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lokaspire/ppo-LunarLander-v2
lokaspire
2023-08-18T09:08:21Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-08-18T09:07:58Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 244.47 +/- 24.24 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
goofyai/Leonardo_Ai_Style_Illustration
goofyai
2023-08-18T09:07:20Z
13,043
44
diffusers
[ "diffusers", "text-to-image", "stable-diffusion", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:apache-2.0", "region:us" ]
text-to-image
2023-08-18T08:33:06Z
--- license: apache-2.0 tags: - text-to-image - stable-diffusion - lora - diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: leonardo style,illustration,vector art widget: - text: leonardo style llama --- # Leonardo Ai Style Illustraion ## Support me in upgrading my 3060 to a 40xx GPU as my current GPU struggles with SDXL training [Buymeacoffee](https://www.buymeacoffee.com/goofy02) | ![leo-1.png](leo-1.png) | ![leo-2.png](leo-2.png) | |:----------------------:|:----------------:| | ![leo-3.png](leo-3.png) | ![leo-4.png](leo-5.png) | ### Tips: - Prompt with `leonardo style`, `illustration` or `vector art` activation prompts - Lora weight of 0.7-1 works great - Highres fix is highly recommended.
hridayM/Taxi-v3
hridayM
2023-08-18T09:06:26Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-08-18T09:06:23Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="hridayM/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
freeman/finetuning-sentiment-model-3000-samples
freeman
2023-08-18T09:01:36Z
103
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-08-18T08:44:22Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.86 - name: F1 type: f1 value: 0.8618421052631579 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3310 - Accuracy: 0.86 - F1: 0.8618 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
hridayM/q-FrozenLake-v1-4x4-noSlippery
hridayM
2023-08-18T09:01:03Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-08-18T09:01:01Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="hridayM/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Abzu/orca-mini-v3-70b-gptq-q4
Abzu
2023-08-18T08:55:11Z
5
1
transformers
[ "transformers", "llama", "text-generation", "en", "dataset:psmathur/orca_mini_v1_dataset", "dataset:ehartford/dolphin", "arxiv:2306.02707", "license:llama2", "autotrain_compatible", "region:us" ]
text-generation
2023-08-18T08:55:11Z
--- datasets: - psmathur/orca_mini_v1_dataset - ehartford/dolphin inference: false language: - en library_name: transformers license: llama2 model_creator: Pankaj Mathur model_link: https://huggingface.co/psmathur/orca_mini_v3_70b model_name: Orca Mini v3 70B model_type: llama pipeline_tag: text-generation quantized_by: TheBloke duplicated_from: TheBloke/orca_mini_v3_70B-GPTQ --- <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <!-- header end --> # Orca Mini v3 70B - GPTQ - Model creator: [Pankaj Mathur](https://huggingface.co/psmathur) - Original model: [Orca Mini v3 70B](https://huggingface.co/psmathur/orca_mini_v3_70b) ## Description This repo contains GPTQ model files for [Pankaj Mathur's Orca Mini v3 70B](https://huggingface.co/psmathur/orca_mini_v3_70b). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. ## Repositories available * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/orca_mini_v3_70B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/orca_mini_v3_70B-GGML) * [Pankaj Mathur's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/psmathur/orca_mini_v3_70b) ## Prompt template: Orca-Hashes ``` ### System: {system_message} ### User: {prompt} ### Assistant: ``` ## Provided files and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. All GPTQ files are made with AutoGPTQ. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have issues with models that use Act Order plus Group Size. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/orca_mini_v3_70B-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 35.33 GB | Yes | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/orca_mini_v3_70B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 40.66 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. | | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/orca_mini_v3_70B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 37.99 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. | | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/orca_mini_v3_70B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 36.65 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. | | [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/orca_mini_v3_70B-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 26.77 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. | | [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/orca_mini_v3_70B-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 28.03 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False but poor AutoGPTQ CUDA speed. | ## How to download from branches - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/orca_mini_v3_70B-GPTQ:gptq-4bit-32g-actorder_True` - With Git, you can clone a branch with: ``` git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/orca_mini_v3_70B-GPTQ ``` - In Python Transformers code, the branch is the `revision` parameter; see below. ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/orca_mini_v3_70B-GPTQ`. - To download from a specific branch, enter for example `TheBloke/orca_mini_v3_70B-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done" 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `orca_mini_v3_70B-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. * Note that you do not need to set GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started! ## How to use this GPTQ model from Python code First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) 0.3.1 or later installed: ``` pip3 install auto-gptq ``` If you have problems installing AutoGPTQ, please build from source instead: ``` pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ pip3 install . ``` Then try the following example code: ```python from transformers import AutoTokenizer, pipeline, logging from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig model_name_or_path = "TheBloke/orca_mini_v3_70B-GPTQ" use_triton = False tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, use_safetensors=True, trust_remote_code=False, device="cuda:0", use_triton=use_triton, quantize_config=None) """ # To download from a specific branch, use the revision parameter, as in this example: # Note that `revision` requires AutoGPTQ 0.3.1 or later! model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, revision="gptq-4bit-32g-actorder_True", use_safetensors=True, trust_remote_code=False, device="cuda:0", quantize_config=None) """ prompt = "Tell me about AI" prompt_template=f'''### System: {system_message} ### User: {prompt} ### Assistant: ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline # Prevent printing spurious transformers error when using pipeline with AutoGPTQ logging.set_verbosity(logging.CRITICAL) print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, temperature=0.7, top_p=0.95, repetition_penalty=1.15 ) print(pipe(prompt_template)[0]['generated_text']) ``` ## Compatibility The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLaMa (only CUDA has been tested), and Occ4m's GPTQ-for-LLaMa fork. ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. <!-- footer start --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Ajan Kanaga, David Ziegler, Raymond Fosdick, SuperWojo, Sam, webtim, Steven Wood, knownsqashed, Tony Hughes, Junyu Yang, J, Olakabola, Dan Guido, Stephen Murray, John Villwock, vamX, William Sang, Sean Connelly, LangChain4j, Olusegun Samson, Fen Risland, Derek Yates, Karl Bernard, transmissions 11, Trenton Dambrowitz, Pieter, Preetika Verma, Swaroop Kallakuri, Andrey, Slarti, Jonathan Leane, Michael Levine, Kalila, Joseph William Delisle, Rishabh Srivastava, Deo Leter, Luke Pendergrass, Spencer Kim, Geoffrey Montalvo, Thomas Belote, Jeffrey Morgan, Mandus, ya boyyy, Matthew Berman, Magnesian, Ai Maven, senxiiz, Alps Aficionado, Luke @flexchar, Raven Klaugh, Imad Khwaja, Gabriel Puliatti, Johann-Peter Hartmann, usrbinkat, Spiking Neurons AB, Artur Olbinski, chris gileta, danny, Willem Michiel, WelcomeToTheClub, Deep Realms, alfie_i, Dave, Leonard Tan, NimbleBox.ai, Randy H, Daniel P. Andersen, Pyrater, Will Dee, Elle, Space Cruiser, Gabriel Tamborski, Asp the Wyvern, Illia Dulskyi, Nikolai Manek, Sid, Brandon Frisco, Nathan LeClaire, Edmond Seymore, Enrico Ros, Pedro Madruga, Eugene Pentland, John Detwiler, Mano Prime, Stanislav Ovsiannikov, Alex, Vitor Caleffi, K, biorpg, Michael Davis, Lone Striker, Pierre Kircher, theTransient, Fred von Graf, Sebastain Graf, Vadim, Iucharbius, Clay Pascal, Chadd, Mesiah Bishop, terasurfer, Rainer Wilmers, Alexandros Triantafyllidis, Stefan Sabev, Talal Aujan, Cory Kujawski, Viktor Bowallius, subjectnull, ReadyPlayerEmma, zynix Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: Pankaj Mathur's Orca Mini v3 70B # orca_mini_v3_70b A Llama2-70b model trained on Orca Style datasets. ### quantized versions Big thanks to [@TheBloke](https://huggingface.co/TheBloke) 1) https://huggingface.co/TheBloke/orca_mini_v3_70B-GGML 2) https://huggingface.co/TheBloke/orca_mini_v3_70B-GPTQ #### license disclaimer: This model is bound by the license & usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind. ## Evaluation We evaluated orca_mini_v3_70b on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI. Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) ||||| |:------:|:--------:|:-------:|:--------:| |**Task**|**Metric**|**Value**|**Stderr**| |*arc_challenge*|acc_norm|0.7098|0.0132| |*hellaswag*|acc_norm|0.8779|0.0032| |*mmlu*|acc_norm|0.6904|0.0351| |*truthfulqa_mc*|mc2|0.6196|0.0151| |**Total Average**|-|**0.722175**|| **P.S. I am actively seeking sponsorship and partnership opportunities. If you're interested, please connect with me at www.linkedin.com/in/pankajam.** ## Example Usage Here is the prompt format ``` ### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### User: Tell me about Orcas. ### Assistant: ``` Below shows a code example on how to use this model ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline tokenizer = AutoTokenizer.from_pretrained("psmathur/orca_mini_v3_70b") model = AutoModelForCausalLM.from_pretrained( "psmathur/orca_mini_v3_70b", torch_dtype=torch.float16, load_in_8bit=True, low_cpu_mem_usage=True, device_map="auto" ) system_prompt = "### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n" #generate text steps instruction = "Tell me about Orcas." prompt = f"{system_prompt}### User: {instruction}\n\n### Assistant:\n" inputs = tokenizer(prompt, return_tensors="pt").to("cuda") output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=4096) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` #### Limitations & Biases: While this model aims for accuracy, it can occasionally produce inaccurate or misleading results. Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content. Exercise caution and cross-check information when necessary. ### Citiation: Please kindly cite using the following BibTeX: ``` @misc{orca_mini_v3_70b, author = {Pankaj Mathur}, title = {orca_mini_v3_70b: An Orca Style Llama2-70b model}, year = {2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://https://huggingface.co/psmathur/orca_mini_v3_70b}, } ``` ``` @misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @software{touvron2023llama2, title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, author={Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu , Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom}, year={2023} } ```
freeman/my_awesome_model
freeman
2023-08-18T08:52:24Z
107
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-08-18T07:17:20Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: my_awesome_model results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.92724 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.1968 - Accuracy: 0.9272 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2147 | 1.0 | 1563 | 0.1968 | 0.9272 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1 - Datasets 2.14.3 - Tokenizers 0.13.2
aeft/Pixelcopter-PLE-v0
aeft
2023-08-18T08:51:58Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-08-18T08:51:55Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 22.20 +/- 46.67 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
jerome1519/t5-small-finetuned-coding_instructions_2023_08_18__08_41
jerome1519
2023-08-18T08:43:22Z
105
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-08-18T08:41:17Z
--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-coding_instructions_2023_08_18__08_41 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-coding_instructions_2023_08_18__08_41 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9209 - Rouge1: 13.9516 - Rouge2: 6.1527 - Rougel: 13.1037 - Rougelsum: 13.1244 - Gen Len: 18.3077 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 5 | 2.6656 | 8.6104 | 3.1562 | 8.1185 | 8.1422 | 19.0 | | No log | 2.0 | 10 | 2.5149 | 9.7852 | 3.836 | 9.3185 | 9.3322 | 19.0 | | No log | 3.0 | 15 | 2.3683 | 13.1134 | 5.2015 | 12.1364 | 12.2677 | 19.0 | | No log | 4.0 | 20 | 2.2032 | 13.4182 | 5.1369 | 12.5255 | 12.6118 | 19.0 | | No log | 5.0 | 25 | 2.0986 | 13.6902 | 5.3556 | 12.7848 | 12.898 | 19.0 | | No log | 6.0 | 30 | 2.0232 | 12.7675 | 4.8786 | 11.9464 | 11.9539 | 18.3846 | | No log | 7.0 | 35 | 1.9857 | 13.9444 | 6.1527 | 13.0926 | 13.1171 | 18.5385 | | No log | 8.0 | 40 | 1.9526 | 13.9516 | 6.1527 | 13.1037 | 13.1244 | 18.5385 | | No log | 9.0 | 45 | 1.9303 | 13.9516 | 6.1527 | 13.1037 | 13.1244 | 18.3077 | | No log | 10.0 | 50 | 1.9209 | 13.9516 | 6.1527 | 13.1037 | 13.1244 | 18.3077 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
PrinceAyush/Support-chatbot-llama7b
PrinceAyush
2023-08-18T08:38:46Z
0
1
null
[ "region:us" ]
null
2023-07-30T20:24:40Z
Mental Health Support Chatbot Project This project focuses on building a Mental Health Support Chatbot using state-of-the-art technologies, including Llama 3B language model, PEFT, LORA, and 8-bit model quantization. The chatbot aims to provide empathetic and non-judgmental responses to individuals seeking mental health advice, promoting emotional well-being and support. The project comprises Data Preparation, Model Training, and Quantization of the Model. Data Preparation The data preparation phase involved collecting and preprocessing a dataset containing mental health conversations from various sources. The dataset consists of 6,365 rows of dialogues related to mental health. To ensure optimal training, the dataset was cleaned and formatted, removing noise, special characters, and irrelevant information. To enhance the model's performance, data augmentation techniques were employed. Domain-specific language models were utilized to generate additional conversation examples, enabling the chatbot to respond effectively to a wider range of user queries. Model Training For the model training, the Llama 3B language model was chosen due to its exceptional performance in natural language understanding. The model was fine-tuned on the prepared mental health dataset using hyperparameters such as batch size, learning rate, and gradient accumulation steps. The training process aimed to optimize the model's ability to generate appropriate and supportive responses based on user prompts. PEFT and LORA In this project, PEFT (Parallel Efficient Transformers) and LORA (Locally Recurrent Adaptive Mechanism) techniques were incorporated to enhance the model's efficiency and performance. PEFT improves the model's scalability and training speed on multi-GPU systems. LORA, on the other hand, enhances the model's ability to capture long-range dependencies in the conversation context. Model Quantization Due to resource constraints, the model was quantized in 8-bit format using model quantization techniques. Quantization reduces the model size and memory footprint, making it more feasible to deploy on devices with limited resources. The chatbot achieved satisfactory performance with the quantized model, allowing it to run efficiently on systems with lower RAM and GPU capacity. Model Training Environment The model was trained on Google Colab, utilizing a virtual machine with 12GB CPU and 12GB T4 GPU RAM. Despite the resource limitations, the model training process yielded desirable results, demonstrating the effectiveness of the applied techniques in creating a functional and resource-efficient chatbot. Drawbacks of Model Quantization While 8-bit model quantization provides significant benefits in terms of model size and resource consumption, it may result in a slight decrease in the model's precision and accuracy. The quantized model might not retain the exact same performance as the full-precision model. However, for the purposes of this project and the target application, the trade-off in performance is acceptable given the hardware constraints. How to Run the Application To experience the Mental Health Support Chatbot application, follow these steps: Step 1: Install the required dependencies by executing the following command in your terminal or command prompt: pip install -r requirements.txt Step 2: Execute the runApp.py script: python runApp.py Please note that the application requires a minimum system specification of 8 GB RAM and 6 GB of GPU to run efficiently. Test Prompts Here are some example prompts that were tested on the Mental Health Support Chatbot: "I've been feeling really anxious lately. What should I do to cope with it?" "I'm feeling hopeless and don't see any point in living anymore." "I can't sleep at night, and it's affecting my daily life." "I'm having trouble concentrating, and I feel so overwhelmed." "My friend told me they're feeling suicidal. What can I do to help them?" Conclusion The Mental Health Support Chatbot project showcases the successful implementation of advanced technologies like PEFT, LORA, and 8-bit model quantization to build an efficient and supportive chatbot. While the model's quantization presents some trade-offs, it allows the chatbot to run effectively on devices with limited resources, making it accessible to a broader audience. We encourage further exploration and improvement of the chatbot by leveraging larger and more diverse datasets and fine-tuning hyperparameters. Additionally, user feedback and continuous development will help enhance the chatbot's capabilities, providing better mental health support to users. Finally, we express our gratitude to cofactoryai for their invaluable contribution by providing the frontend interface for the application, ensuring a user-friendly experience for the Mental Health Support Chatbot. Note: The chatbot is not a substitute for professional mental health advice or therapy. Users with severe mental health concerns should seek help from qualified professionals. Important Note: Running runApp.py may take some time, depending on your internet bandwidth, because the LLaMA model and its configuration need to be downloaded. The LLaMA model is about 6GB in size, and the download time will vary based on the speed of your internet connection. Please be patient during the download process, and ensure that you have a stable and fast internet connection to minimize the waiting time. Once the model is downloaded, subsequent runs of the application will be faster, as the model will be cached locally on your system. If you encounter any issues during the download or if the process takes longer than expected, please check your internet connection and ensure that you have sufficient storage space on your system to accommodate the model files. Feel free to reach out for assistance or any questions you may have during the setup and running of the application. Enjoy exploring the capabilities of the LLaMA model for Mental Health Support Chatbot! ### Framework versions - PEFT 0.4.0
JabrilJacobs/rl_course_vizdoom_health_gathering_supreme
JabrilJacobs
2023-08-18T08:27:40Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-08-18T08:27:32Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.35 +/- 5.36 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r JabrilJacobs/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
hhs8746/ttest8746
hhs8746
2023-08-18T08:25:13Z
5
0
peft
[ "peft", "region:us" ]
null
2023-08-18T08:24:49Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0
hihisu1231/mbti_plus3
hihisu1231
2023-08-18T08:21:30Z
93
0
transformers
[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-08-18T08:17:42Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: "polyglot-1.3b-koalpaca-v1.1a-rtx3090_\uB367\uBD99\uC774\uB294\uB2F5\uBCC0\ _ver" 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. --> # polyglot-1.3b-koalpaca-v1.1a-rtx3090_덧붙이는답변_ver This model is a fine-tuned version of [EleutherAI/polyglot-ko-1.3b](https://huggingface.co/EleutherAI/polyglot-ko-1.3b) 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 200.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
aeft/Reinforce-Cartpole-v1
aeft
2023-08-18T08:11:33Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-08-18T08:11:25Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
SlimeCore/Maeve-Paladins
SlimeCore
2023-08-18T08:10:41Z
0
0
null
[ "license:openrail", "region:us" ]
null
2023-08-18T07:42:17Z
--- license: openrail --- Paladins is part of © 2023 Copyright Hi-Rez Studios, INC. all rights are reserved to them. If theres any copyright issues ill delete the model. Dataset is taken from the Wiki: https://paladins.fandom.com/wiki/Maeve_voice_lines ---
rukeshsekar/phibert-finetuned-ner
rukeshsekar
2023-08-18T07:58:03Z
107
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "base_model:dmis-lab/biobert-v1.1", "base_model:finetune:dmis-lab/biobert-v1.1", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-08-18T07:05:07Z
--- base_model: dmis-lab/biobert-v1.1 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: phibert-finetuned-ner 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. --> # phibert-finetuned-ner This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0269 - Precision: 0.9282 - Recall: 0.9289 - F1: 0.9285 - Accuracy: 0.9952 ## Model description More information needed ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0407 | 1.0 | 5783 | 0.0448 | 0.8798 | 0.8846 | 0.8822 | 0.9920 | | 0.02 | 2.0 | 11566 | 0.0298 | 0.9165 | 0.9144 | 0.9154 | 0.9939 | | 0.0064 | 3.0 | 17349 | 0.0269 | 0.9282 | 0.9289 | 0.9285 | 0.9952 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
jerome1519/flan-t5-base-finetuned-coding_instructions_2023_08_18__07_51
jerome1519
2023-08-18T07:52:20Z
107
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-08-18T07:51:24Z
--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer model-index: - name: flan-t5-base-finetuned-coding_instructions_2023_08_18__07_51 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-base-finetuned-coding_instructions_2023_08_18__07_51 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) 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: 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 5 | nan | 10.5263 | 8.1081 | 10.5263 | 10.5263 | 19.0 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
sitingGZ/german-bert-clinical-ner
sitingGZ
2023-08-18T07:52:03Z
0
0
null
[ "de", "dataset:bigbio/muchmore", "license:afl-3.0", "region:us" ]
null
2023-08-16T15:00:13Z
--- license: afl-3.0 datasets: - bigbio/muchmore language: - de --- --- ### Model Description <!-- Provide a longer summary of what this model is. --> - **Finetuned from model: bert-base-german-cased ### Model Sources <!-- Provide the basic links for the model. --> - **Repository: https://github.com/sitingGZ/bert-sner - **Paper : [BERT-SNER](https://aclanthology.org/2023.clinicalnlp-1.31/) - **Demo (Coming soon) ## Uses import sys sys.path.append('modules') import torch from transformers import AutoConfig, AutoTokenizer, AutoModelForMaskedLM, EncoderDecoderConfig from BERT2span_semantic_disam import BERT2span from helpers import load_config, set_seed from inference import final_label_results_rescaled base_name = "bert-base-german-cased" configs = load_config('configs/step3_gpu_span_semantic_group.yaml') tokenizer = AutoTokenizer.from_pretrained(base_name) bertMLM = AutoModelForMaskedLM.from_pretrained(base_name) bert_sner = BERT2span(configs, bertMLM, tokenizer) checkpoint_path = "checkpoints/german_bert_ex4cds_500_semantic_term.ckpt" state_dict = torch.load(checkpoint_path, map_location=torch.device('cpu')) bert_sner.load_state_dict(state_dict) bert_sner.eval() suggested_terms = {'Condition': 'Zeichen oder Symptom', 'DiagLab': 'Diagnostisch und Laborverfahren', 'LabValues': 'Klinisches Attribut', 'HealthState': 'Gesunder Zustand', 'Measure': 'Quantitatives Konzept', 'Medication': 'Pharmakologische Substanz', 'Process': 'Physiologische Funktion', 'TimeInfo': 'Zeitliches Konzept'} words = "Aktuell Infekt mit Nachweis von E Coli und Pseudomonas im TBS- CRP 99mg/dl".split() words_list = [words] heatmaps, ner_results = final_label_results_rescaled(words_list, tokenizer, bert_sner, suggested_terms, threshold=0.5) <!-- 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] ## 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 [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed]
mmnga/line-corp-japanese-large-lm-3.6b-instruction-sft-ggml
mmnga
2023-08-18T07:48:32Z
0
1
null
[ "ja", "license:apache-2.0", "region:us" ]
null
2023-08-18T07:03:49Z
--- license: apache-2.0 language: - ja --- # line-corporation/japanese-large-lm-3.6b-instruction-sft [line-corporationさんが公開しているjapanese-large-lm-3.6b-instruction-sft](https://huggingface.co/line-corporation/japanese-large-lm-3.6b-instruction-sft)のggml変換版です。 ## Usage ``` git clone https://github.com/ggerganov/ggml.git cd ggml mkdir build && cd build cmake .. make -j ./bin/gpt-neox -m 'line-corp-japanese-large-lm-3.6b-instruction-sft-ggml-q4_0.bin' -n 128 -t 8 -p 'ユーザー: 四国の県名を全て列挙してください。\nシステム: ' ```
Mel-Iza0/Llama2-7B_ZeroShot-20K_classe_bias_port
Mel-Iza0
2023-08-18T07:32:11Z
1
0
peft
[ "peft", "pytorch", "llama", "region:us" ]
null
2023-08-12T14:59:51Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0
ybkim95-mit/medalpaca-pmdata-readiness25
ybkim95-mit
2023-08-18T07:29:42Z
0
0
peft
[ "peft", "region:us" ]
null
2023-08-18T07:14:11Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
ybkim95-mit/medalpaca-pmdata-stress25
ybkim95-mit
2023-08-18T07:29:01Z
0
0
peft
[ "peft", "region:us" ]
null
2023-08-18T07:13:46Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
ybkim95-mit/medalpaca-pmdata-sleep_quality10
ybkim95-mit
2023-08-18T07:25:50Z
0
0
peft
[ "peft", "region:us" ]
null
2023-08-18T07:14:26Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
ybkim95-mit/medalpaca-pmdata-sleep_quality25
ybkim95-mit
2023-08-18T07:25:05Z
0
0
peft
[ "peft", "region:us" ]
null
2023-08-18T07:14:32Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
ybkim95-mit/medalpaca-globem-depression3
ybkim95-mit
2023-08-18T07:23:53Z
0
0
peft
[ "peft", "region:us" ]
null
2023-08-18T07:15:08Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
ybkim95-mit/medalpaca-globem-depression10
ybkim95-mit
2023-08-18T07:23:21Z
0
0
peft
[ "peft", "region:us" ]
null
2023-08-18T07:15:13Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
iamplus/mpt-30b-v2
iamplus
2023-08-18T07:21:45Z
13
10
transformers
[ "transformers", "pytorch", "mpt", "text-generation", "custom_code", "dataset:ehartford/dolphin", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-07-09T17:44:07Z
--- datasets: - ehartford/dolphin license: apache-2.0 --- **Base Model :** mosaicml/mpt-30b **Tool :** MosaicML's llm-foundry (https://github.com/mosaicml/llm-foundry) **Dataset :** Entire flan3m-GPT3.5 dataset. **Config yaml with Model Params :** https://huggingface.co/iamplus/mpt-30b-v2/blob/main/mpt-30b_orca.yaml **Prompt Format :** ``` <system>: [system prompt] <human>: [question] <bot>: ```
ybkim95-mit/medalpaca-lifesnaps-calories3
ybkim95-mit
2023-08-18T07:21:19Z
0
0
peft
[ "peft", "region:us" ]
null
2023-08-18T07:16:13Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
iamplus/mpt-30b-v3
iamplus
2023-08-18T07:20:56Z
11
2
transformers
[ "transformers", "pytorch", "mpt", "text-generation", "custom_code", "dataset:ehartford/dolphin", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-07-10T07:25:53Z
--- datasets: - ehartford/dolphin license: apache-2.0 --- **Base Model :** iamplus/mpt-30b-v2 **Tool :** MosaicML's llm-foundry (https://github.com/mosaicml/llm-foundry) **Dataset :** Entire flan1m-GPT4 dataset **Config yaml with Model Params :** https://huggingface.co/iamplus/mpt-30b-v3/blob/main/mpt-30b_orca.yaml ***Description :*** **mosaicml/mpt-30b** -> Finetuning on (Entire flan3m-GPT3.5 dataset for 1 epoch) -> **iamplus/mpt-30b-v2** -> Finetuning on (Entire flan1m-GPT4 dataset for 1 epoch) -> **iamplus/mpt-30b-v3** **Prompt Format :** ``` <system>: [system prompt] <human>: [question] <bot>: ```
ybkim95-mit/medalpaca-lifesnaps-calories10
ybkim95-mit
2023-08-18T07:20:33Z
0
0
peft
[ "peft", "region:us" ]
null
2023-08-18T07:16:18Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
Ebo88/llama2-70b-toplink-finetunined-french
Ebo88
2023-08-18T07:19:44Z
0
0
peft
[ "peft", "region:us" ]
null
2023-08-18T07:06:41Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions
ybkim95-mit/medalpaca-pmdata-stress3
ybkim95-mit
2023-08-18T07:19:42Z
0
0
peft
[ "peft", "region:us" ]
null
2023-08-18T07:13:33Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
iamplus/gpt-neoxt-20b-v9
iamplus
2023-08-18T07:14:53Z
13
0
transformers
[ "transformers", "pytorch", "gpt_neox", "text-generation", "dataset:iamplus/Instruction_Tuning", "dataset:iamplus/Conversational_Data", "license:bigscience-openrail-m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-06-04T11:34:32Z
--- license: bigscience-openrail-m datasets: - iamplus/Instruction_Tuning - iamplus/Conversational_Data --- Instruction Tuned GPT-NeoXT-20B model on Instruction Tuning dataset as listed below (~5.2M data) using ***Colossal AI*** **Base Model:** togethercomputer/GPT-NeoXT-Chat-Base-20B (GPT-NeoXT-Chat-Base-20B-v0.16 - fine-tuned on feedback data) **Training Details :** * Epochs: 2 * Batch Size : 5 instantaneous per device x 1 gradient accumulation steps x 8 gpus = 40 * Block Size : 2020 * Weight Decay : 0 * Learning Rate : 1e-6 * Learning Rate Scheduler Type : Cosine * Number of warmup steps : 600 * Machine : 8xA100 80GB **Training Data Specifics :** * Labels are similar to Input ids but with "human" responses and pad tokens masked so that they don't contribute during the model's error calculation. * Block Size is 2020, Multiple instructions are clubbed together in each data. * "###" is the EOS Token used in the data.
iamplus/gpt-neoxt-20b-v6
iamplus
2023-08-18T07:13:44Z
15
0
transformers
[ "transformers", "pytorch", "gpt_neox", "text-generation", "dataset:iamplus/Instruction_Tuning", "license:bigscience-openrail-m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-04-13T21:59:22Z
--- license: bigscience-openrail-m datasets: - iamplus/Instruction_Tuning --- Instruction Tuned GPT-NeoXT-20B model on Instruction Tuning dataset as listed below (~560k data) using ***Colossal AI*** **Base Model:** togethercomputer/GPT-NeoXT-Chat-Base-20B (GPT-NeoXT-Chat-Base-20B-v0.16 - fine-tuned on feedback data) **Training Details :** * Epochs: 5 * Batch Size : 16 instantaneous per device x 1 gradient accumulation steps x 8 gpus = 128 * Max Length : 1024 * Weight Decay : 0 * Learning Rate : 2e-5 * Learning Rate Scheduler Type : Cosine * Number of warmup steps : 240 * Machine : 8xA100 80GB **Dataset Details :** Dataset : iamplus/Instruction_Tuning Files : * stanford_alpaca_it_v2.csv * ColossalChat.csv * unified_chip2.csv * iamai_summarization_v1.csv * iamai_v1.csv
iamplus/gpt-neoxt-20b-v4
iamplus
2023-08-18T07:13:23Z
3
0
transformers
[ "transformers", "pytorch", "gpt_neox", "text-generation", "dataset:iamplus/Instruction_Tuning", "license:bigscience-openrail-m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-04-05T06:26:40Z
--- license: bigscience-openrail-m datasets: - iamplus/Instruction_Tuning --- Instruction Tuned GPT-NeoXT-20B model on Instruction Tuning dataset as listed below (~560k data) using ***Colossal AI*** **Base Model:** togethercomputer/GPT-NeoXT-Chat-Base-20B (GPT-NeoXT-Chat-Base-20B-v0.16 - fine-tuned on feedback data) **Training Details :** * Epochs: 2 * Batch Size : 16 instantaneous per device x 1 gradient accumulation steps x 8 gpus = 128 * Max Length : 1024 * Weight Decay : 0 * Learning Rate : 2e-5 * Learning Rate Scheduler Type : Cosine * Number of warmup steps : 240 * Machine : 8xA100 80GB **Dataset Details :** Dataset : iamplus/Instruction_Tuning Files : * stanford_alpaca_it_v2.csv * ColossalChat.csv * unified_chip2.csv * iamai_summarization_v1.csv * iamai_v1.csv
iamplus/bloomz-7b1-v3
iamplus
2023-08-18T07:12:58Z
3
0
transformers
[ "transformers", "bloom", "text-generation", "dataset:iamplus/Instruction_Tuning", "license:bigscience-openrail-m", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2023-03-20T06:30:03Z
--- license: bigscience-openrail-m datasets: - iamplus/Instruction_Tuning --- Instruction Tuned Bloomz-7B1 model on ChatGPT dataset (85k data) using ***Colossal AI*** **Base Model:** bigscience/bloomz-7b1 **Training Details :** * Epochs: 5 * Batch Size : 32 instantaneous per device x 1 gradient accumulation steps x 8 gpus = 256 * Max Length : 512 * Weight Decay : 0 * Learning Rate : 2e-5 * Learning Rate Scheduler Type : Cosine * Number of warmup steps : 0 * Machine : 8xA100 80GB **Dataset Details :** Dataset : iamplus/Instruction_Tuning Files : * chat_gpt_v1.csv
iamplus/gpt-neoxt-20b-v2
iamplus
2023-08-18T07:12:33Z
14
0
transformers
[ "transformers", "pytorch", "gpt_neox", "text-generation", "dataset:iamplus/Instruction_Tuning", "license:bigscience-openrail-m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-03-25T21:20:40Z
--- license: bigscience-openrail-m datasets: - iamplus/Instruction_Tuning --- Instruction Tuned GPT-NeoXT-20B model on Stanford Alpaca-2 Instruction Tuning dataset (outputs from ChatGPT) (52k data) using ***Colossal AI*** **Base Model:** togethercomputer/GPT-NeoXT-Chat-Base-20B (not fine-tuned on feedback data) **Training Details :** * Epochs: 5 * Batch Size : 16 instantaneous per device x 1 gradient accumulation steps x 8 gpus = 128 * Max Length : 1024 * Weight Decay : 0 * Learning Rate : 2e-5 * Learning Rate Scheduler Type : Cosine * Number of warmup steps : 30 * Machine : 8xA100 80GB **Dataset Details :** Dataset : iamplus/Instruction_Tuning Files : * stanford_alpaca_it_v2.csv
iamplus/bloomz-7b1-stanford-alpaca-v2
iamplus
2023-08-18T07:11:35Z
13
0
transformers
[ "transformers", "pytorch", "bloom", "text-generation", "dataset:iamplus/Instruction_Tuning", "license:bigscience-openrail-m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-03-20T06:46:05Z
--- license: bigscience-openrail-m datasets: - iamplus/Instruction_Tuning --- Instruction Tuned Bloomz-7B1 model on Stanford Alpaca Instruction Tuning dataset (52k data) using ***Colossal AI*** **Base Model:** bigscience/bloomz-7b1 **Training Details :** * Epochs: 5 * Batch Size : 32 instantaneous per device x 1 gradient accumulation steps x 8 gpus = 256 * Max Length : 512 * Weight Decay : 0 * Learning Rate : 2e-5 * Learning Rate Scheduler Type : Cosine * Number of warmup steps : 0 * Machine : 8xA100 80GB **Dataset Details :** Dataset : iamplus/Instruction_Tuning Files : * stanford_alpaca_it.csv
iamplus/bloomz-7b1-cot-v1
iamplus
2023-08-18T07:11:15Z
4
0
transformers
[ "transformers", "bloom", "text-generation", "dataset:iamplus/CoT", "license:bigscience-openrail-m", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2023-03-12T05:54:59Z
--- license: bigscience-openrail-m datasets: - iamplus/CoT --- First Version of Fine Tuned Bloomz-7B1 model on CoT dataset from Flan Data Collection (v2) (~64k data) using ***HF Deepspeed*** **Base Model:** bigscience/bloomz-7b1 **Training Details :** * Epochs: 8 * Batch Size : 5 instantaneous per device x 2 gradient accumulation steps x 8 gpus = 80 * Max Length : 1024 * Weight Decay : 0 * Learning Rate : 5e-5 * Learning Rate Scheduler Type : Linear * Number of warmup steps : 0 * Machine : 8xA100 80GB **Dataset Details :** Dataset : iamplus/CoT Files : * cot_fsnoopt.csv * cot_fsopt.csv * cot_zsnoopt.csv * cot_zsopt.csv **Final Review :** * The model has just memorized/overfitted on the data and is not working good on the samples outside the training data. * Also looks like it has changed the base model weights by too much (catastrophic forgetting). * Similar problems with the Epoch 6 model as well. * Epoch 2 model couldn't find middle ground and not performing well on training data and not on new data as well and increasing just the Epochs is leading to memorization as stated above. **Conclusion :** * Need more quality data for the model to really learn the patterns. Increasing just the epochs with less data only leads to overfitting.
iamplus/bloomz-7b1-stanford-alpaca-v1
iamplus
2023-08-18T07:10:59Z
12
0
transformers
[ "transformers", "pytorch", "bloom", "text-generation", "dataset:iamplus/Instruction_Tuning", "license:bigscience-openrail-m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-03-17T15:02:39Z
--- license: bigscience-openrail-m datasets: - iamplus/Instruction_Tuning --- First Version of Instruction Tuned Bloomz-7B1 model on Stanford Alpaca Instruction Tuning dataset (52k data) using ***HF Deepspeed*** **Base Model:** bigscience/bloomz-7b1 **Training Details :** * Epochs: 4 * Batch Size : 5 instantaneous per device x 3 gradient accumulation steps x 8 gpus = 120 * Max Length : 1024 * Weight Decay : 0 * Learning Rate : 5e-5 * Learning Rate Scheduler Type : Linear * Number of warmup steps : 40 * Machine : 8xA100 80GB **Dataset Details :** Dataset : iamplus/Instruction_Tuning Files : * stanford_alpaca_it.csv
aratshimyanga/q-taxi-v3
aratshimyanga
2023-08-18T07:09:56Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-08-18T07:09:54Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="aratshimyanga/q-taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
BenjaminOcampo/model-contrastive-bert__trained-in-dynahate__seed-42
BenjaminOcampo
2023-08-18T07:09:37Z
4
0
transformers
[ "transformers", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-08-18T07:08:45Z
--- language: en --- # Model Card for BenjaminOcampo/model-contrastive-bert__trained-in-dynahate__seed-42 <!-- 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:** BenjaminOcampo - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** en - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** https://github.com/huggingface/huggingface_hub - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
F-Haru/WMT_Metrics_da_data
F-Haru
2023-08-18T07:08:07Z
0
0
null
[ "region:us" ]
null
2023-08-17T10:56:50Z
metrics_finetuning_teacher.py で「paraphrase-mpnet-base-v2」をファインチューニングをするコードになっている metrics_finetuning_student.py で「paraphrase-multilingual-mpnet-base-v2」をファインチューニングをするコードになっている distillation.py で知識蒸留をするコードになっている
aratshimyanga/q-FrozenLake-v1-4x4-noSlippery
aratshimyanga
2023-08-18T07:08:02Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-08-18T07:08:00Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="aratshimyanga/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
huytx267/function_retrieval
huytx267
2023-08-18T07:05:02Z
8
0
sentence-transformers
[ "sentence-transformers", "pytorch", "distilbert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-08-18T06:39:44Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 5 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
cecb/nuixmodel
cecb
2023-08-18T07:04:35Z
1
0
peft
[ "peft", "safetensors", "llama", "region:us" ]
null
2023-08-17T21:05:23Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
zhangbo2008/best_llm_train06P55P27p2023
zhangbo2008
2023-08-18T06:55:28Z
1
0
peft
[ "peft", "region:us" ]
null
2023-08-18T06:55:27Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
zhangbo2008/best_llm_train6p54p37p2023
zhangbo2008
2023-08-18T06:54:38Z
0
0
peft
[ "peft", "region:us" ]
null
2023-08-18T06:54:37Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
kimdwan/klue-roberta-finetuned-korquad-LOGAN
kimdwan
2023-08-18T06:53:40Z
101
3
transformers
[ "transformers", "pytorch", "roberta", "question-answering", "license:other", "endpoints_compatible", "region:us" ]
question-answering
2023-08-18T06:28:18Z
--- license: other --- 허깅페이스 korquad를 추가적으로 파인튜닝한 모델입니다. 코드는 아래와 같습니다. 많은 사랑 부탁드립니다. ``` from transformers import RobertaForQuestionAnswering,AutoTokenizer import torch path = 'kimdwan/klue-roberta-finetuned-korquad-LOGAN' model = RobertaForQuestionAnswering.from_pretrained(path) tokenizer = AutoTokenizer.from_pretrained(path) # 문장예시 입니다. text = """ 대한민국(大韓民國, 영어: Republic of Korea, ROK), 약칭 한국(韓國, 영어: Korea), 남한(南韓, 영어: South Korea), 남조선(南朝鮮)은 동아시아의 한반도 남부에 위치한 국가이다. 현정체제는 대한민국 제6공화국이다. 대한민국의 국기는 대한민국 국기법에 따라 태극기[5]이며, 국가는 관습상 애국가, 국화는 관습상 무궁화이다. 공용어는 한국어와 한국 수어이다. 수도는 서울이다. 인구는 2023년을 기준으로 약 5,174만 명으로, 전체 인구 중 절반이(약 2,624만 명) 수도권에 살고 있다.[6] 대한민국은 1948년 5월 10일 총선거를 통해 제헌국회를 구성하였고, 1948년 8월 15일 대한민국 정부를 수립하였다. 1948년 제헌 국회에서 대한민국의 국호를 계승하여 헌법에 명시하였고, 다시 1950년 1월 16일 국무원 고시 제7호 ‘국호 및 일부 지방명과 지도색 사용에 관한 건’에 의해 확정하였다. """ # 질문을 적습니다. question = "대한민국 정부 수립 시기는?" #이제 부터입니다. confix = question + "[SEP]" + text token = tokenizer(confix,return_tensors="pt") model.eval() output = model(**token) _,start = torch.max(output['start_logits'],dim=-1) _,end = torch.max(output['end_logits'],dim=-1) last = token["input_ids"][0][start.item():end.item()+1] tokenizer.decode(last) ```
Kadan2023/PlantsMix
Kadan2023
2023-08-18T06:51:16Z
0
2
null
[ "stable-diffusion", "text-to-image", "ja", "en", "license:other", "region:us" ]
text-to-image
2023-08-17T11:39:05Z
--- license: other language: - ja - en tags: - stable-diffusion - text-to-image --- <div class="flex justify-center"> <div class="container p-0 w-100"> <img class="mt-0 object-cover rounded-t-lg w-100" style="height: 320px;" src="https://huggingface.co/Kadan2023/PlantsMix/resolve/main/images/header.png" width="100%"/> <div class="flex px-4"> <div class="flex-auto"> <h1 class="mb-2 text-3xl font-bold leading-tight" style="color: rgb(77 124 147/var(--tw-text-opacity));"> PlantsMix <a href="https://huggingface.co/Kadan2023/PlantsMix/resolve/main/README.md" class="ml-2 inline-block"> <svg xmlns="http://www.w3.org/2000/svg" class="h-5 w-5" fill="none" viewBox="0 0 24 24" strokeWidth={1.5} stroke="currentColor" className="w-6 h-6"> <path strokeLinecap="round" strokeLinejoin="round" d="M3.75 3.75v4.5m0-4.5h4.5m-4.5 0L9 9M3.75 20.25v-4.5m0 4.5h4.5m-4.5 0L9 15M20.25 3.75h-4.5m4.5 0v4.5m0-4.5L15 9m5.25 11.25h-4.5m4.5 0v-4.5m0 4.5L15 15" /> </svg> </a> </h1> <p class="mb-4 text-base text-neutral-600 dark:text-neutral-200"> 注意:SD1.5、レシピ非公開プライベートモデルです。使用方法、リスクについてはご自身でお調べになり、ご理解の上使用ください。 <br> NOTE: This is an SD1.5 recipe private model. Please research and understand the usage and risks yourself before making a decision to use this product. </p> </div> </div> </div> </div> <hr class="my-6 h-0.5 border-t-0 opacity-100 dark:opacity-50" style="background-color: rgb(245 245 245/var(--tw-bg-opacity));"> <h3 id="PlantsMix" class="mt-0 text-2xl"> <code>PlantsMix</code> <small>(<code>@75a24d317f</code>)</small> </h3> <div> <h4>ライセンス / License</h4> <div class="px-2"> <table class="table-fixed border mt-0 text-xs"> <tbody> <tr> <td class="px-4 text-base" colspan="2"> <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license"> 修正 CreativeML OpenRAIL-M ライセンス / Modified CreativeML OpenRAIL-M license </a> </td> </tr> <tr> <td class="align-middle px-2 w-8"> <span class="text-green-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" /> </svg> </span> </td> <td> このモデルのクレジットを入れずに使用する<br> Use the model without crediting the creator </td> </tr> <tr> <td class="align-middle px-2 w-8"> <span class="text-green-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" /> </svg> </span> </td> <td> このモデルで生成した画像を商用利用する<br> Sell images they generate </td> </tr> <tr class="bg-danger-100"> <td class="align-middle px-2 w-8"> <span class="text-red-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" /> </svg> </span> </td> <td> このモデルを商用の画像生成サービスで利用する</br> Run on services that generate images for money </td> </tr> <tr> <td class="align-middle px-2 w-8"> <span class="text-green-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" /> </svg> </span> </td> <td> このモデルを使用したマージモデルを共有する<br> Share merges using this model </td> </tr> <tr class="bg-danger-100"> <td class="align-middle px-2 w-8"> <span class="text-red-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" /> </svg> </span> </td> <td> このモデル、またはこのモデルをマージしたモデルを販売する</br> Sell this model or merges using this model </td> </tr> <tr class="bg-danger-100"> <td class="align-middle px-2 w-8"> <span class="text-red-500"> <svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6"> <path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" /> </svg> </span> </td> <td> このモデルをマージしたモデルに異なる権限を設定する</br> Have different permissions when sharing merges </td> </tr> </tbody> </table> </div> <h4>推奨設定 / Recommended Settings</h4> <div class="px-2"> <table class="table-auto border mt-0 text-sm"> <tbody> <tr> <td class="pl-2" style="width: 12rem;"> VAE </td> <td> <a href="https://civitai.com/models/22354/clearvae">ClearVAE</a> </td> </tr> <tr> <td class="pl-2"> Negative Embedding </td> <td> <a href="https://huggingface.co/gsdf/Counterfeit-V3.0/tree/main/embedding">EasyNegativeV2</a><br> <a href="https://huggingface.co/yesyeahvh/bad-hands-5/tree/main">badhandv4</a> ※badhandv4はなくても良い、ある方が崩壊するかも </td> </tr> </tbody> </table> </div> <h4>例 / Examples</h4> <div class="container mx-auto px-2"> <div class="flex flex-wrap min-w-min items-baseline"> <div class="p-1 flex-1" style="width: 50%; min-width: 320px;"> <div class="flex-1"> <img alt="gallery" class="block h-full w-full rounded-t-lg object-contain object-center" src="https://huggingface.co/Kadan2023/PlantsMix/resolve/main/images/1.png" loading="lazy" /> </div> <div class="w-full"> <pre class="w-full" style="white-space: pre-line;"> Ultra detailed, best quality, official art, masterpiece, illustration,Detailed face and long eyelashes, ray tracing, depth of field. BREAK 13 year old (boy) and a girl. They are standing next to each other. Victorian style royalcore twin-corded fashion. They are very similar but with different elements. BREAK The season is summer, Cute and dreamy backgrounds are the beauty they deserve. art by Representing Artists. Negative prompt: nsfw, blush, EasyNegativeV2, signature, watermark, username, blurry, artist name. Steps: 25 Sampler: DDIM CFG scale: 7 Seed: 3126641670 Clip Skip: 1 </pre> </div> </div> <div class="p-1 flex-1" style="width: 50%; min-width: 320px;"> <div class="w-full"> <img alt="gallery" class="block h-full w-full rounded-t-lg object-contain object-center" src="https://huggingface.co/Kadan2023/PlantsMix/resolve/main/images/2.png" loading="lazy" /> </div> <div class="w-full"> <pre class="w-full" style="white-space: pre-line;"> Ultra detailed, best quality, official art, masterpiece, illustration, Detailed face and long eyelashes, solo, cowboy shot, ray tracing, depth of field. BREAK.girl in magical fantasy Night. cute magicalcore fashion. The season is summer. Stars and light effects, Cute and dreamy backgrounds are the beauty deserve. art by Representing Artists. Negative prompt: EasyNegativeV2, badhandv4, nsfw, blush, signature, watermark, username, blurry, artist name. Steps: 25 Sampler: DDIM CFG scale: 7 Seed: 170819351 Clip Skip: 1 </pre> </div> </div> </div> </div> </div>
camus-ng/lora-trained-xl-cory-10
camus-ng
2023-08-18T06:34:51Z
1
0
diffusers
[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]
text-to-image
2023-08-16T10:22:22Z
--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of <ntvc> man tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - camus-ng/lora-trained-xl-cory-10 These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of <ntvc> man 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: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
xianbin/a2c-PandaReachDense-v2
xianbin
2023-08-18T06:31:12Z
10
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "arxiv:2106.13687", "model-index", "region:us" ]
reinforcement-learning
2023-07-28T03:03:48Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -2.58 +/- 0.78 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ``` Panda Gym environments: [arxiv.org/abs/2106.13687](https://arxiv.org/abs/2106.13687)
PM-AI/sts_paraphrase_xlm-roberta-base_de-en
PM-AI
2023-08-18T06:30:56Z
9
4
transformers
[ "transformers", "pytorch", "xlm-roberta", "feature-extraction", "semantic textual similarity", "sts", "semantic search", "sentence similarity", "paraphrasing", "sentence-transformer", "sentence-similarity", "de", "en", "arxiv:2004.09813", "license:mit", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-01-09T12:35:09Z
--- language: - de - en pipeline_tag: sentence-similarity tags: - semantic textual similarity - sts - semantic search - sentence similarity - paraphrasing - sentence-transformer - feature-extraction - transformers license: mit --- # Model card for PM-AI/sts_paraphrase_xlm-roberta-base_de-en ## Model summary Transformer model for **Semantic Textual Similarity (STS)** for _German_ and _Englisch_ sentences/texts. The embeddings output can be used for **semantic search**, **paraphrasing** and **retrieval** with _cosine similarity_. The Model is applicable to _Englisch-German-Mixed_ sentences/texts but also for _Englisch only_ and _German only_. The model can be easily used with [Sentence Transformer](https://github.com/UKPLab/sentence-transformers) library. ## Training This model is based on a training approach from 2020 by Philip May, who published the [T-Systems-onsite/cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer) model. We updated this approach by a new base model for fine-tuning and some extensions to the training data. These changes are discussed in the next sections. ### Training Data The model is based on training with samples from [STSb](https://huggingface.co/datasets/stsb_multi_mt), [SICK](https://huggingface.co/datasets/mteb/sickr-sts) and [Priya22 semantic textual relatedness](https://github.com/Priya22/semantic-textual-relatedness) datasets. They contain about 76.000 sentence pairs in total. These sentence pairs are based on _German-German_, _English-English_ and _German-English mixed_. The training object is to optimize for _cosine similarity loss_ based on a human annoted sentence similarity score. In terms of content, the samples are based on rather simple sentences. When the TSystems model was published, only the STSb dataset was used for STS training. Therefore it is included in our model, but expanded to include SICK and Priya22 semantic textual relatedness: - SICK was partly used in STSb, but our custom translation using [DeepL](https://www.deepl.com/) leads to slightly different phrases. This approach allows more examples to be included in the training. - The Priya22 semantic textual relatedness dataset published in 2022 was also translated into German via DeepL and added to the training data. Since it does not have a train-test-split, it was created independently at a ratio of 80:20. All training and test data (STSb, Sick, Priya22) were checked for duplicates within and with each other and removed if found. Because the test data is prioritized, duplicated entries between test-train are exclusively removed from train split. The final used datasets can be viewed here: [datasets_sts_paraphrase_xlm-roberta-base_de-en](https://gitlab.com/sense.ai.tion-public/datasets_sts_paraphrase_xlm-roberta-base_de-en) ### Training Befor fine-tuning for STS we made the English paraphrasing model [paraphrase-distilroberta-base-v1](https://huggingface.co/sentence-transformers/paraphrase-distilroberta-base-v1) usable for German by applying **[Knowledge Distillation](https://arxiv.org/abs/2004.09813)** (_Teacher-Student_ approach). The TSystems model used version 1, which is based on 7 different datasets and contains around 24.6 million samples. We are using version 2 with 12 datasets and about 83.3 million examples. Details for this process here: [PM-AI/paraphrase-distilroberta-base-v2_de-en](https://huggingface.co/PM-AI/paraphrase-distilroberta-base-v2_de-en) For fine-tuning we are using SBERT's [training_stsbenchmark_continue_training.py](https://github.com/UKPLab/sentence-transformers/blob/b86eec31cf0a102ad786ba1ff31bfeb4998d3ca5/examples/training/sts/training_stsbenchmark_continue_training.py) training script. One thing has been changed in this training script: when a sentence pair consists of identical utterances the score is set to 5.0 (maximum). It makes no sense to say identical sentences have a score of 4.8 or 4.9. #### Parameterization of training - **Script:** [training_stsbenchmark_continue_training.py](https://github.com/UKPLab/sentence-transformers/blob/b86eec31cf0a102ad786ba1ff31bfeb4998d3ca5/examples/training/sts/training_stsbenchmark_continue_training.py) - **Datasets:** [datasets_sts_paraphrase_xlm-roberta-base_de-en/all_cross_train_unique.csv](https://gitlab.com/sense.ai.tion-public/datasets_sts_paraphrase_xlm-roberta-base_de-en) - **GPU:** NVIDIA A40 (Driver Version: 515.48.07; CUDA Version: 11.7) - **Batch Size:** 32 - **Base Model:** [PM-AI/paraphrase-distilroberta-base-v2_de-en](PM-AI/paraphrase-distilroberta-base-v2_de-en) - **Loss Function:** Cosine Similarity - **Learning Rate:** 2e-5 - **Epochs:** 3 - **Evaluation Samples:** 500 - **Evaluation Steps:** 1000 - **Warmup Steps:** 10% ### Evaluation <a name="evaluation"></a> Now the performance is measured cross-lingually as well as for German and English only. In addition, the test samples used are evaluated individually for each data set (STSb, SICK, Priya22), as well as in a large combined test data set (all). This subdivision per data set allows for a fair overall assessment, since external models are not built on the same data basis as the model presented here. The data is not evenly distributed in either training or testing! **❗Some models are only usable for one language (because they are monolingual). They will almost not perform at all in the other two tables. Still, they are good models in certain applications❗** The first table shows the evaluation results for **cross-lingual (German-English-Mixed)** based on _Spearman_: **model**|**STSb**|**SICK**|**Priya22**|**all**| :-----:|:-----:|:-----:|:-----:|:-----: [PM-AI/sts_paraphrase_xlm-roberta-base_de-en (ours)](https://huggingface.co/PM-AI/sts_paraphrase_xlm-roberta-base_de-en) | 0.8672 <br /> 🏆 | 0.8639 <br /> 🏆 | 0.8354 <br /> 🏆 | 0.8711 <br /> 🏆 [T-Systems-onsite/cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer) | 0.8525 | 0.7642 | 0.7998 | 0.8216 [PM-AI/paraphrase-distilroberta-base-v2_de-en (ours, no fine-tuning)](PM-AI/paraphrase-distilroberta-base-v2_de-en) | 0.8225 | 0.7579 | 0.8255 | 0.8109 [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) | 0.8310 | 0.7529 | 0.8184 | 0.8102 [sentence-transformers/stsb-xlm-r-multilingual](https://huggingface.co/sentence-transformers/stsb-xlm-r-multilingual) | 0.8194 | 0.7703 | 0.7566 | 0.7998 [sentence-transformers/paraphrase-xlm-r-multilingual-v1](https://huggingface.co/sentence-transformers/paraphrase-xlm-r-multilingual-v1) | 0.7985 | 0.7217 | 0.7975 | 0.7838 [sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1](https://huggingface.co/sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1) | 0.7985 | 0.7217 | 0.7975 | 0.7838 [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) | 0.7823 | 0.7090 | 0.7830 | 0.7834 [sentence-transformers/distiluse-base-multilingual-cased-v1](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1) | 0.7449 | 0.6941 | 0.7607 | 0.7534 [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2) | 0.7517 | 0.6950 | 0.7619 | 0.7496 [sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking](https://huggingface.co/sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking) | 0.7211 | 0.6650 | 0.7382 | 0.7200 [Sahajtomar/German-semantic](https://huggingface.co/Sahajtomar/German-semantic) | 0.7170 | 0.5871 | 0.7204 | 0.6802 [symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli](https://huggingface.co/symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli) | 0.6488 | 0.5489 | 0.6688 | 0.6303 [sentence-transformers/sentence-t5-large](https://huggingface.co/sentence-transformers/sentence-t5-large) | 0.6849 | 0.6063 | 0.7360 | 0.5843 [sentence-transformers/sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.6013 | 0.5213 | 0.6671 | 0.5068 [sentence-transformers/gtr-t5-large](https://huggingface.co/sentence-transformers/gtr-t5-large) | 0.5881 | 0.5168 | 0.6674 | 0.4984 [deepset/gbert-large-sts](https://huggingface.co/deepset/gbert-large-sts) | 0.3842 | 0.3537 | 0.4105 | 0.4362 [sentence-transformers/gtr-t5-base](https://huggingface.co/sentence-transformers/gtr-t5-base) | 0.5204 | 0.4346 | 0.6008 | 0.4276 [textattack/bert-base-uncased-STS-B](https://huggingface.co/textattack/bert-base-uncased-STS-B) | 0.0669 | 0.1135 | 0.0105 | 0.1514 [symanto/xlm-roberta-base-snli-mnli-anli-xnli](https://huggingface.co/symanto/xlm-roberta-base-snli-mnli-anli-xnli) | 0.1694 | 0.0440 | 0.0521 | 0.1156 The second table shows the evaluation results for **German only** based on _Spearman_: **model**|**STSb**|**SICK**|**Priya22**|**all**| :-----:|:-----:|:-----:|:-----:|:-----: [PM-AI/sts_paraphrase_xlm-roberta-base_de-en (ours)](https://huggingface.co/PM-AI/sts_paraphrase_xlm-roberta-base_de-en) | 0.8658 <br /> 🏆 | 0.8775 <br /> 🏆 | 0.8432 <br /> 🏆 | 0.8747 <br /> 🏆 [T-Systems-onsite/cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer) | 0.8547 | 0.8047 | 0.8068 | 0.8327 [Sahajtomar/German-semantic](https://huggingface.co/Sahajtomar/German-semantic) | 0.8485 | 0.7915 | 0.8139 | 0.8280 [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) | 0.8360 | 0.7941 | 0.8237 | 0.8178 [PM-AI/paraphrase-distilroberta-base-v2_de-en (ours, no fine-tuning)](PM-AI/paraphrase-distilroberta-base-v2_de-en) | 0.8297 | 0.7930 | 0.8341 | 0.8170 [sentence-transformers/stsb-xlm-r-multilingual](https://huggingface.co/sentence-transformers/stsb-xlm-r-multilingual) | 0.8190 | 0.8027 | 0.7674 | 0.8072 [sentence-transformers/paraphrase-xlm-r-multilingual-v1](https://huggingface.co/sentence-transformers/paraphrase-xlm-r-multilingual-v1) | 0.8079 | 0.7844 | 0.8126 | 0.8034 [sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1](https://huggingface.co/sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1) | 0.8079 | 0.7844 | 0.8126 | 0.8034 [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) | 0.7891 | 0.7830 | 0.8010 | 0.7981 [sentence-transformers/distiluse-base-multilingual-cased-v1](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1) | 0.7705 | 0.7612 | 0.7899 | 0.7780 [sentence-transformers/sentence-t5-large](https://huggingface.co/sentence-transformers/sentence-t5-large) | 0.7771 | 0.7724 | 0.7829 | 0.7727 [sentence-transformers/sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.7361 | 0.7613 | 0.7643 | 0.7602 [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2) | 0.7467 | 0.7494 | 0.7684 | 0.7584 [sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking](https://huggingface.co/sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking) | 0.7419 | 0.7420 | 0.7692 | 0.7566 [sentence-transformers/gtr-t5-large](https://huggingface.co/sentence-transformers/gtr-t5-large) | 0.7252 | 0.7201 | 0.7613 | 0.7447 [sentence-transformers/gtr-t5-base](https://huggingface.co/sentence-transformers/gtr-t5-base) | 0.7058 | 0.6943 | 0.7462 | 0.7271 [symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli](https://huggingface.co/symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli) | 0.7284 | 0.7136 | 0.7109 | 0.6997 [deepset/gbert-large-sts](https://huggingface.co/deepset/gbert-large-sts) | 0.6576 | 0.7141 | 0.6769 | 0.6959 [textattack/bert-base-uncased-STS-B](https://huggingface.co/textattack/bert-base-uncased-STS-B) | 0.4427 | 0.6023 | 0.4380 | 0.5380 [symanto/xlm-roberta-base-snli-mnli-anli-xnli](https://huggingface.co/symanto/xlm-roberta-base-snli-mnli-anli-xnli) | 0.4154 | 0.5048 | 0.3478 | 0.4540 And last but not least our third table which shows the evaluation results for **English only** based on _Spearman_: **model**|**STSb**|**SICK**|**Priya22**|**all**| :-----:|:-----:|:-----:|:-----:|:-----: [PM-AI/sts_paraphrase_xlm-roberta-base_de-en (ours)](https://huggingface.co/PM-AI/sts_paraphrase_xlm-roberta-base_de-en) | 0.8768 <br /> 🏆 | 0.8705 <br /> 🏆 | 0.8402 | 0.8748 <br /> 🏆 [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) | 0.8682 | 0.8065 | 0.8430 | 0.8378 [PM-AI/paraphrase-distilroberta-base-v2_de-en (ours, no fine-tuning)](PM-AI/paraphrase-distilroberta-base-v2_de-en) | 0.8597 | 0.8105 | 0.8399 | 0.8363 [T-Systems-onsite/cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer) | 0.8660 | 0.7897 | 0.8097 | 0.8308 [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) | 0.8441 | 0.8059 | 0.8175 | 0.8300 [sentence-transformers/sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.8551 | 0.8063 | 0.8434 | 0.8235 [sentence-transformers/sentence-t5-large](https://huggingface.co/sentence-transformers/sentence-t5-large) | 0.8536 | 0.8097 | 0.8475 <br /> 🏆 | 0.8191 [sentence-transformers/stsb-xlm-r-multilingual](https://huggingface.co/sentence-transformers/stsb-xlm-r-multilingual) | 0.8503 | 0.8009 | 0.7675 | 0.8162 [sentence-transformers/paraphrase-xlm-r-multilingual-v1](https://huggingface.co/sentence-transformers/paraphrase-xlm-r-multilingual-v1) | 0.8350 | 0.7645 | 0.8211 | 0.8050 [sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1](https://huggingface.co/sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1) | 0.8350 | 0.7645 | 0.8211 | 0.8050 [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2) | 0.8075 | 0.7534 | 0.7908 | 0.7828 [sentence-transformers/distiluse-base-multilingual-cased-v1](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1) | 0.8061 | 0.7421 | 0.7923 | 0.7784 [Sahajtomar/German-semantic](https://huggingface.co/Sahajtomar/German-semantic) | 0.8061 | 0.7098 | 0.7709 | 0.7712 [sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking](https://huggingface.co/sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking) | 0.7866 | 0.7477 | 0.7700 | 0.7691 [sentence-transformers/gtr-t5-large](https://huggingface.co/sentence-transformers/gtr-t5-large) | 0.7763 | 0.7258 | 0.8124 | 0.7675 [sentence-transformers/gtr-t5-base](https://huggingface.co/sentence-transformers/gtr-t5-base) | 0.7961 | 0.7129 | 0.8147 | 0.7669 [symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli](https://huggingface.co/symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli) | 0.7799 | 0.7415 | 0.7335 | 0.7376 [deepset/gbert-large-sts](https://huggingface.co/deepset/gbert-large-sts) | 0.5703 | 0.6011 | 0.5673 | 0.6060 [textattack/bert-base-uncased-STS-B](https://huggingface.co/textattack/bert-base-uncased-STS-B) | 0.4978 | 0.6099 | 0.5505 | 0.5754 [symanto/xlm-roberta-base-snli-mnli-anli-xnli](https://huggingface.co/symanto/xlm-roberta-base-snli-mnli-anli-xnli) | 0.3830 | 0.5180 | 0.3056 | 0.4414 **❗It is crucial to understand that:** - Only our model has seen training data from STSb, SICK and Priya22 combined, which is one reason for better results. The model has simply been trained to be more sensitive to these type of samples. - The datasets are not proportionally aligned in terms of their number of examples. For example, Priya22 is significantly underrepresented. - The compared models are of different sizes, which affects resource consumption (CPU, RAM) and inference speed (benchmark). So-called "large" models usually perform better, but also cost more (resources, monetary value) than e.g. "base" models. - Multilingual models are usually made multilingual by Knowledge Distillation, starting from a monolingual state. Therefore, they usually perform somewhat better in the original language. ### Acknowledgment This work is a collaboration between [Technical University of Applied Sciences Wildau (TH Wildau)](https://en.th-wildau.de/) and [sense.ai.tion GmbH](https://senseaition.com/). You can contact us via: * [Philipp Müller (M.Eng.)](https://www.linkedin.com/in/herrphilipps); Author * [Prof. Dr. Janett Mohnke](mailto:[email protected]); TH Wildau * [Dr. Matthias Boldt, Jörg Oehmichen](mailto:[email protected]); sense.AI.tion GmbH This work was funded by the European Regional Development Fund (EFRE) and the State of Brandenburg. Project/Vorhaben: "ProFIT: Natürlichsprachliche Dialogassistenten in der Pflege". <div style="display:flex"> <div style="padding-left:20px;"> <a href="https://efre.brandenburg.de/efre/de/"><img src="https://huggingface.co/datasets/PM-AI/germandpr-beir/resolve/main/res/EFRE-Logo_rechts_oweb_en_rgb.jpeg" alt="Logo of European Regional Development Fund (EFRE)" width="200"/></a> </div> <div style="padding-left:20px;"> <a href="https://www.senseaition.com"><img src="https://senseaition.com/wp-content/uploads/thegem-logos/logo_c847aaa8f42141c4055d4a8665eb208d_3x.png" alt="Logo of senseaition GmbH" width="200"/></a> </div> <div style="padding-left:20px;"> <a href="https://www.th-wildau.de"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/f/f6/TH_Wildau_Logo.png/640px-TH_Wildau_Logo.png" alt="Logo of TH Wildau" width="180"/></a> </div> </div>
Joe-Joe/QuandaleDingle
Joe-Joe
2023-08-18T06:10:20Z
0
0
null
[ "arxiv:1910.09700", "license:openrail", "region:us" ]
null
2023-08-18T06:01:35Z
--- license: openrail --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
vishwanath2003/mymlappp
vishwanath2003
2023-08-18T05:48:17Z
0
0
null
[ "license:bigscience-openrail-m", "region:us" ]
null
2023-08-18T05:47:11Z
--- license: bigscience-openrail-m ---
LarryAIDraw/noa
LarryAIDraw
2023-08-18T05:28:23Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-07-18T20:08:31Z
--- license: creativeml-openrail-m --- https://civitai.com/models/106525/ushio-noa-blue-archive-or-goofy-ai
warshakhan/donut-base-ISynHMP
warshakhan
2023-08-18T05:26:59Z
4
0
transformers
[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base", "base_model:finetune:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]
image-text-to-text
2023-08-16T10:36:36Z
--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-base-ISynHMP results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # donut-base-ISynHMP This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. ## Model description Fine_tuned Donut_Model for Document Parsing task on ISynGen_HMP Dataset ## Intended uses & limitations More information needed ## Training and evaluation data ISynGen_HMP - private Handwritten Medical Prescription dataset. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
JabrilJacobs/ppo-LunarLander-v2
JabrilJacobs
2023-08-18T05:26:07Z
3
0
transformers
[ "transformers", "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "endpoints_compatible", "region:us" ]
reinforcement-learning
2022-12-11T06:58:46Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -203.44 +/- 98.41 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'JabrilJacobs/ppo-LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
rohn132/dqn-SpaceInvadersNoFrameskip-v4
rohn132
2023-08-18T05:15:09Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-08-18T05:14:33Z
--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 626.00 +/- 178.90 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rohn132 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rohn132 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga rohn132 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
heegyu/polyglot-ko-5.8b-chat
heegyu
2023-08-18T05:08:42Z
2,282
3
transformers
[ "transformers", "pytorch", "gpt_neox", "text-generation", "ko", "dataset:beomi/KoAlpaca-v1.1a", "dataset:dbdu/ShareGPT-74k-ko", "dataset:heegyu/korquad-chat-v1", "dataset:HAERAE-HUB/KoInstruct-QA", "dataset:changpt/ko-lima-vicuna", "dataset:nlpai-lab/kullm-v2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-08-18T00:24:41Z
--- datasets: - beomi/KoAlpaca-v1.1a - dbdu/ShareGPT-74k-ko - heegyu/korquad-chat-v1 - HAERAE-HUB/KoInstruct-QA - changpt/ko-lima-vicuna - nlpai-lab/kullm-v2 language: - ko --- # heegyu/polyglot-ko-5.8b-chat - [EleutherAI/polyglot-ko-5.8b](https://huggingface.co/EleutherAI/polyglot-ko-5.8b)를 여러 한국어 instruction 데이터셋으로 학습한 모델 ## 사용한 데이터셋 | Dataset | # instance | 타입 | | --- | --- | --- | | [KoAlpaca v1.1](https://raw.githubusercontent.com/Beomi/KoAlpaca/main/KoAlpaca_v1.1.jsonl) | 50K | 싱글턴 | | [dbdu/ShareGPT-74k-ko 의 part2_ko_uncleaned](https://huggingface.co/datasets/dbdu/ShareGPT-74k-ko/resolve/main/part2_ko_uncleaned.json) | 36K | 멀티턴 | | [heegyu/korquad-chat-v1](https://huggingface.co/datasets/heegyu/korquad-chat-v1) | 9.6K | 멀티턴, 지식기반 | | [lcw99/evolve-instruct](https://github.com/lcw99/evolve-instruct/) | 37K | 싱글턴 | | [HAERAE-HUB/KoInstruct-QA](https://huggingface.co/datasets/HAERAE-HUB/KoInstruct-QA) | 50.3k | 싱글턴 | | [changpt/ko-lima-vicuna](https://huggingface.co/datasets/changpt/ko-lima-vicuna) | 1K | 싱글턴, 멀티턴(극히 일부) | | [nlpai-lab/kullm-v2](https://huggingface.co/datasets/nlpai-lab/kullm-v2) | 15K | 싱글턴 | - KULLM v2 데이터셋에서는 GPT4ALL, Dolly 데이터만 추출해서 사용했습니다. - 다양한 학습 데이터셋은 [HeegyuKim/open-korean-instructions](https://github.com/HeegyuKim/open-korean-instructions) GitHub repository를 참고하세요. ## 생성 Prompt - EOS token(<|endoftext|>)이 나올 때까지 생성하면 됩니다. - 최상단 프롬프트는 있는게 좋은 답변이 더 자주 나오는 것 같아요. ``` 당신은 AI 챗봇입니다. 사용자에게 도움이 되고 유익한 내용을 제공해야합니다. 답변은 길고 자세하며 친절한 설명을 덧붙여서 작성하세요. ### 사용자: 서울 관광지 추천해줘요 ### 챗봇: ``` 생성된 답변: ``` 서울 관광지를 추천하겠습니다. 경복궁 (경복궁 또는 경복궁) 경복궁은 한국 전통 궁궐로서 조선 시대의 역사와 건축 철학을 체험할 수 있는 공간입니다. 궁을 방문하면 조선시대 생활과 통치방식에 대해 배울 수 있으며, 동시에 전통적인 궁궐 문화를 체험할 수 있습니다. 창덕궁 (창덕궁) 창덕궁은 조선시대에 왕실의 정원으로 유명한 궁입니다. 한국의 르네상스 시대를 보여주는 아름다움을 경험할 수 있으며, 전통적인 건축양식을 경험할 수 있는 기회가 주어집니다. 덕수궁 (덕수궁) 덕수궁은 1896년에 설립된 왕이 사는 궁궐입니다. 당시의 아름다움과 건축 철학을 동시에 느낄 수 있는 궁궐입니다. 한국의 궁궐 문화, 역사 및 자연 환경에 대한 학습을 원하는 사람에게 추천됩니다. 종묘 (종묘) 종묘는 조선시대의 왕실 정원으로서 궁궐과 함께 왕의 거처 및 제사를 지내는 곳입니다. 전통적인 의례, 역사 및 건축 철학을 느끼고 체험할 수 있는 곳입니다. 한옥마을 (한옥마을) 한옥마을은 서울 중심부에 위치한 유명한 관광지로, 한국 전통 문화 체험을 원하는 사람과 외국인에게도 인기가 많습니다. 한옥 건물과 전통 정원을 경험할 수 있으며, 도시 속의 전통 문화를 경험할 수 있는 기회를 제공합니다. 인사동 (인사동) 인사동은 한국의 전통적인 문화와 예술, 음식 및 쇼핑을 체험할 수 있는 곳입니다. 한국의 전통문화를 체험하고, 전통 문화의 아름다움을 느낄 수 있는 장소입니다. 위의 관광지를 방문함으로써, 우리는 한국의 전통적인 문화와 역사를 경험하고 배울 수 있습니다. 또한, 아름다움과 건축 철학에 대한 이해도를 높일 수 있으며, 우리 지역의 문화, 전통, 역사를 더욱 이해하고 사랑하게 될 수 있습니다. ``` 멀티턴 대화 예시 ``` 당신은 AI 챗봇입니다. 사용자에게 도움이 되고 유익한 내용을 제공해야합니다. 답변은 길고 자세하며 친절한 설명을 덧붙여서 작성하세요. ### 사용자: 저녁 메뉴 추천해주세요 ### 챗봇: 한식을 선호하세요? ### 사용자: 네네 ### 챗봇: 한식 중에서는 찌게류를 추천드리겠어요. 된장찌개, 김치찌개, 부대찌개와 같은 전통적인 찌게 메뉴를 선택해보세요. 더 좋은 선택을 위해 다양한 종류의 찌게를 시도해보세요. 만약에 다른 음식을 먹고 싶다면 그것도 좋아요! ```
BenjaminOcampo/model-contrastive-bert__trained-in-dynahate__seed-3
BenjaminOcampo
2023-08-18T04:57:10Z
3
0
transformers
[ "transformers", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-08-18T04:56:22Z
--- language: en --- # Model Card for BenjaminOcampo/model-contrastive-bert__trained-in-dynahate__seed-3 <!-- 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:** BenjaminOcampo - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** en - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** https://github.com/huggingface/huggingface_hub - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
zijuncheng/results
zijuncheng
2023-08-18T04:50:36Z
163
0
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:imdb", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-08-18T04:50:11Z
--- license: mit base_model: roberta-base tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 - precision - recall model-index: - name: results results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.9133333333333333 - name: F1 type: f1 value: 0.9161290322580645 - name: Precision type: precision value: 0.8875 - name: Recall type: recall value: 0.9466666666666667 --- <!-- 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 imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2250 - Accuracy: 0.9133 - F1: 0.9161 - Precision: 0.8875 - Recall: 0.9467 ## Model description More information needed ## 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.6922 | 0.98 | 46 | 0.6867 | 0.7433 | 0.6778 | 0.9101 | 0.54 | | 0.2634 | 1.98 | 93 | 0.3428 | 0.8833 | 0.8736 | 0.9528 | 0.8067 | | 0.1736 | 2.94 | 138 | 0.2250 | 0.9133 | 0.9161 | 0.8875 | 0.9467 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3
BenjaminOcampo/model-contrastive-bert__trained-in-dynahate__seed-2
BenjaminOcampo
2023-08-18T04:43:27Z
3
0
transformers
[ "transformers", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-08-18T04:42:38Z
--- language: en --- # Model Card for BenjaminOcampo/model-contrastive-bert__trained-in-dynahate__seed-2 <!-- 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:** BenjaminOcampo - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** en - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** https://github.com/huggingface/huggingface_hub - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
yzzhong/pixelcopter_policy
yzzhong
2023-08-18T04:31:58Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-08-17T05:58:50Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: pixelcopter_policy results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 12.00 +/- 9.23 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Yntec/samdoesartsUlt
Yntec
2023-08-18T04:07:41Z
334
4
diffusers
[ "diffusers", "safetensors", "art", "anime", "style", "checkpoint", "jinofcoolnes", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-08-07T12:27:42Z
--- license: creativeml-openrail-m language: - en pipeline_tag: text-to-image tags: - art - anime - style - checkpoint - jinofcoolnes --- This model with the MoistMix VAE baked. Previews and prompts: ![sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/AgsivwO8s9FGLA4HdLx9_.png) (lora)0.5 , (amakawa hano)0.5 , 1 girl, ray tracing, {best quality}, {{masterpiece}}, {highres}, original, extremely detailed 8K wallpaper, {an extremely delicate and beautiful}, , incredibly_absurdres, colorful, intricate detail, artbook ![sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/hfblYHsWcyjKLsbG_Ea4I.png) pretty cute little girl in tricycle, Screenshot of an surreal streetwear 70s round minimalist architecture, Sharp, 35mm still from a sci fi light blockbuster color movie made in 2022, beautiful portrait, set in 1860, in front of a spaceship that has just landed on an alien planet, are all wearing, a robot stands nearby Original pages: https://huggingface.co/jinofcoolnes/sammod https://civitai.com/api/download/models/14459?type=VAE
Harsha100/disneyPixarCartoonTypeA_10
Harsha100
2023-08-18T03:57:59Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-08-18T03:45:33Z
--- license: creativeml-openrail-m ---
sartmis1/starcoder-wikisql
sartmis1
2023-08-18T03:55:38Z
7
0
transformers
[ "transformers", "pytorch", "gpt_bigcode", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-08-17T14:39:23Z
Starcoder model trained on wikisql dataset.
aphi/ownppo-LunarLander-v2
aphi
2023-08-18T03:21:10Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-08-17T17:31:52Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -25.85 +/- 39.85 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'aphi_experiment' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 500000 'learning_rate': 0.0001 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.1 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'aphi/ownppo-LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
cecb/super_awesome_llama2
cecb
2023-08-18T03:18:17Z
0
0
peft
[ "peft", "region:us" ]
null
2023-08-11T19:08:48Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
asenella/incomplete_mhd_MMVAEPlus_beta_5_scale_True_seed_1
asenella
2023-08-18T03:04:48Z
0
0
null
[ "multivae", "en", "license:apache-2.0", "region:us" ]
null
2023-08-13T23:22:54Z
--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
Spico/Humback-M0
Spico
2023-08-18T02:51:51Z
9
3
transformers
[ "transformers", "pytorch", "llama", "text-generation", "en", "dataset:OpenAssistant/oasst1", "arxiv:2308.06259", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-08-16T05:58:07Z
--- license: apache-2.0 datasets: - OpenAssistant/oasst1 language: - en --- ## 🐋 Humback The proposed Humback is a novel framework that can augment the instruction data for supervised fine-tuning with high quality. This is a SFT (supervised fine-tuning) model $M_{0}$ for [Humback](https://arxiv.org/pdf/2308.06259.pdf) reproduction. This model is trained on the seed data. The seed data is a sampled dataset from [oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1). You may find more details and usage examples in [Spico197/Humback](https://github.com/Spico197/Humback) . ## 📜 Reference ```bibtex @misc{li2023selfalignment, title={Self-Alignment with Instruction Backtranslation}, author={Xian Li and Ping Yu and Chunting Zhou and Timo Schick and Luke Zettlemoyer and Omer Levy and Jason Weston and Mike Lewis}, year={2023}, eprint={2308.06259}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
gonglinyuan/metro_t0p_base
gonglinyuan
2023-08-18T02:41:17Z
133
0
transformers
[ "transformers", "pytorch", "fairseq_t5", "text2text-generation", "t5", "custom_code", "en", "arxiv:2305.12567", "arxiv:2110.08207", "license:mit", "model-index", "autotrain_compatible", "region:us" ]
text2text-generation
2023-05-19T19:40:01Z
--- license: mit language: - en tags: - t5 model-index: - name: metro_t0p_base results: - task: type: natural-language-inference dataset: type: super_glue name: RTE config: rte split: validation metrics: - type: accuracy value: 64.90974729241879 - task: type: natural-language-inference dataset: type: super_glue name: CB config: cb split: validation metrics: - type: accuracy value: 44.642857142857146 - task: type: natural-language-inference dataset: type: anli name: ANLI R1 split: dev_r1 metrics: - type: accuracy value: 32.35333333333333 - task: type: natural-language-inference dataset: type: anli name: ANLI R2 split: dev_r2 metrics: - type: accuracy value: 32.199999999999996 - task: type: natural-language-inference dataset: type: anli name: ANLI R3 split: dev_r3 metrics: - type: accuracy value: 32.9 - task: type: coreference-resolution dataset: type: super_glue name: WSC config: wsc.fixed split: validation metrics: - type: accuracy value: 61.34615384615385 - task: type: coreference-resolution dataset: type: winogrande name: Winogrande XL config: winogrande_xl split: validation metrics: - type: accuracy value: 50.860299921073405 - task: type: multiple-choice-qa dataset: type: super_glue name: COPA config: copa split: validation metrics: - type: accuracy value: 61.5 - task: type: multiple-choice-qa dataset: type: story_cloze name: StoryCloze 2016 config: '2016' split: validation metrics: - type: accuracy value: 82.59754142169962 - task: type: multiple-choice-qa dataset: type: hellaswag name: HellaSwag split: validation metrics: - type: accuracy value: 43.22097191794464 - task: type: word-sense-disambiguation dataset: type: super_glue name: WiC config: wic split: validation metrics: - type: accuracy value: 51.20689655172414 --- Official repository: https://github.com/gonglinyuan/metro_t0 # METRO-T0 Paper: [Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers](https://arxiv.org/abs/2305.12567) (ACL 2023) METRO-T0 is a T5-style text-to-text Transformer pretrained using model-generated pretraining signals, prompt-finetuned on a family of public NLP tasks proposed in [T0](https://arxiv.org/abs/2110.08207). METRO-T0 is highly parameter efficient. For example, METRO-T0-Large++ (775M parameters) outperforms GPT-3 (175B parameters) and T0-3B (3B parameters) on a wide range of NLP tasks. ![The architecture of METRO-T0 during pretraining using BERT as the auxiliary model to generate signals](https://github.com/gonglinyuan/metro_t0/raw/main/assets/metro_t0_method.png) ![Prompt learning results of METRO-T0 versus our T0 baseline and T03B by Sanh et al. (2022) on 4 tasks in the T0 Eval benchmark. Each point denotes the accuracy using one prompt template, except that the median accuracy over all templates of T03B is indicated by the blue point. The plots of other tasks are in our paper.](https://github.com/gonglinyuan/metro_t0/raw/main/assets/metro_t0_selected_results.png) ## Use METRO-T0+-Base To use METRO-T0+-Base in PyTorch (Python 3.7+, PyTorch 1.12+ and transformers 4.17+ are prerequisites), refer to the code snippet below: ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained("gonglinyuan/metro_t0p_base", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("gonglinyuan/metro_t0p_base", trust_remote_code=True) input_text = "Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy" inputs = tokenizer([input_text], max_length=512, truncation=True, add_special_tokens=True, return_tensors="pt").input_ids outputs = model.generate(inputs, max_new_tokens=256, do_sample=False) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) # expected: positive ``` ## Other METRO-T0 Models | | # Parameters | Pretraining Data | Prompt-Finetuning Data | |--------------------|--------------|------------------|------------------------| | [METRO-T0-Base](https://huggingface.co/gonglinyuan/metro_t0_base) | 226M | Wikibook (16G) | T0 Train | | [METRO-T0+-Base](https://huggingface.co/gonglinyuan/metro_t0p_base) | 226M | Wikibook (16G) | T0+ Train | | [METRO-T0++-Base](https://huggingface.co/gonglinyuan/metro_t0pp_base) | 226M | Wikibook (16G) | T0++ Train | | [METRO-T0-Base++](https://huggingface.co/gonglinyuan/metro_t0_basepp) | 256M | 160G corpus | T0 Train | | [METRO-T0+-Base++](https://huggingface.co/gonglinyuan/metro_t0p_basepp) | 256M | 160G corpus | T0+ Train | | [METRO-T0++-Base++](https://huggingface.co/gonglinyuan/metro_t0pp_basepp) | 256M | 160G corpus | T0++ Train | | [METRO-T0-Large++](https://huggingface.co/gonglinyuan/metro_t0_largepp) | 775M | 160G corpus | T0 Train | | [METRO-T0+-Large++](https://huggingface.co/gonglinyuan/metro_t0p_largepp) | 775M | 160G corpus | T0+ Train | | [METRO-T0++-Large++](https://huggingface.co/gonglinyuan/metro_t0pp_largepp) | 775M | 160G corpus | T0++ Train | ## Citation If you find the code and models useful for your research, please cite the following paper: ``` @misc{gong2023modelgenerated, title={Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers}, author={Linyuan Gong and Chenyan Xiong and Xiaodong Liu and Payal Bajaj and Yiqing Xie and Alvin Cheung and Jianfeng Gao and Xia Song}, year={2023}, eprint={2305.12567}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2305.12567} } ```
gonglinyuan/metro_t0p_basepp
gonglinyuan
2023-08-18T02:41:04Z
138
0
transformers
[ "transformers", "pytorch", "fairseq_t5", "text2text-generation", "t5", "custom_code", "en", "arxiv:2305.12567", "arxiv:2110.08207", "license:mit", "model-index", "autotrain_compatible", "region:us" ]
text2text-generation
2023-05-19T20:39:10Z
--- license: mit language: - en tags: - t5 model-index: - name: metro_t0p_basepp results: - task: type: natural-language-inference dataset: type: super_glue name: RTE config: rte split: validation metrics: - type: accuracy value: 71.44404332129963 - task: type: natural-language-inference dataset: type: super_glue name: CB config: cb split: validation metrics: - type: accuracy value: 60.714285714285715 - task: type: natural-language-inference dataset: type: anli name: ANLI R1 split: dev_r1 metrics: - type: accuracy value: 36.906666666666666 - task: type: natural-language-inference dataset: type: anli name: ANLI R2 split: dev_r2 metrics: - type: accuracy value: 35.24 - task: type: natural-language-inference dataset: type: anli name: ANLI R3 split: dev_r3 metrics: - type: accuracy value: 36.46666666666666 - task: type: coreference-resolution dataset: type: super_glue name: WSC config: wsc.fixed split: validation metrics: - type: accuracy value: 62.21153846153847 - task: type: coreference-resolution dataset: type: winogrande name: Winogrande XL config: winogrande_xl split: validation metrics: - type: accuracy value: 54.08050513022889 - task: type: multiple-choice-qa dataset: type: super_glue name: COPA config: copa split: validation metrics: - type: accuracy value: 78.875 - task: type: multiple-choice-qa dataset: type: story_cloze name: StoryCloze 2016 config: '2016' split: validation metrics: - type: accuracy value: 90.29396044895778 - task: type: multiple-choice-qa dataset: type: hellaswag name: HellaSwag split: validation metrics: - type: accuracy value: 67.56871141206932 - task: type: word-sense-disambiguation dataset: type: super_glue name: WiC config: wic split: validation metrics: - type: accuracy value: 51.5987460815047 --- Official repository: https://github.com/gonglinyuan/metro_t0 # METRO-T0 Paper: [Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers](https://arxiv.org/abs/2305.12567) (ACL 2023) METRO-T0 is a T5-style text-to-text Transformer pretrained using model-generated pretraining signals, prompt-finetuned on a family of public NLP tasks proposed in [T0](https://arxiv.org/abs/2110.08207). METRO-T0 is highly parameter efficient. For example, METRO-T0-Large++ (775M parameters) outperforms GPT-3 (175B parameters) and T0-3B (3B parameters) on a wide range of NLP tasks. ![The architecture of METRO-T0 during pretraining using BERT as the auxiliary model to generate signals](https://github.com/gonglinyuan/metro_t0/raw/main/assets/metro_t0_method.png) ![Prompt learning results of METRO-T0 versus our T0 baseline and T03B by Sanh et al. (2022) on 4 tasks in the T0 Eval benchmark. Each point denotes the accuracy using one prompt template, except that the median accuracy over all templates of T03B is indicated by the blue point. The plots of other tasks are in our paper.](https://github.com/gonglinyuan/metro_t0/raw/main/assets/metro_t0_selected_results.png) ## Use METRO-T0+-Base++ To use METRO-T0+-Base++ in PyTorch (Python 3.7+, PyTorch 1.12+ and transformers 4.17+ are prerequisites), refer to the code snippet below: ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained("gonglinyuan/metro_t0p_basepp", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("gonglinyuan/metro_t0p_basepp", trust_remote_code=True) input_text = "Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy" inputs = tokenizer([input_text], max_length=512, truncation=True, add_special_tokens=True, return_tensors="pt").input_ids outputs = model.generate(inputs, max_new_tokens=256, do_sample=False) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) # expected: positive ``` ## Other METRO-T0 Models | | # Parameters | Pretraining Data | Prompt-Finetuning Data | |--------------------|--------------|------------------|------------------------| | [METRO-T0-Base](https://huggingface.co/gonglinyuan/metro_t0_base) | 226M | Wikibook (16G) | T0 Train | | [METRO-T0+-Base](https://huggingface.co/gonglinyuan/metro_t0p_base) | 226M | Wikibook (16G) | T0+ Train | | [METRO-T0++-Base](https://huggingface.co/gonglinyuan/metro_t0pp_base) | 226M | Wikibook (16G) | T0++ Train | | [METRO-T0-Base++](https://huggingface.co/gonglinyuan/metro_t0_basepp) | 256M | 160G corpus | T0 Train | | [METRO-T0+-Base++](https://huggingface.co/gonglinyuan/metro_t0p_basepp) | 256M | 160G corpus | T0+ Train | | [METRO-T0++-Base++](https://huggingface.co/gonglinyuan/metro_t0pp_basepp) | 256M | 160G corpus | T0++ Train | | [METRO-T0-Large++](https://huggingface.co/gonglinyuan/metro_t0_largepp) | 775M | 160G corpus | T0 Train | | [METRO-T0+-Large++](https://huggingface.co/gonglinyuan/metro_t0p_largepp) | 775M | 160G corpus | T0+ Train | | [METRO-T0++-Large++](https://huggingface.co/gonglinyuan/metro_t0pp_largepp) | 775M | 160G corpus | T0++ Train | ## Citation If you find the code and models useful for your research, please cite the following paper: ``` @misc{gong2023modelgenerated, title={Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers}, author={Linyuan Gong and Chenyan Xiong and Xiaodong Liu and Payal Bajaj and Yiqing Xie and Alvin Cheung and Jianfeng Gao and Xia Song}, year={2023}, eprint={2305.12567}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2305.12567} } ```
gonglinyuan/metro_t0pp_base
gonglinyuan
2023-08-18T02:40:48Z
136
0
transformers
[ "transformers", "pytorch", "fairseq_t5", "text2text-generation", "t5", "custom_code", "en", "arxiv:2305.12567", "arxiv:2110.08207", "license:mit", "model-index", "autotrain_compatible", "region:us" ]
text2text-generation
2023-05-19T20:51:14Z
--- license: mit language: - en tags: - t5 model-index: - name: metro_t0pp_base results: - task: type: natural-language-inference dataset: type: super_glue name: RTE config: rte split: validation metrics: - type: accuracy value: 75.41516245487364 - task: type: natural-language-inference dataset: type: super_glue name: CB config: cb split: validation metrics: - type: accuracy value: 46.904761904761905 - task: type: natural-language-inference dataset: type: anli name: ANLI R1 split: dev_r1 metrics: - type: accuracy value: 34.233333333333334 - task: type: natural-language-inference dataset: type: anli name: ANLI R2 split: dev_r2 metrics: - type: accuracy value: 33.906666666666666 - task: type: natural-language-inference dataset: type: anli name: ANLI R3 split: dev_r3 metrics: - type: accuracy value: 35.71111111111111 - task: type: coreference-resolution dataset: type: super_glue name: WSC config: wsc.fixed split: validation metrics: - type: accuracy value: 55.0 - task: type: coreference-resolution dataset: type: winogrande name: Winogrande XL config: winogrande_xl split: validation metrics: - type: accuracy value: 51.22336227308604 - task: type: multiple-choice-qa dataset: type: super_glue name: COPA config: copa split: validation metrics: - type: accuracy value: 69.5 - task: type: multiple-choice-qa dataset: type: story_cloze name: StoryCloze 2016 config: '2016' split: validation metrics: - type: accuracy value: 84.17958311063602 - task: type: multiple-choice-qa dataset: type: hellaswag name: HellaSwag split: validation metrics: - type: accuracy value: 43.432583150766774 - task: type: word-sense-disambiguation dataset: type: super_glue name: WiC config: wic split: validation metrics: - type: accuracy value: 65.12539184952979 --- Official repository: https://github.com/gonglinyuan/metro_t0 # METRO-T0 Paper: [Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers](https://arxiv.org/abs/2305.12567) (ACL 2023) METRO-T0 is a T5-style text-to-text Transformer pretrained using model-generated pretraining signals, prompt-finetuned on a family of public NLP tasks proposed in [T0](https://arxiv.org/abs/2110.08207). METRO-T0 is highly parameter efficient. For example, METRO-T0-Large++ (775M parameters) outperforms GPT-3 (175B parameters) and T0-3B (3B parameters) on a wide range of NLP tasks. ![The architecture of METRO-T0 during pretraining using BERT as the auxiliary model to generate signals](https://github.com/gonglinyuan/metro_t0/raw/main/assets/metro_t0_method.png) ![Prompt learning results of METRO-T0 versus our T0 baseline and T03B by Sanh et al. (2022) on 4 tasks in the T0 Eval benchmark. Each point denotes the accuracy using one prompt template, except that the median accuracy over all templates of T03B is indicated by the blue point. The plots of other tasks are in our paper.](https://github.com/gonglinyuan/metro_t0/raw/main/assets/metro_t0_selected_results.png) ## Use METRO-T0++-Base To use METRO-T0++-Base in PyTorch (Python 3.7+, PyTorch 1.12+ and transformers 4.17+ are prerequisites), refer to the code snippet below: ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained("gonglinyuan/metro_t0pp_base", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("gonglinyuan/metro_t0pp_base", trust_remote_code=True) input_text = "Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy" inputs = tokenizer([input_text], max_length=512, truncation=True, add_special_tokens=True, return_tensors="pt").input_ids outputs = model.generate(inputs, max_new_tokens=256, do_sample=False) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) # expected: positive ``` ## Other METRO-T0 Models | | # Parameters | Pretraining Data | Prompt-Finetuning Data | |--------------------|--------------|------------------|------------------------| | [METRO-T0-Base](https://huggingface.co/gonglinyuan/metro_t0_base) | 226M | Wikibook (16G) | T0 Train | | [METRO-T0+-Base](https://huggingface.co/gonglinyuan/metro_t0p_base) | 226M | Wikibook (16G) | T0+ Train | | [METRO-T0++-Base](https://huggingface.co/gonglinyuan/metro_t0pp_base) | 226M | Wikibook (16G) | T0++ Train | | [METRO-T0-Base++](https://huggingface.co/gonglinyuan/metro_t0_basepp) | 256M | 160G corpus | T0 Train | | [METRO-T0+-Base++](https://huggingface.co/gonglinyuan/metro_t0p_basepp) | 256M | 160G corpus | T0+ Train | | [METRO-T0++-Base++](https://huggingface.co/gonglinyuan/metro_t0pp_basepp) | 256M | 160G corpus | T0++ Train | | [METRO-T0-Large++](https://huggingface.co/gonglinyuan/metro_t0_largepp) | 775M | 160G corpus | T0 Train | | [METRO-T0+-Large++](https://huggingface.co/gonglinyuan/metro_t0p_largepp) | 775M | 160G corpus | T0+ Train | | [METRO-T0++-Large++](https://huggingface.co/gonglinyuan/metro_t0pp_largepp) | 775M | 160G corpus | T0++ Train | ## Citation If you find the code and models useful for your research, please cite the following paper: ``` @misc{gong2023modelgenerated, title={Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers}, author={Linyuan Gong and Chenyan Xiong and Xiaodong Liu and Payal Bajaj and Yiqing Xie and Alvin Cheung and Jianfeng Gao and Xia Song}, year={2023}, eprint={2305.12567}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2305.12567} } ```
gonglinyuan/metro_t0pp_basepp
gonglinyuan
2023-08-18T02:40:35Z
139
1
transformers
[ "transformers", "pytorch", "fairseq_t5", "text2text-generation", "t5", "custom_code", "en", "arxiv:2305.12567", "arxiv:2110.08207", "license:mit", "model-index", "autotrain_compatible", "region:us" ]
text2text-generation
2023-05-19T21:26:47Z
--- license: mit language: - en tags: - t5 model-index: - name: metro_t0pp_basepp results: - task: type: natural-language-inference dataset: type: super_glue name: RTE config: rte split: validation metrics: - type: accuracy value: 77.79783393501806 - task: type: natural-language-inference dataset: type: super_glue name: CB config: cb split: validation metrics: - type: accuracy value: 69.52380952380955 - task: type: natural-language-inference dataset: type: anli name: ANLI R1 split: dev_r1 metrics: - type: accuracy value: 39.693333333333335 - task: type: natural-language-inference dataset: type: anli name: ANLI R2 split: dev_r2 metrics: - type: accuracy value: 36.61333333333334 - task: type: natural-language-inference dataset: type: anli name: ANLI R3 split: dev_r3 metrics: - type: accuracy value: 40.08333333333334 - task: type: coreference-resolution dataset: type: super_glue name: WSC config: wsc.fixed split: validation metrics: - type: accuracy value: 61.44230769230769 - task: type: coreference-resolution dataset: type: winogrande name: Winogrande XL config: winogrande_xl split: validation metrics: - type: accuracy value: 54.55406471981057 - task: type: multiple-choice-qa dataset: type: super_glue name: COPA config: copa split: validation metrics: - type: accuracy value: 83.875 - task: type: multiple-choice-qa dataset: type: story_cloze name: StoryCloze 2016 config: '2016' split: validation metrics: - type: accuracy value: 90.88188134687333 - task: type: multiple-choice-qa dataset: type: hellaswag name: HellaSwag split: validation metrics: - type: accuracy value: 68.5421230830512 - task: type: word-sense-disambiguation dataset: type: super_glue name: WiC config: wic split: validation metrics: - type: accuracy value: 67.58620689655174 --- Official repository: https://github.com/gonglinyuan/metro_t0 # METRO-T0 Paper: [Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers](https://arxiv.org/abs/2305.12567) (ACL 2023) METRO-T0 is a T5-style text-to-text Transformer pretrained using model-generated pretraining signals, prompt-finetuned on a family of public NLP tasks proposed in [T0](https://arxiv.org/abs/2110.08207). METRO-T0 is highly parameter efficient. For example, METRO-T0-Large++ (775M parameters) outperforms GPT-3 (175B parameters) and T0-3B (3B parameters) on a wide range of NLP tasks. ![The architecture of METRO-T0 during pretraining using BERT as the auxiliary model to generate signals](https://github.com/gonglinyuan/metro_t0/raw/main/assets/metro_t0_method.png) ![Prompt learning results of METRO-T0 versus our T0 baseline and T03B by Sanh et al. (2022) on 4 tasks in the T0 Eval benchmark. Each point denotes the accuracy using one prompt template, except that the median accuracy over all templates of T03B is indicated by the blue point. The plots of other tasks are in our paper.](https://github.com/gonglinyuan/metro_t0/raw/main/assets/metro_t0_selected_results.png) ## Use METRO-T0++-Base++ To use METRO-T0++-Base++ in PyTorch (Python 3.7+, PyTorch 1.12+ and transformers 4.17+ are prerequisites), refer to the code snippet below: ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained("gonglinyuan/metro_t0pp_basepp", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("gonglinyuan/metro_t0pp_basepp", trust_remote_code=True) input_text = "Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy" inputs = tokenizer([input_text], max_length=512, truncation=True, add_special_tokens=True, return_tensors="pt").input_ids outputs = model.generate(inputs, max_new_tokens=256, do_sample=False) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) # expected: positive ``` ## Other METRO-T0 Models | | # Parameters | Pretraining Data | Prompt-Finetuning Data | |--------------------|--------------|------------------|------------------------| | [METRO-T0-Base](https://huggingface.co/gonglinyuan/metro_t0_base) | 226M | Wikibook (16G) | T0 Train | | [METRO-T0+-Base](https://huggingface.co/gonglinyuan/metro_t0p_base) | 226M | Wikibook (16G) | T0+ Train | | [METRO-T0++-Base](https://huggingface.co/gonglinyuan/metro_t0pp_base) | 226M | Wikibook (16G) | T0++ Train | | [METRO-T0-Base++](https://huggingface.co/gonglinyuan/metro_t0_basepp) | 256M | 160G corpus | T0 Train | | [METRO-T0+-Base++](https://huggingface.co/gonglinyuan/metro_t0p_basepp) | 256M | 160G corpus | T0+ Train | | [METRO-T0++-Base++](https://huggingface.co/gonglinyuan/metro_t0pp_basepp) | 256M | 160G corpus | T0++ Train | | [METRO-T0-Large++](https://huggingface.co/gonglinyuan/metro_t0_largepp) | 775M | 160G corpus | T0 Train | | [METRO-T0+-Large++](https://huggingface.co/gonglinyuan/metro_t0p_largepp) | 775M | 160G corpus | T0+ Train | | [METRO-T0++-Large++](https://huggingface.co/gonglinyuan/metro_t0pp_largepp) | 775M | 160G corpus | T0++ Train | ## Citation If you find the code and models useful for your research, please cite the following paper: ``` @misc{gong2023modelgenerated, title={Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers}, author={Linyuan Gong and Chenyan Xiong and Xiaodong Liu and Payal Bajaj and Yiqing Xie and Alvin Cheung and Jianfeng Gao and Xia Song}, year={2023}, eprint={2305.12567}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2305.12567} } ```
gonglinyuan/metro_t0_largepp
gonglinyuan
2023-08-18T02:40:21Z
138
0
transformers
[ "transformers", "pytorch", "fairseq_t5", "text2text-generation", "t5", "custom_code", "en", "arxiv:2305.12567", "arxiv:2110.08207", "license:mit", "model-index", "autotrain_compatible", "region:us" ]
text2text-generation
2023-05-19T22:05:11Z
--- license: mit language: - en tags: - t5 model-index: - name: metro_t0_largepp results: - task: type: natural-language-inference dataset: type: super_glue name: RTE config: rte split: validation metrics: - type: accuracy value: 76.7509025270758 - task: type: natural-language-inference dataset: type: super_glue name: CB config: cb split: validation metrics: - type: accuracy value: 65.47619047619047 - task: type: natural-language-inference dataset: type: anli name: ANLI R1 split: dev_r1 metrics: - type: accuracy value: 41.486666666666665 - task: type: natural-language-inference dataset: type: anli name: ANLI R2 split: dev_r2 metrics: - type: accuracy value: 36.29333333333333 - task: type: natural-language-inference dataset: type: anli name: ANLI R3 split: dev_r3 metrics: - type: accuracy value: 40.18333333333333 - task: type: coreference-resolution dataset: type: super_glue name: WSC config: wsc.fixed split: validation metrics: - type: accuracy value: 60.57692307692308 - task: type: coreference-resolution dataset: type: winogrande name: Winogrande XL config: winogrande_xl split: validation metrics: - type: accuracy value: 54.50670876085242 - task: type: multiple-choice-qa dataset: type: super_glue name: COPA config: copa split: validation metrics: - type: accuracy value: 88.0 - task: type: multiple-choice-qa dataset: type: story_cloze name: StoryCloze 2016 config: '2016' split: validation metrics: - type: accuracy value: 94.06734366648851 - task: type: multiple-choice-qa dataset: type: hellaswag name: HellaSwag split: validation metrics: - type: accuracy value: 29.30691097390958 - task: type: word-sense-disambiguation dataset: type: super_glue name: WiC config: wic split: validation metrics: - type: accuracy value: 50.9717868338558 --- Official repository: https://github.com/gonglinyuan/metro_t0 # METRO-T0 Paper: [Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers](https://arxiv.org/abs/2305.12567) (ACL 2023) METRO-T0 is a T5-style text-to-text Transformer pretrained using model-generated pretraining signals, prompt-finetuned on a family of public NLP tasks proposed in [T0](https://arxiv.org/abs/2110.08207). METRO-T0 is highly parameter efficient. For example, METRO-T0-Large++ (775M parameters) outperforms GPT-3 (175B parameters) and T0-3B (3B parameters) on a wide range of NLP tasks. ![The architecture of METRO-T0 during pretraining using BERT as the auxiliary model to generate signals](https://github.com/gonglinyuan/metro_t0/raw/main/assets/metro_t0_method.png) ![Prompt learning results of METRO-T0 versus our T0 baseline and T03B by Sanh et al. (2022) on 4 tasks in the T0 Eval benchmark. Each point denotes the accuracy using one prompt template, except that the median accuracy over all templates of T03B is indicated by the blue point. The plots of other tasks are in our paper.](https://github.com/gonglinyuan/metro_t0/raw/main/assets/metro_t0_selected_results.png) ## Use METRO-T0-Large++ To use METRO-T0-Large++ in PyTorch (Python 3.7+, PyTorch 1.12+ and transformers 4.17+ are prerequisites), refer to the code snippet below: ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained("gonglinyuan/metro_t0_largepp", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("gonglinyuan/metro_t0_largepp", trust_remote_code=True) input_text = "Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy" inputs = tokenizer([input_text], max_length=512, truncation=True, add_special_tokens=True, return_tensors="pt").input_ids outputs = model.generate(inputs, max_new_tokens=256, do_sample=False) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) # expected: positive ``` ## Other METRO-T0 Models | | # Parameters | Pretraining Data | Prompt-Finetuning Data | |--------------------|--------------|------------------|------------------------| | [METRO-T0-Base](https://huggingface.co/gonglinyuan/metro_t0_base) | 226M | Wikibook (16G) | T0 Train | | [METRO-T0+-Base](https://huggingface.co/gonglinyuan/metro_t0p_base) | 226M | Wikibook (16G) | T0+ Train | | [METRO-T0++-Base](https://huggingface.co/gonglinyuan/metro_t0pp_base) | 226M | Wikibook (16G) | T0++ Train | | [METRO-T0-Base++](https://huggingface.co/gonglinyuan/metro_t0_basepp) | 256M | 160G corpus | T0 Train | | [METRO-T0+-Base++](https://huggingface.co/gonglinyuan/metro_t0p_basepp) | 256M | 160G corpus | T0+ Train | | [METRO-T0++-Base++](https://huggingface.co/gonglinyuan/metro_t0pp_basepp) | 256M | 160G corpus | T0++ Train | | [METRO-T0-Large++](https://huggingface.co/gonglinyuan/metro_t0_largepp) | 775M | 160G corpus | T0 Train | | [METRO-T0+-Large++](https://huggingface.co/gonglinyuan/metro_t0p_largepp) | 775M | 160G corpus | T0+ Train | | [METRO-T0++-Large++](https://huggingface.co/gonglinyuan/metro_t0pp_largepp) | 775M | 160G corpus | T0++ Train | ## Citation If you find the code and models useful for your research, please cite the following paper: ``` @misc{gong2023modelgenerated, title={Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers}, author={Linyuan Gong and Chenyan Xiong and Xiaodong Liu and Payal Bajaj and Yiqing Xie and Alvin Cheung and Jianfeng Gao and Xia Song}, year={2023}, eprint={2305.12567}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2305.12567} } ```
gonglinyuan/metro_t0p_largepp
gonglinyuan
2023-08-18T02:39:37Z
139
0
transformers
[ "transformers", "pytorch", "fairseq_t5", "text2text-generation", "t5", "custom_code", "en", "arxiv:2305.12567", "arxiv:2110.08207", "license:mit", "model-index", "autotrain_compatible", "region:us" ]
text2text-generation
2023-05-19T23:29:24Z
--- license: mit language: - en tags: - t5 model-index: - name: metro_t0p_largepp results: - task: type: natural-language-inference dataset: type: super_glue name: RTE config: rte split: validation metrics: - type: accuracy value: 81.26353790613719 - task: type: natural-language-inference dataset: type: super_glue name: CB config: cb split: validation metrics: - type: accuracy value: 70.0 - task: type: natural-language-inference dataset: type: anli name: ANLI R1 split: dev_r1 metrics: - type: accuracy value: 45.059999999999995 - task: type: natural-language-inference dataset: type: anli name: ANLI R2 split: dev_r2 metrics: - type: accuracy value: 38.593333333333334 - task: type: natural-language-inference dataset: type: anli name: ANLI R3 split: dev_r3 metrics: - type: accuracy value: 42.35 - task: type: coreference-resolution dataset: type: super_glue name: WSC config: wsc.fixed split: validation metrics: - type: accuracy value: 60.67307692307692 - task: type: coreference-resolution dataset: type: winogrande name: Winogrande XL config: winogrande_xl split: validation metrics: - type: accuracy value: 57.521704814522494 - task: type: multiple-choice-qa dataset: type: super_glue name: COPA config: copa split: validation metrics: - type: accuracy value: 90.5 - task: type: multiple-choice-qa dataset: type: story_cloze name: StoryCloze 2016 config: '2016' split: validation metrics: - type: accuracy value: 95.41421699625869 - task: type: multiple-choice-qa dataset: type: hellaswag name: HellaSwag split: validation metrics: - type: accuracy value: 83.81796454889465 - task: type: word-sense-disambiguation dataset: type: super_glue name: WiC config: wic split: validation metrics: - type: accuracy value: 52.31974921630094 --- Official repository: https://github.com/gonglinyuan/metro_t0 # METRO-T0 Paper: [Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers](https://arxiv.org/abs/2305.12567) (ACL 2023) METRO-T0 is a T5-style text-to-text Transformer pretrained using model-generated pretraining signals, prompt-finetuned on a family of public NLP tasks proposed in [T0](https://arxiv.org/abs/2110.08207). METRO-T0 is highly parameter efficient. For example, METRO-T0-Large++ (775M parameters) outperforms GPT-3 (175B parameters) and T0-3B (3B parameters) on a wide range of NLP tasks. ![The architecture of METRO-T0 during pretraining using BERT as the auxiliary model to generate signals](https://github.com/gonglinyuan/metro_t0/raw/main/assets/metro_t0_method.png) ![Prompt learning results of METRO-T0 versus our T0 baseline and T03B by Sanh et al. (2022) on 4 tasks in the T0 Eval benchmark. Each point denotes the accuracy using one prompt template, except that the median accuracy over all templates of T03B is indicated by the blue point. The plots of other tasks are in our paper.](https://github.com/gonglinyuan/metro_t0/raw/main/assets/metro_t0_selected_results.png) ## Use METRO-T0+-Large++ To use METRO-T0+-Large++ in PyTorch (Python 3.7+, PyTorch 1.12+ and transformers 4.17+ are prerequisites), refer to the code snippet below: ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained("gonglinyuan/metro_t0p_largepp", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("gonglinyuan/metro_t0p_largepp", trust_remote_code=True) input_text = "Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy" inputs = tokenizer([input_text], max_length=512, truncation=True, add_special_tokens=True, return_tensors="pt").input_ids outputs = model.generate(inputs, max_new_tokens=256, do_sample=False) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) # expected: positive ``` ## Other METRO-T0 Models | | # Parameters | Pretraining Data | Prompt-Finetuning Data | |--------------------|--------------|------------------|------------------------| | [METRO-T0-Base](https://huggingface.co/gonglinyuan/metro_t0_base) | 226M | Wikibook (16G) | T0 Train | | [METRO-T0+-Base](https://huggingface.co/gonglinyuan/metro_t0p_base) | 226M | Wikibook (16G) | T0+ Train | | [METRO-T0++-Base](https://huggingface.co/gonglinyuan/metro_t0pp_base) | 226M | Wikibook (16G) | T0++ Train | | [METRO-T0-Base++](https://huggingface.co/gonglinyuan/metro_t0_basepp) | 256M | 160G corpus | T0 Train | | [METRO-T0+-Base++](https://huggingface.co/gonglinyuan/metro_t0p_basepp) | 256M | 160G corpus | T0+ Train | | [METRO-T0++-Base++](https://huggingface.co/gonglinyuan/metro_t0pp_basepp) | 256M | 160G corpus | T0++ Train | | [METRO-T0-Large++](https://huggingface.co/gonglinyuan/metro_t0_largepp) | 775M | 160G corpus | T0 Train | | [METRO-T0+-Large++](https://huggingface.co/gonglinyuan/metro_t0p_largepp) | 775M | 160G corpus | T0+ Train | | [METRO-T0++-Large++](https://huggingface.co/gonglinyuan/metro_t0pp_largepp) | 775M | 160G corpus | T0++ Train | ## Citation If you find the code and models useful for your research, please cite the following paper: ``` @misc{gong2023modelgenerated, title={Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers}, author={Linyuan Gong and Chenyan Xiong and Xiaodong Liu and Payal Bajaj and Yiqing Xie and Alvin Cheung and Jianfeng Gao and Xia Song}, year={2023}, eprint={2305.12567}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2305.12567} } ```
gonglinyuan/metro_t0pp_largepp
gonglinyuan
2023-08-18T02:32:53Z
135
3
transformers
[ "transformers", "pytorch", "fairseq_t5", "text2text-generation", "t5", "custom_code", "en", "arxiv:2305.12567", "arxiv:2110.08207", "license:mit", "model-index", "autotrain_compatible", "region:us" ]
text2text-generation
2023-05-20T04:20:21Z
--- license: mit language: - en tags: - t5 model-index: - name: metro_t0pp_largepp results: - task: type: natural-language-inference dataset: type: super_glue name: RTE config: rte split: validation metrics: - type: accuracy value: 83.68231046931406 - task: type: natural-language-inference dataset: type: super_glue name: CB config: cb split: validation metrics: - type: accuracy value: 74.8809523809524 - task: type: natural-language-inference dataset: type: anli name: ANLI R1 split: dev_r1 metrics: - type: accuracy value: 46.84 - task: type: natural-language-inference dataset: type: anli name: ANLI R2 split: dev_r2 metrics: - type: accuracy value: 40.373333333333335 - task: type: natural-language-inference dataset: type: anli name: ANLI R3 split: dev_r3 metrics: - type: accuracy value: 44.949999999999996 - task: type: coreference-resolution dataset: type: super_glue name: WSC config: wsc.fixed split: validation metrics: - type: accuracy value: 71.82692307692307 - task: type: coreference-resolution dataset: type: winogrande name: Winogrande XL config: winogrande_xl split: validation metrics: - type: accuracy value: 62.74664561957379 - task: type: multiple-choice-qa dataset: type: super_glue name: COPA config: copa split: validation metrics: - type: accuracy value: 92.625 - task: type: multiple-choice-qa dataset: type: story_cloze name: StoryCloze 2016 config: '2016' split: validation metrics: - type: accuracy value: 95.64938535542491 - task: type: multiple-choice-qa dataset: type: hellaswag name: HellaSwag split: validation metrics: - type: accuracy value: 83.74327823142801 - task: type: word-sense-disambiguation dataset: type: super_glue name: WiC config: wic split: validation metrics: - type: accuracy value: 70.4858934169279 --- Official repository: https://github.com/gonglinyuan/metro_t0 # METRO-T0 Paper: [Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers](https://arxiv.org/abs/2305.12567) (ACL 2023) METRO-T0 is a T5-style text-to-text Transformer pretrained using model-generated pretraining signals, prompt-finetuned on a family of public NLP tasks proposed in [T0](https://arxiv.org/abs/2110.08207). METRO-T0 is highly parameter efficient. For example, METRO-T0-Large++ (775M parameters) outperforms GPT-3 (175B parameters) and T0-3B (3B parameters) on a wide range of NLP tasks. ![The architecture of METRO-T0 during pretraining using BERT as the auxiliary model to generate signals](https://github.com/gonglinyuan/metro_t0/raw/main/assets/metro_t0_method.png) ![Prompt learning results of METRO-T0 versus our T0 baseline and T03B by Sanh et al. (2022) on 4 tasks in the T0 Eval benchmark. Each point denotes the accuracy using one prompt template, except that the median accuracy over all templates of T03B is indicated by the blue point. The plots of other tasks are in our paper.](https://github.com/gonglinyuan/metro_t0/raw/main/assets/metro_t0_selected_results.png) ## Use METRO-T0++-Large++ To use METRO-T0++-Large++ in PyTorch (Python 3.7+, PyTorch 1.12+ and transformers 4.17+ are prerequisites), refer to the code snippet below: ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained("gonglinyuan/metro_t0pp_largepp", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("gonglinyuan/metro_t0pp_largepp", trust_remote_code=True) input_text = "Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy" inputs = tokenizer([input_text], max_length=512, truncation=True, add_special_tokens=True, return_tensors="pt").input_ids outputs = model.generate(inputs, max_new_tokens=256, do_sample=False) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) # expected: positive ``` ## Other METRO-T0 Models | | # Parameters | Pretraining Data | Prompt-Finetuning Data | |--------------------|--------------|------------------|------------------------| | [METRO-T0-Base](https://huggingface.co/gonglinyuan/metro_t0_base) | 226M | Wikibook (16G) | T0 Train | | [METRO-T0+-Base](https://huggingface.co/gonglinyuan/metro_t0p_base) | 226M | Wikibook (16G) | T0+ Train | | [METRO-T0++-Base](https://huggingface.co/gonglinyuan/metro_t0pp_base) | 226M | Wikibook (16G) | T0++ Train | | [METRO-T0-Base++](https://huggingface.co/gonglinyuan/metro_t0_basepp) | 256M | 160G corpus | T0 Train | | [METRO-T0+-Base++](https://huggingface.co/gonglinyuan/metro_t0p_basepp) | 256M | 160G corpus | T0+ Train | | [METRO-T0++-Base++](https://huggingface.co/gonglinyuan/metro_t0pp_basepp) | 256M | 160G corpus | T0++ Train | | [METRO-T0-Large++](https://huggingface.co/gonglinyuan/metro_t0_largepp) | 775M | 160G corpus | T0 Train | | [METRO-T0+-Large++](https://huggingface.co/gonglinyuan/metro_t0p_largepp) | 775M | 160G corpus | T0+ Train | | [METRO-T0++-Large++](https://huggingface.co/gonglinyuan/metro_t0pp_largepp) | 775M | 160G corpus | T0++ Train | ## Citation If you find the code and models useful for your research, please cite the following paper: ``` @misc{gong2023modelgenerated, title={Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers}, author={Linyuan Gong and Chenyan Xiong and Xiaodong Liu and Payal Bajaj and Yiqing Xie and Alvin Cheung and Jianfeng Gao and Xia Song}, year={2023}, eprint={2305.12567}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2305.12567} } ```
rodriguezj314/pixel_train_model_beta_hoodie_gap
rodriguezj314
2023-08-18T02:26:35Z
29
0
diffusers
[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-08-18T02:08:30Z
--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks hoodie tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - rodriguezj314/pixel_train_model_beta_hoodie_gap This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks hoodie using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
hoangdeeptry/whisper-vietnamese-3
hoangdeeptry
2023-08-18T02:23:19Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:duytran3112/whisper-sm-vivos", "base_model:finetune:duytran3112/whisper-sm-vivos", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-08-17T15:39:28Z
--- license: apache-2.0 base_model: duytran3112/whisper-sm-vivos tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-vietnamese-3 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-vietnamese-3 This model is a fine-tuned version of [duytran3112/whisper-sm-vivos](https://huggingface.co/duytran3112/whisper-sm-vivos) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6164 - Wer: 104.1257 - Cer: 100.8264 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| | 0.0204 | 7.19 | 1000 | 0.4900 | 123.6149 | 105.0861 | | 0.0022 | 14.39 | 2000 | 0.5671 | 111.9607 | 104.7015 | | 0.0009 | 21.58 | 3000 | 0.6017 | 109.1316 | 101.5308 | | 0.0006 | 28.78 | 4000 | 0.6164 | 104.1257 | 100.8264 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.0 - Datasets 2.14.4 - Tokenizers 0.13.3
BenjaminOcampo/model-contrastive-bert__trained-in-dynahate__seed-0
BenjaminOcampo
2023-08-18T02:16:38Z
3
0
transformers
[ "transformers", "bert", "text-classification", "en", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-08-18T02:15:48Z
--- language: en --- # Model Card for BenjaminOcampo/model-contrastive-bert__trained-in-dynahate__seed-0 <!-- 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:** BenjaminOcampo - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** en - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** https://github.com/huggingface/huggingface_hub - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
0sunfire0/a2c-PandaReachDense-v2
0sunfire0
2023-08-18T01:57:22Z
2
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "arxiv:2106.13687", "model-index", "region:us" ]
reinforcement-learning
2023-07-15T22:28:39Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -2.68 +/- 0.53 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ``` Panda Gym environments: [arxiv.org/abs/2106.13687](https://arxiv.org/abs/2106.13687)
abdiharyadi/indobart-v2-amr-to-text-linearized-penman-ilmy
abdiharyadi
2023-08-18T01:53:25Z
174
0
transformers
[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "base_model:indobenchmark/indobart-v2", "base_model:finetune:indobenchmark/indobart-v2", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-08-18T01:43:12Z
--- license: mit base_model: indobenchmark/indobart-v2 tags: - generated_from_trainer model-index: - name: indobart-v2-amr-to-text-linearized-penman-ilmy 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. --> # indobart-v2-amr-to-text-linearized-penman-ilmy This model is a fine-tuned version of [indobenchmark/indobart-v2](https://huggingface.co/indobenchmark/indobart-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1927 ## Model description More information needed ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 331 | 0.1928 | | 0.2519 | 2.0 | 662 | 0.1978 | | 0.2519 | 3.0 | 993 | 0.1927 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
reachosen/autotrain-in-basket-3.42-83100142189
reachosen
2023-08-18T01:31:02Z
103
0
transformers
[ "transformers", "pytorch", "safetensors", "deberta", "text-classification", "autotrain", "en", "dataset:reachosen/autotrain-data-in-basket-3.42", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-08-18T01:29:17Z
--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain" datasets: - reachosen/autotrain-data-in-basket-3.42 co2_eq_emissions: emissions: 0.7228932272231364 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 83100142189 - CO2 Emissions (in grams): 0.7229 ## Validation Metrics - Loss: 0.584 - Accuracy: 0.851 - Macro F1: 0.841 - Micro F1: 0.851 - Weighted F1: 0.847 - Macro Precision: 0.851 - Micro Precision: 0.851 - Weighted Precision: 0.853 - Macro Recall: 0.843 - Micro Recall: 0.851 - Weighted Recall: 0.851 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/reachosen/autotrain-in-basket-3.42-83100142189 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("reachosen/autotrain-in-basket-3.42-83100142189", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("reachosen/autotrain-in-basket-3.42-83100142189", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
nightdude/config_5111
nightdude
2023-08-18T01:27:04Z
0
0
peft
[ "peft", "region:us" ]
null
2023-08-18T01:25:34Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
f4falalu/q-taxis
f4falalu
2023-08-18T01:15:02Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-08-18T01:14:59Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxis results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.72 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="f4falalu/q-taxis", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
f4falalu/q-FrozenLake-v1-4x4-noSlippery
f4falalu
2023-08-18T01:08:54Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-08-18T00:30:59Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="f4falalu/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
scarlett623/wav2vec2-timit-xls-r-53-wandb-colab
scarlett623
2023-08-18T00:56:48Z
5
0
transformers
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-08-17T16:06:19Z
--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-timit-xls-r-53-wandb-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-timit-xls-r-53-wandb-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3325 - Wer: 0.2897 - Cer: 0.0940 ## Model description More information needed ## 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: 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: 1000 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | No log | 0.69 | 400 | 3.1507 | 1.0 | 0.9806 | | 4.3857 | 1.38 | 800 | 3.0109 | 1.0 | 0.9806 | | 2.6835 | 2.08 | 1200 | 0.6181 | 0.5756 | 0.1795 | | 0.9327 | 2.77 | 1600 | 0.4239 | 0.4718 | 0.1456 | | 0.5602 | 3.46 | 2000 | 0.3691 | 0.4141 | 0.1301 | | 0.5602 | 4.15 | 2400 | 0.3386 | 0.3894 | 0.1231 | | 0.4407 | 4.84 | 2800 | 0.3122 | 0.3676 | 0.1177 | | 0.3437 | 5.54 | 3200 | 0.3149 | 0.3601 | 0.1152 | | 0.3154 | 6.23 | 3600 | 0.3146 | 0.3495 | 0.1119 | | 0.267 | 6.92 | 4000 | 0.3039 | 0.3427 | 0.1089 | | 0.267 | 7.61 | 4400 | 0.3313 | 0.3409 | 0.1092 | | 0.2354 | 8.3 | 4800 | 0.2986 | 0.3365 | 0.1064 | | 0.2191 | 9.0 | 5200 | 0.3235 | 0.3353 | 0.1074 | | 0.1937 | 9.69 | 5600 | 0.3117 | 0.3320 | 0.1071 | | 0.1803 | 10.38 | 6000 | 0.3102 | 0.3233 | 0.1040 | | 0.1803 | 11.07 | 6400 | 0.3176 | 0.3196 | 0.1030 | | 0.1635 | 11.76 | 6800 | 0.3166 | 0.3220 | 0.1036 | | 0.1551 | 12.46 | 7200 | 0.2836 | 0.3160 | 0.1021 | | 0.1566 | 13.15 | 7600 | 0.3146 | 0.3186 | 0.1032 | | 0.1424 | 13.84 | 8000 | 0.3392 | 0.3167 | 0.1036 | | 0.1424 | 14.53 | 8400 | 0.3254 | 0.3109 | 0.1001 | | 0.1379 | 15.22 | 8800 | 0.3249 | 0.3127 | 0.1009 | | 0.1192 | 15.92 | 9200 | 0.3408 | 0.3119 | 0.1010 | | 0.1178 | 16.61 | 9600 | 0.3551 | 0.3061 | 0.0997 | | 0.1112 | 17.3 | 10000 | 0.3250 | 0.3059 | 0.0991 | | 0.1112 | 17.99 | 10400 | 0.3127 | 0.3037 | 0.0983 | | 0.1022 | 18.69 | 10800 | 0.3370 | 0.3067 | 0.0994 | | 0.1031 | 19.38 | 11200 | 0.3351 | 0.3048 | 0.0991 | | 0.0926 | 20.07 | 11600 | 0.3433 | 0.2994 | 0.0974 | | 0.0861 | 20.76 | 12000 | 0.3145 | 0.3003 | 0.0971 | | 0.0861 | 21.45 | 12400 | 0.3367 | 0.2980 | 0.0973 | | 0.0935 | 22.15 | 12800 | 0.3139 | 0.3016 | 0.0986 | | 0.0784 | 22.84 | 13200 | 0.3181 | 0.2990 | 0.0972 | | 0.078 | 23.53 | 13600 | 0.3347 | 0.2938 | 0.0961 | | 0.0761 | 24.22 | 14000 | 0.3371 | 0.2921 | 0.0949 | | 0.0761 | 24.91 | 14400 | 0.3274 | 0.2916 | 0.0952 | | 0.0784 | 25.61 | 14800 | 0.3152 | 0.2927 | 0.0942 | | 0.0714 | 26.3 | 15200 | 0.3237 | 0.2924 | 0.0943 | | 0.0671 | 26.99 | 15600 | 0.3183 | 0.2914 | 0.0945 | | 0.0684 | 27.68 | 16000 | 0.3307 | 0.2931 | 0.0950 | | 0.0684 | 28.37 | 16400 | 0.3383 | 0.2913 | 0.0940 | | 0.07 | 29.07 | 16800 | 0.3318 | 0.2901 | 0.0940 | | 0.0624 | 29.76 | 17200 | 0.3325 | 0.2897 | 0.0940 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 1.18.3 - Tokenizers 0.13.3
IngeniousArtist/llama2-finance
IngeniousArtist
2023-08-18T00:54:31Z
0
0
null
[ "generated_from_trainer", "dataset:financial_phrasebank", "base_model:meta-llama/Llama-2-7b-hf", "base_model:finetune:meta-llama/Llama-2-7b-hf", "region:us" ]
null
2023-08-09T04:32:51Z
--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer datasets: - financial_phrasebank model-index: - name: llama2-finance results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama2-finance This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the financial_phrasebank 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: 0.0002 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 20 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
Noct-Blib/lora-trained-xl-colab
Noct-Blib
2023-08-18T00:49:48Z
4
1
diffusers
[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]
text-to-image
2023-08-17T23:12:39Z
--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: zkz tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - Noct-Blib/lora-trained-xl-colab These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on zkz using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
mekjr1/opus-mt-en-es-finetuned-es-to-pbb-v2
mekjr1
2023-08-18T00:45:04Z
7
0
transformers
[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "generated_from_trainer", "base_model:Helsinki-NLP/opus-mt-en-es", "base_model:finetune:Helsinki-NLP/opus-mt-en-es", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-08-17T03:20:54Z
--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-es tags: - generated_from_trainer metrics: - bleu model-index: - name: opus-mt-en-es-finetuned-es-to-pbb-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. --> # opus-mt-en-es-finetuned-es-to-pbb-v2 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-es](https://huggingface.co/Helsinki-NLP/opus-mt-en-es) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6535 - Bleu: 1.2729 - Gen Len: 90.5316 ## 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: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| | No log | 1.0 | 199 | 2.3626 | 0.171 | 109.5972 | | No log | 2.0 | 398 | 2.0302 | 0.3065 | 95.3081 | | 2.712 | 3.0 | 597 | 1.8861 | 0.7019 | 96.8497 | | 2.712 | 4.0 | 796 | 1.8081 | 0.6924 | 93.4432 | | 2.712 | 5.0 | 995 | 1.7496 | 0.9599 | 90.7563 | | 1.942 | 6.0 | 1194 | 1.7133 | 1.0843 | 92.4646 | | 1.942 | 7.0 | 1393 | 1.6859 | 1.1072 | 92.8725 | | 1.7861 | 8.0 | 1592 | 1.6696 | 1.243 | 91.2184 | | 1.7861 | 9.0 | 1791 | 1.6569 | 1.2595 | 90.1641 | | 1.7861 | 10.0 | 1990 | 1.6535 | 1.2729 | 90.5316 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
gang21/llama2-icd10-common2
gang21
2023-08-18T00:34:00Z
1
0
peft
[ "peft", "region:us" ]
null
2023-08-18T00:33:57Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
asenella/incomplete_mhd_MMVAE_beta_5_scale_True_seed_2
asenella
2023-08-18T00:28:35Z
0
0
null
[ "multivae", "en", "license:apache-2.0", "region:us" ]
null
2023-08-13T22:50:11Z
--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
asenella/incomplete_mhd_MMVAE_beta_5_scale_True_seed_3
asenella
2023-08-18T00:27:50Z
0
0
null
[ "multivae", "en", "license:apache-2.0", "region:us" ]
null
2023-08-13T23:02:14Z
--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
jordyvl/vit-base_rvl_cdip_crl_softmax_rank1_fixed
jordyvl
2023-08-18T00:26:47Z
163
0
transformers
[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-07-31T11:36:47Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base_rvl_cdip_crl_softmax_rank1_fixed results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base_rvl_cdip_crl_softmax_rank1_fixed This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7091 - Accuracy: 0.9032 - Brier Loss: 0.1756 - Nll: 1.0964 - F1 Micro: 0.9032 - F1 Macro: 0.9033 - Ece: 0.0854 - Aurc: 0.0188 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:------:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | 0.2289 | 1.0 | 10000 | 0.4298 | 0.8826 | 0.1794 | 1.2461 | 0.8826 | 0.8841 | 0.0553 | 0.0199 | | 0.1972 | 2.0 | 20000 | 0.4350 | 0.8859 | 0.1769 | 1.3140 | 0.8859 | 0.8862 | 0.0558 | 0.0197 | | 0.1414 | 3.0 | 30000 | 0.4423 | 0.8938 | 0.1702 | 1.2433 | 0.8938 | 0.8948 | 0.0639 | 0.0181 | | 0.0903 | 4.0 | 40000 | 0.5076 | 0.8943 | 0.1753 | 1.2033 | 0.8943 | 0.8941 | 0.0766 | 0.0181 | | 0.0684 | 5.0 | 50000 | 0.5592 | 0.8963 | 0.1783 | 1.2422 | 0.8963 | 0.8965 | 0.0811 | 0.0194 | | 0.0313 | 6.0 | 60000 | 0.6384 | 0.8956 | 0.1836 | 1.2359 | 0.8957 | 0.8957 | 0.0861 | 0.0218 | | 0.0163 | 7.0 | 70000 | 0.6673 | 0.9005 | 0.1788 | 1.1927 | 0.9005 | 0.9006 | 0.0855 | 0.0215 | | 0.0104 | 8.0 | 80000 | 0.6929 | 0.9001 | 0.1791 | 1.1768 | 0.9001 | 0.9000 | 0.0860 | 0.0204 | | 0.0036 | 9.0 | 90000 | 0.7131 | 0.9018 | 0.1780 | 1.1295 | 0.9018 | 0.9018 | 0.0866 | 0.0195 | | 0.0023 | 10.0 | 100000 | 0.7091 | 0.9032 | 0.1756 | 1.0964 | 0.9032 | 0.9033 | 0.0854 | 0.0188 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
asenella/incomplete_mhd_MMVAE_beta_5_scale_True_seed_0
asenella
2023-08-18T00:25:38Z
0
0
null
[ "multivae", "en", "license:apache-2.0", "region:us" ]
null
2023-08-10T15:07:29Z
--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
KingKazma/cnn_dailymail_gpt2_lora_500_4_50000_8_e3_s6789_v4_l5_r2
KingKazma
2023-08-18T00:18:25Z
0
0
peft
[ "peft", "region:us" ]
null
2023-08-18T00:18:23Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
ihgn/gpt2-paraphrase
ihgn
2023-08-18T00:10:26Z
114
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "en", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-06-11T16:25:56Z
--- language: - en metrics: - bleu - rouge pipeline_tag: text2text-generation --- def paraphrase( question, num_beams=5, num_beam_groups=5, num_return_sequences=1, repetition_penalty=10.0, diversity_penalty=3.0, no_repeat_ngram_size=2, temperature=0.7, max_length=128 ): input_ids = tokenizer( f'paraphrase: {question}', return_tensors="pt", padding="longest", max_length=max_length, truncation=True, ).input_ids outputs = model.generate( input_ids, temperature=temperature, repetition_penalty=repetition_penalty, num_return_sequences=num_return_sequences, no_repeat_ngram_size=no_repeat_ngram_size, num_beams=num_beams, num_beam_groups=num_beam_groups, max_length=max_length, diversity_penalty=diversity_penalty ) res = tokenizer.batch_decode(outputs, skip_special_tokens=True) return res
yummyummy/bark-cpp-ggml-models-test
yummyummy
2023-08-17T23:38:02Z
0
0
null
[ "region:us" ]
null
2023-08-17T08:23:28Z
initial test on https://github.com/PABannier/bark.cpp/commit/58d24ea9f1836247d2aee520d2056c46a6f09c5a 73s 10.3g ram q4 f16mixed 50s 10.2g ram f16 mixed 67s 12.1g f32
siacus/llama-v2-huff-test
siacus
2023-08-17T23:19:01Z
2
0
peft
[ "peft", "region:us" ]
null
2023-08-17T23:05:10Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0