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AntoineGourru/Mistral_qlora_telecom_R512A1024BS4E2	AntoineGourru	2024-02-16T11:02:11Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]	null	2024-02-16T11:00:30Z	--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.2 --- # 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. --> - 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] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.7.0
Joshua265/fingpt-forecaster_llama2-7b-gguf	Joshua265	2024-02-16T11:01:07Z	25	2	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-02-16T10:39:34Z	Merged GGUF Model of LLama2-7B and the FinGPT LoRA https://huggingface.co/FinGPT/fingpt-forecaster_dow30_llama2-7b_lora
Shijia/furina_seed42_eng_amh_hau_cross_latin_2e-05	Shijia	2024-02-16T11:00:21Z	89	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:yihongLiu/furina", "base_model:finetune:yihongLiu/furina", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-16T10:59:27Z	--- base_model: yihongLiu/furina tags: - generated_from_trainer model-index: - name: furina_seed42_eng_amh_hau_cross_latin_2e-05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # furina_seed42_eng_amh_hau_cross_latin_2e-05 This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0213 - Spearman Corr: 0.7832 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Spearman Corr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------:\| \| No log \| 0.52 \| 200 \| 0.0297 \| 0.6717 \| \| No log \| 1.04 \| 400 \| 0.0242 \| 0.7298 \| \| No log \| 1.55 \| 600 \| 0.0238 \| 0.7583 \| \| 0.0472 \| 2.07 \| 800 \| 0.0234 \| 0.7528 \| \| 0.0472 \| 2.59 \| 1000 \| 0.0233 \| 0.7682 \| \| 0.0472 \| 3.11 \| 1200 \| 0.0210 \| 0.7792 \| \| 0.0472 \| 3.63 \| 1400 \| 0.0206 \| 0.7805 \| \| 0.0222 \| 4.15 \| 1600 \| 0.0211 \| 0.7751 \| \| 0.0222 \| 4.66 \| 1800 \| 0.0213 \| 0.7796 \| \| 0.0222 \| 5.18 \| 2000 \| 0.0207 \| 0.7920 \| \| 0.0222 \| 5.7 \| 2200 \| 0.0196 \| 0.7933 \| \| 0.0157 \| 6.22 \| 2400 \| 0.0204 \| 0.7839 \| \| 0.0157 \| 6.74 \| 2600 \| 0.0204 \| 0.7848 \| \| 0.0157 \| 7.25 \| 2800 \| 0.0216 \| 0.7791 \| \| 0.0157 \| 7.77 \| 3000 \| 0.0218 \| 0.7844 \| \| 0.0116 \| 8.29 \| 3200 \| 0.0214 \| 0.7887 \| \| 0.0116 \| 8.81 \| 3400 \| 0.0209 \| 0.7908 \| \| 0.0116 \| 9.33 \| 3600 \| 0.0208 \| 0.7768 \| \| 0.0116 \| 9.84 \| 3800 \| 0.0213 \| 0.7832 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_4_torch.bfloat16_32_32_0.01_2_0.002	ferrazzipietro	2024-02-16T10:59:48Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-16T10:59:25Z	--- library_name: transformers tags: [] --- # 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]
itsmefaiz/image_classification	itsmefaiz	2024-02-16T10:55:40Z	176	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-16T10:53:19Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: image_classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1721 - Accuracy: 0.9420 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 52 \| 0.2860 \| 0.9179 \| \| No log \| 2.0 \| 104 \| 0.1864 \| 0.9517 \| \| No log \| 3.0 \| 156 \| 0.1810 \| 0.9565 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_4_torch.bfloat16_32_32_0.01_2_0.0002	ferrazzipietro	2024-02-16T10:53:27Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-16T10:53:04Z	--- library_name: transformers tags: [] --- # 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]
itsmefaiz/audio_classification	itsmefaiz	2024-02-16T10:49:39Z	148	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2024-02-16T10:46:05Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: audio_classification results: - task: name: Audio Classification type: audio-classification dataset: name: minds14 type: minds14 config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.08849557522123894 --- <!-- 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. --> # audio_classification This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6427 - Accuracy: 0.0885 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 15 \| 2.6419 \| 0.0885 \| \| No log \| 2.0 \| 30 \| 2.6427 \| 0.0885 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
Noel-lawrence/PPO-LunarLander-v2-TEST	Noel-lawrence	2024-02-16T10:48:18Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-16T10:47:49Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 250.56 +/- 17.51 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_4_torch.bfloat16_32_32_0.05_8_0.002	ferrazzipietro	2024-02-16T10:47:05Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-16T10:46:43Z	--- library_name: transformers tags: [] --- # 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]
basab1142/a2c-PandaReachDense-v3	basab1142	2024-02-16T10:43:08Z	4	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-16T10:38:49Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.20 +/- 0.09 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_4_torch.bfloat16_32_32_0.05_8_0.0002	ferrazzipietro	2024-02-16T10:41:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-16T10:40:41Z	--- library_name: transformers tags: [] --- # 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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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. 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sam749/Airavata-GGUF	sam749	2024-02-16T10:37:00Z	11	2	transformers	[ "transformers", "safetensors", "gguf", "llama", "text-generation", "multilingual", "instruction-tuning", "llama2", "en", "hi", "dataset:ai4bharat/indic-instruct-data-v0.1", "arxiv:2401.15006", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-16T10:09:13Z	--- language: - en - hi license: llama2 tags: - multilingual - instruction-tuning - llama2 datasets: - ai4bharat/indic-instruct-data-v0.1 --- # Airavata 7B GGUF (Q4_K_M) This model is a 7B [OpenHathi](https://huggingface.co/sarvamai/OpenHathi-7B-Hi-v0.1-Base) model finetuned on [IndicInstruct dataset](https://huggingface.co/datasets/ai4bharat/indic-instruct-data-v0.1) which is a collection of instruction datasets (Anudesh, wikiHow, Flan v2, Dolly, Anthropic-HHH, OpenAssistant v1, and LymSys-Chat). Please check the corresponding huggingface dataset card for more details. This was trained as part of the technical report [Airavata: Introducing Hindi Instruction-tuned LLM](https://arxiv.org/abs/2401.15006). The codebase used to train and evaluate this model can be found at [https://github.com/AI4Bharat/IndicInstruct](https://github.com/AI4Bharat/IndicInstruct). ## Usage Clone [https://github.com/AI4Bharat/IndicInstruct](https://github.com/AI4Bharat/IndicInstruct) and install the required dependencies. Then download or clone this model to the same machine. ## Input Format The model is trained to use the chat format similar to [open-instruct code repository](https://github.com/allenai/open-instruct) (note the newlines): ``` <\|user\|> Your message here! <\|assistant\|> ``` For best results, format all inputs in this manner. Make sure to include a newline after `<\|assistant\|>`, this can affect generation quality quite a bit. ## Hyperparameters We fine-tune OpenHathi base model on the aforementioned IndicInstruct dataset with LoRA. The hyperparameters for the LoRA fine-tuning are listed below: - LoRA Rank: 16 - LoRA alpha: 32 - LoRA Dropout: 0.05 - LoRA Target Modules: ["q_proj", "v_proj", "k_proj", "down_proj", "gate_proj", "up_proj"] - Epochs: 4 - Learning rate: 5e-4 - Batch Size: 128 - Floating Point Precision: bfloat16 We recommend the readers to check out [our official blog post](https://ai4bharat.github.io/airavata) for more details on the model training, ablations and evaluation results. ## Example ```python3 import torch from transformers import AutoTokenizer, AutoModelForCausalLM device = "cuda" if torch.cuda.is_available() else "cpu" def create_prompt_with_chat_format(messages, bos="<s>", eos="</s>", add_bos=True): formatted_text = "" for message in messages: if message["role"] == "system": formatted_text += "<\|system\|>\n" + message["content"] + "\n" elif message["role"] == "user": formatted_text += "<\|user\|>\n" + message["content"] + "\n" elif message["role"] == "assistant": formatted_text += "<\|assistant\|>\n" + message["content"].strip() + eos + "\n" else: raise ValueError( "Tulu chat template only supports 'system', 'user' and 'assistant' roles. Invalid role: {}.".format( message["role"] ) ) formatted_text += "<\|assistant\|>\n" formatted_text = bos + formatted_text if add_bos else formatted_text return formatted_text def inference(input_prompts, model, tokenizer): input_prompts = [ create_prompt_with_chat_format([{"role": "user", "content": input_prompt}], add_bos=False) for input_prompt in input_prompts ] encodings = tokenizer(input_prompts, padding=True, return_tensors="pt") encodings = encodings.to(device) with torch.inference_mode(): outputs = model.generate(encodings.input_ids, do_sample=False, max_new_tokens=250) output_texts = tokenizer.batch_decode(outputs.detach(), skip_special_tokens=True) input_prompts = [ tokenizer.decode(tokenizer.encode(input_prompt), skip_special_tokens=True) for input_prompt in input_prompts ] output_texts = [output_text[len(input_prompt) :] for input_prompt, output_text in zip(input_prompts, output_texts)] return output_texts model_name = "ai4bharat/Airavata" tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left") tokenizer.pad_token = tokenizer.eos_token model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to(device) input_prompts = [ "मैं अपने समय प्रबंधन कौशल को कैसे सुधार सकता हूँ? मुझे पांच बिंदु बताएं।", "मैं अपने समय प्रबंधन कौशल को कैसे सुधार सकता हूँ? मुझे पांच बिंदु बताएं और उनका वर्णन करें।", ] outputs = inference(input_prompts, model, tokenizer) print(outputs) ``` ## Citation ```bibtex @article{gala2024airavata, title = {Airavata: Introducing Hindi Instruction-tuned LLM}, author = {Jay Gala and Thanmay Jayakumar and Jaavid Aktar Husain and Aswanth Kumar M and Mohammed Safi Ur Rahman Khan and Diptesh Kanojia and Ratish Puduppully and Mitesh M. Khapra and Raj Dabre and Rudra Murthy and Anoop Kunchukuttan}, year = {2024}, journal = {arXiv preprint arXiv: 2401.15006} } ```
stablediffusionapi/hair-base-inpainting	stablediffusionapi	2024-02-16T10:36:13Z	32	0	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-16T10:34:24Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # API Inference ![generated from modelslab.com](https://cdn2.stablediffusionapi.com/generations/bf190b5a-fe19-437c-ba05-82f29cb1f7ad-0.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "hair-base-inpainting" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs) Try model for free: [Generate Images](https://modelslab.com/models/hair-base-inpainting) Model link: [View model](https://modelslab.com/models/hair-base-inpainting) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "hair-base-inpainting", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_4_torch.bfloat16_32_32_0.05_4_0.002	ferrazzipietro	2024-02-16T10:34:57Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-16T10:34:34Z	--- library_name: transformers tags: [] --- # 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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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]
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_4_torch.bfloat16_32_32_0.05_4_0.0002	ferrazzipietro	2024-02-16T10:28:37Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-16T10:28:15Z	--- library_name: transformers tags: [] --- # 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. 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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. 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Ahmed007/VIT_ara_gpt2	Ahmed007	2024-02-16T10:27:51Z	34	0	transformers	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-02-16T10:27:21Z	--- tags: - generated_from_trainer model-index: - name: VIT_ara_gpt2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # VIT_ara_gpt2 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 3.3934 - eval_rouge2_precision: 0.0 - eval_rouge2_recall: 0.0 - eval_rouge2_fmeasure: 0.0 - eval_runtime: 1818.7738 - eval_samples_per_second: 1.329 - eval_steps_per_second: 0.042 - epoch: 1.0 - step: 1360 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1024 - num_epochs: 3 ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
unanam/smallloraft-v4	unanam	2024-02-16T10:23:41Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai/whisper-small", "base_model:adapter:openai/whisper-small", "region:us" ]	null	2024-02-16T08:52:05Z	--- library_name: peft base_model: openai/whisper-small --- # 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. --> - 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. 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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. 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shanhy/xlm-roberta-base_lr2e-05_seed42_basic_original_esp-kin-eng_train	shanhy	2024-02-16T10:21:39Z	100	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-16T10:20:31Z	--- license: mit base_model: FacebookAI/xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlm-roberta-base_lr2e-05_seed42_basic_original_esp-kin-eng_train results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base_lr2e-05_seed42_basic_original_esp-kin-eng_train This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0284 - Spearman Corr: 0.7240 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Spearman Corr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------:\| \| No log \| 1.63 \| 200 \| 0.0303 \| 0.6450 \| \| 0.0362 \| 3.27 \| 400 \| 0.0274 \| 0.6899 \| \| 0.0204 \| 4.9 \| 600 \| 0.0238 \| 0.6971 \| \| 0.0161 \| 6.53 \| 800 \| 0.0278 \| 0.7107 \| \| 0.0124 \| 8.16 \| 1000 \| 0.0294 \| 0.7245 \| \| 0.0124 \| 9.8 \| 1200 \| 0.0284 \| 0.7332 \| \| 0.0093 \| 11.43 \| 1400 \| 0.0294 \| 0.7160 \| \| 0.0075 \| 13.06 \| 1600 \| 0.0275 \| 0.7203 \| \| 0.0061 \| 14.69 \| 1800 \| 0.0267 \| 0.7250 \| \| 0.0053 \| 16.33 \| 2000 \| 0.0308 \| 0.7266 \| \| 0.0053 \| 17.96 \| 2200 \| 0.0316 \| 0.7213 \| \| 0.0049 \| 19.59 \| 2400 \| 0.0293 \| 0.7216 \| \| 0.0042 \| 21.22 \| 2600 \| 0.0284 \| 0.7240 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
AumBarai/ppo-Huggy	AumBarai	2024-02-16T10:21:32Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-02-16T10:21:28Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: AumBarai/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
sinux-l5d/results	sinux-l5d	2024-02-16T10:20:42Z	91	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-16T10:19:29Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results 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: - eval_loss: 0.0863 - eval_runtime: 129.7995 - eval_samples_per_second: 41.803 - eval_steps_per_second: 10.455 - epoch: 1.0 - step: 10853 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
shahzebnaveed/NeuralPipe-7B-slerp	shahzebnaveed	2024-02-16T10:17:31Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1218", "mlabonne/NeuralHermes-2.5-Mistral-7B", "base_model:OpenPipe/mistral-ft-optimized-1218", "base_model:merge:OpenPipe/mistral-ft-optimized-1218", "base_model:mlabonne/NeuralHermes-2.5-Mistral-7B", "base_model:merge:mlabonne/NeuralHermes-2.5-Mistral-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-16T10:13:20Z	--- tags: - merge - mergekit - lazymergekit - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B base_model: - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B --- # NeuralPipe-7B-slerp NeuralPipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218) * [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: OpenPipe/mistral-ft-optimized-1218 layer_range: [0, 32] - model: mlabonne/NeuralHermes-2.5-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: OpenPipe/mistral-ft-optimized-1218 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "shahzebnaveed/NeuralPipe-7B-slerp" 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"]) ```
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_4_torch.bfloat16_32_32_0.05_2_0.0002	ferrazzipietro	2024-02-16T10:16:01Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-16T10:15:38Z	--- library_name: transformers tags: [] --- # 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. 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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. 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Hamzaharman/imageclassification	Hamzaharman	2024-02-16T10:07:37Z	35	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-16T00:34:25Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: imageclassification results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.59375 --- <!-- 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. --> # imageclassification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1467 - Accuracy: 0.5938 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 40 \| 1.8113 \| 0.35 \| \| No log \| 2.0 \| 80 \| 1.5533 \| 0.3937 \| \| No log \| 3.0 \| 120 \| 1.4193 \| 0.4688 \| \| No log \| 4.0 \| 160 \| 1.3237 \| 0.5687 \| \| No log \| 5.0 \| 200 \| 1.2989 \| 0.4938 \| \| No log \| 6.0 \| 240 \| 1.2901 \| 0.5 \| \| No log \| 7.0 \| 280 \| 1.2380 \| 0.5625 \| \| No log \| 8.0 \| 320 \| 1.1773 \| 0.6125 \| \| No log \| 9.0 \| 360 \| 1.2149 \| 0.5625 \| \| No log \| 10.0 \| 400 \| 1.2280 \| 0.5312 \| \| No log \| 11.0 \| 440 \| 1.2326 \| 0.5625 \| \| No log \| 12.0 \| 480 \| 1.1488 \| 0.5875 \| \| 1.0601 \| 13.0 \| 520 \| 1.1597 \| 0.6062 \| \| 1.0601 \| 14.0 \| 560 \| 1.1953 \| 0.5563 \| \| 1.0601 \| 15.0 \| 600 \| 1.2011 \| 0.55 \| \| 1.0601 \| 16.0 \| 640 \| 1.2294 \| 0.55 \| \| 1.0601 \| 17.0 \| 680 \| 1.1972 \| 0.5687 \| \| 1.0601 \| 18.0 \| 720 \| 1.3043 \| 0.525 \| \| 1.0601 \| 19.0 \| 760 \| 1.2796 \| 0.525 \| \| 1.0601 \| 20.0 \| 800 \| 1.1781 \| 0.5813 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
hs4jk24erfc/test_fine_tuned_model	hs4jk24erfc	2024-02-16T10:04:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-16T10:03:14Z	--- library_name: transformers tags: [] --- # 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]
maramzarkaoui/facebook1	maramzarkaoui	2024-02-16T10:04:18Z	0	0	null	[ "safetensors", "autotrain", "text-generation", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-02-16T10:04:16Z	--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
shanhy/xlm-roberta-base_lr0.0001_seed42_basic_original_esp-kin-eng_train	shanhy	2024-02-16T10:04:02Z	89	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-16T10:03:04Z	--- license: mit base_model: FacebookAI/xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlm-roberta-base_lr0.0001_seed42_basic_original_esp-kin-eng_train results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base_lr0.0001_seed42_basic_original_esp-kin-eng_train This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0394 - Spearman Corr: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Spearman Corr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------:\| \| No log \| 1.63 \| 200 \| 0.0478 \| 0.0585 \| \| 0.059 \| 3.27 \| 400 \| 0.0393 \| 0.0110 \| \| 0.0534 \| 4.9 \| 600 \| 0.0413 \| nan \| \| 0.0524 \| 6.53 \| 800 \| 0.0384 \| nan \| \| 0.0517 \| 8.16 \| 1000 \| 0.0424 \| nan \| \| 0.0517 \| 9.8 \| 1200 \| 0.0384 \| nan \| \| 0.0509 \| 11.43 \| 1400 \| 0.0467 \| nan \| \| 0.0511 \| 13.06 \| 1600 \| 0.0404 \| nan \| \| 0.0505 \| 14.69 \| 1800 \| 0.0392 \| -0.0320 \| \| 0.05 \| 16.33 \| 2000 \| 0.0382 \| -0.0245 \| \| 0.05 \| 17.96 \| 2200 \| 0.0395 \| nan \| \| 0.0498 \| 19.59 \| 2400 \| 0.0389 \| -0.0672 \| \| 0.0498 \| 21.22 \| 2600 \| 0.0384 \| -0.0760 \| \| 0.05 \| 22.86 \| 2800 \| 0.0399 \| nan \| \| 0.0492 \| 24.49 \| 3000 \| 0.0387 \| nan \| \| 0.0493 \| 26.12 \| 3200 \| 0.0385 \| nan \| \| 0.0493 \| 27.76 \| 3400 \| 0.0392 \| nan \| \| 0.0491 \| 29.39 \| 3600 \| 0.0394 \| nan \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_4_torch.bfloat16_16_32_0.01_8_0.0002	ferrazzipietro	2024-02-16T10:03:41Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-16T10:03:28Z	--- library_name: transformers tags: [] --- # 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. 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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]
anjith672/dillu_high_train	anjith672	2024-02-16T10:01:58Z	1	1	diffusers	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2024-02-16T07:31:37Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: dil tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was trained.
sadhaklal/bert-base-uncased-finetuned-mrpc-v2	sadhaklal	2024-02-16T09:59:17Z	92	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "en", "dataset:glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-14T10:14:44Z	--- license: apache-2.0 datasets: - glue language: - en metrics: - accuracy - f1 library_name: transformers pipeline_tag: text-classification widget: - text: The company didn 't detail the costs of the replacement and repairs . [SEP] But company officials expect the costs of the replacement work to run into the millions of dollars . example_title: not_equivalent - text: According to the federal Centers for Disease Control and Prevention ( news - web sites ) , there were 19 reported cases of measles in the United States in 2002 . [SEP] The Centers for Disease Control and Prevention said there were 19 reported cases of measles in the United States in 2002 . example_title: equivalent --- # bert-base-uncased-finetuned-mrpc-v2 BERT (`"bert-base-uncased"`) finetuned on MRPC (Microsoft Research Paraphrase Corpus). The model predicts whether two sentences are semantically equivalent. It pertains to section 4 of chapter 3 of the Hugging Face "NLP Course" (https://huggingface.co/learn/nlp-course/chapter3/4). It was trained using a custom PyTorch loop with Hugging Face Accelerate. Code: https://github.com/sambitmukherjee/huggingface-notebooks/blob/main/course/en/chapter3/section4.ipynb Experiment tracking: https://wandb.ai/sadhaklal/bert-base-uncased-finetuned-mrpc-v2 ## Usage ``` from transformers import pipeline classifier = pipeline("text-classification", model="sadhaklal/bert-base-uncased-finetuned-mrpc-v2") sentence1 = "A tropical storm rapidly developed in the Gulf of Mexico Sunday and was expected to hit somewhere along the Texas or Louisiana coasts by Monday night ." sentence2 = "A tropical storm rapidly developed in the Gulf of Mexico on Sunday and could have hurricane-force winds when it hits land somewhere along the Louisiana coast Monday night ." sentence_pair = sentence1 + " [SEP] " + sentence2 print(classifier(sentence_pair)) sentence1 = "The settling companies would also assign their possible claims against the underwriters to the investor plaintiffs , he added ." sentence2 = "Under the agreement , the settling companies will also assign their potential claims against the underwriters to the investors , he added ." sentence_pair = sentence1 + " [SEP] " + sentence2 print(classifier(sentence_pair)) ``` ## Dataset From the dataset page: > The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent. Examples: https://huggingface.co/datasets/glue/viewer/mrpc ## Metrics Accuracy on the 'validation' split of MRPC: 0.875 F1 on the 'validation' split of MRPC: 0.9128
chienweichang/Breeze-7B-Instruct-v0_1-TaiwanChat	chienweichang	2024-02-16T09:55:20Z	7	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:MediaTek-Research/Breeze-7B-Instruct-v0_1", "base_model:finetune:MediaTek-Research/Breeze-7B-Instruct-v0_1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-02-16T09:08:24Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: MediaTek-Research/Breeze-7B-Instruct-v0_1 --- # Uploaded model - Developed by: chienweichang - License: apache-2.0 - Finetuned from model : MediaTek-Research/Breeze-7B-Instruct-v0_1 This mistral 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)
Morningheart/commentary-generator	Morningheart	2024-02-16T09:52:54Z	64	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-15T15:30:05Z	--- library_name: transformers tags: [] widget: - text: "My name is Julien and I like to" example_title: "Julien" - text: "My name is Merve and my favorite" example_title: "Merve" --- # 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]
logicker/SkkuDS-DPO-72B-v1	logicker	2024-02-16T09:51:54Z	52	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "pretrained, dpo", "conversational", "en", "arxiv:2309.16609", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-15T08:14:26Z	--- license: other license_name: tongyi-qianwen license_link: >- https://huggingface.co/Qwen/Qwen1.5-72B/blob/main/LICENSE language: - en pipeline_tag: text-generation tags: - pretrained, dpo --- # Qwen1.5-72B ## DPO Tuning - Dataset: Intel/orca_dpo_pairs ## Introduction Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include: * Multilingual support of both base and chat models; * Stable support of 32K context length for models of all sizes * No need of `trust_remote_code`. For more details, please refer to [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5). ## Model Details Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA and the mixture of SWA and full attention. ## Requirements The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error: ``` KeyError: 'qwen2'. ``` ## Citation ``` @article{qwen, title={Qwen Technical Report}, author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu}, journal={arXiv preprint arXiv:2309.16609}, year={2023} } ```
hewonty/longformer-ner-finetuned-pii	hewonty	2024-02-16T09:51:32Z	91	1	transformers	[ "transformers", "tensorboard", "safetensors", "longformer", "token-classification", "generated_from_trainer", "base_model:allenai/longformer-base-4096", "base_model:finetune:allenai/longformer-base-4096", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-02-15T15:02:46Z	--- license: apache-2.0 base_model: allenai/longformer-base-4096 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: longformer-ner-finetuned-pii results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # longformer-ner-finetuned-pii This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0034 - Precision: 0.9772 - Recall: 0.9879 - F1: 0.9825 - Accuracy: 0.9993 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0063 \| 1.0 \| 1324 \| 0.0050 \| 0.9613 \| 0.9842 \| 0.9726 \| 0.9990 \| \| 0.0037 \| 2.0 \| 2648 \| 0.0038 \| 0.9735 \| 0.9873 \| 0.9803 \| 0.9992 \| \| 0.002 \| 3.0 \| 3972 \| 0.0034 \| 0.9772 \| 0.9879 \| 0.9825 \| 0.9993 \| ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
qkrwnstj/lora_mid_journal_1	qkrwnstj	2024-02-16T09:50:39Z	3	1	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-02-16T08:43:31Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - qkrwnstj/lora_mid_journal_1 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the qkrwnstj/mid-captioning-dataset dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
mindlywork/BandW2Char	mindlywork	2024-02-16T09:46:24Z	34	1	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:cc", "region:us" ]	text-to-image	2024-02-16T09:43:27Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: BandW2Char output: url: images/l1DJDaP05ESt6MK9MtxN5bJj (1).png base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: BandW2Char license: cc --- # BandW2Char <Gallery /> ## Model description BandW2Char ## Trigger words You should use `BandW2Char` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/dasdsff/BandW2Char/tree/main) them in the Files & versions tab.
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_4_torch.bfloat16_16_32_0.01_2_0.0002	ferrazzipietro	2024-02-16T09:39:20Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-16T09:39:08Z	--- library_name: transformers tags: [] --- # 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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ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_4_torch.bfloat16_16_32_0.05_8_0.002	ferrazzipietro	2024-02-16T09:33:11Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-16T09:32:57Z	--- library_name: transformers tags: [] --- # 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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thrunlab/Mistral_Sparse_refined_web_50p_2024-02-16	thrunlab	2024-02-16T09:28:06Z	6	0	transformers	[ "transformers", "safetensors", "sparse_mistral", "text-generation", "generated_from_trainer", "custom_code", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-generation	2024-02-16T08:22:09Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer model-index: - name: Mistral_Sparse_refined_web_50p_2024-02-16 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Mistral_Sparse_refined_web_50p_2024-02-16 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1260 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 0 - distributed_type: multi-GPU - num_devices: 3 - gradient_accumulation_steps: 3 - total_train_batch_size: 9 - total_eval_batch_size: 3 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.5975 \| 0.01 \| 25 \| 2.6362 \| \| 2.3082 \| 0.01 \| 50 \| 2.5659 \| \| 2.4024 \| 0.02 \| 75 \| 2.5151 \| \| 2.3358 \| 0.02 \| 100 \| 2.4817 \| \| 2.2267 \| 0.03 \| 125 \| 2.4660 \| \| 2.271 \| 0.04 \| 150 \| 2.4456 \| \| 2.1709 \| 0.04 \| 175 \| 2.4413 \| \| 2.2549 \| 0.05 \| 200 \| 2.4306 \| \| 2.2536 \| 0.05 \| 225 \| 2.4243 \| \| 2.2234 \| 0.06 \| 250 \| 2.4212 \| \| 2.2516 \| 0.07 \| 275 \| 2.4202 \| \| 2.2827 \| 0.07 \| 300 \| 2.4146 \| \| 2.1774 \| 0.08 \| 325 \| 2.4156 \| \| 2.278 \| 0.08 \| 350 \| 2.4094 \| \| 2.204 \| 0.09 \| 375 \| 2.4088 \| \| 2.1987 \| 0.1 \| 400 \| 2.4073 \| \| 2.1985 \| 0.1 \| 425 \| 2.4041 \| \| 2.2198 \| 0.11 \| 450 \| 2.4069 \| \| 2.2555 \| 0.11 \| 475 \| 2.4014 \| \| 2.1567 \| 0.12 \| 500 \| 2.4017 \| \| 2.2918 \| 0.13 \| 525 \| 2.3998 \| \| 2.2559 \| 0.13 \| 550 \| 2.3959 \| \| 2.2234 \| 0.14 \| 575 \| 2.3978 \| \| 2.2001 \| 0.14 \| 600 \| 2.3944 \| \| 2.1409 \| 0.15 \| 625 \| 2.3957 \| \| 2.2034 \| 0.16 \| 650 \| 2.3981 \| \| 2.1863 \| 0.16 \| 675 \| 2.3941 \| \| 2.2372 \| 0.17 \| 700 \| 2.3936 \| \| 2.2438 \| 0.17 \| 725 \| 2.3953 \| \| 2.2172 \| 0.18 \| 750 \| 2.3943 \| \| 2.1917 \| 0.19 \| 775 \| 2.3921 \| \| 2.1137 \| 0.19 \| 800 \| 2.3912 \| \| 2.0766 \| 0.07 \| 825 \| 2.3935 \| \| 2.1926 \| 0.08 \| 850 \| 2.3913 \| \| 2.2948 \| 0.08 \| 875 \| 2.3915 \| \| 2.1349 \| 0.08 \| 900 \| 2.3917 \| \| 2.2446 \| 0.08 \| 925 \| 2.3876 \| \| 2.253 \| 0.09 \| 950 \| 2.3880 \| \| 2.0729 \| 0.09 \| 975 \| 2.3890 \| \| 2.1965 \| 0.09 \| 1000 \| 2.3873 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
chaouch/distilhubert-finetuned-gtzan-finetuned-gtzan	chaouch	2024-02-16T09:26:52Z	146	0	transformers	[ "transformers", "tensorboard", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:chaouch/distilhubert-finetuned-gtzan", "base_model:finetune:chaouch/distilhubert-finetuned-gtzan", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2024-02-16T07:59:54Z	--- license: apache-2.0 base_model: chaouch/distilhubert-finetuned-gtzan tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan-finetuned-gtzan2 results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.85 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilhubert-finetuned-gtzan-finetuned-gtzan2 This model is a fine-tuned version of [chaouch/distilhubert-finetuned-gtzan](https://huggingface.co/chaouch/distilhubert-finetuned-gtzan) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.7901 - Accuracy: 0.85 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0776 \| 1.0 \| 57 \| 0.7597 \| 0.81 \| \| 0.0272 \| 2.0 \| 114 \| 0.6991 \| 0.84 \| \| 0.0132 \| 3.0 \| 171 \| 0.7621 \| 0.85 \| \| 0.0089 \| 4.0 \| 228 \| 0.7702 \| 0.85 \| \| 0.0071 \| 5.0 \| 285 \| 0.7901 \| 0.85 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
ArthurZ/rwkv-5	ArthurZ	2024-02-16T09:24:17Z	0	1	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-16T09:20:58Z	--- library_name: transformers tags: [] --- # 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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Azma-AI/azma-open-hermes-2.5-mistral-7B-conversation-agent-v1	Azma-AI	2024-02-16T09:23:44Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "dataset:Azma-AI/azma-final-conversation-dataset", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-16T08:54:19Z	--- library_name: transformers datasets: - Azma-AI/azma-final-conversation-dataset --- # 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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(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]
nold/OpenMath-Mistral-7B-v0.1-hf-GGUF	nold	2024-02-16T09:21:21Z	50	0	null	[ "gguf", "nvidia", "code", "math", "en", "dataset:nvidia/OpenMathInstruct-1", "arxiv:2402.10176", "base_model:mistralai/Mistral-7B-v0.1", "base_model:quantized:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-02-16T07:41:27Z	--- license: apache-2.0 base_model: - mistralai/Mistral-7B-v0.1 datasets: - nvidia/OpenMathInstruct-1 language: - en tags: - nvidia - code - math --- # OpenMath-Mistral-7B-v0.1-hf OpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks executed by Python interpreter. The models were trained on [OpenMathInstruct-1](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1), a math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed [Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) model. <table border="1"> <tr> <td></td> <td colspan="2" style="text-align: center;">greedy</td> <td colspan="2" style="text-align: center;">majority@50</td> </tr> <tr> <td style="text-align: center;">model</td> <td style="text-align: center;">GSM8K</td> <td style="text-align: center;">MATH</td> <td style="text-align: center;">GMS8K</td> <td style="text-align: center;">MATH</td> </tr> <tr> <td style="text-align: right;">OpenMath-CodeLlama-7B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python">nemo</a> \| <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python-hf">HF</a>)</td> <td style="text-align: center;">75.9</td> <td style="text-align: center;">43.6</td> <td style="text-align: center;">84.8</td> <td style="text-align: center;">55.6</td> </tr> <tr> <td style="text-align: right;">OpenMath-Mistral-7B (<a href="https://huggingface.co/nvidia/OpenMath-Mistral-7B-v0.1">nemo</a> \| <a href="https://huggingface.co/nvidia/OpenMath-Mistral-7B-v0.1-hf">HF</a>)</td> <td style="text-align: center;">80.2</td> <td style="text-align: center;">44.5</td> <td style="text-align: center;">86.9</td> <td style="text-align: center;">57.2</td> </tr> <tr> <td style="text-align: right;">OpenMath-CodeLlama-13B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-13b-Python">nemo</a> \| <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-13b-Python-hf">HF</a>)</td> <td style="text-align: center;">78.8</td> <td style="text-align: center;">45.5</td> <td style="text-align: center;">86.8</td> <td style="text-align: center;">57.6</td> </tr> <tr> <td style="text-align: right;">OpenMath-CodeLlama-34B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python">nemo</a> \| <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python-hf">HF</a>)</td> <td style="text-align: center;">80.7</td> <td style="text-align: center;">48.3</td> <td style="text-align: center;">88.0</td> <td style="text-align: center;">60.2</td> </tr> <tr> <td style="text-align: right;">OpenMath-Llama2-70B (<a href="https://huggingface.co/nvidia/OpenMath-Llama-2-70b">nemo</a> \| <a href="https://huggingface.co/nvidia/OpenMath-Llama-2-70b-hf">HF</a>)</td> <td style="text-align: center;"><b>84.7</b></td> <td style="text-align: center;">46.3</td> <td style="text-align: center;">90.1</td> <td style="text-align: center;">58.3</td> </tr> <tr> <td style="text-align: right;">OpenMath-CodeLlama-70B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-70b-Python">nemo</a> \| <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-70b-Python-hf">HF</a>)</td> <td style="text-align: center;">84.6</td> <td style="text-align: center;"><b>50.7</b></td> <td style="text-align: center;"><b>90.8</b></td> <td style="text-align: center;"><b>60.4</b></td> </tr> </table> The pipeline we used to produce these models is fully open-sourced! - [Code](https://github.com/Kipok/NeMo-Skills) - [Models](https://huggingface.co/collections/nvidia/openmath-65c5619de2ba059be0775014) - [Dataset](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1) See our [paper](https://arxiv.org/abs/2402.10176) for more details! # How to use the models? Try to [run inference with our models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/inference.md) with just a few commands! # Reproducing our results We provide [all instructions](https://github.com/Kipok/NeMo-Skills/blob/main/docs/reproducing-results.md) to fully reproduce our results. # Improving other models To improve other models or to learn more about our code, read through the docs below. - [NeMo-Skills Pipeline](https://github.com/Kipok/NeMo-Skills) - [Generating synthetic data](https://github.com/Kipok/NeMo-Skills/blob/main/docs/synthetic-data-generation.md) - [Finetuning models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/finetuning.md) - [Evaluating models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/evaluation.md) In our pipeline we use [NVIDIA NeMo](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/), an end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere. It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models, offering enterprises an easy, cost-effective, and fast way to adopt generative AI. # Citation If you find our work useful, please consider citing us! ```bibtex @article{toshniwal2024openmath, title = {OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset}, author = {Shubham Toshniwal and Ivan Moshkov and Sean Narenthiran and Daria Gitman and Fei Jia and Igor Gitman}, year = {2024}, journal = {arXiv preprint arXiv: Arxiv-2402.10176} } ``` *** Quantization of Model [nvidia/OpenMath-Mistral-7B-v0.1-hf](https://huggingface.co/nvidia/OpenMath-Mistral-7B-v0.1-hf). Created using [llm-quantizer](https://github.com/Nold360/llm-quantizer) Pipeline
seatond/EXTRACTION_rank64_lr2.2e-05_target7_epochs50step_laplha128_batch1_gradacc4	seatond	2024-02-16T09:21:20Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "arxiv:1910.09700", "base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "region:us" ]	null	2024-02-16T09:02:17Z	--- library_name: peft base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ --- # 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. --> - 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. 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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] ### Framework versions - PEFT 0.7.1.dev0
HenryNgan/mistral_7b_now_news	HenryNgan	2024-02-16T09:20:59Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-16T09:16:41Z	--- library_name: transformers tags: [] --- # 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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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. 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ggomma/aika-lora-dreambooth-test-f2b2b1b5-bf99-41e8-83be-60985e953bc0	ggomma	2024-02-16T09:20:41Z	1	0	diffusers	[ "diffusers", "text-to-image", "lora", "stable-diffusion", "stable-diffusion-diffusers", "base_model:KantoRegion/99mix-converted", "base_model:adapter:KantoRegion/99mix-converted", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-02-16T09:01:06Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - diffusers - lora - stable-diffusion - stable-diffusion-diffusers inference: true base_model: ggomma/test instance_prompt: '"An image of Aika"--resolution=512' --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA DreamBooth - ggomma/aika-lora-dreambooth-test-f2b2b1b5-bf99-41e8-83be-60985e953bc0 These are LoRA adaption weights for ggomma/test. The weights were trained on "An image of Aika"--resolution=512 using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Menouar/saqr-7b-merged	Menouar	2024-02-16T09:20:31Z	233	1	transformers	[ "transformers", "safetensors", "falcon", "text-generation", "saqr-7b-instrcut", "Pytorch", "conversational", "custom_code", "en", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:openbmb/UltraFeedback", "dataset:gsm8k", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-16T09:10:53Z	--- library_name: transformers tags: - saqr-7b-instrcut - Pytorch license: apache-2.0 datasets: - HuggingFaceH4/ultrachat_200k - openbmb/UltraFeedback - gsm8k language: - en pipeline_tag: text-generation --- # saqr-7b-merged This model is a merged version of [saqr-7b-instruct](https://huggingface.co/Menouar/saqr-7b-instruct) with LoRA Adapters. <img src="https://huggingface.co/Menouar/saqr-7b-instruct/resolve/main/saqr.jpg" alt="Saqr Logo" width="800" style="margin-left:auto; margin-right:auto; display:block;"/>
Jise/controlnet-wireframe	Jise	2024-02-16T09:18:37Z	1	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "controlnet", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-11-02T03:18:32Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet base_model: runwayml/stable-diffusion-v1-5 inference: true --- # controlnet-Jise/controlnet-wireframe These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images below. prompt: Interior design ![images_0)](./images_0.png)
Reshphil/lab1_finetuning	Reshphil	2024-02-16T09:16:48Z	118	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "dataset:kde4", "base_model:Helsinki-NLP/opus-mt-en-fr", "base_model:finetune:Helsinki-NLP/opus-mt-en-fr", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-15T09:05:21Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-fr tags: - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: marian-finetuned-kde4-en-to-fr results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 config: en-fr split: train args: en-fr metrics: - name: Bleu type: bleu value: 52.88398487672078 --- <!-- 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. --> # marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8556 - Bleu: 52.8840 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_4_torch.bfloat16_16_32_0.05_4_0.0002	ferrazzipietro	2024-02-16T09:15:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-16T09:15:01Z	--- library_name: transformers tags: [] --- # 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. 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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. 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muyuanma/marian-finetuned-kde4-en-to-fr	muyuanma	2024-02-16T09:12:40Z	118	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "dataset:kde4", "base_model:Helsinki-NLP/opus-mt-en-fr", "base_model:finetune:Helsinki-NLP/opus-mt-en-fr", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-16T07:12:00Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-fr tags: - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: marian-finetuned-kde4-en-to-fr results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 config: en-fr split: train args: en-fr metrics: - name: Bleu type: bleu value: 53.03371127884619 --- <!-- 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. --> # marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8558 - Bleu: 53.0337 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_4_torch.bfloat16_16_32_0.05_2_0.002	ferrazzipietro	2024-02-16T09:09:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-16T09:08:49Z	--- library_name: transformers tags: [] --- # 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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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. 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ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_4_torch.bfloat16_16_32_0.05_2_0.0002	ferrazzipietro	2024-02-16T09:02:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-16T09:02:35Z	--- library_name: transformers tags: [] --- # 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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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. 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rAIfle/ohno-8x7B-exl2-rpcal	rAIfle	2024-02-16T08:54:25Z	0	0	null	[ "region:us" ]	null	2024-02-15T13:28:29Z	same deal as always, 200 rows, 8192 tokens, PIPPA, yadayada. home-merged, check [rAIfle/ohno-8x7B-fp16](https://huggingface.co/rAIfle/ohno-8x7B-fp16) for source. no clue what prompt to use, nor what settings. model deemed fit for human consumption, at least, though it is pretty braindamaged.
rAIfle/ohno-8x7B-fp16	rAIfle	2024-02-16T08:53:58Z	8	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora", "base_model:merge:Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora", "base_model:Envoid/Mixtral-Instruct-ITR-8x7B", "base_model:merge:Envoid/Mixtral-Instruct-ITR-8x7B", "base_model:NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss", "base_model:merge:NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss", "base_model:retrieval-bar/Mixtral-8x7B-v0.1_case-briefs", "base_model:merge:retrieval-bar/Mixtral-8x7B-v0.1_case-briefs", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-15T11:33:35Z	--- base_model: - Envoid/Mixtral-Instruct-ITR-8x7B - Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora - Envoid/Mixtral-Instruct-ITR-8x7B - retrieval-bar/Mixtral-8x7B-v0.1_case-briefs - NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss - Envoid/Mixtral-Instruct-ITR-8x7B tags: - mergekit - merge --- # ohno-8x7b this... will either be my magnum opus... or terrible. no inbetweens! Post-test verdict: It's mostly braindamaged. Might be my settings or something, idk. the `./output` mentioned below is my own merge using identical recipe as [Envoid/Mixtral-Instruct-ITR-8x7B](https://huggingface.co/Envoid/Mixtral-Instruct-ITR-8x7B). # output_merge2 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 [Envoid/Mixtral-Instruct-ITR-8x7B](https://huggingface.co/Envoid/Mixtral-Instruct-ITR-8x7B) as a base. ### Models Merged The following models were included in the merge: * ./output/ + /ai/LLM/tmp/pefts/daybreak-peft/mixtral-8x7b * [Envoid/Mixtral-Instruct-ITR-8x7B](https://huggingface.co/Envoid/Mixtral-Instruct-ITR-8x7B) + [Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora](https://huggingface.co/Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora) * [Envoid/Mixtral-Instruct-ITR-8x7B](https://huggingface.co/Envoid/Mixtral-Instruct-ITR-8x7B) + [retrieval-bar/Mixtral-8x7B-v0.1_case-briefs](https://huggingface.co/retrieval-bar/Mixtral-8x7B-v0.1_case-briefs) * [NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss](https://huggingface.co/NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: ./output/+/ai/LLM/tmp/pefts/daybreak-peft/mixtral-8x7b parameters: density: 0.66 weight: 1.0 - model: Envoid/Mixtral-Instruct-ITR-8x7B+retrieval-bar/Mixtral-8x7B-v0.1_case-briefs parameters: density: 0.1 weight: 0.25 - model: Envoid/Mixtral-Instruct-ITR-8x7B+Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora parameters: density: 0.66 weight: 0.5 - model: NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss parameters: density: 0.15 weight: 0.3 merge_method: dare_ties base_model: Envoid/Mixtral-Instruct-ITR-8x7B dtype: float16 ```
DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel-MISTRAL-notrescaled_30000	DragosGorduza	2024-02-16T08:49:03Z	46	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-16T08:48:23Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 64875 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `gpl.toolkit.loss.MarginDistillationLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 80000, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
golesheed/whisper-5-dutch	golesheed	2024-02-16T08:48:27Z	86	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "nl", "base_model:openai/whisper-large-v2", "base_model:finetune:openai/whisper-large-v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-15T21:46:22Z	--- language: - nl license: apache-2.0 base_model: openai/whisper-large-v2 tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper Large V2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Large V2 This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2801 - Wer: 9.8223 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 20 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.5984 \| 0.09 \| 30 \| 0.3378 \| 18.2664 \| \| 0.3397 \| 0.19 \| 60 \| 0.3041 \| 17.6296 \| \| 0.3062 \| 0.28 \| 90 \| 0.2931 \| 16.5683 \| \| 0.2995 \| 0.38 \| 120 \| 0.2821 \| 12.3463 \| \| 0.2783 \| 0.47 \| 150 \| 0.2799 \| 12.5440 \| \| 0.2946 \| 0.57 \| 180 \| 0.2626 \| 12.3870 \| \| 0.2615 \| 0.66 \| 210 \| 0.2652 \| 11.8054 \| \| 0.2456 \| 0.76 \| 240 \| 0.2654 \| 12.1282 \| \| 0.2446 \| 0.85 \| 270 \| 0.2579 \| 12.3172 \| \| 0.2836 \| 0.95 \| 300 \| 0.2494 \| 12.5149 \| \| 0.1951 \| 1.04 \| 330 \| 0.2531 \| 13.0267 \| \| 0.1233 \| 1.14 \| 360 \| 0.2517 \| 11.3402 \| \| 0.1219 \| 1.23 \| 390 \| 0.2512 \| 11.8926 \| \| 0.144 \| 1.33 \| 420 \| 0.2510 \| 11.6775 \| \| 0.1268 \| 1.42 \| 450 \| 0.2509 \| 11.9624 \| \| 0.1366 \| 1.52 \| 480 \| 0.2496 \| 13.8350 \| \| 0.134 \| 1.61 \| 510 \| 0.2467 \| 11.1279 \| \| 0.139 \| 1.71 \| 540 \| 0.2465 \| 12.2823 \| \| 0.1449 \| 1.8 \| 570 \| 0.2428 \| 13.7158 \| \| 0.1257 \| 1.9 \| 600 \| 0.2400 \| 12.9452 \| \| 0.1319 \| 1.99 \| 630 \| 0.2335 \| 13.2738 \| \| 0.066 \| 2.09 \| 660 \| 0.2525 \| 13.3291 \| \| 0.0517 \| 2.18 \| 690 \| 0.2553 \| 12.6225 \| \| 0.0595 \| 2.28 \| 720 \| 0.2530 \| 13.2273 \| \| 0.0638 \| 2.37 \| 750 \| 0.2533 \| 10.6452 \| \| 0.0612 \| 2.47 \| 780 \| 0.2565 \| 12.9191 \| \| 0.0633 \| 2.56 \| 810 \| 0.2512 \| 10.7935 \| \| 0.056 \| 2.66 \| 840 \| 0.2549 \| 13.8263 \| \| 0.0614 \| 2.75 \| 870 \| 0.2526 \| 11.1803 \| \| 0.062 \| 2.85 \| 900 \| 0.2555 \| 10.2585 \| \| 0.0586 \| 2.94 \| 930 \| 0.2456 \| 10.5900 \| \| 0.0462 \| 3.04 \| 960 \| 0.2538 \| 10.5754 \| \| 0.0225 \| 3.13 \| 990 \| 0.2629 \| 11.0320 \| \| 0.0244 \| 3.23 \| 1020 \| 0.2659 \| 10.0695 \| \| 0.0246 \| 3.32 \| 1050 \| 0.2651 \| 9.8863 \| \| 0.0217 \| 3.42 \| 1080 \| 0.2652 \| 10.0550 \| \| 0.0222 \| 3.51 \| 1110 \| 0.2625 \| 10.7063 \| \| 0.023 \| 3.61 \| 1140 \| 0.2660 \| 10.7470 \| \| 0.0236 \| 3.7 \| 1170 \| 0.2651 \| 9.9183 \| \| 0.0214 \| 3.8 \| 1200 \| 0.2642 \| 9.9386 \| \| 0.0208 \| 3.89 \| 1230 \| 0.2635 \| 9.9619 \| \| 0.021 \| 3.99 \| 1260 \| 0.2674 \| 9.9794 \| \| 0.0127 \| 4.08 \| 1290 \| 0.2700 \| 9.6391 \| \| 0.0096 \| 4.18 \| 1320 \| 0.2783 \| 9.6217 \| \| 0.0082 \| 4.27 \| 1350 \| 0.2822 \| 9.6799 \| \| 0.009 \| 4.37 \| 1380 \| 0.2799 \| 9.7729 \| \| 0.0077 \| 4.46 \| 1410 \| 0.2806 \| 9.9154 \| \| 0.007 \| 4.56 \| 1440 \| 0.2803 \| 9.7526 \| \| 0.0078 \| 4.65 \| 1470 \| 0.2805 \| 9.7264 \| \| 0.0083 \| 4.75 \| 1500 \| 0.2804 \| 9.8427 \| \| 0.0085 \| 4.84 \| 1530 \| 0.2807 \| 9.8543 \| \| 0.0081 \| 4.94 \| 1560 \| 0.2801 \| 9.8223 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.0
DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel-MISTRAL-notrescaled_40000	DragosGorduza	2024-02-16T08:48:22Z	49	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-16T08:47:38Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 64875 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `gpl.toolkit.loss.MarginDistillationLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 80000, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
shanhy/xlm-roberta-base_lr0.0001_seed42_basic_original_esp-hau-eng_train	shanhy	2024-02-16T08:48:02Z	101	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-16T08:46:59Z	--- license: mit base_model: FacebookAI/xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlm-roberta-base_lr0.0001_seed42_basic_original_esp-hau-eng_train results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base_lr0.0001_seed42_basic_original_esp-hau-eng_train This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0382 - Spearman Corr: 0.5403 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Spearman Corr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------:\| \| No log \| 1.45 \| 200 \| 0.0446 \| 0.0681 \| \| 0.0622 \| 2.91 \| 400 \| 0.0538 \| nan \| \| 0.0586 \| 4.36 \| 600 \| 0.0468 \| -0.0637 \| \| 0.0586 \| 5.82 \| 800 \| 0.0450 \| -0.0228 \| \| 0.0578 \| 7.27 \| 1000 \| 0.0468 \| nan \| \| 0.0569 \| 8.73 \| 1200 \| 0.0437 \| 0.1100 \| \| 0.0538 \| 10.18 \| 1400 \| 0.0431 \| 0.2566 \| \| 0.0538 \| 11.64 \| 1600 \| 0.0465 \| nan \| \| 0.0539 \| 13.09 \| 1800 \| 0.0447 \| 0.0202 \| \| 0.0558 \| 14.55 \| 2000 \| 0.0463 \| 0.1008 \| \| 0.0561 \| 16.0 \| 2200 \| 0.0456 \| nan \| \| 0.0561 \| 17.45 \| 2400 \| 0.0448 \| 0.0437 \| \| 0.0552 \| 18.91 \| 2600 \| 0.0473 \| 0.0297 \| \| 0.0512 \| 20.36 \| 2800 \| 0.0522 \| 0.1850 \| \| 0.0512 \| 21.82 \| 3000 \| 0.0379 \| 0.4856 \| \| 0.0401 \| 23.27 \| 3200 \| 0.0401 \| 0.4589 \| \| 0.0315 \| 24.73 \| 3400 \| 0.0396 \| 0.4645 \| \| 0.0277 \| 26.18 \| 3600 \| 0.0387 \| 0.4813 \| \| 0.0277 \| 27.64 \| 3800 \| 0.0376 \| 0.5494 \| \| 0.0258 \| 29.09 \| 4000 \| 0.0382 \| 0.5403 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel-MISTRAL-notrescaled_80000	DragosGorduza	2024-02-16T08:46:15Z	47	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-16T08:45:34Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 64875 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `gpl.toolkit.loss.MarginDistillationLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 80000, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel-MISTRAL-notrescaled_60000	DragosGorduza	2024-02-16T08:45:34Z	48	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-16T08:44:51Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 64875 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `gpl.toolkit.loss.MarginDistillationLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 80000, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel-MISTRAL-notrescaled_70000	DragosGorduza	2024-02-16T08:44:51Z	46	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-16T08:44:08Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 64875 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `gpl.toolkit.loss.MarginDistillationLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 80000, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
wesleyyip/pony	wesleyyip	2024-02-16T08:37:39Z	0	0	null	[ "license:other", "region:us" ]	null	2024-02-16T08:36:41Z	--- license: other license_name: other license_link: LICENSE ---
Haary/TinyLlama-1.1B-indo-v1	Haary	2024-02-16T08:35:13Z	96	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-15T09:58:05Z	--- license: llama2 --- # TinyLlama-1.1B-indo-v1 (Contoh Card Model) <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="margin-left: auto; margin-right: auto"> <img src="https://cdn.pixabay.com/photo/2023/01/13/22/11/ai-art-7716983_1280.jpg" alt="Sky" style="width: 300px; margin:auto"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> Base Model [TinyLlama/TinyLlama-1.1B-Chat-v1.0]("https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0") ## Python code example: inference from this GPTQ model ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install --upgrade transformers optimum # If using PyTorch 2.1 + CUDA 12.x: pip3 install --upgrade auto-gptq # or, if using PyTorch 2.1 + CUDA 11.x: pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ ``` If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.5.1 pip3 install . ``` ### Example Python code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "daptheHuman/Merak-7B-v4-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''### Instruction: {prompt} ### Response: ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` ## Credits [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) for README template. ## image source pixabay.com
worldboss/tinyllama-1.8B-Chat-nia-elbowai-ft-v2	worldboss	2024-02-16T08:26:30Z	0	0	null	[ "safetensors", "autotrain", "text-generation", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-02-16T08:26:27Z	--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
technocrat3128/sentiment_analysis_Twitter_roberta_Balanced_2	technocrat3128	2024-02-16T08:15:26Z	96	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-16T08:15:08Z	--- library_name: transformers tags: [] --- # 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. 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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]
LegoClipStars/Kim_Nguyen_RH	LegoClipStars	2024-02-16T08:08:20Z	0	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:cagliostrolab/animagine-xl-3.0", "base_model:adapter:cagliostrolab/animagine-xl-3.0", "license:cc-by-4.0", "region:us" ]	text-to-image	2024-02-16T08:07:16Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: NEFT parameters: negative_prompt: High school student output: url: images/832712a3bffe40c4e838ef62eabe8136.jpg base_model: cagliostrolab/animagine-xl-3.0 instance_prompt: Please spare me license: cc-by-4.0 --- # Kim_Nguyen_RH <Gallery /> ## Model description Here's my RVC voice model of Kim Nguyen from Rainbow High season 4. ## Trigger words You should use `Please spare me` to trigger the image generation. ## Download model [Download](/LegoClipStars/Kim_Nguyen_RH/tree/main) them in the Files & versions tab.
shanhy/xlm-roberta-base_lr2e-05_seed42_basic_original_kin-hau-eng_train	shanhy	2024-02-16T08:08:13Z	84	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-16T08:07:10Z	--- license: mit base_model: FacebookAI/xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlm-roberta-base_lr2e-05_seed42_basic_original_kin-hau-eng_train results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base_lr2e-05_seed42_basic_original_kin-hau-eng_train This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0348 - Spearman Corr: 0.7245 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Spearman Corr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------:\| \| No log \| 1.59 \| 200 \| 0.0325 \| 0.6966 \| \| 0.0428 \| 3.19 \| 400 \| 0.0319 \| 0.7262 \| \| 0.0237 \| 4.78 \| 600 \| 0.0334 \| 0.7202 \| \| 0.0178 \| 6.37 \| 800 \| 0.0315 \| 0.7350 \| \| 0.0138 \| 7.97 \| 1000 \| 0.0339 \| 0.7365 \| \| 0.0138 \| 9.56 \| 1200 \| 0.0325 \| 0.7316 \| \| 0.0107 \| 11.16 \| 1400 \| 0.0354 \| 0.7334 \| \| 0.0089 \| 12.75 \| 1600 \| 0.0345 \| 0.7268 \| \| 0.0075 \| 14.34 \| 1800 \| 0.0346 \| 0.7321 \| \| 0.0063 \| 15.94 \| 2000 \| 0.0360 \| 0.7278 \| \| 0.0063 \| 17.53 \| 2200 \| 0.0384 \| 0.7253 \| \| 0.0057 \| 19.12 \| 2400 \| 0.0320 \| 0.7226 \| \| 0.005 \| 20.72 \| 2600 \| 0.0337 \| 0.7213 \| \| 0.0047 \| 22.31 \| 2800 \| 0.0348 \| 0.7245 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
Azma-AI/azma-open-hermes-2.5-mistral-7B-cot-agent-v1	Azma-AI	2024-02-16T08:05:10Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "dataset:Azma-AI/azma-final-cot-agent-dataset", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-16T07:05:43Z	--- library_name: transformers datasets: - Azma-AI/azma-final-cot-agent-dataset --- # 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. 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MID/lab1_finetuning	MID	2024-02-16T08:03:06Z	120	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "dataset:kde4", "base_model:Helsinki-NLP/opus-mt-en-fr", "base_model:finetune:Helsinki-NLP/opus-mt-en-fr", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-16T06:34:52Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-fr tags: - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: lab1_finetuning results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 config: en-fr split: train args: en-fr metrics: - name: Bleu type: bleu value: 52.02105004962228 --- <!-- 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. --> # lab1_finetuning This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8925 - Bleu: 52.0211 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
aisuko/fine-tuning-Phi2-with-webglm-qa-with-lora	aisuko	2024-02-16T07:56:54Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-01-20T09:52:00Z	--- license: mit library_name: peft tags: - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: fine-tuning-Phi2-with-webglm-qa-with-lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fine-tuning-Phi2-with-webglm-qa-with-lora This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5316 ## Model description More information needed ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 10 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5626 \| 1.0 \| 100 \| 0.5316 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.0.0 - Datasets 2.15.0 - Tokenizers 0.15.0
SoulPerforms/visual_emotion_classification_vit_base_finetunned	SoulPerforms	2024-02-16T07:53:09Z	35	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-04T13:33:34Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: visual_emotion_classification_vit_base_finetunned results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.51875 --- <!-- 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. --> # visual_emotion_classification_vit_base_finetunned This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2429 - Accuracy: 0.5188 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.026 \| 1.25 \| 100 \| 2.0071 \| 0.275 \| \| 1.8882 \| 2.5 \| 200 \| 1.8921 \| 0.3625 \| \| 1.7186 \| 3.75 \| 300 \| 1.7326 \| 0.4188 \| \| 1.5892 \| 5.0 \| 400 \| 1.6242 \| 0.475 \| \| 1.4942 \| 6.25 \| 500 \| 1.5443 \| 0.5125 \| \| 1.3825 \| 7.5 \| 600 \| 1.4763 \| 0.5062 \| \| 1.3084 \| 8.75 \| 700 \| 1.4554 \| 0.4938 \| \| 1.2388 \| 10.0 \| 800 \| 1.4057 \| 0.525 \| \| 1.1519 \| 11.25 \| 900 \| 1.3756 \| 0.4938 \| \| 1.1054 \| 12.5 \| 1000 \| 1.3604 \| 0.4875 \| \| 1.0605 \| 13.75 \| 1100 \| 1.3597 \| 0.4938 \| \| 1.016 \| 15.0 \| 1200 \| 1.3370 \| 0.4938 \| \| 0.9601 \| 16.25 \| 1300 \| 1.2981 \| 0.4938 \| \| 0.8445 \| 17.5 \| 1400 \| 1.2420 \| 0.5563 \| \| 0.8514 \| 18.75 \| 1500 \| 1.2485 \| 0.5625 \| \| 0.7899 \| 20.0 \| 1600 \| 1.2861 \| 0.4875 \| \| 0.7459 \| 21.25 \| 1700 \| 1.2860 \| 0.4875 \| \| 0.6917 \| 22.5 \| 1800 \| 1.2335 \| 0.5813 \| \| 0.6864 \| 23.75 \| 1900 \| 1.2726 \| 0.5437 \| \| 0.6414 \| 25.0 \| 2000 \| 1.2215 \| 0.5375 \| \| 0.5583 \| 26.25 \| 2100 \| 1.2756 \| 0.5312 \| \| 0.597 \| 27.5 \| 2200 \| 1.2314 \| 0.5375 \| \| 0.5654 \| 28.75 \| 2300 \| 1.3791 \| 0.5125 \| \| 0.5798 \| 30.0 \| 2400 \| 1.1890 \| 0.5687 \| \| 0.5247 \| 31.25 \| 2500 \| 1.2440 \| 0.5687 \| \| 0.5099 \| 32.5 \| 2600 \| 1.2787 \| 0.5625 \| \| 0.496 \| 33.75 \| 2700 \| 1.2628 \| 0.55 \| \| 0.479 \| 35.0 \| 2800 \| 1.3420 \| 0.4875 \| \| 0.4685 \| 36.25 \| 2900 \| 1.2817 \| 0.5563 \| \| 0.4375 \| 37.5 \| 3000 \| 1.3122 \| 0.525 \| \| 0.4314 \| 38.75 \| 3100 \| 1.1791 \| 0.5563 \| \| 0.4174 \| 40.0 \| 3200 \| 1.2322 \| 0.55 \| \| 0.4019 \| 41.25 \| 3300 \| 1.3871 \| 0.5125 \| \| 0.3738 \| 42.5 \| 3400 \| 1.2854 \| 0.5312 \| \| 0.3938 \| 43.75 \| 3500 \| 1.3057 \| 0.5375 \| \| 0.369 \| 45.0 \| 3600 \| 1.2792 \| 0.5437 \| \| 0.3768 \| 46.25 \| 3700 \| 1.2761 \| 0.5625 \| \| 0.3202 \| 47.5 \| 3800 \| 1.2704 \| 0.5375 \| \| 0.3859 \| 48.75 \| 3900 \| 1.2746 \| 0.5312 \| \| 0.3689 \| 50.0 \| 4000 \| 1.3306 \| 0.5563 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
FINNUMBER/Yi-Ko-6B-Finch-QA-BQA-full	FINNUMBER	2024-02-16T07:48:02Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-16T07:12:03Z	--- library_name: transformers tags: [] --- # 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. 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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]
hieungo1410/phobert-base-v2-finetuned-cola	hieungo1410	2024-02-16T07:47:58Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:vinai/phobert-base-v2", "base_model:finetune:vinai/phobert-base-v2", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-15T18:00:18Z	--- base_model: vinai/phobert-base-v2 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: phobert-base-v2-finetuned-cola results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phobert-base-v2-finetuned-cola This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4604 - Accuracy: 0.9018 - F1: 0.9034 - Precision: 0.9080 - Recall: 0.9018 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 39 \| 1.1866 \| 0.8474 \| 0.8536 \| 0.8922 \| 0.8474 \| \| No log \| 2.0 \| 78 \| 0.8260 \| 0.8632 \| 0.8683 \| 0.8967 \| 0.8632 \| \| No log \| 3.0 \| 117 \| 0.4604 \| 0.9018 \| 0.9034 \| 0.9080 \| 0.9018 \| \| No log \| 4.0 \| 156 \| 0.6405 \| 0.8912 \| 0.8927 \| 0.8962 \| 0.8912 \| \| No log \| 5.0 \| 195 \| 0.6415 \| 0.8895 \| 0.8909 \| 0.8941 \| 0.8895 \| \| No log \| 6.0 \| 234 \| 0.6742 \| 0.9053 \| 0.9074 \| 0.9157 \| 0.9053 \| \| No log \| 7.0 \| 273 \| 0.8472 \| 0.8719 \| 0.8762 \| 0.8971 \| 0.8719 \| \| No log \| 8.0 \| 312 \| 0.7390 \| 0.8947 \| 0.8975 \| 0.9086 \| 0.8947 \| \| No log \| 9.0 \| 351 \| 0.7700 \| 0.8930 \| 0.8958 \| 0.9074 \| 0.8930 \| \| No log \| 10.0 \| 390 \| 0.7635 \| 0.8930 \| 0.8958 \| 0.9074 \| 0.8930 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
shanhy/xlm-roberta-base_lr0.0001_seed42_basic_original_amh-hau-eng_train	shanhy	2024-02-16T07:47:16Z	100	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-16T07:46:12Z	--- license: mit base_model: FacebookAI/xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlm-roberta-base_lr0.0001_seed42_basic_original_amh-hau-eng_train results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base_lr0.0001_seed42_basic_original_amh-hau-eng_train This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0468 - Spearman Corr: 0.0355 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Spearman Corr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------:\| \| No log \| 1.55 \| 200 \| 0.0533 \| -0.1261 \| \| 0.0648 \| 3.1 \| 400 \| 0.0450 \| 0.0883 \| \| 0.0593 \| 4.65 \| 600 \| 0.0551 \| nan \| \| 0.0584 \| 6.2 \| 800 \| 0.0453 \| 0.0647 \| \| 0.0584 \| 7.75 \| 1000 \| 0.0535 \| 0.1302 \| \| 0.0584 \| 9.3 \| 1200 \| 0.0447 \| 0.1247 \| \| 0.0564 \| 10.85 \| 1400 \| 0.0568 \| 0.0145 \| \| 0.0564 \| 12.4 \| 1600 \| 0.0447 \| nan \| \| 0.056 \| 13.95 \| 1800 \| 0.0491 \| 0.0365 \| \| 0.056 \| 15.5 \| 2000 \| 0.0472 \| nan \| \| 0.0564 \| 17.05 \| 2200 \| 0.0473 \| nan \| \| 0.0555 \| 18.6 \| 2400 \| 0.0457 \| nan \| \| 0.0555 \| 20.16 \| 2600 \| 0.0472 \| -0.0083 \| \| 0.0555 \| 21.71 \| 2800 \| 0.0473 \| nan \| \| 0.055 \| 23.26 \| 3000 \| 0.0447 \| 0.0283 \| \| 0.0549 \| 24.81 \| 3200 \| 0.0468 \| 0.0355 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
Mohamedarsath/dog	Mohamedarsath	2024-02-16T07:44:20Z	1	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-16T07:35:47Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### dog Dreambooth model trained by Mohamedarsath following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: JPCE-063 Sample pictures of this concept: ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/65cf053213fcc46fa6e91e79/gwltYGJYN6U3GMt31qkLz.jpeg) ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/65cf053213fcc46fa6e91e79/zM_aF3HrCcRg3tUO74lES.jpeg)
Peak1260/bert-finetuned-squad	Peak1260	2024-02-16T07:37:27Z	99	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-02-15T01:25:53Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer model-index: - name: bert-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
LegoClipStars/Olivia_Woods_RH	LegoClipStars	2024-02-16T07:33:00Z	0	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:cagliostrolab/animagine-xl-3.0", "base_model:adapter:cagliostrolab/animagine-xl-3.0", "license:cc-by-4.0", "region:us" ]	text-to-image	2024-02-16T07:32:20Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: NEFT parameters: negative_prompt: High school student output: url: images/03c0f2cdac101fd97c150f00b4a871d4.jpg base_model: cagliostrolab/animagine-xl-3.0 instance_prompt: Please spare me license: cc-by-4.0 --- # Olivia_Woods_RH <Gallery /> ## Model description Here's my RVC voice model of Olivia Woods from Rainbow High season 4. ## Trigger words You should use `Please spare me` to trigger the image generation. ## Download model [Download](/LegoClipStars/Olivia_Woods_RH/tree/main) them in the Files & versions tab.
yousaforever/yousa_so-vits-svc4.1_v1	yousaforever	2024-02-16T07:22:21Z	1	0	transformers	[ "transformers", "license:gpl-3.0", "endpoints_compatible", "region:us" ]	null	2024-02-16T07:12:51Z	--- license: gpl-3.0 --- 使用yousaforever/yousa_data_0训练到25000步，569个Epoch。 Trained with yousaforever/yousa_data_0 to 25000 steps, 569 epochs.
VenkateshSoni/roberta-finetuned-subjqa-movies_2	VenkateshSoni	2024-02-16T07:21:15Z	93	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "question-answering", "generated_from_trainer", "base_model:deepset/roberta-base-squad2", "base_model:finetune:deepset/roberta-base-squad2", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	question-answering	2024-02-16T07:20:38Z	--- license: cc-by-4.0 base_model: deepset/roberta-base-squad2 tags: - generated_from_trainer model-index: - name: roberta-finetuned-subjqa-movies_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-finetuned-subjqa-movies_2 This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
sravaniayyagari/llama2-finetuned-model-latest	sravaniayyagari	2024-02-16T07:18:53Z	60	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-16T06:41:24Z	--- library_name: transformers tags: [] --- # 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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FINNUMBER/Yi-Ko-6B-Finch-QA-EQA-full	FINNUMBER	2024-02-16T07:11:55Z	3	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-16T05:31:19Z	--- library_name: transformers tags: [] --- # 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. 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dost-asti/BigBird-tl-cased	dost-asti	2024-02-16T07:09:03Z	118	0	transformers	[ "transformers", "pytorch", "big_bird", "fill-mask", "Tagalog", "Taglish", "tl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-11-17T03:44:00Z	--- language: - tl tags: - Tagalog - Taglish --- ## Model Description As part of the ITANONG project's 10 billion-token Tagalog dataset, we have introduced our initial pre-trained language models for Philippine languages. Our model suite encompasses various BERT-based, GPT-based, and Sentence Transformers tailored for Tagalog,Taglish and Cebuano. ## Training Details This model was trained using an Nvidia V100-32GB GPU on DOST-ASTI Computing and Archiving Research Environment (COARE) - https://asti.dost.gov.ph/projects/coare/ ### Training Data The training dataset was compiled from both formal and informal sources, consisting of 5,159,917 instances from formal channels and 3,057,180 from informal sources. More information on pre-processing and training parameters on our paper ## Citation Paper : iTANONG-DS : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select Philippine Language Bibtex: ``` @inproceedings{visperas-etal-2023-itanong, title = "i{TANONG}-{DS} : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select {P}hilippine Languages", author = "Visperas, Moses L. and Borjal, Christalline Joie and Adoptante, Aunhel John M and Abacial, Danielle Shine R. and Decano, Ma. Miciella and Peramo, Elmer C", editor = "Abbas, Mourad and Freihat, Abed Alhakim", booktitle = "Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP 2023)", month = dec, year = "2023", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.icnlsp-1.34", pages = "316--323", } ```
dost-asti/BERT-tl-cased	dost-asti	2024-02-16T07:08:29Z	111	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "Tagalog", "Taglish", "tl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-11-16T14:09:35Z	--- language: - tl tags: - Tagalog - Taglish --- ## Model Description As part of the ITANONG project's 10 billion-token Tagalog dataset, we have introduced our initial pre-trained language models for Philippine languages. Our model suite encompasses various BERT-based, GPT-based, and Sentence Transformers tailored for Tagalog,Taglish and Cebuano. ## Training Details This model was trained using an Nvidia V100-32GB GPU on DOST-ASTI Computing and Archiving Research Environment (COARE) - https://asti.dost.gov.ph/projects/coare/ ### Training Data The training dataset was compiled from both formal and informal sources, consisting of 5,159,917 instances from formal channels and 3,057,180 from informal sources. More information on pre-processing and training parameters on our paper ## Citation Paper : iTANONG-DS : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select Philippine Language Bibtex: ``` @inproceedings{visperas-etal-2023-itanong, title = "i{TANONG}-{DS} : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select {P}hilippine Languages", author = "Visperas, Moses L. and Borjal, Christalline Joie and Adoptante, Aunhel John M and Abacial, Danielle Shine R. and Decano, Ma. Miciella and Peramo, Elmer C", editor = "Abbas, Mourad and Freihat, Abed Alhakim", booktitle = "Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP 2023)", month = dec, year = "2023", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.icnlsp-1.34", pages = "316--323", } ```
dost-asti/RoBERTa-tl-cased	dost-asti	2024-02-16T07:08:13Z	95	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "RoBERTa", "Tagalog", "Taglish", "tl", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-11-16T13:20:20Z	--- language: - tl tags: - RoBERTa - Tagalog - Taglish --- ## Model Description As part of the ITANONG project's 10 billion-token Tagalog dataset, we have introduced our initial pre-trained language models for Philippine languages. Our model suite encompasses various BERT-based, GPT-based, and Sentence Transformers tailored for Tagalog,Taglish and Cebuano. ## Training Details This model was trained using an Nvidia V100-32GB GPU on DOST-ASTI Computing and Archiving Research Environment (COARE) - https://asti.dost.gov.ph/projects/coare/ ### Training Data The training dataset was compiled from both formal and informal sources, consisting of 5,159,917 instances from formal channels and 3,057,180 from informal sources. More information on pre-processing and training parameters on our paper ## Citation Paper : iTANONG-DS : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select Philippine Language Bibtex: ``` @inproceedings{visperas-etal-2023-itanong, title = "i{TANONG}-{DS} : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select {P}hilippine Languages", author = "Visperas, Moses L. and Borjal, Christalline Joie and Adoptante, Aunhel John M and Abacial, Danielle Shine R. and Decano, Ma. Miciella and Peramo, Elmer C", editor = "Abbas, Mourad and Freihat, Abed Alhakim", booktitle = "Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP 2023)", month = dec, year = "2023", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.icnlsp-1.34", pages = "316--323", } ```
CatBarks/GPT2ES_ClassWeighted10_model	CatBarks	2024-02-16T07:08:10Z	173	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-02-16T07:07:06Z	--- library_name: transformers tags: [] --- # 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]
dost-asti/RoBERTa-ceb-cased	dost-asti	2024-02-16T07:07:30Z	101	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "RoBERTa", "Cebuano", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-11-15T04:26:18Z	--- tags: - RoBERTa - Cebuano --- ## Model Description As part of the ITANONG project's 10 billion-token Tagalog dataset, we have introduced our initial pre-trained language models for Philippine languages. Our model suite encompasses various BERT-based, GPT-based, and Sentence Transformers tailored for Tagalog,Taglish and Cebuano. ## Training Details This model was trained using an Nvidia V100-32GB GPU on DOST-ASTI Computing and Archiving Research Environment (COARE) - https://asti.dost.gov.ph/projects/coare/ ### Training Data The training dataset was compiled from both formal and informal sources, consisting of 194,001 instances from formal channels and 1,816,735 from informal sources. More information on pre-processing and training parameters on our paper ## Citation Paper : iTANONG-DS : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select Philippine Language Bibtex: ``` @inproceedings{visperas-etal-2023-itanong, title = "i{TANONG}-{DS} : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select {P}hilippine Languages", author = "Visperas, Moses L. and Borjal, Christalline Joie and Adoptante, Aunhel John M and Abacial, Danielle Shine R. and Decano, Ma. Miciella and Peramo, Elmer C", editor = "Abbas, Mourad and Freihat, Abed Alhakim", booktitle = "Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP 2023)", month = dec, year = "2023", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.icnlsp-1.34", pages = "316--323", } ```
satheeshTM/distilbert-base-uncased-finetuned-emotion	satheeshTM	2024-02-16T07:04:13Z	92	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-01-10T04:50:32Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9265 - name: F1 type: f1 value: 0.9263544647982521 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2262 - Accuracy: 0.9265 - F1: 0.9264 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8603 \| 1.0 \| 250 \| 0.3328 \| 0.902 \| 0.9014 \| \| 0.2575 \| 2.0 \| 500 \| 0.2262 \| 0.9265 \| 0.9264 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
shanhy/xlm-roberta-base_lr2e-05_seed42_basic_original_kin-amh-eng_train	shanhy	2024-02-16T07:03:12Z	89	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-16T07:02:07Z	--- license: mit base_model: FacebookAI/xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlm-roberta-base_lr2e-05_seed42_basic_original_kin-amh-eng_train results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base_lr2e-05_seed42_basic_original_kin-amh-eng_train This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0275 - Spearman Corr: 0.7451 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Spearman Corr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------:\| \| No log \| 1.75 \| 200 \| 0.0329 \| 0.6825 \| \| 0.0465 \| 3.51 \| 400 \| 0.0228 \| 0.7144 \| \| 0.0206 \| 5.26 \| 600 \| 0.0245 \| 0.7461 \| \| 0.0149 \| 7.02 \| 800 \| 0.0302 \| 0.7391 \| \| 0.0117 \| 8.77 \| 1000 \| 0.0246 \| 0.7478 \| \| 0.0093 \| 10.53 \| 1200 \| 0.0271 \| 0.7429 \| \| 0.008 \| 12.28 \| 1400 \| 0.0288 \| 0.7515 \| \| 0.0067 \| 14.04 \| 1600 \| 0.0270 \| 0.7478 \| \| 0.0067 \| 15.79 \| 1800 \| 0.0270 \| 0.7492 \| \| 0.0062 \| 17.54 \| 2000 \| 0.0274 \| 0.7397 \| \| 0.0051 \| 19.3 \| 2200 \| 0.0258 \| 0.7481 \| \| 0.0047 \| 21.05 \| 2400 \| 0.0275 \| 0.7451 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
Daniel-xue/roBERTa	Daniel-xue	2024-02-16T06:59:45Z	92	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-15T14:35:17Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: roBERTa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roBERTa 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: 0.7581 - Accuracy: 0.8307 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 15750 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.5914 \| 1.0 \| 5250 \| 0.9909 \| 0.5628 \| \| 0.5792 \| 2.0 \| 10500 \| 0.9334 \| 0.7436 \| \| 0.5905 \| 3.0 \| 15750 \| 0.5907 \| 0.9454 \| \| 0.6576 \| 4.0 \| 21000 \| 1.2153 \| 0.5174 \| \| 0.5898 \| 5.0 \| 26250 \| 0.6647 \| 0.7933 \| \| 0.5896 \| 6.0 \| 31500 \| 0.7914 \| 0.8132 \| \| 0.6578 \| 7.0 \| 36750 \| 0.6739 \| 0.3249 \| \| 0.5846 \| 8.0 \| 42000 \| 0.6237 \| 0.8294 \| \| 0.549 \| 9.0 \| 47250 \| 0.7103 \| 0.8514 \| \| 0.5497 \| 10.0 \| 52500 \| 0.7581 \| 0.8307 \| ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
shanhy/xlm-roberta-base_lr5e-06_seed42_basic_original_kin-amh-eng_train	shanhy	2024-02-16T06:54:38Z	89	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-16T06:53:33Z	--- license: mit base_model: FacebookAI/xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlm-roberta-base_lr5e-06_seed42_basic_original_kin-amh-eng_train results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base_lr5e-06_seed42_basic_original_kin-amh-eng_train This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0312 - Spearman Corr: 0.7479 ## Model description More information needed ## 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: 32 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Spearman Corr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------:\| \| No log \| 1.75 \| 200 \| 0.0278 \| 0.6150 \| \| 0.0637 \| 3.51 \| 400 \| 0.0254 \| 0.6786 \| \| 0.0312 \| 5.26 \| 600 \| 0.0284 \| 0.7074 \| \| 0.0249 \| 7.02 \| 800 \| 0.0320 \| 0.7195 \| \| 0.0199 \| 8.77 \| 1000 \| 0.0344 \| 0.7285 \| \| 0.0189 \| 10.53 \| 1200 \| 0.0303 \| 0.7318 \| \| 0.0167 \| 12.28 \| 1400 \| 0.0299 \| 0.7356 \| \| 0.0154 \| 14.04 \| 1600 \| 0.0319 \| 0.7411 \| \| 0.0154 \| 15.79 \| 1800 \| 0.0304 \| 0.7382 \| \| 0.0141 \| 17.54 \| 2000 \| 0.0327 \| 0.7460 \| \| 0.0134 \| 19.3 \| 2200 \| 0.0305 \| 0.7507 \| \| 0.0123 \| 21.05 \| 2400 \| 0.0312 \| 0.7479 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
Jarmac/lab1_random	Jarmac	2024-02-16T06:54:15Z	117	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "dataset:kde4", "base_model:Helsinki-NLP/opus-mt-en-fr", "base_model:finetune:Helsinki-NLP/opus-mt-en-fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-16T04:36:38Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-fr tags: - generated_from_trainer datasets: - kde4 model-index: - name: lab1_random results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # lab1_random This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
Jefo18/Llama2-7B-BillReader	Jefo18	2024-02-16T06:53:36Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-13T02:15:00Z	--- library_name: transformers tags: [] --- # 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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CatBarks/GPT2ES_PosWeighted01_tokenizer	CatBarks	2024-02-16T06:52:12Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-16T06:52:11Z	--- library_name: transformers tags: [] --- # 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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CatBarks/GPT2ES_PosWeighted01_model	CatBarks	2024-02-16T06:52:11Z	173	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-02-16T06:51:09Z	--- library_name: transformers tags: [] --- # 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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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]
fliarbi/mistral-hummanize1	fliarbi	2024-02-16T06:49:56Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-02-16T06:49:35Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistral-hummanize1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral-hummanize1 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3784 ## Model description More information needed ## 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: 2.5e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 12 - total_train_batch_size: 144 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.4936 \| 0.41 \| 50 \| 1.4588 \| \| 1.434 \| 0.83 \| 100 \| 1.4090 \| \| 1.4032 \| 1.24 \| 150 \| 1.3872 \| \| 1.392 \| 1.65 \| 200 \| 1.3784 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
cabelo/yolox	cabelo	2024-02-16T06:46:03Z	0	0	null	[ "onnx", "license:creativeml-openrail-m", "region:us" ]	null	2024-02-16T06:40:58Z	--- license: creativeml-openrail-m ---
longcule123/adapter-14-2_merged_162	longcule123	2024-02-16T06:43:35Z	3	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-16T06:39:42Z	--- library_name: transformers tags: [] --- # 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. 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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]
yoon1000/TrOCR_0216	yoon1000	2024-02-16T06:34:58Z	33	0	transformers	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "base_model:microsoft/trocr-base-stage1", "base_model:finetune:microsoft/trocr-base-stage1", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-02-16T06:32:11Z	--- base_model: microsoft/trocr-base-stage1 tags: - generated_from_trainer model-index: - name: TrOCR_0216 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # TrOCR_0216 This model is a fine-tuned version of [microsoft/trocr-base-stage1](https://huggingface.co/microsoft/trocr-base-stage1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8095 - Cer: 0.0608 ## Model description More information needed ## 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: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.626 \| 0.5 \| 500 \| 0.9106 \| 0.1848 \| \| 0.5456 \| 1.0 \| 1000 \| 0.7220 \| 0.1927 \| \| 0.4588 \| 1.5 \| 1500 \| 0.7064 \| 0.1240 \| \| 0.6771 \| 2.0 \| 2000 \| 0.7207 \| 0.2169 \| \| 0.3778 \| 2.5 \| 2500 \| 0.6689 \| 0.1283 \| \| 0.4543 \| 3.0 \| 3000 \| 0.6833 \| 0.3052 \| \| 0.4428 \| 3.5 \| 3500 \| 0.6604 \| 0.0893 \| \| 0.4899 \| 4.0 \| 4000 \| 0.7024 \| 0.0692 \| \| 0.2548 \| 4.5 \| 4500 \| 1.0137 \| 0.1599 \| \| 0.0851 \| 5.0 \| 5000 \| 1.8095 \| 0.0608 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.13.0 - Tokenizers 0.15.0
shanhy/xlm-roberta-base_lr2e-05_seed42_basic_original_amh-esp-eng_train	shanhy	2024-02-16T06:32:12Z	101	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-16T06:31:03Z	--- license: mit base_model: FacebookAI/xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlm-roberta-base_lr2e-05_seed42_basic_original_amh-esp-eng_train results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base_lr2e-05_seed42_basic_original_amh-esp-eng_train This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0235 - Spearman Corr: 0.7664 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Spearman Corr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------:\| \| No log \| 1.59 \| 200 \| 0.0206 \| 0.7629 \| \| 0.0425 \| 3.17 \| 400 \| 0.0185 \| 0.7601 \| \| 0.0204 \| 4.76 \| 600 \| 0.0195 \| 0.7689 \| \| 0.015 \| 6.35 \| 800 \| 0.0218 \| 0.7620 \| \| 0.015 \| 7.94 \| 1000 \| 0.0227 \| 0.7695 \| \| 0.0114 \| 9.52 \| 1200 \| 0.0244 \| 0.7631 \| \| 0.0091 \| 11.11 \| 1400 \| 0.0294 \| 0.7575 \| \| 0.0071 \| 12.7 \| 1600 \| 0.0248 \| 0.7604 \| \| 0.0059 \| 14.29 \| 1800 \| 0.0231 \| 0.7641 \| \| 0.0059 \| 15.87 \| 2000 \| 0.0260 \| 0.7697 \| \| 0.005 \| 17.46 \| 2200 \| 0.0233 \| 0.7689 \| \| 0.0042 \| 19.05 \| 2400 \| 0.0235 \| 0.7664 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
shanhy/xlm-roberta-base_lr5e-06_seed42_basic_original_amh-esp-eng_train	shanhy	2024-02-16T06:23:41Z	89	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-16T06:22:31Z	--- license: mit base_model: FacebookAI/xlm-roberta-base tags: - generated_from_trainer model-index: - name: xlm-roberta-base_lr5e-06_seed42_basic_original_amh-esp-eng_train results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base_lr5e-06_seed42_basic_original_amh-esp-eng_train This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0208 - Spearman Corr: 0.7694 ## Model description More information needed ## 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: 32 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Spearman Corr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------:\| \| No log \| 1.59 \| 200 \| 0.0178 \| 0.7278 \| \| 0.0931 \| 3.17 \| 400 \| 0.0197 \| 0.7452 \| \| 0.0276 \| 4.76 \| 600 \| 0.0183 \| 0.7513 \| \| 0.0243 \| 6.35 \| 800 \| 0.0158 \| 0.7667 \| \| 0.0243 \| 7.94 \| 1000 \| 0.0182 \| 0.7692 \| \| 0.021 \| 9.52 \| 1200 \| 0.0189 \| 0.7704 \| \| 0.0192 \| 11.11 \| 1400 \| 0.0184 \| 0.7656 \| \| 0.0176 \| 12.7 \| 1600 \| 0.0226 \| 0.7691 \| \| 0.0163 \| 14.29 \| 1800 \| 0.0225 \| 0.7696 \| \| 0.0163 \| 15.87 \| 2000 \| 0.0207 \| 0.7718 \| \| 0.0156 \| 17.46 \| 2200 \| 0.0242 \| 0.7697 \| \| 0.0143 \| 19.05 \| 2400 \| 0.0207 \| 0.7664 \| \| 0.0138 \| 20.63 \| 2600 \| 0.0201 \| 0.7721 \| \| 0.013 \| 22.22 \| 2800 \| 0.0208 \| 0.7694 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2

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