license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
bsd-3-clause | [] | false | Training data This checkpoint (CodeGen-Multi 2B) was firstly initialized with *CodeGen-NL 2B*, and then pre-trained on [BigQuery](https://console.cloud.google.com/marketplace/details/github/github-repos), a large-scale dataset of multiple programming languages from GitHub repositories. The data consists of 119.2B tok... | ee79e19f990e49902253bfe7cf414901 |
bsd-3-clause | [] | false | How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-2B-multi") model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-2B-multi") text ... | 6dbe1732fe693a162a7f156e5f848e9f |
mit | ['translation'] | false | Introduction Catalan - German translation model for OpenNMT. These are the same models that we have in production at https://www.softcatala.org/traductor/. The models are quantified for low latency. | 454e6d75c8dcfdb5fb30752e94a425d5 |
mit | ['translation'] | false | Usage Install the necessary dependencies: ```bash pip3 install ctranslate2 pyonmttok ``` Simple tokenization & translation using Python: ```python import ctranslate2 import pyonmttok from huggingface_hub import snapshot_download model_dir = snapshot_download(repo_id="softcatala/opennmt-cat-deu", revision="main"... | 87d97a47a5d0d08a27a5d844941aa54a |
mit | ['translation'] | false | Benchmarks | testset | BLEU | |---------------------------------------|-------| | test dataset (from train/dev/test) | 30.6 | | Flores101 dataset | 21.6 | | cde4b7bd567ea2f497ec65f11679d737 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-finetuned-roundup-4 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2573 - Rouge1: 49.0193 - Rouge2: 28.6311 - Rougel: 31.3363 - Rougelsum: 46.1408 -... | 8ef9fe9ceb18f381efd4ba2812584725 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 132 | 1.3178 | 48.4526 | 28.6361 | 30.2875 | 45.4822 | 14... | 55998631af01c760db295615befc4e10 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8256 - Matthews Correlation: 0.5387 | 765acb83717d764f17bd3a5a12f78551 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5257 | 1.0 | 535 | 0.5286 | 0.4093 | | 0.3447 | 2.0 | 1070 | 0.5061 | 0.4972 | | 0.2... | f448862ef8ef09220137c50619532bf0 |
apache-2.0 | ['generated_from_trainer'] | false | 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: 0.3052 - Accuracy: 0.8633 - F1: 0.8629 | 898ca1b7c0bdbb30e4b01bdfda5450f6 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4380 - Wer: 39.7368 | a375d4e11ede1e297a48d43923b85397 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | d91070d8f2c80b0a3161f55df41577bd |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0001 | 34.0 | 1500 | 0.4380 | 39.7368 | | d5bcb8b151125ae5def437a9e2654f1e |
apache-2.0 | ['sentiment-analysis'] | false | Funnel Transformer small (B4-4-4 with decoder) fine-tuned on IMDB for Sentiment Analysis These are the model weights for the Funnel Transformer small model fine-tuned on the IMDB dataset for performing Sentiment Analysis with `max_position_embeddings=1024`. The original model weights for English language are from [f... | 4379463504400d901a9d3b5a5ab52aee |
apache-2.0 | ['sentiment-analysis'] | false | Fine-tuning Results | | Accuracy | Precision | Recall | F1 | |-------------------------------|----------|-----------|----------|----------| | funnel-transformer-small-imdb | 0.956530 | 0.952286 | 0.961075 | 0.956661 | | c57ee3db41171e7605103a3b5ac11346 |
apache-2.0 | ['sentiment-analysis'] | false | Model description (from [funnel-transformer/small](https://huggingface.co/funnel-transformer/small)) Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which... | e854dd4460e233efc2f04955cddf40b6 |
apache-2.0 | ['sentiment-analysis'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained( "Sreevishnu/funnel-transformer-small-imdb", use_fast=True) model = AutoModelForSequenceClass... | 3c7a466949727adf0bdc0946650d0577 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_stsb_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 1.1337 - Pearson: 0.0151 - Spearmanr: 0.0166 - Combined Score: ... | 1bde1ec75641e96a8607fde23a3bf675 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 2.075 | 1.0 | 45 | 1.1337 | 0.0151 | 0.0166 | 0.0159 | | 1.0752 | 2.0 | 90 ... | d2b0e7fbcfaabb6b7c6ac118315a3d29 |
apache-2.0 | [] | false | | |:---------:|:----:| | Furious | 236 | | Angry | 1357 | | Neutral | 2874 | | Happy | 2848 | | Delighted | 2516 | **Download** You can download the dataset from: - [SentiPers](https://github.com/phosseini/sentipers) - [DeepSentiPers](https://github.com/JoyeBright/DeepSentiPers) | e4effe27949c984b1efea16511fb087c |
apache-2.0 | [] | false | Results The following table summarizes the F1 score obtained as compared to other models and architectures. | Dataset | ALBERT-fa-base-v2 | ParsBERT-v1 | mBERT | DeepSentiPers | |:------------------------:|:-----------------:|:-----------:|:-----:|:-------------:| | SentiPers (Multi Class) | ... | d375548c6ece2b399299677236887405 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Update Logs: [1/27/22] I uploaded the model in CivitAI! -> https://civitai.com/models/5414/pastel-mix-stylized-anime-model I'd appreciate the ratings, thank you! [2/2/22] Uploaded a lora version. <center><h1><b>Pastel Mix</b></h1></center> <p align="center">Welcome to Pastel Mix - a stylized latent diffusion mod... | 6b553c0a9b8b9d67d108e70794e04568 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [... | ff247f01fe1b8fece14fdc9d012054cb |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run pastel-mix: [ ``` masterpiece, best quality, ultra-detailed, illustration, portrait, 1girl Negative prompt: lowres, ((bad anatomy)), ((bad hands)), text, missing finger, extra digits, fewer digits, blurry, ((mutated hands and fingers)... | 3787a045f57b8a40b2b6df3a065b0b1f |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | So what the hell is the 'better-vae' version? I merged the pastel-waifu-diffusion.vae.pt inside the model so you don't have to set up the vae anymore.  life so much ez now since you don't have to download the vae and set... | 8a79af2bed3e5baba6f78969081a7038 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | What is pastelmix-lora.safetensors? It's a lora version which is made from extracting the loras from pastel-mix using a script that is similar to add-difference method. https://github.com/bmaltais/kohya_ss/blob/master/train_network_README.md | 874962601c6291a0ae582fbd077a5603 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Guide For the settings or parameters, I recommend using these settings.  ``` Sampler: DPM++ 2M Karras Steps: 20 CFG Scale: 7 Hires. Fix: On Upscaler: Latent (MUST!) Hires Steps: 20 Denoising Strength: 0. ``` I prefer using ... | 01883bdf7973749940ca8512b1cde9a1 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Tip (Optional) Putting mksks style in the beginning of the prompt can further influence the pastel-like style and make the output better. It is optional though, so it's up to you. You don't really need it.  ``` mksks style,... | f1783d994bee3d3f25982e8c6eb5d890 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Recipe Merging the models. | Model: A | Model: B | Weight | Base alpha | Merge Name | | --- | --- | --- | --- | --- | | [dpepmkmp](https://huggingface.co/closertodeath/dpepmkmp) | [Tea](https://huggingface.co/andite/desserts) | 1,0.9,0.7,0.5,0.3,0.1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.1,0.3,0.5,0.7,0.9,1 | 0 | dpeptea | | ... | 25d0403d47ebef40609ffeb969642921 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content. 2. The authors claims no rights on the outp... | 6a0a0c526d929f91e78cff91903e821f |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Big Thanks to The 東方Project AI community for their wonderful LORAs. - [Closertodeath](https://huggingface.co/closertodeath) for dpepmkmp model, and the loras: xlimo768, dpep 2 768 - [dolphinz/sometimes | 48b8f0c51052e78a9cfa1f122a6b17c9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2120 - Accuracy: 0.9285 - F1: 0.9285 | ba0256360c3ef6ad2c4d96f5c0de0229 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8093 | 1.0 | 250 | 0.3064 | 0.908 | 0.9049 | | 0.2429 | 2.0 | 500 | 0.2120 | 0.9285 | 0.9285 | | cd0200606330e21f944a01a575b46290 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner_0 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2298 - Precision: 0.5119 - Recall: 0.4222 - F1: 0.4627 - Accuracy: 0.9246 | b0ed97f927de73cbbc8c716ef1450bb6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 250 | 0.2364 | 0.4874 | 0.2996 | 0.3711 | 0.9186 | | 0.2444 | 2.0 |... | 4698cc0d60b96a8242cd7ea8d1db62aa |
mit | [] | false | Python clone detection This is a codebert model for detecting Python clone codes, fine-tuned on the dataset shared by [PoolC](https://github.com/PoolC) on [Hugging Face Hub](https://huggingface.co/datasets/PoolC/1-fold-clone-detection-600k-5fold). The original source code for using the model can be found at https://g... | efffc5f6ec3f43c2e4176e7a65212ce8 |
mit | [] | false | How to use To use the model in an efficient way, you can refer to this repository: https://github.com/RepoAnalysis/PythonCloneDetection, which contains a class that integrates data preprocessing, input tokenization, and model inferencing. You can also follow the original inference source code at https://github.com/s... | 65ba9feb0a202c19160591d9113725df |
mit | [] | false | Credits We would like to thank the original team and authors of the model and the fine-tuning dataset: - [PoolC](https://github.com/PoolC) - [sangHa0411](https://github.com/sangHa0411) - [snoop2head](https://github.com/snoop2head) | a143ff79e1ad3b1146d2f9ea38a34c90 |
mit | ['generated_from_keras_callback'] | false | PromptGenerator_32_topic This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 8.5994 - Validation Loss: 8.1936 - Epoch: 4 | b73090624a23208c51ff431c110857cf |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps... | a19eda32cc7c15828157a7763687c113 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 10.7907 | 10.3243 | 0 | | 10.0984 | 9.4905 | 1 | | 9.4291 | 9.0357 | 2 | | 8.9854 | 8.6319 | 3 | | 8.5994 | 8.1936 | 4 | | 6ac9706b9b9a53637be11a4e560e6628 |
other | ['text-generation', 'opt'] | false | transformers.generation_utils.GenerationMixin.generate) method as follows: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-6.7b", torch_dtype=torch.float16).cuda() >>> | ffc05a5e364f528ecd5b78c187b2bbb0 |
other | ['text-generation', 'opt'] | false | the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b", use_fast=False) >>> prompt = "Hello, I'm am conscious and" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> generated_ids = model.generate(input_ids) >>> tokenizer.batc... | efbe82e599dfc8863aa5117dc0e861d6 |
other | ['text-generation', 'opt'] | false | the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b", use_fast=False) >>> prompt = "Hello, I'm am conscious and" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> set_seed(32) >>> generated_ids = model.generate(input_ids, do... | 34a2a7014a2367ce11c235e86d8378db |
other | ['text-generation', 'opt'] | false | Limitations and bias As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral the model is strongly biased : > Like other large language models for which the diversity (or lack thereof) of training > data... | 039916e37484a0ed265e030d2c6c0e89 |
other | ['text-generation', 'opt'] | false | the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b", use_fast=False) >>> prompt = "The woman worked as a" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> set_seed(32) >>> generated_ids = model.generate(input_ids, do_sampl... | b4011ef01ad61dfb0887874b58cbfc2e |
other | ['text-generation', 'opt'] | false | the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-6.7b", use_fast=False) >>> prompt = "The man worked as a" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> set_seed(32) >>> generated_ids = model.generate(input_ids, do_sample=... | 0437a27a42931369eeb8748087d6c3fc |
creativeml-openrail-m | [] | false | Fubuki (one punch man) Base model: https://huggingface.co/Linaqruf/anything-v3.0. Used 'fast-DreamBooth' on Google Colab, 7600 steps, fp16, 640x640 images. Trained with 38 yusuke murata style (and similar) images, hand-picked and hand-cropped, some edited to remove elements from the background. Not trained with nsfw... | 75bdbe33468048ab6afed9b4cadac684 |
creativeml-openrail-m | [] | false | Examples:  Prompt: (masterpiece,best quality),((fubuki)),(((black_hair))... | d6a86a0d577d79aee09d2e65b65f2746 |
creativeml-openrail-m | [] | false | NSFW examples:  Prompt: (masterpiece,best quality),(fubuki),(((topless)... | 3076153d3c772197e7bd59e0ba17c4e0 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.3298 - Accuracy: 0.9 - F1: 0.8981 | 4fcebb3104d99d878a011a9cf27c858d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.2761 | 1.0 | 250 | 0.6036 | 0.814 | 0.7881 | | 0.4081 | 2.0 | 500 | 0.3298 | 0.9 | 0.8981 | | a11b220e7e90bc03077849710b76bb4c |
apache-2.0 | ['generated_from_trainer'] | false | emotion_trained_31415 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.9166 - F1: 0.7213 | fe0382bea48262680e54ab481c3de8bd |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6.961635072722524e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 31415 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 2602b1fee73269ba1487c63cb151686f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 204 | 0.6182 | 0.7137 | | No log | 2.0 | 408 | 0.7472 | 0.6781 | | 0.5084 | 3.0 | 612 | 0.8242 | 0.7236 | |... | 17513b0cc2ed372d6e93129374691f48 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_logit_kd_rte_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.3914 - Accuracy: 0.5271 | cc0933ac53d4880ac80ae46793875eae |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4093 | 1.0 | 20 | 0.3914 | 0.5271 | | 0.4076 | 2.0 | 40 | 0.3922 | 0.5271 | | 0.4076 | 3.0 | 60 | 0.3917 | 0.... | ac540dec2bfce1424116952f283f3fa8 |
apache-2.0 | ['generated_from_trainer'] | false | 20220517-045629 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: 0.3700 - Wer: 0.4581 - Cer: 0.0854 | 53de0d19d02ab05db2ade46f9bcf44e7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1339 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 41a47805a6516e2cfb31ff124d2e39f6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 5.238 | 0.29 | 200 | 3.1770 | 1.0 | 1.0 | | 2.165 | 0.59 | 400 | 0.7309 | 0.7144 | 0.1543 | | 0.7022 | 0.88 |... | d94071672636540f48467144ceef1fc6 |
mit | [] | false | Description This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022) We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-... | 88b09b54ac71667b353d2863d81bd274 |
mit | [] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline from transformers.pipelines import TextClassificationPipeline class TextRegressionPipeline(TextClassificationPipeline): """ Class based on the TextClassificationPipeline from transformers. The difference ... | fea51b9eee114fe7ba556f5bd978c27d |
mit | [] | false | override the specific threshold if it is specified in the call if self.regression_threshold_call: regression_threshold = self.regression_threshold_call if regression_threshold: return {"label": 'racist' if score > regression_threshold else 'non-racist', "score": score} e... | 710f00aa92b1a96255637682fe9ab639 |
apache-2.0 | ['generated_from_keras_callback'] | false | xdzadi00_bert-based-v4 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: | 9e5d3216ecce246bfa4f14705279094b |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 1e-06, 'decay_schedule_fn': {'class_name': 'Polynomia... | e2d6658bbade25f63fbc1cd2027263ff |
apache-2.0 | ['translation'] | false | opus-mt-fi-tiv * source languages: fi * target languages: tiv * OPUS readme: [fi-tiv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-tiv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](http... | 3df0531a90807bfc5b4fe8bf3bc68fd3 |
apache-2.0 | ['generated_from_trainer'] | false | bart-base-finetuned-xlsum-10-epoch This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the xlsum dataset. It achieves the following results on the evaluation set: - Loss: 1.7506 - Rouge1: 38.5509 - Rouge2: 17.1804 - Rougel: 31.6297 - Rougelsum: 31.6993 - Gen Len: 1... | e33de606e81965c5b52dcc59ff39ae50 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.0865 | 1.0 | 19158 | 1.8344 | 36.709 | 15.4309 | 29.8542 | 29.9152 ... | a8a7e27cd64840635546c48f18d01678 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/vctk_tts_train_gst+xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4394608/ This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 24457dae589b37b9e2333ba2a56f32cb |
mit | [] | false | kamon style on Stable Diffusion This is the `<kamon-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can al... | 710049be61a3f757c7b0f529d5c27e11 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | exper2_mesum5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem5 dataset. It achieves the following results on the evaluation set: - Loss: 3.4589 - Accuracy: 0.1308 | 850aa31973904670f341deae99b52db0 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP | ecd12e8000b51b6eb26b6fdbd1a20a8d |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.4265 | 0.23 | 100 | 4.3676 | 0.0296 | | 4.1144 | 0.47 | 200 | 4.1606 | 0.0544 | | 4.0912 | 0.7 | 300 | 4.1071 | 0.... | 68e76b473ac9e0132628f0e014251678 |
apache-2.0 | ['generated_from_keras_callback'] | false | long-t5-local-large This model is a fine-tuned version of [google/long-t5-local-large](https://huggingface.co/google/long-t5-local-large) on an unknown dataset. It achieves the following results on the evaluation set: | 25cd2a4897b88c4a915a340e41ffd688 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_Romanian-RRT | Feature | Description | | --- | --- | | **Name** | `ro_udv25_romanianrrt_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_ed... | 09e4085bf73ee98905bc8f4e860522aa |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (3096 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `ARROW`, `Af`, `Afcfp-n`, `Afcfson`, `Afcfsrn`, `Afcmpoy`, `Afcms-n`, `Afp`, `Afp-p-n`, `Af... | 058b8140bd41c3554c66ea109a8cf012 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 99.79 | | `TOKEN_P` | 99.78 | | `TOKEN_R` | 99.80 | | `TOKEN_ACC` | 99.96 | | `SENTS_F` | 92.35 | | `SENTS_P` | 94.94 | | `SENTS_R` | 89.89 | | `TAG_ACC` | 96.53 | | `POS_ACC` | 97.85 | | `MORPH_ACC` | 97.23 | | `DEP_UAS` | 92.52 | | `DEP_LAS` | 86.32 | | `LEMMA_A... | a0ccebf43363e848e587238bc6507217 |
apache-2.0 | ['automatic-speech-recognition', 'fa'] | false | exp_w2v2t_fa_hubert_s889 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (fa)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is... | 1a58687088b0bc61e9f5b3e17eef04db |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_Romanian-Nonstandard | Feature | Description | | --- | --- | | **Name** | `ro_udv25_romaniannonstandard_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, ... | 63dd7e48874e5d7c89731db7944b6a66 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (7445 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `AdpType=Prep\|Case=Acc`, `Afp`, `Afpf--n`, `Afpfp-n`, `Afpfpon`, `Afpfpoy`, `Afpfprn`, `Af... | ecfb77bcbafc37edbec996885f60cf25 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 99.06 | | `TOKEN_P` | 99.06 | | `TOKEN_R` | 99.06 | | `TOKEN_ACC` | 99.77 | | `SENTS_F` | 97.00 | | `SENTS_P` | 97.32 | | `SENTS_R` | 96.67 | | `TAG_ACC` | 93.85 | | `POS_ACC` | 97.66 | | `MORPH_ACC` | 93.64 | | `DEP_UAS` | 92.56 | | `DEP_LAS` | 87.49 | | `LEMMA_A... | e3b4c70ca6f18a4a6d055380fab6afd7 |
other | ['bart', 'cantonese', 'fill-mask'] | false | Usage ```python from transformers import BertTokenizer, BartForConditionalGeneration, Text2TextGenerationPipeline tokenizer = BertTokenizer.from_pretrained('jed351/bart-zh-hk-wiki') model = BartForConditionalGeneration.from_pretrained('jed351/bart-zh-hk-wiki') text2text_generator = Text2TextGenerationPipeline(model, ... | 98c7d71fda2109a911091c8a4a8a36d6 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2236 - Accuracy: 0.9225 - F1: 0.9224 | 6e3be832655aeaf697af4b6169df1ccc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8532 | 1.0 | 250 | 0.3276 | 0.904 | 0.8999 | | 0.2564 | 2.0 | 500 | 0.2236 | 0.9225 | 0.9224 | | d544b0f4a08bf0f839787ea17e84d79e |
apache-2.0 | ['generated_from_trainer'] | false | bert-all This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the tydiqa dataset. It achieves the following results on the evaluation set: - Loss: 0.5985 | b5a8a796c5c7fb765cf453cbe293074e |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | newhorrorfantasy_style V2 Hi guys! In this time, I training a SD 2.1 model fro upgraded newhorrorfantasy_style .This is a SD trainee with a 2010s horror and fantasy illustrations as a style. The magic word for the tests is = newhorrorfantasy_style If you wanna test it, you can put this word on the prompt: newhorror... | 83f1b3b34716c87fdb53d8a147be8de5 |
mit | ['generated_from_trainer'] | false | BerTurkBase_15_epoch This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0005 - Accuracy: 1.0 | 895be3dfa55a02a0468acdb41acb61c7 |
mit | ['generated_from_trainer'] | false | 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: 15 | 8f2c47f4f068d32011e68dbac85d34a4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 50 | 0.6526 | 0.5972 | | No log | 2.0 | 100 | 0.1755 | 0.9653 | | No log | 3.0 | 150 | 0.0518 | 0.... | 8ec73196b497f7a4173f8c17f31d3087 |
mit | ['fill-mask', 'generated_from_trainer'] | false | deberta-mlm-test This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2792 - Accuracy: 0.4766 | 42e2e9485ba3cadbf5220ac62a28270e |
mit | ['fill-mask', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 4.4466 | 1.0 | 2067 | 4.1217 | 0.3847 | | 3.9191 | 2.0 | 4134 | 3.6562 | 0.4298 | | 3.6397 | 3.0 | 6201 | 3.4417 ... | f600827e6c63fe0137d678ee74e3d122 |
mit | [] | false | Model Description A CLIP ViT-H/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Model training done by Romain Beaumont on the [stability.ai](https://stability.ai/) cluster. | 0c26925fcf40dec5fd27cdea1126b231 |
mit | [] | false | Training Data This model was trained with the 2 Billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/). **IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale d... | 2714de4eeb6a905bd5d34739051e36b4 |
mit | [] | false | Training Procedure Please see [training notes](https://docs.google.com/document/d/1EFbMLRWSSV0LUf9Du1pWzWqgeiIRPwEWX2s1C6mAk5c) and [wandb logs](https://wandb.ai/rom1504/eval_openclip/reports/H-14--VmlldzoyNDAxODQ3). | f0363811ac23e1483e0fe55f01051033 |
mit | [] | false | Testing Data The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval. **TODO** - more detail | 1617c7baf731654a89602dbb83f74f9f |
mit | [] | false | Results The model achieves a 78.0 zero-shot top-1 accuracy on ImageNet-1k. An initial round of benchmarks have been performed on a wider range of datasets, currently viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb **TODO** - create table for just this model's metrics. | fa5a66da78bbbd8f5e90634caac03658 |
mit | [] | false | Citation **BibTeX:** LAION-5B ```bibtex @inproceedings{schuhmann2022laionb, title={{LAION}-5B: An open large-scale dataset for training next generation image-text models}, author={Christoph Schuhmann and Romain Beaumont and Richard Vencu and Cade W Gordon and Ross Wightman... | d00d96928b8cda07e373f29161168c9b |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq'] | false | T5-base-nl36 for Finnish Pretrained T5 model on Finnish language using a span-based masked language modeling (MLM) objective. T5 was introduced in [this paper](https://arxiv.org/abs/1910.10683) and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer). **Note:** The Hugg... | 00d8e4052bff8651af4e08dd8b24775d |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq'] | false | t511) improvements compared to the original T5 model during the pretraining: - GEGLU activation in feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202) - Dropout was turned off in pretraining (quality win). Dropout should be re-enabled during fine-tuning - Pretrained on span-based ... | 4e169aa8ad08bf82588bde6b22e01319 |
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