.6218 | 0.6689 | 0.6680 | | 0.5624 | 39.58 | 7600 | 0.6139 | 0.6722 | 0.6722 | | 0.56 | 40.62 | 7800 | 0.6328 | 0.6586 | 0.6562 | | 0.5583 | 41.67 | 8000 | 0.6191 | 0.6650 | 0.6634 | | 0.5563 | 42.71 | 8200 | 0.6189 | 0.6708 | 0.6706 | | 0.5599 | 43.75 | 8400 | 0.6180 | 0.6674 | 0.6663 | | 0.5572 | 44.79 | 8600 | 0.6239 | 0.6643 | 0.6624 | | 0.5543 | 45.83 | 8800 | 0.6204 | 0.6676 | 0.6670 | | 0.5576 | 46.88 | 9000 | 0.6294 | 0.6597 | 0.6575 | | 0.5544 | 47.92 | 9200 | 0.6281 | 0.6599 | 0.6579 | | 0.555 | 48.96 | 9400 | 0.6271 | 0.6637 | 0.6621 | | 0.555 | 50.0 | 9600 | 0.6273 | 0.6652 | 0.6634 | | 0.5544 | 51.04 | 9800 | 0.6263 | 0.6641 | 0.6624 | | 0.5523 | 52.08 | 10000 | 0.6264 | 0.6642 | 0.6624 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_4096_512_15M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:44:48+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

{"library_name": "transformers", "tags": []}

golf2248/ox9od86

null

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T17:44:59+00:00

text-generation

transformers

# sft-fsi-masked-loss This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3788 ## 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 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-------:|:----:|:---------------:| | 17.1473 | 0.5333 | 1 | 18.7762 | | 17.1473 | 1.6 | 3 | 9.0847 | | 11.7791 | 2.6667 | 5 | 3.0550 | | 11.7791 | 3.7333 | 7 | 0.6706 | | 11.7791 | 4.8 | 9 | 0.6697 | | 1.4045 | 5.8667 | 11 | 0.5653 | | 1.4045 | 6.9333 | 13 | 0.4982 | | 0.6622 | 8.0 | 15 | 0.4756 | | 0.6622 | 8.5333 | 16 | 0.4777 | | 0.6622 | 9.6 | 18 | 0.4338 | | 0.5586 | 10.6667 | 20 | 0.3788 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"tags": ["trl", "sft", "generated_from_trainer"], "model-index": [{"name": "sft-fsi-masked-loss", "results": []}]}

jamesoneill12/sft-fsi-masked-loss

null

[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T17:45:22+00:00

text2text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

{"library_name": "transformers", "tags": []}

Ragab167/m2m_translation_v2

null

[ "transformers", "safetensors", "m2m_100", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T17:45:31+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

{"library_name": "transformers", "tags": []}

cilantro9246/vlrcskl

null

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T17:45:32+00:00

text-classification

transformers

{}

philgrey/question

null

[ "transformers", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T17:45:40+00:00

text-generation

transformers

<br/><br/> 3bpw/h6 exl2 quantization of [NeverSleep/Llama-3-Lumimaid-70B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1) using default exllamav2 calibration dataset. --- **ORIGINAL CARD:** ## Lumimaid 0.1 <center><div style="width: 100%;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/630dfb008df86f1e5becadc3/d3QMaxy3peFTpSlWdWF-k.png" style="display: block; margin: auto;"> </div></center> This model uses the Llama3 **prompting format** Llama3 trained on our RP datasets, we tried to have a balance between the ERP and the RP, not too horny, but just enough. We also added some non-RP dataset, making the model less dumb overall. It should look like a 40%/60% ratio for Non-RP/RP+ERP data. This model includes the new Luminae dataset from Ikari. If you consider trying this model please give us some feedback either on the Community tab on hf or on our [Discord Server](https://discord.gg/MtCVRWTZXY). ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of Lumimaid-70B-v0.1. Switch: [8B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) - [70B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1) - [70B-alt](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt) ## Training data used: - [Aesir datasets](https://huggingface.co/MinervaAI) - [NoRobots](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) - [limarp](https://huggingface.co/datasets/lemonilia/LimaRP) - 8k ctx - [toxic-dpo-v0.1-sharegpt](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt) - [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) - Luminae-i1 (70B/70B-alt) (i2 was not existing when the 70b started training) | Luminae-i2 (8B) (this one gave better results on the 8b) - Ikari's Dataset - [Squish42/bluemoon-fandom-1-1-rp-cleaned](https://huggingface.co/datasets/Squish42/bluemoon-fandom-1-1-rp-cleaned) - 50% (randomly) - [NobodyExistsOnTheInternet/PIPPAsharegptv2test](https://huggingface.co/datasets/NobodyExistsOnTheInternet/PIPPAsharegptv2test) - 5% (randomly) - [cgato/SlimOrcaDedupCleaned](https://huggingface.co/datasets/cgato/SlimOrcaDedupCleaned) - 5% (randomly) - Airoboros (reduced) - [Capybara](https://huggingface.co/datasets/Undi95/Capybara-ShareGPT/) (reduced) ## Models used (only for 8B) - Initial LumiMaid 8B Finetune - Undi95/Llama-3-Unholy-8B-e4 - Undi95/Llama-3-LewdPlay-8B ## Prompt template: Llama3 ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {input}<|eot_id|><|start_header_id|>assistant<|end_header_id|> {output}<|eot_id|> ``` ## Others Undi: If you want to support us, you can [here](https://ko-fi.com/undiai). IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek

{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}

JayhC/Llama-3-Lumimaid-70B-v0.1-3bpw-h6-exl2

null

[ "transformers", "safetensors", "llama", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "3-bit", "region:us" ]

null

2024-05-03T17:45:41+00:00

null

peft

# GUE_EMP_H3K4me2-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.6006 - F1 Score: 0.6727 - Accuracy: 0.6748 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6452 | 1.04 | 200 | 0.6178 | 0.6311 | 0.6566 | | 0.6153 | 2.08 | 400 | 0.6301 | 0.6434 | 0.6409 | | 0.6065 | 3.12 | 600 | 0.6049 | 0.6694 | 0.6804 | | 0.603 | 4.17 | 800 | 0.6251 | 0.6584 | 0.6559 | | 0.5966 | 5.21 | 1000 | 0.6064 | 0.6686 | 0.6755 | | 0.5941 | 6.25 | 1200 | 0.6047 | 0.6722 | 0.6745 | | 0.5852 | 7.29 | 1400 | 0.6158 | 0.6688 | 0.6673 | | 0.5845 | 8.33 | 1600 | 0.6197 | 0.6646 | 0.6624 | | 0.5814 | 9.38 | 1800 | 0.6251 | 0.6585 | 0.6559 | | 0.5764 | 10.42 | 2000 | 0.6045 | 0.6777 | 0.6804 | | 0.5778 | 11.46 | 2200 | 0.6041 | 0.6711 | 0.6758 | | 0.5668 | 12.5 | 2400 | 0.6185 | 0.6726 | 0.6722 | | 0.5644 | 13.54 | 2600 | 0.6260 | 0.6671 | 0.6657 | | 0.5642 | 14.58 | 2800 | 0.6139 | 0.6665 | 0.6670 | | 0.5637 | 15.62 | 3000 | 0.6193 | 0.6636 | 0.6631 | | 0.5576 | 16.67 | 3200 | 0.6239 | 0.6590 | 0.6579 | | 0.5523 | 17.71 | 3400 | 0.6274 | 0.6560 | 0.6546 | | 0.556 | 18.75 | 3600 | 0.6327 | 0.6570 | 0.6553 | | 0.5516 | 19.79 | 3800 | 0.6394 | 0.6645 | 0.6628 | | 0.5438 | 20.83 | 4000 | 0.6292 | 0.6633 | 0.6621 | | 0.5438 | 21.88 | 4200 | 0.6535 | 0.6475 | 0.6448 | | 0.5386 | 22.92 | 4400 | 0.6413 | 0.6594 | 0.6579 | | 0.5357 | 23.96 | 4600 | 0.6465 | 0.6519 | 0.6497 | | 0.5325 | 25.0 | 4800 | 0.6459 | 0.6539 | 0.6517 | | 0.5274 | 26.04 | 5000 | 0.6459 | 0.6504 | 0.6484 | | 0.526 | 27.08 | 5200 | 0.6466 | 0.6535 | 0.6520 | | 0.523 | 28.12 | 5400 | 0.6561 | 0.6495 | 0.6471 | | 0.5191 | 29.17 | 5600 | 0.6623 | 0.6535 | 0.6514 | | 0.5115 | 30.21 | 5800 | 0.6637 | 0.6552 | 0.6533 | | 0.5137 | 31.25 | 6000 | 0.6703 | 0.6423 | 0.6396 | | 0.5119 | 32.29 | 6200 | 0.6508 | 0.6502 | 0.6487 | | 0.5088 | 33.33 | 6400 | 0.6721 | 0.6439 | 0.6413 | | 0.5057 | 34.38 | 6600 | 0.6668 | 0.6495 | 0.6491 | | 0.5043 | 35.42 | 6800 | 0.6701 | 0.6503 | 0.6481 | | 0.506 | 36.46 | 7000 | 0.6517 | 0.6510 | 0.6497 | | 0.4961 | 37.5 | 7200 | 0.6784 | 0.6473 | 0.6452 | | 0.4929 | 38.54 | 7400 | 0.6843 | 0.6489 | 0.6471 | | 0.4942 | 39.58 | 7600 | 0.6631 | 0.6505 | 0.6510 | | 0.4938 | 40.62 | 7800 | 0.6954 | 0.6413 | 0.6386 | | 0.4898 | 41.67 | 8000 | 0.6708 | 0.6492 | 0.6474 | | 0.4866 | 42.71 | 8200 | 0.6798 | 0.6518 | 0.6504 | | 0.4901 | 43.75 | 8400 | 0.6709 | 0.6427 | 0.6413 | | 0.4866 | 44.79 | 8600 | 0.6799 | 0.6513 | 0.6494 | | 0.4819 | 45.83 | 8800 | 0.6798 | 0.6502 | 0.6494 | | 0.4847 | 46.88 | 9000 | 0.6948 | 0.6396 | 0.6370 | | 0.4809 | 47.92 | 9200 | 0.6960 | 0.6417 | 0.6393 | | 0.4816 | 48.96 | 9400 | 0.6919 | 0.6486 | 0.6468 | | 0.482 | 50.0 | 9600 | 0.6903 | 0.6467 | 0.6445 | | 0.4792 | 51.04 | 9800 | 0.6937 | 0.6468 | 0.6445 | | 0.4762 | 52.08 | 10000 | 0.6930 | 0.6458 | 0.6435 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_4096_512_15M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:45:43+00:00

null

peft

# GUE_EMP_H3K9ac-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4994 - F1 Score: 0.7632 - Accuracy: 0.7625 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6178 | 1.15 | 200 | 0.5812 | 0.7080 | 0.7074 | | 0.5678 | 2.3 | 400 | 0.6382 | 0.6436 | 0.6582 | | 0.5459 | 3.45 | 600 | 0.5881 | 0.7031 | 0.7071 | | 0.5401 | 4.6 | 800 | 0.5820 | 0.7005 | 0.7046 | | 0.5342 | 5.75 | 1000 | 0.5525 | 0.7202 | 0.7200 | | 0.5258 | 6.9 | 1200 | 0.5593 | 0.7173 | 0.7179 | | 0.5225 | 8.05 | 1400 | 0.5496 | 0.7250 | 0.7247 | | 0.5197 | 9.2 | 1600 | 0.5916 | 0.6860 | 0.6923 | | 0.5155 | 10.34 | 1800 | 0.5553 | 0.7197 | 0.7200 | | 0.5146 | 11.49 | 2000 | 0.5560 | 0.7191 | 0.7200 | | 0.508 | 12.64 | 2200 | 0.5824 | 0.7011 | 0.7053 | | 0.5132 | 13.79 | 2400 | 0.5530 | 0.7193 | 0.7211 | | 0.506 | 14.94 | 2600 | 0.5556 | 0.7127 | 0.7143 | | 0.504 | 16.09 | 2800 | 0.5451 | 0.7312 | 0.7316 | | 0.503 | 17.24 | 3000 | 0.5652 | 0.7205 | 0.7222 | | 0.4994 | 18.39 | 3200 | 0.5591 | 0.7246 | 0.7262 | | 0.5011 | 19.54 | 3400 | 0.5456 | 0.7289 | 0.7298 | | 0.497 | 20.69 | 3600 | 0.5430 | 0.7267 | 0.7269 | | 0.4967 | 21.84 | 3800 | 0.5407 | 0.7314 | 0.7319 | | 0.4947 | 22.99 | 4000 | 0.5471 | 0.7285 | 0.7290 | | 0.4959 | 24.14 | 4200 | 0.5297 | 0.7354 | 0.7352 | | 0.4894 | 25.29 | 4400 | 0.5519 | 0.7314 | 0.7319 | | 0.4965 | 26.44 | 4600 | 0.5460 | 0.7324 | 0.7326 | | 0.4902 | 27.59 | 4800 | 0.5525 | 0.7269 | 0.7280 | | 0.487 | 28.74 | 5000 | 0.5480 | 0.7240 | 0.7251 | | 0.4945 | 29.89 | 5200 | 0.5410 | 0.7337 | 0.7341 | | 0.4869 | 31.03 | 5400 | 0.5507 | 0.7291 | 0.7301 | | 0.4896 | 32.18 | 5600 | 0.5256 | 0.7396 | 0.7391 | | 0.4832 | 33.33 | 5800 | 0.5439 | 0.7342 | 0.7344 | | 0.4921 | 34.48 | 6000 | 0.5405 | 0.7330 | 0.7337 | | 0.4814 | 35.63 | 6200 | 0.5309 | 0.7376 | 0.7373 | | 0.4888 | 36.78 | 6400 | 0.5390 | 0.7330 | 0.7334 | | 0.4837 | 37.93 | 6600 | 0.5416 | 0.7329 | 0.7330 | | 0.4815 | 39.08 | 6800 | 0.5345 | 0.7384 | 0.7384 | | 0.4833 | 40.23 | 7000 | 0.5349 | 0.7385 | 0.7384 | | 0.486 | 41.38 | 7200 | 0.5310 | 0.7382 | 0.7380 | | 0.483 | 42.53 | 7400 | 0.5359 | 0.7330 | 0.7334 | | 0.4805 | 43.68 | 7600 | 0.5332 | 0.7385 | 0.7384 | | 0.4801 | 44.83 | 7800 | 0.5450 | 0.7309 | 0.7316 | | 0.4821 | 45.98 | 8000 | 0.5359 | 0.7349 | 0.7352 | | 0.4806 | 47.13 | 8200 | 0.5407 | 0.7325 | 0.7330 | | 0.4819 | 48.28 | 8400 | 0.5387 | 0.7352 | 0.7355 | | 0.4829 | 49.43 | 8600 | 0.5323 | 0.7389 | 0.7388 | | 0.4819 | 50.57 | 8800 | 0.5356 | 0.7366 | 0.7366 | | 0.48 | 51.72 | 9000 | 0.5423 | 0.7321 | 0.7326 | | 0.4766 | 52.87 | 9200 | 0.5446 | 0.7321 | 0.7326 | | 0.4815 | 54.02 | 9400 | 0.5420 | 0.7328 | 0.7334 | | 0.48 | 55.17 | 9600 | 0.5405 | 0.7329 | 0.7334 | | 0.476 | 56.32 | 9800 | 0.5379 | 0.7349 | 0.7352 | | 0.4808 | 57.47 | 10000 | 0.5386 | 0.7342 | 0.7344 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_4096_512_15M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:45:55+00:00

null

peft

# GUE_EMP_H3K9ac-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4829 - F1 Score: 0.7825 - Accuracy: 0.7819 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5985 | 1.15 | 200 | 0.5713 | 0.7153 | 0.7154 | | 0.5469 | 2.3 | 400 | 0.6351 | 0.6395 | 0.6564 | | 0.5227 | 3.45 | 600 | 0.5475 | 0.7292 | 0.7294 | | 0.5141 | 4.6 | 800 | 0.5387 | 0.7369 | 0.7373 | | 0.5053 | 5.75 | 1000 | 0.5242 | 0.7497 | 0.7492 | | 0.4998 | 6.9 | 1200 | 0.5305 | 0.7395 | 0.7391 | | 0.4963 | 8.05 | 1400 | 0.5248 | 0.7393 | 0.7388 | | 0.4931 | 9.2 | 1600 | 0.5473 | 0.7262 | 0.7283 | | 0.4876 | 10.34 | 1800 | 0.5194 | 0.7439 | 0.7434 | | 0.4872 | 11.49 | 2000 | 0.5172 | 0.7510 | 0.7506 | | 0.4807 | 12.64 | 2200 | 0.5475 | 0.7303 | 0.7319 | | 0.4855 | 13.79 | 2400 | 0.5089 | 0.7587 | 0.7582 | | 0.4776 | 14.94 | 2600 | 0.5157 | 0.7514 | 0.7510 | | 0.4752 | 16.09 | 2800 | 0.5177 | 0.7500 | 0.7499 | | 0.4758 | 17.24 | 3000 | 0.5201 | 0.7531 | 0.7528 | | 0.4699 | 18.39 | 3200 | 0.5240 | 0.7504 | 0.7503 | | 0.4728 | 19.54 | 3400 | 0.5102 | 0.7498 | 0.7496 | | 0.4662 | 20.69 | 3600 | 0.5063 | 0.7545 | 0.7542 | | 0.4668 | 21.84 | 3800 | 0.5230 | 0.7458 | 0.7460 | | 0.4627 | 22.99 | 4000 | 0.5297 | 0.7412 | 0.7416 | | 0.4655 | 24.14 | 4200 | 0.5121 | 0.7515 | 0.7510 | | 0.4565 | 25.29 | 4400 | 0.5336 | 0.7506 | 0.7506 | | 0.463 | 26.44 | 4600 | 0.5167 | 0.7540 | 0.7535 | | 0.4583 | 27.59 | 4800 | 0.5223 | 0.7470 | 0.7474 | | 0.4553 | 28.74 | 5000 | 0.5166 | 0.7515 | 0.7513 | | 0.4595 | 29.89 | 5200 | 0.5159 | 0.7546 | 0.7542 | | 0.4532 | 31.03 | 5400 | 0.5204 | 0.7508 | 0.7506 | | 0.4546 | 32.18 | 5600 | 0.5063 | 0.7537 | 0.7531 | | 0.4474 | 33.33 | 5800 | 0.5128 | 0.7562 | 0.7557 | | 0.4565 | 34.48 | 6000 | 0.5174 | 0.7511 | 0.7506 | | 0.4419 | 35.63 | 6200 | 0.5137 | 0.7540 | 0.7535 | | 0.4492 | 36.78 | 6400 | 0.5112 | 0.7576 | 0.7571 | | 0.4456 | 37.93 | 6600 | 0.5413 | 0.7403 | 0.7402 | | 0.4434 | 39.08 | 6800 | 0.5180 | 0.7519 | 0.7513 | | 0.4448 | 40.23 | 7000 | 0.5249 | 0.7538 | 0.7535 | | 0.4468 | 41.38 | 7200 | 0.5210 | 0.7503 | 0.7499 | | 0.444 | 42.53 | 7400 | 0.5156 | 0.7479 | 0.7474 | | 0.4406 | 43.68 | 7600 | 0.5162 | 0.7490 | 0.7485 | | 0.4386 | 44.83 | 7800 | 0.5258 | 0.7495 | 0.7492 | | 0.443 | 45.98 | 8000 | 0.5153 | 0.7486 | 0.7481 | | 0.4409 | 47.13 | 8200 | 0.5243 | 0.7488 | 0.7485 | | 0.4412 | 48.28 | 8400 | 0.5204 | 0.7493 | 0.7488 | | 0.4385 | 49.43 | 8600 | 0.5198 | 0.7501 | 0.7496 | | 0.4406 | 50.57 | 8800 | 0.5227 | 0.7521 | 0.7517 | | 0.4391 | 51.72 | 9000 | 0.5283 | 0.7511 | 0.7510 | | 0.4376 | 52.87 | 9200 | 0.5288 | 0.7484 | 0.7481 | | 0.4383 | 54.02 | 9400 | 0.5270 | 0.7479 | 0.7478 | | 0.4378 | 55.17 | 9600 | 0.5240 | 0.7488 | 0.7485 | | 0.4332 | 56.32 | 9800 | 0.5228 | 0.7514 | 0.7510 | | 0.4382 | 57.47 | 10000 | 0.5228 | 0.7503 | 0.7499 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_4096_512_15M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:46:02+00:00

null

peft

# GUE_EMP_H3K9ac-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4683 - F1 Score: 0.7846 - Accuracy: 0.7841 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5805 | 1.15 | 200 | 0.5699 | 0.7096 | 0.7121 | | 0.5311 | 2.3 | 400 | 0.6196 | 0.6574 | 0.6711 | | 0.5054 | 3.45 | 600 | 0.5329 | 0.7385 | 0.7384 | | 0.4987 | 4.6 | 800 | 0.5223 | 0.7407 | 0.7406 | | 0.4901 | 5.75 | 1000 | 0.5191 | 0.7511 | 0.7506 | | 0.4858 | 6.9 | 1200 | 0.5215 | 0.7479 | 0.7478 | | 0.4814 | 8.05 | 1400 | 0.5293 | 0.7434 | 0.7431 | | 0.4752 | 9.2 | 1600 | 0.5324 | 0.7414 | 0.7427 | | 0.4677 | 10.34 | 1800 | 0.5228 | 0.7472 | 0.7467 | | 0.4686 | 11.49 | 2000 | 0.5185 | 0.7565 | 0.7560 | | 0.4585 | 12.64 | 2200 | 0.5343 | 0.7408 | 0.7409 | | 0.4611 | 13.79 | 2400 | 0.5132 | 0.7546 | 0.7542 | | 0.4551 | 14.94 | 2600 | 0.5177 | 0.7486 | 0.7481 | | 0.45 | 16.09 | 2800 | 0.5290 | 0.7467 | 0.7470 | | 0.4485 | 17.24 | 3000 | 0.5097 | 0.7583 | 0.7578 | | 0.4414 | 18.39 | 3200 | 0.5293 | 0.7483 | 0.7481 | | 0.4412 | 19.54 | 3400 | 0.5122 | 0.7461 | 0.7456 | | 0.4354 | 20.69 | 3600 | 0.5108 | 0.7502 | 0.7499 | | 0.4326 | 21.84 | 3800 | 0.5305 | 0.7444 | 0.7445 | | 0.4262 | 22.99 | 4000 | 0.5570 | 0.7396 | 0.7406 | | 0.4284 | 24.14 | 4200 | 0.5263 | 0.7511 | 0.7506 | | 0.4186 | 25.29 | 4400 | 0.5468 | 0.7512 | 0.7510 | | 0.4232 | 26.44 | 4600 | 0.5302 | 0.7490 | 0.7485 | | 0.4159 | 27.59 | 4800 | 0.5412 | 0.7507 | 0.7506 | | 0.4109 | 28.74 | 5000 | 0.5274 | 0.7464 | 0.7460 | | 0.4147 | 29.89 | 5200 | 0.5354 | 0.7479 | 0.7481 | | 0.4047 | 31.03 | 5400 | 0.5491 | 0.7428 | 0.7427 | | 0.4047 | 32.18 | 5600 | 0.5310 | 0.7433 | 0.7427 | | 0.3938 | 33.33 | 5800 | 0.5478 | 0.7511 | 0.7506 | | 0.4018 | 34.48 | 6000 | 0.5339 | 0.7508 | 0.7503 | | 0.3872 | 35.63 | 6200 | 0.5474 | 0.7439 | 0.7434 | | 0.3911 | 36.78 | 6400 | 0.5366 | 0.7428 | 0.7424 | | 0.3877 | 37.93 | 6600 | 0.5748 | 0.7417 | 0.7413 | | 0.3853 | 39.08 | 6800 | 0.5557 | 0.7392 | 0.7388 | | 0.3846 | 40.23 | 7000 | 0.5654 | 0.7439 | 0.7434 | | 0.3872 | 41.38 | 7200 | 0.5705 | 0.7375 | 0.7373 | | 0.3829 | 42.53 | 7400 | 0.5605 | 0.7393 | 0.7388 | | 0.3754 | 43.68 | 7600 | 0.5542 | 0.7450 | 0.7445 | | 0.3755 | 44.83 | 7800 | 0.5678 | 0.7403 | 0.7398 | | 0.3758 | 45.98 | 8000 | 0.5571 | 0.7418 | 0.7413 | | 0.3735 | 47.13 | 8200 | 0.5867 | 0.7398 | 0.7395 | | 0.3728 | 48.28 | 8400 | 0.5711 | 0.7382 | 0.7377 | | 0.371 | 49.43 | 8600 | 0.5742 | 0.7407 | 0.7402 | | 0.3695 | 50.57 | 8800 | 0.5821 | 0.7402 | 0.7398 | | 0.368 | 51.72 | 9000 | 0.5897 | 0.7393 | 0.7391 | | 0.3675 | 52.87 | 9200 | 0.5823 | 0.7362 | 0.7359 | | 0.3668 | 54.02 | 9400 | 0.5857 | 0.7365 | 0.7362 | | 0.3671 | 55.17 | 9600 | 0.5799 | 0.7396 | 0.7391 | | 0.3637 | 56.32 | 9800 | 0.5779 | 0.7410 | 0.7406 | | 0.3655 | 57.47 | 10000 | 0.5769 | 0.7406 | 0.7402 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_4096_512_15M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:46:07+00:00

text-classification

transformers

{}

philgrey/question_classification

null

[ "transformers", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T17:46:22+00:00

null

{"license": "apache-2.0"}

MarkLogic/marklogic-connect

null

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

null

2024-05-03T17:48:04+00:00

null

{}

LongDaHo/finetuned-gemma-2b

null

[ "region:us" ]

null

2024-05-03T17:48:14+00:00

null

peft

# prompt_fine_tuned_CB_bert This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1636 - Accuracy: 0.3182 - F1: 0.1536 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "google-bert/bert-base-uncased", "model-index": [{"name": "prompt_fine_tuned_CB_bert", "results": []}]}

lenatr99/prompt_fine_tuned_CB_bert

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "license:apache-2.0", "region:us" ]

null

2024-05-03T17:48:19+00:00

null

{}

vinven7/Llama2-ft-BandGap

null

[ "region:us" ]

null

2024-05-03T17:49:11+00:00

null

# Phi0503B1 This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0800 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.5697 | 0.09 | 10 | 0.7185 | | 0.345 | 0.18 | 20 | 0.1655 | | 0.1552 | 0.27 | 30 | 0.1343 | | 0.1345 | 0.36 | 40 | 0.1175 | | 0.121 | 0.45 | 50 | 0.1152 | | 0.1088 | 0.54 | 60 | 0.0861 | | 0.0923 | 0.63 | 70 | 0.0942 | | 0.0773 | 0.73 | 80 | 0.0681 | | 0.0606 | 0.82 | 90 | 0.0686 | | 0.0647 | 0.91 | 100 | 0.0624 | | 0.062 | 1.0 | 110 | 0.0663 | | 0.0434 | 1.09 | 120 | 0.0687 | | 0.042 | 1.18 | 130 | 0.0675 | | 0.0503 | 1.27 | 140 | 0.0681 | | 0.0445 | 1.36 | 150 | 0.0654 | | 0.0511 | 1.45 | 160 | 0.0593 | | 0.0462 | 1.54 | 170 | 0.0687 | | 0.0498 | 1.63 | 180 | 0.0651 | | 0.0448 | 1.72 | 190 | 0.0640 | | 0.043 | 1.81 | 200 | 0.0636 | | 0.04 | 1.9 | 210 | 0.0617 | | 0.043 | 1.99 | 220 | 0.0613 | | 0.0226 | 2.08 | 230 | 0.0657 | | 0.0165 | 2.18 | 240 | 0.0788 | | 0.011 | 2.27 | 250 | 0.0943 | | 0.0097 | 2.36 | 260 | 0.0946 | | 0.0167 | 2.45 | 270 | 0.0864 | | 0.0105 | 2.54 | 280 | 0.0827 | | 0.0118 | 2.63 | 290 | 0.0819 | | 0.0156 | 2.72 | 300 | 0.0802 | | 0.0137 | 2.81 | 310 | 0.0800 | | 0.013 | 2.9 | 320 | 0.0800 | | 0.0098 | 2.99 | 330 | 0.0800 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1

{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/Phi-3-mini-4k-instruct", "model-index": [{"name": "Phi0503B1", "results": []}]}

Litzy619/Phi0503B1

null

[ "safetensors", "generated_from_trainer", "base_model:microsoft/Phi-3-mini-4k-instruct", "license:mit", "region:us" ]

null

2024-05-03T17:49:24+00:00

null

# Phi0503B2 This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0690 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.4837 | 0.09 | 10 | 5.4342 | | 5.4537 | 0.18 | 20 | 5.2266 | | 4.774 | 0.27 | 30 | 3.6419 | | 2.4745 | 0.36 | 40 | 1.0488 | | 0.5621 | 0.45 | 50 | 0.2015 | | 0.1739 | 0.54 | 60 | 0.1465 | | 0.1373 | 0.63 | 70 | 0.1350 | | 0.1328 | 0.73 | 80 | 0.1258 | | 0.1091 | 0.82 | 90 | 0.1152 | | 0.1142 | 0.91 | 100 | 0.0968 | | 0.0918 | 1.0 | 110 | 0.1021 | | 0.0773 | 1.09 | 120 | 0.0807 | | 0.0711 | 1.18 | 130 | 0.0793 | | 0.0751 | 1.27 | 140 | 0.0661 | | 0.06 | 1.36 | 150 | 0.0651 | | 0.0647 | 1.45 | 160 | 0.0658 | | 0.0577 | 1.54 | 170 | 0.0657 | | 0.0575 | 1.63 | 180 | 0.0644 | | 0.0534 | 1.72 | 190 | 0.0661 | | 0.0594 | 1.81 | 200 | 0.0622 | | 0.0473 | 1.9 | 210 | 0.0628 | | 0.0522 | 1.99 | 220 | 0.0643 | | 0.0402 | 2.08 | 230 | 0.0644 | | 0.0436 | 2.18 | 240 | 0.0674 | | 0.0343 | 2.27 | 250 | 0.0708 | | 0.0358 | 2.36 | 260 | 0.0724 | | 0.0411 | 2.45 | 270 | 0.0720 | | 0.0359 | 2.54 | 280 | 0.0706 | | 0.0366 | 2.63 | 290 | 0.0702 | | 0.0397 | 2.72 | 300 | 0.0697 | | 0.044 | 2.81 | 310 | 0.0692 | | 0.0415 | 2.9 | 320 | 0.0688 | | 0.037 | 2.99 | 330 | 0.0690 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1

{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/Phi-3-mini-4k-instruct", "model-index": [{"name": "Phi0503B2", "results": []}]}

Litzy619/Phi0503B2

null

[ "safetensors", "generated_from_trainer", "base_model:microsoft/Phi-3-mini-4k-instruct", "license:mit", "region:us" ]

null

2024-05-03T17:49:39+00:00

null

peft

# GUE_EMP_H3K4me3-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.6104 - F1 Score: 0.6754 - Accuracy: 0.6755 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6748 | 0.87 | 200 | 0.6571 | 0.6173 | 0.6188 | | 0.6536 | 1.74 | 400 | 0.6488 | 0.6324 | 0.6323 | | 0.6479 | 2.61 | 600 | 0.6438 | 0.6414 | 0.6410 | | 0.6365 | 3.48 | 800 | 0.6347 | 0.6385 | 0.6397 | | 0.6346 | 4.35 | 1000 | 0.6307 | 0.6387 | 0.6416 | | 0.6286 | 5.22 | 1200 | 0.6395 | 0.6339 | 0.6391 | | 0.6242 | 6.09 | 1400 | 0.6266 | 0.6497 | 0.6511 | | 0.6186 | 6.96 | 1600 | 0.6242 | 0.6600 | 0.6606 | | 0.6171 | 7.83 | 1800 | 0.6186 | 0.6623 | 0.6630 | | 0.6159 | 8.7 | 2000 | 0.6171 | 0.6646 | 0.6644 | | 0.6094 | 9.57 | 2200 | 0.6146 | 0.6613 | 0.6611 | | 0.6144 | 10.43 | 2400 | 0.6139 | 0.6648 | 0.6647 | | 0.6105 | 11.3 | 2600 | 0.6175 | 0.6571 | 0.6584 | | 0.6118 | 12.17 | 2800 | 0.6119 | 0.6676 | 0.6674 | | 0.6086 | 13.04 | 3000 | 0.6103 | 0.6679 | 0.6677 | | 0.6053 | 13.91 | 3200 | 0.6114 | 0.6620 | 0.6625 | | 0.6039 | 14.78 | 3400 | 0.6115 | 0.6615 | 0.6628 | | 0.606 | 15.65 | 3600 | 0.6125 | 0.6653 | 0.6660 | | 0.6002 | 16.52 | 3800 | 0.6121 | 0.6665 | 0.6668 | | 0.6016 | 17.39 | 4000 | 0.6084 | 0.6693 | 0.6696 | | 0.603 | 18.26 | 4200 | 0.6086 | 0.6690 | 0.6690 | | 0.597 | 19.13 | 4400 | 0.6072 | 0.6692 | 0.6693 | | 0.5983 | 20.0 | 4600 | 0.6074 | 0.6661 | 0.6666 | | 0.5986 | 20.87 | 4800 | 0.6091 | 0.6645 | 0.6649 | | 0.5976 | 21.74 | 5000 | 0.6116 | 0.6619 | 0.6630 | | 0.5976 | 22.61 | 5200 | 0.6068 | 0.6666 | 0.6677 | | 0.5978 | 23.48 | 5400 | 0.6129 | 0.6573 | 0.6611 | | 0.5943 | 24.35 | 5600 | 0.6047 | 0.6673 | 0.6674 | | 0.5966 | 25.22 | 5800 | 0.6116 | 0.6578 | 0.6617 | | 0.5934 | 26.09 | 6000 | 0.6113 | 0.6585 | 0.6614 | | 0.5951 | 26.96 | 6200 | 0.6116 | 0.6622 | 0.6652 | | 0.5948 | 27.83 | 6400 | 0.6180 | 0.6534 | 0.6592 | | 0.5914 | 28.7 | 6600 | 0.6068 | 0.6609 | 0.6628 | | 0.5915 | 29.57 | 6800 | 0.6048 | 0.6677 | 0.6690 | | 0.5893 | 30.43 | 7000 | 0.6109 | 0.6600 | 0.6633 | | 0.5974 | 31.3 | 7200 | 0.6085 | 0.6625 | 0.6652 | | 0.5923 | 32.17 | 7400 | 0.6108 | 0.6596 | 0.6639 | | 0.5891 | 33.04 | 7600 | 0.6036 | 0.6659 | 0.6671 | | 0.5919 | 33.91 | 7800 | 0.6048 | 0.6618 | 0.6633 | | 0.5906 | 34.78 | 8000 | 0.6055 | 0.6651 | 0.6666 | | 0.5927 | 35.65 | 8200 | 0.6027 | 0.6657 | 0.6668 | | 0.5891 | 36.52 | 8400 | 0.6069 | 0.6614 | 0.6639 | | 0.5908 | 37.39 | 8600 | 0.6063 | 0.6635 | 0.6655 | | 0.5857 | 38.26 | 8800 | 0.6095 | 0.6630 | 0.6660 | | 0.5921 | 39.13 | 9000 | 0.6070 | 0.6622 | 0.6649 | | 0.5895 | 40.0 | 9200 | 0.6047 | 0.6643 | 0.6660 | | 0.5884 | 40.87 | 9400 | 0.6029 | 0.6672 | 0.6679 | | 0.5909 | 41.74 | 9600 | 0.6040 | 0.6656 | 0.6668 | | 0.5906 | 42.61 | 9800 | 0.6042 | 0.6650 | 0.6666 | | 0.5892 | 43.48 | 10000 | 0.6047 | 0.6640 | 0.6658 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_4096_512_15M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:51:35+00:00

text-generation

transformers

{}

PB7-DUT-2023/finetuned_Bloomz_1b1_v5

null

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

null

2024-05-03T17:51:49+00:00

null

{}

UC3M-LCPM/Bart_large_mnli_task1a_en_normal

null

[ "region:us" ]

null

2024-05-03T17:52:50+00:00

null

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

{"library_name": "transformers", "tags": []}

ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_64_0.05_2_5e-05

null

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-03T17:53:04+00:00

null

# LlamaJarvis-7B LlamaJarvis-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) ## 🧩 Configuration ```yaml models: - model: NousResearch/Meta-Llama-3-8B # No parameters necessary for base model - model: NousResearch/Meta-Llama-3-8B-Instruct parameters: density: 0.6 weight: 0.5 - model: mlabonne/OrpoLlama-3-8B parameters: density: 0.55 weight: 0.05 merge_method: dare_ties base_model: NousResearch/Meta-Llama-3-8B parameters: int8_mask: true dtype: float16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/LlamaJarvis-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"], "base_model": ["NousResearch/Meta-Llama-3-8B-Instruct", "mlabonne/OrpoLlama-3-8B"]}

automerger/LlamaJarvis-7B

null

[ "merge", "mergekit", "lazymergekit", "automerger", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:mlabonne/OrpoLlama-3-8B", "license:apache-2.0", "region:us" ]

null

2024-05-03T17:53:49+00:00

null

peft

# GUE_EMP_H3K4me3-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.6053 - F1 Score: 0.6811 - Accuracy: 0.6823 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6682 | 0.87 | 200 | 0.6471 | 0.6349 | 0.6359 | | 0.6393 | 1.74 | 400 | 0.6363 | 0.6456 | 0.6457 | | 0.6281 | 2.61 | 600 | 0.6212 | 0.6612 | 0.6609 | | 0.6181 | 3.48 | 800 | 0.6148 | 0.6642 | 0.6641 | | 0.6148 | 4.35 | 1000 | 0.6139 | 0.6601 | 0.6603 | | 0.6098 | 5.22 | 1200 | 0.6154 | 0.6526 | 0.6552 | | 0.6042 | 6.09 | 1400 | 0.6202 | 0.6480 | 0.6527 | | 0.5989 | 6.96 | 1600 | 0.6141 | 0.6615 | 0.6639 | | 0.5962 | 7.83 | 1800 | 0.6078 | 0.6712 | 0.6709 | | 0.5953 | 8.7 | 2000 | 0.6030 | 0.6731 | 0.6731 | | 0.5864 | 9.57 | 2200 | 0.5979 | 0.6767 | 0.6766 | | 0.5919 | 10.43 | 2400 | 0.6012 | 0.6721 | 0.6723 | | 0.5862 | 11.3 | 2600 | 0.6009 | 0.6692 | 0.6715 | | 0.5893 | 12.17 | 2800 | 0.5981 | 0.6709 | 0.6717 | | 0.5824 | 13.04 | 3000 | 0.5966 | 0.6752 | 0.6758 | | 0.5807 | 13.91 | 3200 | 0.5975 | 0.6735 | 0.6747 | | 0.5772 | 14.78 | 3400 | 0.6008 | 0.6742 | 0.6766 | | 0.5799 | 15.65 | 3600 | 0.6016 | 0.6730 | 0.6758 | | 0.5746 | 16.52 | 3800 | 0.5983 | 0.6759 | 0.6764 | | 0.5731 | 17.39 | 4000 | 0.5999 | 0.6770 | 0.6777 | | 0.5756 | 18.26 | 4200 | 0.5986 | 0.6797 | 0.6815 | | 0.5684 | 19.13 | 4400 | 0.5978 | 0.6775 | 0.6780 | | 0.5707 | 20.0 | 4600 | 0.5995 | 0.6755 | 0.6769 | | 0.5702 | 20.87 | 4800 | 0.5974 | 0.6778 | 0.6791 | | 0.5675 | 21.74 | 5000 | 0.6075 | 0.6707 | 0.6720 | | 0.569 | 22.61 | 5200 | 0.5955 | 0.6776 | 0.6785 | | 0.5645 | 23.48 | 5400 | 0.6137 | 0.6672 | 0.6723 | | 0.5628 | 24.35 | 5600 | 0.6011 | 0.6756 | 0.6769 | | 0.5664 | 25.22 | 5800 | 0.6027 | 0.6728 | 0.6764 | | 0.5609 | 26.09 | 6000 | 0.6073 | 0.6746 | 0.6772 | | 0.5618 | 26.96 | 6200 | 0.6067 | 0.6739 | 0.6769 | | 0.5603 | 27.83 | 6400 | 0.6151 | 0.6679 | 0.6728 | | 0.5578 | 28.7 | 6600 | 0.5997 | 0.6778 | 0.6796 | | 0.559 | 29.57 | 6800 | 0.5980 | 0.6795 | 0.6807 | | 0.5551 | 30.43 | 7000 | 0.6067 | 0.6740 | 0.6772 | | 0.5636 | 31.3 | 7200 | 0.6002 | 0.6794 | 0.6810 | | 0.5549 | 32.17 | 7400 | 0.6016 | 0.6790 | 0.6807 | | 0.5543 | 33.04 | 7600 | 0.5994 | 0.6770 | 0.6783 | | 0.5558 | 33.91 | 7800 | 0.5993 | 0.6776 | 0.6793 | | 0.5546 | 34.78 | 8000 | 0.6022 | 0.6781 | 0.6793 | | 0.5567 | 35.65 | 8200 | 0.5980 | 0.6793 | 0.6807 | | 0.553 | 36.52 | 8400 | 0.6025 | 0.6756 | 0.6783 | | 0.5553 | 37.39 | 8600 | 0.6016 | 0.6774 | 0.6788 | | 0.5478 | 38.26 | 8800 | 0.6096 | 0.6733 | 0.6764 | | 0.5536 | 39.13 | 9000 | 0.6045 | 0.6756 | 0.6777 | | 0.5508 | 40.0 | 9200 | 0.6035 | 0.6800 | 0.6818 | | 0.5521 | 40.87 | 9400 | 0.6018 | 0.6760 | 0.6769 | | 0.5512 | 41.74 | 9600 | 0.6028 | 0.6758 | 0.6772 | | 0.552 | 42.61 | 9800 | 0.6021 | 0.6789 | 0.6802 | | 0.5521 | 43.48 | 10000 | 0.6031 | 0.6783 | 0.6799 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_4096_512_15M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:54:40+00:00

null

peft

# GUE_EMP_H3K4me3-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.6214 - F1 Score: 0.6871 - Accuracy: 0.6872 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6604 | 0.87 | 200 | 0.6356 | 0.6419 | 0.6446 | | 0.6283 | 1.74 | 400 | 0.6323 | 0.6420 | 0.6451 | | 0.6159 | 2.61 | 600 | 0.6089 | 0.6698 | 0.6696 | | 0.6064 | 3.48 | 800 | 0.6044 | 0.6734 | 0.6731 | | 0.6011 | 4.35 | 1000 | 0.6067 | 0.6681 | 0.6679 | | 0.5945 | 5.22 | 1200 | 0.6015 | 0.6739 | 0.6742 | | 0.5889 | 6.09 | 1400 | 0.6102 | 0.6588 | 0.6630 | | 0.5839 | 6.96 | 1600 | 0.6055 | 0.6744 | 0.6764 | | 0.5777 | 7.83 | 1800 | 0.6038 | 0.6778 | 0.6777 | | 0.5768 | 8.7 | 2000 | 0.6082 | 0.6716 | 0.6717 | | 0.5664 | 9.57 | 2200 | 0.5990 | 0.6784 | 0.6785 | | 0.5693 | 10.43 | 2400 | 0.6050 | 0.6708 | 0.6726 | | 0.5635 | 11.3 | 2600 | 0.5996 | 0.6714 | 0.675 | | 0.566 | 12.17 | 2800 | 0.5940 | 0.6735 | 0.6747 | | 0.556 | 13.04 | 3000 | 0.5968 | 0.6770 | 0.6780 | | 0.553 | 13.91 | 3200 | 0.6026 | 0.6703 | 0.6720 | | 0.5486 | 14.78 | 3400 | 0.6150 | 0.6675 | 0.6709 | | 0.5497 | 15.65 | 3600 | 0.6032 | 0.6709 | 0.6731 | | 0.5432 | 16.52 | 3800 | 0.6059 | 0.6764 | 0.6766 | | 0.5393 | 17.39 | 4000 | 0.6131 | 0.6752 | 0.6772 | | 0.5427 | 18.26 | 4200 | 0.6093 | 0.6747 | 0.6785 | | 0.5304 | 19.13 | 4400 | 0.6131 | 0.6716 | 0.6739 | | 0.5329 | 20.0 | 4600 | 0.6077 | 0.6777 | 0.6793 | | 0.531 | 20.87 | 4800 | 0.6070 | 0.6769 | 0.6783 | | 0.5239 | 21.74 | 5000 | 0.6174 | 0.6708 | 0.6723 | | 0.5272 | 22.61 | 5200 | 0.6096 | 0.6799 | 0.6813 | | 0.5188 | 23.48 | 5400 | 0.6364 | 0.6696 | 0.6731 | | 0.5177 | 24.35 | 5600 | 0.6255 | 0.6697 | 0.6736 | | 0.5185 | 25.22 | 5800 | 0.6251 | 0.6740 | 0.6777 | | 0.513 | 26.09 | 6000 | 0.6339 | 0.6707 | 0.6742 | | 0.5119 | 26.96 | 6200 | 0.6245 | 0.6742 | 0.6777 | | 0.5078 | 27.83 | 6400 | 0.6367 | 0.6723 | 0.6766 | | 0.504 | 28.7 | 6600 | 0.6171 | 0.6765 | 0.6772 | | 0.5056 | 29.57 | 6800 | 0.6165 | 0.6755 | 0.6769 | | 0.5021 | 30.43 | 7000 | 0.6280 | 0.6777 | 0.6804 | | 0.5093 | 31.3 | 7200 | 0.6212 | 0.6818 | 0.6826 | | 0.4991 | 32.17 | 7400 | 0.6257 | 0.6770 | 0.6783 | | 0.4968 | 33.04 | 7600 | 0.6238 | 0.6776 | 0.6791 | | 0.4957 | 33.91 | 7800 | 0.6232 | 0.6764 | 0.6785 | | 0.4945 | 34.78 | 8000 | 0.6249 | 0.6765 | 0.6780 | | 0.4986 | 35.65 | 8200 | 0.6241 | 0.6784 | 0.6802 | | 0.4907 | 36.52 | 8400 | 0.6303 | 0.6738 | 0.6761 | | 0.495 | 37.39 | 8600 | 0.6312 | 0.6758 | 0.6769 | | 0.4868 | 38.26 | 8800 | 0.6352 | 0.6774 | 0.6793 | | 0.4894 | 39.13 | 9000 | 0.6343 | 0.6773 | 0.6791 | | 0.4875 | 40.0 | 9200 | 0.6298 | 0.6787 | 0.6802 | | 0.4871 | 40.87 | 9400 | 0.6313 | 0.6760 | 0.6769 | | 0.4861 | 41.74 | 9600 | 0.6330 | 0.6773 | 0.6791 | | 0.4892 | 42.61 | 9800 | 0.6306 | 0.6777 | 0.6791 | | 0.4891 | 43.48 | 10000 | 0.6317 | 0.6775 | 0.6791 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_4096_512_15M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T17:55:10+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

{"library_name": "transformers", "tags": []}

LongDHo/finetuned-gemma-2b

null

[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T17:55:44+00:00

null

peft

# cosmosDPO_CodeTest2 This model is a fine-tuned version of [ytu-ce-cosmos/turkish-gpt2-large-750m-instruct-v0.1](https://huggingface.co/ytu-ce-cosmos/turkish-gpt2-large-750m-instruct-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5356 - Rewards/chosen: -1.3639 - Rewards/rejected: -3.6411 - Rewards/accuracies: 0.2640 - Rewards/margins: 2.2772 - Logps/rejected: -477.7171 - Logps/chosen: -224.9044 - Logits/rejected: -4.1447 - Logits/chosen: -3.7114 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6915 | 0.0524 | 8 | 0.6855 | -0.0225 | -0.0387 | 0.2171 | 0.0162 | -117.4774 | -90.7611 | -2.7135 | -2.4688 | | 0.6639 | 0.1047 | 16 | 0.6480 | -0.2509 | -0.4015 | 0.2189 | 0.1506 | -153.7584 | -113.6010 | -3.2208 | -2.9343 | | 0.6251 | 0.1571 | 24 | 0.6436 | -0.7453 | -1.1570 | 0.2217 | 0.4117 | -229.3032 | -163.0413 | -3.8702 | -3.5589 | | 0.6238 | 0.2095 | 32 | 0.6047 | -0.7237 | -1.2597 | 0.2355 | 0.5360 | -239.5777 | -160.8856 | -3.7913 | -3.4555 | | 0.5586 | 0.2619 | 40 | 0.5789 | -1.0590 | -1.9755 | 0.2474 | 0.9164 | -311.1551 | -194.4169 | -3.9560 | -3.5940 | | 0.5389 | 0.3142 | 48 | 0.5577 | -1.0922 | -2.3486 | 0.2548 | 1.2564 | -348.4677 | -197.7312 | -3.9027 | -3.5251 | | 0.5102 | 0.3666 | 56 | 0.5606 | -1.4904 | -3.3229 | 0.2548 | 1.8325 | -445.8979 | -237.5522 | -4.0088 | -3.6310 | | 0.5506 | 0.4190 | 64 | 0.5529 | -1.4084 | -3.4076 | 0.2585 | 1.9992 | -454.3663 | -229.3532 | -3.9314 | -3.5543 | | 0.5696 | 0.4714 | 72 | 0.5365 | -0.7411 | -2.1788 | 0.2621 | 1.4377 | -331.4860 | -162.6252 | -3.6733 | -3.2798 | | 0.5265 | 0.5237 | 80 | 0.5355 | -0.8770 | -2.4950 | 0.2612 | 1.6180 | -363.1028 | -176.2112 | -3.7304 | -3.3452 | | 0.5199 | 0.5761 | 88 | 0.5482 | -1.5559 | -3.7745 | 0.2585 | 2.2186 | -491.0597 | -244.1054 | -3.9633 | -3.5958 | | 0.5163 | 0.6285 | 96 | 0.5464 | -1.5899 | -3.8545 | 0.2594 | 2.2646 | -499.0518 | -247.5011 | -4.0472 | -3.6688 | | 0.5421 | 0.6809 | 104 | 0.5408 | -1.4973 | -3.8002 | 0.2631 | 2.3029 | -493.6231 | -238.2402 | -4.1221 | -3.7151 | | 0.5416 | 0.7332 | 112 | 0.5356 | -1.2811 | -3.4299 | 0.2640 | 2.1488 | -456.5994 | -216.6231 | -4.0861 | -3.6611 | | 0.4967 | 0.7856 | 120 | 0.5347 | -1.2626 | -3.4278 | 0.2640 | 2.1653 | -456.3912 | -214.7687 | -4.1048 | -3.6705 | | 0.4783 | 0.8380 | 128 | 0.5345 | -1.2666 | -3.4477 | 0.2640 | 2.1811 | -458.3748 | -215.1744 | -4.1066 | -3.6704 | | 0.508 | 0.8903 | 136 | 0.5352 | -1.3287 | -3.5746 | 0.2640 | 2.2459 | -471.0667 | -221.3868 | -4.1311 | -3.6966 | | 0.5417 | 0.9427 | 144 | 0.5356 | -1.3619 | -3.6366 | 0.2640 | 2.2746 | -477.2621 | -224.7045 | -4.1435 | -3.7103 | | 0.5414 | 0.9951 | 152 | 0.5356 | -1.3639 | -3.6411 | 0.2640 | 2.2772 | -477.7171 | -224.9044 | -4.1447 | -3.7114 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "mit", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "ytu-ce-cosmos/turkish-gpt2-large-750m-instruct-v0.1", "model-index": [{"name": "cosmosDPO_CodeTest2", "results": []}]}

meguzn/cosmosDPO_CodeTest2

null

[ "peft", "tensorboard", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:ytu-ce-cosmos/turkish-gpt2-large-750m-instruct-v0.1", "license:mit", "region:us" ]

null

2024-05-03T17:55:54+00:00

null

{}

lanzv/model_output_dir

null

[ "region:us" ]

null

2024-05-03T17:56:00+00:00

null

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

{"library_name": "transformers", "tags": ["unsloth", "trl", "sft"]}

MohamedSaeed-dev/gemma7b-unsloth

null

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-03T17:56:25+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

{"library_name": "transformers", "tags": ["unsloth", "trl", "sft"]}

MohamedSaeed-dev/phi-unsloth

null

[ "transformers", "pytorch", "mistral", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T17:56:38+00:00

null

{}

UC3M-LCPM/Bart_large_task1a_en_normal

null

[ "region:us" ]

null

2024-05-03T17:56:40+00:00

null

peft

# loha_fine_tuned_cb_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2436 - Accuracy: 0.3182 - F1: 0.1536 ## 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 - training_steps: 400 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:| | 0.9874 | 3.5714 | 50 | 1.1351 | 0.3182 | 0.1591 | | 0.8665 | 7.1429 | 100 | 1.1589 | 0.3182 | 0.1536 | | 0.8359 | 10.7143 | 150 | 1.1890 | 0.3182 | 0.1536 | | 0.7662 | 14.2857 | 200 | 1.2116 | 0.3182 | 0.1536 | | 0.769 | 17.8571 | 250 | 1.2287 | 0.3182 | 0.1536 | | 0.7534 | 21.4286 | 300 | 1.2380 | 0.3182 | 0.1536 | | 0.7359 | 25.0 | 350 | 1.2421 | 0.3182 | 0.1536 | | 0.7449 | 28.5714 | 400 | 1.2436 | 0.3182 | 0.1536 | ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "cc-by-4.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "loha_fine_tuned_cb_croslo", "results": []}]}

lenatr99/loha_fine_tuned_cb_croslo

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:EMBEDDIA/crosloengual-bert", "license:cc-by-4.0", "region:us" ]

null

2024-05-03T17:56:45+00:00

null

peft

# lora_fine_tuned_cb_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3172 - Accuracy: 0.3182 - F1: 0.1536 ## 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 - training_steps: 400 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:| | 1.0956 | 3.5714 | 50 | 1.1024 | 0.3636 | 0.3027 | | 0.8669 | 7.1429 | 100 | 1.1540 | 0.3182 | 0.1536 | | 0.7634 | 10.7143 | 150 | 1.2351 | 0.3182 | 0.1536 | | 0.7 | 14.2857 | 200 | 1.2885 | 0.3182 | 0.1536 | | 0.6951 | 17.8571 | 250 | 1.3121 | 0.3182 | 0.1536 | | 0.7047 | 21.4286 | 300 | 1.3145 | 0.3182 | 0.1536 | | 0.6769 | 25.0 | 350 | 1.3154 | 0.3182 | 0.1536 | | 0.6886 | 28.5714 | 400 | 1.3172 | 0.3182 | 0.1536 | ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "cc-by-4.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "lora_fine_tuned_cb_croslo", "results": []}]}

lenatr99/lora_fine_tuned_cb_croslo

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:EMBEDDIA/crosloengual-bert", "license:cc-by-4.0", "region:us" ]

null

2024-05-03T17:56:45+00:00

null

peft

# prompt_fine_tuned_CB_croslo This model is a fine-tuned version of [EMBEDDIA/crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2046 - Accuracy: 0.3182 - F1: 0.1536 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:| | 1.0278 | 0.4545 | 50 | 1.1158 | 0.3182 | 0.2306 | | 0.9865 | 0.9091 | 100 | 1.1195 | 0.3636 | 0.2430 | | 0.8601 | 1.3636 | 150 | 1.1357 | 0.3182 | 0.1536 | | 0.8769 | 1.8182 | 200 | 1.1595 | 0.3182 | 0.1536 | | 0.9026 | 2.2727 | 250 | 1.1733 | 0.3182 | 0.1536 | | 0.8002 | 2.7273 | 300 | 1.1885 | 0.3182 | 0.1536 | | 0.8093 | 3.1818 | 350 | 1.1996 | 0.3182 | 0.1536 | | 0.7259 | 3.6364 | 400 | 1.2046 | 0.3182 | 0.1536 | ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "cc-by-4.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "EMBEDDIA/crosloengual-bert", "model-index": [{"name": "prompt_fine_tuned_CB_croslo", "results": []}]}

lenatr99/prompt_fine_tuned_CB_croslo

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:EMBEDDIA/crosloengual-bert", "license:cc-by-4.0", "region:us" ]

null

2024-05-03T17:57:06+00:00

null

{}

mvera/my-awesome-model

null

[ "region:us" ]

null

2024-05-03T17:57:27+00:00

null

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

{"library_name": "transformers", "tags": []}

ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_64_0.05_2_0.0002

null

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-03T17:59:42+00:00

null

{}

Aryan0310/bart-large-cnn-finetuned-xsum

null

[ "region:us" ]

null

2024-05-03T17:59:49+00:00

null

{}

GeronimoYeah/Harvester_v2

null

[ "region:us" ]

null

2024-05-03T18:00:20+00:00

null

{"license": "mit"}

omarques/testgb

null

[ "license:mit", "region:us" ]

null

2024-05-03T18:02:07+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

{"library_name": "transformers", "tags": []}

Pruz0/Len2GPT

null

[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T18:02:18+00:00

null

diffusers

{"license": "wtfpl"}

Poomz/loreitup

null

[ "diffusers", "safetensors", "license:wtfpl", "diffusers:DiTPipeline", "region:us" ]

null

2024-05-03T18:02:37+00:00

null

{}

HarshitSheoran/birdclef24_comp_data

null

[ "region:us" ]

null

2024-05-03T18:03:31+00:00

text-classification

transformers

# roberta-base_bedtype This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0771 - Accuracy: 0.7143 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 27 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.6124 | 1.67 | 10 | 1.6887 | 0.2857 | | 1.1698 | 3.33 | 20 | 1.6470 | 0.2571 | | 0.9559 | 5.0 | 30 | 1.3456 | 0.5429 | | 0.765 | 6.67 | 40 | 1.3860 | 0.4571 | | 0.5258 | 8.33 | 50 | 1.0184 | 0.5714 | | 0.2822 | 10.0 | 60 | 0.7994 | 0.6571 | | 0.1576 | 11.67 | 70 | 0.8624 | 0.7429 | | 0.0648 | 13.33 | 80 | 0.6489 | 0.8 | | 0.0483 | 15.0 | 90 | 0.7762 | 0.7429 | | 0.0148 | 16.67 | 100 | 0.7915 | 0.7714 | | 0.0086 | 18.33 | 110 | 1.0589 | 0.7429 | | 0.0058 | 20.0 | 120 | 0.8353 | 0.8 | | 0.005 | 21.67 | 130 | 0.8960 | 0.8 | | 0.0042 | 23.33 | 140 | 0.9228 | 0.7429 | | 0.0036 | 25.0 | 150 | 0.9461 | 0.7429 | | 0.0032 | 26.67 | 160 | 0.9904 | 0.7714 | | 0.003 | 28.33 | 170 | 0.9968 | 0.7429 | | 0.0029 | 30.0 | 180 | 0.9978 | 0.7429 | | 0.0026 | 31.67 | 190 | 1.0043 | 0.7429 | | 0.0029 | 33.33 | 200 | 1.0712 | 0.7143 | | 0.0423 | 35.0 | 210 | 1.0914 | 0.7429 | | 0.0187 | 36.67 | 220 | 0.9988 | 0.7714 | | 0.0025 | 38.33 | 230 | 1.0863 | 0.7143 | | 0.0023 | 40.0 | 240 | 1.1078 | 0.7143 | | 0.0022 | 41.67 | 250 | 1.1058 | 0.7143 | | 0.0023 | 43.33 | 260 | 1.0903 | 0.7143 | | 0.0021 | 45.0 | 270 | 1.0833 | 0.7143 | | 0.0021 | 46.67 | 280 | 1.0805 | 0.7143 | | 0.0021 | 48.33 | 290 | 1.0777 | 0.7143 | | 0.0021 | 50.0 | 300 | 1.0771 | 0.7143 | ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base_bedtype", "results": []}]}

JBhug/roberta-base_bedtype

null

[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T18:03:58+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

{"library_name": "transformers", "tags": []}

golf2248/qkuzzuz

null

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-03T18:04:11+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

{"library_name": "transformers", "tags": []}

abc88767/model57

null

[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-03T18:05:08+00:00

null

peft

# GUE_EMP_H4-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2686 - F1 Score: 0.8975 - Accuracy: 0.8973 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.4376 | 2.17 | 200 | 0.3131 | 0.8777 | 0.8775 | | 0.3136 | 4.35 | 400 | 0.3095 | 0.8718 | 0.8713 | | 0.2965 | 6.52 | 600 | 0.2957 | 0.8853 | 0.8850 | | 0.2931 | 8.7 | 800 | 0.2967 | 0.8853 | 0.8850 | | 0.2855 | 10.87 | 1000 | 0.2915 | 0.8921 | 0.8919 | | 0.279 | 13.04 | 1200 | 0.2921 | 0.8921 | 0.8919 | | 0.2774 | 15.22 | 1400 | 0.2821 | 0.8941 | 0.8939 | | 0.2731 | 17.39 | 1600 | 0.2847 | 0.8935 | 0.8932 | | 0.2726 | 19.57 | 1800 | 0.2780 | 0.8891 | 0.8891 | | 0.2711 | 21.74 | 2000 | 0.2868 | 0.8908 | 0.8905 | | 0.2622 | 23.91 | 2200 | 0.2883 | 0.8949 | 0.8946 | | 0.2659 | 26.09 | 2400 | 0.2896 | 0.8908 | 0.8905 | | 0.2646 | 28.26 | 2600 | 0.2777 | 0.8928 | 0.8925 | | 0.2609 | 30.43 | 2800 | 0.2813 | 0.8942 | 0.8939 | | 0.2605 | 32.61 | 3000 | 0.2823 | 0.8963 | 0.8960 | | 0.2589 | 34.78 | 3200 | 0.2771 | 0.8990 | 0.8987 | | 0.2556 | 36.96 | 3400 | 0.2850 | 0.8942 | 0.8939 | | 0.2551 | 39.13 | 3600 | 0.2833 | 0.8970 | 0.8966 | | 0.2518 | 41.3 | 3800 | 0.2831 | 0.8963 | 0.8960 | | 0.2515 | 43.48 | 4000 | 0.2820 | 0.8963 | 0.8960 | | 0.255 | 45.65 | 4200 | 0.2756 | 0.8969 | 0.8966 | | 0.248 | 47.83 | 4400 | 0.2804 | 0.8956 | 0.8953 | | 0.2508 | 50.0 | 4600 | 0.2736 | 0.8969 | 0.8966 | | 0.2465 | 52.17 | 4800 | 0.2755 | 0.8942 | 0.8939 | | 0.2485 | 54.35 | 5000 | 0.2929 | 0.8855 | 0.8850 | | 0.2436 | 56.52 | 5200 | 0.2922 | 0.8855 | 0.8850 | | 0.2435 | 58.7 | 5400 | 0.2806 | 0.8881 | 0.8877 | | 0.2428 | 60.87 | 5600 | 0.2852 | 0.8888 | 0.8884 | | 0.2452 | 63.04 | 5800 | 0.2816 | 0.8881 | 0.8877 | | 0.2428 | 65.22 | 6000 | 0.2777 | 0.8915 | 0.8912 | | 0.242 | 67.39 | 6200 | 0.2873 | 0.8841 | 0.8836 | | 0.2387 | 69.57 | 6400 | 0.2821 | 0.8861 | 0.8857 | | 0.2393 | 71.74 | 6600 | 0.2894 | 0.8854 | 0.8850 | | 0.2409 | 73.91 | 6800 | 0.2829 | 0.8888 | 0.8884 | | 0.236 | 76.09 | 7000 | 0.2819 | 0.8881 | 0.8877 | | 0.2364 | 78.26 | 7200 | 0.2800 | 0.8908 | 0.8905 | | 0.2363 | 80.43 | 7400 | 0.2764 | 0.8915 | 0.8912 | | 0.2361 | 82.61 | 7600 | 0.2743 | 0.8935 | 0.8932 | | 0.2391 | 84.78 | 7800 | 0.2879 | 0.8875 | 0.8871 | | 0.2357 | 86.96 | 8000 | 0.2767 | 0.8922 | 0.8919 | | 0.237 | 89.13 | 8200 | 0.2769 | 0.8915 | 0.8912 | | 0.2348 | 91.3 | 8400 | 0.2780 | 0.8915 | 0.8912 | | 0.2338 | 93.48 | 8600 | 0.2758 | 0.8935 | 0.8932 | | 0.2339 | 95.65 | 8800 | 0.2753 | 0.8935 | 0.8932 | | 0.2358 | 97.83 | 9000 | 0.2763 | 0.8929 | 0.8925 | | 0.2353 | 100.0 | 9200 | 0.2829 | 0.8888 | 0.8884 | | 0.2349 | 102.17 | 9400 | 0.2784 | 0.8915 | 0.8912 | | 0.2346 | 104.35 | 9600 | 0.2794 | 0.8908 | 0.8905 | | 0.2327 | 106.52 | 9800 | 0.2779 | 0.8929 | 0.8925 | | 0.2332 | 108.7 | 10000 | 0.2783 | 0.8929 | 0.8925 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H4-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H4-seqsight_4096_512_15M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T18:05:36+00:00

null

peft

# GUE_EMP_H4-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2787 - F1 Score: 0.9048 - Accuracy: 0.9049 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.3874 | 2.17 | 200 | 0.2947 | 0.8811 | 0.8809 | | 0.2921 | 4.35 | 400 | 0.2955 | 0.8888 | 0.8884 | | 0.2805 | 6.52 | 600 | 0.2811 | 0.8927 | 0.8925 | | 0.2752 | 8.7 | 800 | 0.2907 | 0.8888 | 0.8884 | | 0.2682 | 10.87 | 1000 | 0.2885 | 0.8874 | 0.8871 | | 0.2612 | 13.04 | 1200 | 0.2793 | 0.8962 | 0.8960 | | 0.2565 | 15.22 | 1400 | 0.2839 | 0.8902 | 0.8898 | | 0.2495 | 17.39 | 1600 | 0.2838 | 0.8949 | 0.8946 | | 0.2489 | 19.57 | 1800 | 0.2698 | 0.9030 | 0.9028 | | 0.2432 | 21.74 | 2000 | 0.2757 | 0.8942 | 0.8939 | | 0.2346 | 23.91 | 2200 | 0.2817 | 0.8929 | 0.8925 | | 0.234 | 26.09 | 2400 | 0.2747 | 0.8935 | 0.8932 | | 0.2307 | 28.26 | 2600 | 0.2722 | 0.9043 | 0.9042 | | 0.2248 | 30.43 | 2800 | 0.2782 | 0.8874 | 0.8871 | | 0.224 | 32.61 | 3000 | 0.2796 | 0.8989 | 0.8987 | | 0.2198 | 34.78 | 3200 | 0.2762 | 0.9017 | 0.9014 | | 0.2167 | 36.96 | 3400 | 0.2808 | 0.8983 | 0.8980 | | 0.2139 | 39.13 | 3600 | 0.2760 | 0.8982 | 0.8980 | | 0.2062 | 41.3 | 3800 | 0.2803 | 0.8983 | 0.8980 | | 0.2082 | 43.48 | 4000 | 0.2833 | 0.8976 | 0.8973 | | 0.2089 | 45.65 | 4200 | 0.2781 | 0.9030 | 0.9028 | | 0.2016 | 47.83 | 4400 | 0.2828 | 0.9056 | 0.9055 | | 0.2043 | 50.0 | 4600 | 0.2781 | 0.8962 | 0.8960 | | 0.1996 | 52.17 | 4800 | 0.2825 | 0.8969 | 0.8966 | | 0.1967 | 54.35 | 5000 | 0.2907 | 0.8977 | 0.8973 | | 0.1945 | 56.52 | 5200 | 0.3027 | 0.8841 | 0.8836 | | 0.1929 | 58.7 | 5400 | 0.2808 | 0.8990 | 0.8987 | | 0.1909 | 60.87 | 5600 | 0.2814 | 0.8990 | 0.8987 | | 0.1904 | 63.04 | 5800 | 0.2880 | 0.8915 | 0.8912 | | 0.19 | 65.22 | 6000 | 0.2885 | 0.8970 | 0.8966 | | 0.187 | 67.39 | 6200 | 0.2886 | 0.8963 | 0.8960 | | 0.1834 | 69.57 | 6400 | 0.2960 | 0.8922 | 0.8919 | | 0.1855 | 71.74 | 6600 | 0.2953 | 0.8983 | 0.8980 | | 0.1826 | 73.91 | 6800 | 0.2914 | 0.8956 | 0.8953 | | 0.1796 | 76.09 | 7000 | 0.2983 | 0.8929 | 0.8925 | | 0.1791 | 78.26 | 7200 | 0.2885 | 0.9010 | 0.9008 | | 0.1798 | 80.43 | 7400 | 0.2929 | 0.8963 | 0.8960 | | 0.178 | 82.61 | 7600 | 0.2925 | 0.9044 | 0.9042 | | 0.1767 | 84.78 | 7800 | 0.3021 | 0.8909 | 0.8905 | | 0.1783 | 86.96 | 8000 | 0.2919 | 0.8989 | 0.8987 | | 0.1754 | 89.13 | 8200 | 0.2978 | 0.8949 | 0.8946 | | 0.1753 | 91.3 | 8400 | 0.2952 | 0.8942 | 0.8939 | | 0.1735 | 93.48 | 8600 | 0.2969 | 0.8996 | 0.8994 | | 0.1745 | 95.65 | 8800 | 0.2928 | 0.9037 | 0.9035 | | 0.1725 | 97.83 | 9000 | 0.2929 | 0.9030 | 0.9028 | | 0.1732 | 100.0 | 9200 | 0.3001 | 0.8915 | 0.8912 | | 0.1718 | 102.17 | 9400 | 0.2951 | 0.9003 | 0.9001 | | 0.1729 | 104.35 | 9600 | 0.2937 | 0.8996 | 0.8994 | | 0.1718 | 106.52 | 9800 | 0.2948 | 0.8996 | 0.8994 | | 0.1713 | 108.7 | 10000 | 0.2957 | 0.8996 | 0.8994 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H4-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H4-seqsight_4096_512_15M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T18:05:39+00:00

null

transformers

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

{"library_name": "transformers", "tags": []}

ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_64_0.05_4_5e-05

null

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-05-03T18:06:11+00:00

null

peft

# GUE_EMP_H3-seqsight_4096_512_15M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset. It achieves the following results on the evaluation set: - Loss: 0.3157 - F1 Score: 0.8771 - Accuracy: 0.8771 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.5141 | 2.13 | 200 | 0.4359 | 0.7886 | 0.7902 | | 0.3998 | 4.26 | 400 | 0.3945 | 0.8303 | 0.8303 | | 0.3759 | 6.38 | 600 | 0.3907 | 0.8321 | 0.8323 | | 0.3625 | 8.51 | 800 | 0.3761 | 0.8377 | 0.8377 | | 0.3542 | 10.64 | 1000 | 0.3715 | 0.8382 | 0.8383 | | 0.3365 | 12.77 | 1200 | 0.3685 | 0.8462 | 0.8464 | | 0.3269 | 14.89 | 1400 | 0.3503 | 0.8470 | 0.8470 | | 0.3101 | 17.02 | 1600 | 0.3486 | 0.8503 | 0.8504 | | 0.3011 | 19.15 | 1800 | 0.3602 | 0.8527 | 0.8530 | | 0.2962 | 21.28 | 2000 | 0.3519 | 0.8593 | 0.8597 | | 0.2926 | 23.4 | 2200 | 0.3292 | 0.8570 | 0.8570 | | 0.284 | 25.53 | 2400 | 0.3457 | 0.8575 | 0.8577 | | 0.2853 | 27.66 | 2600 | 0.3263 | 0.8590 | 0.8591 | | 0.284 | 29.79 | 2800 | 0.3354 | 0.8582 | 0.8584 | | 0.2801 | 31.91 | 3000 | 0.3358 | 0.8622 | 0.8624 | | 0.2764 | 34.04 | 3200 | 0.3264 | 0.8656 | 0.8657 | | 0.2755 | 36.17 | 3400 | 0.3383 | 0.8622 | 0.8624 | | 0.2724 | 38.3 | 3600 | 0.3252 | 0.8663 | 0.8664 | | 0.2728 | 40.43 | 3800 | 0.3269 | 0.8676 | 0.8677 | | 0.2718 | 42.55 | 4000 | 0.3340 | 0.8676 | 0.8677 | | 0.2695 | 44.68 | 4200 | 0.3235 | 0.8657 | 0.8657 | | 0.2667 | 46.81 | 4400 | 0.3381 | 0.8655 | 0.8657 | | 0.2664 | 48.94 | 4600 | 0.3301 | 0.8689 | 0.8691 | | 0.2654 | 51.06 | 4800 | 0.3266 | 0.8697 | 0.8697 | | 0.2641 | 53.19 | 5000 | 0.3489 | 0.8607 | 0.8611 | | 0.2677 | 55.32 | 5200 | 0.3200 | 0.8677 | 0.8677 | | 0.2607 | 57.45 | 5400 | 0.3333 | 0.8683 | 0.8684 | | 0.2581 | 59.57 | 5600 | 0.3247 | 0.8690 | 0.8691 | | 0.2612 | 61.7 | 5800 | 0.3181 | 0.8677 | 0.8677 | | 0.2565 | 63.83 | 6000 | 0.3443 | 0.8682 | 0.8684 | | 0.2626 | 65.96 | 6200 | 0.3354 | 0.8676 | 0.8677 | | 0.2579 | 68.09 | 6400 | 0.3338 | 0.8663 | 0.8664 | | 0.2546 | 70.21 | 6600 | 0.3380 | 0.8683 | 0.8684 | | 0.2562 | 72.34 | 6800 | 0.3305 | 0.8683 | 0.8684 | | 0.2545 | 74.47 | 7000 | 0.3513 | 0.8600 | 0.8604 | | 0.2507 | 76.6 | 7200 | 0.3392 | 0.8670 | 0.8671 | | 0.2568 | 78.72 | 7400 | 0.3329 | 0.8676 | 0.8677 | | 0.2515 | 80.85 | 7600 | 0.3307 | 0.8670 | 0.8671 | | 0.2494 | 82.98 | 7800 | 0.3388 | 0.8649 | 0.8651 | | 0.2537 | 85.11 | 8000 | 0.3287 | 0.8663 | 0.8664 | | 0.2535 | 87.23 | 8200 | 0.3317 | 0.8663 | 0.8664 | | 0.25 | 89.36 | 8400 | 0.3356 | 0.8663 | 0.8664 | | 0.2493 | 91.49 | 8600 | 0.3397 | 0.8649 | 0.8651 | | 0.2504 | 93.62 | 8800 | 0.3360 | 0.8656 | 0.8657 | | 0.2532 | 95.74 | 9000 | 0.3389 | 0.8649 | 0.8651 | | 0.2502 | 97.87 | 9200 | 0.3313 | 0.8676 | 0.8677 | | 0.2497 | 100.0 | 9400 | 0.3302 | 0.8683 | 0.8684 | | 0.2516 | 102.13 | 9600 | 0.3324 | 0.8676 | 0.8677 | | 0.2499 | 104.26 | 9800 | 0.3338 | 0.8663 | 0.8664 | | 0.2503 | 106.38 | 10000 | 0.3336 | 0.8663 | 0.8664 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3-seqsight_4096_512_15M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3-seqsight_4096_512_15M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T18:06:35+00:00

null

peft

# GUE_EMP_H3-seqsight_4096_512_15M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset. It achieves the following results on the evaluation set: - Loss: 0.3240 - F1 Score: 0.8737 - Accuracy: 0.8737 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.4577 | 2.13 | 200 | 0.3877 | 0.8286 | 0.8290 | | 0.3469 | 4.26 | 400 | 0.3609 | 0.8490 | 0.8490 | | 0.3103 | 6.38 | 600 | 0.3633 | 0.8493 | 0.8497 | | 0.2955 | 8.51 | 800 | 0.3413 | 0.8589 | 0.8591 | | 0.2851 | 10.64 | 1000 | 0.3289 | 0.8663 | 0.8664 | | 0.2766 | 12.77 | 1200 | 0.3486 | 0.8540 | 0.8544 | | 0.2727 | 14.89 | 1400 | 0.3322 | 0.8649 | 0.8651 | | 0.2675 | 17.02 | 1600 | 0.3172 | 0.8731 | 0.8731 | | 0.2608 | 19.15 | 1800 | 0.3377 | 0.8601 | 0.8604 | | 0.2563 | 21.28 | 2000 | 0.3345 | 0.8643 | 0.8644 | | 0.2561 | 23.4 | 2200 | 0.3228 | 0.8751 | 0.8751 | | 0.2447 | 25.53 | 2400 | 0.3772 | 0.8566 | 0.8570 | | 0.2497 | 27.66 | 2600 | 0.3324 | 0.8663 | 0.8664 | | 0.2466 | 29.79 | 2800 | 0.3237 | 0.8744 | 0.8744 | | 0.2406 | 31.91 | 3000 | 0.3300 | 0.8730 | 0.8731 | | 0.2368 | 34.04 | 3200 | 0.3247 | 0.8737 | 0.8737 | | 0.2362 | 36.17 | 3400 | 0.3424 | 0.8696 | 0.8697 | | 0.2321 | 38.3 | 3600 | 0.3380 | 0.8730 | 0.8731 | | 0.2306 | 40.43 | 3800 | 0.3357 | 0.8750 | 0.8751 | | 0.2297 | 42.55 | 4000 | 0.3661 | 0.8669 | 0.8671 | | 0.2267 | 44.68 | 4200 | 0.3540 | 0.8730 | 0.8731 | | 0.2239 | 46.81 | 4400 | 0.3621 | 0.8722 | 0.8724 | | 0.2206 | 48.94 | 4600 | 0.3667 | 0.8668 | 0.8671 | | 0.2184 | 51.06 | 4800 | 0.3429 | 0.8751 | 0.8751 | | 0.2186 | 53.19 | 5000 | 0.3682 | 0.8695 | 0.8697 | | 0.2168 | 55.32 | 5200 | 0.3536 | 0.8717 | 0.8717 | | 0.2109 | 57.45 | 5400 | 0.3656 | 0.8696 | 0.8697 | | 0.2103 | 59.57 | 5600 | 0.3503 | 0.8764 | 0.8764 | | 0.2118 | 61.7 | 5800 | 0.3421 | 0.8784 | 0.8784 | | 0.2059 | 63.83 | 6000 | 0.3849 | 0.8716 | 0.8717 | | 0.2099 | 65.96 | 6200 | 0.3856 | 0.8654 | 0.8657 | | 0.2062 | 68.09 | 6400 | 0.3654 | 0.8737 | 0.8737 | | 0.2016 | 70.21 | 6600 | 0.3694 | 0.8730 | 0.8731 | | 0.2023 | 72.34 | 6800 | 0.3634 | 0.8770 | 0.8771 | | 0.2009 | 74.47 | 7000 | 0.4164 | 0.8619 | 0.8624 | | 0.1965 | 76.6 | 7200 | 0.3775 | 0.8696 | 0.8697 | | 0.2008 | 78.72 | 7400 | 0.3728 | 0.8730 | 0.8731 | | 0.1949 | 80.85 | 7600 | 0.3727 | 0.8723 | 0.8724 | | 0.1923 | 82.98 | 7800 | 0.3806 | 0.8722 | 0.8724 | | 0.195 | 85.11 | 8000 | 0.3777 | 0.8702 | 0.8704 | | 0.1949 | 87.23 | 8200 | 0.3796 | 0.8736 | 0.8737 | | 0.1924 | 89.36 | 8400 | 0.3854 | 0.8709 | 0.8711 | | 0.1899 | 91.49 | 8600 | 0.4060 | 0.8688 | 0.8691 | | 0.1873 | 93.62 | 8800 | 0.3931 | 0.8722 | 0.8724 | | 0.1945 | 95.74 | 9000 | 0.3907 | 0.8716 | 0.8717 | | 0.1907 | 97.87 | 9200 | 0.3828 | 0.8716 | 0.8717 | | 0.1898 | 100.0 | 9400 | 0.3809 | 0.8737 | 0.8737 | | 0.1906 | 102.13 | 9600 | 0.3861 | 0.8729 | 0.8731 | | 0.1884 | 104.26 | 9800 | 0.3847 | 0.8716 | 0.8717 | | 0.1891 | 106.38 | 10000 | 0.3846 | 0.8710 | 0.8711 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H3-seqsight_4096_512_15M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3-seqsight_4096_512_15M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

null

2024-05-03T18:06:45+00:00

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peft

# GUE_EMP_H4-seqsight_4096_512_15M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_15M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_15M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2642 - F1 Score: 0.9023 - Accuracy: 0.9021 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.3648 | 2.17 | 200 | 0.2892 | 0.8852 | 0.8850 | | 0.2836 | 4.35 | 400 | 0.2803 | 0.8954 | 0.8953 | | 0.2716 | 6.52 | 600 | 0.2752 | 0.9001 | 0.9001 | | 0.2644 | 8.7 | 800 | 0.2867 | 0.8874 | 0.8871 | | 0.2542 | 10.87 | 1000 | 0.2844 | 0.8936 | 0.8932 | | 0.2455 | 13.04 | 1200 | 0.2891 | 0.8875 | 0.8871 | | 0.2348 | 15.22 | 1400 | 0.2901 | 0.8889 | 0.8884 | | 0.226 | 17.39 | 1600 | 0.2846 | 0.8949 | 0.8946 | | 0.2204 | 19.57 | 1800 | 0.2804 | 0.9037 | 0.9035 | | 0.2127 | 21.74 | 2000 | 0.2843 | 0.8963 | 0.8960 | | 0.1998 | 23.91 | 2200 | 0.2911 | 0.8935 | 0.8932 | | 0.1956 | 26.09 | 2400 | 0.2842 | 0.8982 | 0.8980 | | 0.1909 | 28.26 | 2600 | 0.2996 | 0.9016 | 0.9014 | | 0.1785 | 30.43 | 2800 | 0.3167 | 0.8847 | 0.8843 | | 0.1761 | 32.61 | 3000 | 0.3012 | 0.8974 | 0.8973 | | 0.1675 | 34.78 | 3200 | 0.3298 | 0.8820 | 0.8816 | | 0.158 | 36.96 | 3400 | 0.3107 | 0.8934 | 0.8932 | | 0.1546 | 39.13 | 3600 | 0.3235 | 0.8893 | 0.8891 | | 0.1427 | 41.3 | 3800 | 0.3383 | 0.8827 | 0.8823 | | 0.1421 | 43.48 | 4000 | 0.3797 | 0.8739 | 0.8734 | | 0.1371 | 45.65 | 4200 | 0.3425 | 0.8907 | 0.8905 | | 0.1291 | 47.83 | 4400 | 0.3497 | 0.8819 | 0.8816 | | 0.1265 | 50.0 | 4600 | 0.3615 | 0.8866 | 0.8864 | | 0.1208 | 52.17 | 4800 | 0.3905 | 0.8752 | 0.8747 | | 0.1148 | 54.35 | 5000 | 0.3784 | 0.8839 | 0.8836 | | 0.115 | 56.52 | 5200 | 0.3857 | 0.8732 | 0.8727 | | 0.1046 | 58.7 | 5400 | 0.3877 | 0.8786 | 0.8782 | | 0.1019 | 60.87 | 5600 | 0.3844 | 0.8772 | 0.8768 | | 0.1023 | 63.04 | 5800 | 0.3925 | 0.8790 | 0.8789 | | 0.0957 | 65.22 | 6000 | 0.4220 | 0.8797 | 0.8795 | | 0.095 | 67.39 | 6200 | 0.4142 | 0.8765 | 0.8761 | | 0.0902 | 69.57 | 6400 | 0.4613 | 0.8739 | 0.8734 | | 0.0899 | 71.74 | 6600 | 0.4272 | 0.8812 | 0.8809 | | 0.0829 | 73.91 | 6800 | 0.4464 | 0.8690 | 0.8686 | | 0.0841 | 76.09 | 7000 | 0.4438 | 0.8764 | 0.8761 | | 0.0817 | 78.26 | 7200 | 0.4425 | 0.8751 | 0.8747 | | 0.0782 | 80.43 | 7400 | 0.4576 | 0.8785 | 0.8782 | | 0.0765 | 82.61 | 7600 | 0.4707 | 0.8744 | 0.8741 | | 0.0689 | 84.78 | 7800 | 0.5113 | 0.8704 | 0.8700 | | 0.0742 | 86.96 | 8000 | 0.4925 | 0.8738 | 0.8734 | | 0.0721 | 89.13 | 8200 | 0.4897 | 0.8758 | 0.8754 | | 0.0746 | 91.3 | 8400 | 0.4792 | 0.8717 | 0.8713 | | 0.0713 | 93.48 | 8600 | 0.4866 | 0.8738 | 0.8734 | | 0.0695 | 95.65 | 8800 | 0.4749 | 0.8791 | 0.8789 | | 0.0676 | 97.83 | 9000 | 0.4816 | 0.8757 | 0.8754 | | 0.0684 | 100.0 | 9200 | 0.5062 | 0.8725 | 0.8720 | | 0.0661 | 102.17 | 9400 | 0.4917 | 0.8730 | 0.8727 | | 0.0642 | 104.35 | 9600 | 0.4921 | 0.8751 | 0.8747 | | 0.0628 | 106.52 | 9800 | 0.4966 | 0.8751 | 0.8747 | | 0.062 | 108.7 | 10000 | 0.5035 | 0.8744 | 0.8741 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_15M", "model-index": [{"name": "GUE_EMP_H4-seqsight_4096_512_15M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H4-seqsight_4096_512_15M-L32_f

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[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_4096_512_15M", "region:us" ]

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2024-05-03T18:06:50+00:00