license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'lv', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-latvian This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - LV dataset. It achieves the following results on the evaluation set: - Loss: 0.1892 - Wer: 0.1698 | 0c1a6aebe36673a0bd0ab6d96d54cc31 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'lv', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.4235 | 12.82 | 2000 | 0.4475 | 0.4551 | | 0.9383 | 25.64 | 4000 | 0.2235 | 0.2328 | | 0.8359 | 38.46 | 6000 | 0.2004 | 0.209... | c288a067526a2327e71cb57510d1bad9 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned-token-argumentative This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1573 - Precision: 0.3777 - Recall: 0.391... | 19cedf19fbc695154c73b2ca5a4e84b0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 75 | 0.3241 | 0.1109 | 0.2178 | 0.1470 | 0.8488 | | No log | 2.0 |... | d9c4ec14608908cd6458d74bd95b5092 |
cc | ['named-entity-recognition', 'token-classification', 'entity_extraction', 'multi_class_classification'] | false | Intended uses: This model is intended to be used for named entity recoginition tasks. The model will identify disease entities in text. The model will predict lables based upon the NCBI-disease dataset, please see the dataset information for details. | 787a88b7b88dbbb830ba622173d7e79e |
cc | ['named-entity-recognition', 'token-classification', 'entity_extraction', 'multi_class_classification'] | false | Limitations: Note that the dataset and model may not be fully represetative or suitable for all needs it is recommended that the paper for the dataset and the base model card should be reviewed before using the model - - [NCBI Disease](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951655/pdf/nihms557856.pdf) - [micro... | 6527484b835c5b3c99a5cbbd1b12e661 |
cc | ['named-entity-recognition', 'token-classification', 'entity_extraction', 'multi_class_classification'] | false | How to use: Load the model from the library using the following checkpoints: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sarahmiller137/BiomedNLP-PubMedBERT-base-uncased-abstract-ft-ncbi-disease") model = AutoModel.from_pretrained("sarahmiller137/BiomedNLP-Pub... | 76207de5a39b91039a85f0f674fd2522 |
mit | [] | false | model by machinelearnear This your the Stable Diffusion model fine-tuned the mirtha legrand concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks mirtha legrand** You can also train your own concepts and upload them to the library by using [this notebo... | 3e05303eb5cdae91d476677a7e10adb0 |
mit | [] | false | wojaks-now-now-now on Stable Diffusion This is the `<red-wojak>` 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 c... | 460471045040a4c53575772f28e4912a |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1815 - Accuracy: 0.9663 - F1: 0.9686 | 48c06f20e38cc669cb172920dc13782a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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 | 6c942c1298cc48903d07b5b61fd8b496 |
apache-2.0 | Text Classification | false | BatterySciBERT-cased for Battery Abstract Classification
**Language model:** batteryscibert-cased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructu... | 6e0979636242d294971e70d0c91fefe2 |
apache-2.0 | Text Classification | false | a) Get predictions
nlp = pipeline('text-classification', model=model_name, tokenizer=model_name)
input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'}
res = nlp(input)
| 021ff6b75b9fa08704b4bf7a89237787 |
apache-2.0 | ['generated_from_keras_callback'] | false | ksabeh/bert_attrs_qa_large This model is a fine-tuned version of [ksabeh/distilbert-attribute-correction-mlm](https://huggingface.co/ksabeh/distilbert-attribute-correction-mlm) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0560 - Validation Loss: 0.0722 - Epoch: 1 | f1cb7941299238b5dcedb30dc24df602 |
apache-2.0 | ['generated_from_trainer'] | false | bert-tweet-disaster This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9563 - Accuracy: 0.8320 - F1: 0.8095 | e4ede3ab38b711e1ae47deca90a60e3d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 | 559ae73b2bdbbe24ad2b67d7aeeac99d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.605 | 1.0 | 108 | 0.4455 | 0.8123 | 0.7741 | | 0.3878 | 2.0 | 216 | 0.3940 | 0.8438 | 0.8126 | | 0.3228 |... | 097b353582791ebee6c33cb43dff2f3f |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - label_smoothing_facto... | 1d4945dd3d61034ef1d9e02f3971e633 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - total_eval_batch_size: 18 - optimizer: Adam with betas=(0.9,0.999) and... | 1a39540b184f3324632d67bdfb11af42 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | nan | 1.0 | 291 | nan | | nan | 2.0 | 582 | nan | | nan | 3.0 | 873 | nan | | 2a24ef4022ccc5290611494e28a56e2b |
apache-2.0 | ['generated_from_trainer'] | false | roberta-large-finetuned-chunking 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: 1.4192 - Precision: 0.3222 - Recall: 0.3161 - F1: 0.3191 - Accuracy: 0.8632 | 0a1d2dd992951a5fb1607929422155a5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0373 | 1.0 | 2498 | 0.9545 | 0.3166 | 0.2545 | 0.2822 | 0.8656 | | 0.0045 | 2.0 ... | 9d757666d977611fe686630815609431 |
apache-2.0 | ['generated_from_trainer'] | false | poem-gen-gpt2-small-spanish This model is a fine-tuned version of [datificate/gpt2-small-spanish](https://huggingface.co/datificate/gpt2-small-spanish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.9229 | f586324752a64cae2f93e630d4e1ca82 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | d9213753b692d6ad9f5d1f2b71b3e11d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.2121 | 1.0 | 2569 | 3.9954 | | 4.0612 | 2.0 | 5138 | 3.9375 | | 3.9988 | 3.0 | 7707 | 3.9229 | | dd6558fff37f017f8571a947990f08f8 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large-v2 Marathi This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 mr dataset. It achieves the following results on the evaluation set: - Loss: 0.3108 - Wer: 15.2206 | cde1339c48c3b545bd514a12b25e3e15 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_s... | 61099b37df016591c2729adab3593f3b |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1931 | 3.04 | 200 | 0.2491 | 16.9270 | | 0.1108 | 7.03 | 400 | 0.2379 | 15.2711 | | 0.0548 | 11.02 | 600 | 0.2668 | 15.312... | 98f0cca6034f6fdf12bd95a6d8a563ea |
apache-2.0 | ['bert', 'stsb', 'glue', 'torchdistill'] | false | `bert-large-uncased` fine-tuned on STS-B dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb). The hyperparameters are the same as th... | 6d99fd2dc374862c4414260333d67ab0 |
openrail | [] | false | Yes, he is back, better than ever. And with a beautiful Green Hill Zone. Renders in Automatic1111  . | ccee1cd6b631c7770f7f6b242691710b |
openrail | ['code'] | false | Model Description this model is developed as a multi-approach knn recommending system using sklearn & pytorch. <!-- Provide a longer summary of what this model is. --> - **Developed by:** [AmirHossein Advari, Parsa MohammadPour] - **Model type:** [KNN] | 65e1dbf8139faed46a3844e903d3fb6d |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1454 - F1: 0.8732 | ed187af8ce21181c412cd3e2289fe628 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.297 | 1.0 | 739 | 0.1785 | 0.8273 | | 0.1536 | 2.0 | 1478 | 0.1524 | 0.8574 | | 0.0998 | 3.0 | 2217 | 0.1454 | 0.8732 | ... | 628e2b810d7a25f25013ec5b91ba0058 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large Amharic FLEURS This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the google/fleurs am_et dataset. It achieves the following results on the evaluation set: - Loss: 12.2408 - Wer: 102.9412 | d786da0c314f59b4ec39cbe9266f535b |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Intended uses & limitations - For experimentation and curiosity. - Based on the paper [AXRIV](https://arxiv.org/abs/2212.04356) and [Benchmarking OpenAI Whisper for non-English ASR - Dan Shafer](https://blog.deepgram.com/benchmarking-openai-whisper-for-non-english-asr/), there is a performance bias towards certain l... | 47e74d0e80792f39ff91a11c67fbb6af |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training procedure - The training was done in Lambda Cloud GPU on A100/40GB GPUs, which were provided by OpenAI Community Events [Whisper Fine Tuning Event - Dec 2022](https://github.com/huggingface/community-events/tree/main/whisper-fine-tuning-event | 6508e942d9ae5421ea3be552a35761f1 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | fine-tune-whisper). The training was done using [HuggingFace Community Events - Whisper - run_speech_recognition_seq2seq_streaming.py](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/run_speech_recognition_seq2seq_streaming.py) using the included [whisper_python_am_et.ipynb](https://... | 43f70af321b0eb2a3ef2df8ee4a23bdc |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | fine-tune-whisper). The notebook sets up the environment, logs into your huggingface account, and generates a bash script. The bash script generated in the IPYNB, `run.sh` was run from the terminal to train `bash run.sh`, as described on the Whisper community events GITHUB page. | f4917fc1eabb7b6383febdab4e4ea8a9 |
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: 128 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_preci... | 2b28ab5a243357d617101f9c76f1b618 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.0 | 1000.0 | 1000 | 8.3822 | 156.0160 | | 0.0 | 2000.0 | 2000 | 9.7961 | 110.4278 | | 0.0 | 3000.0 | 3000 | 12.0014 ... | 5ff675da5a5625ad5f9be44c082084fc |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Recommendations Limit training duration for smaller datasets to ~ 2000 to 3000 steps to avoid overfitting. 5000 steps using the [HuggingFace - Whisper Small](https://huggingface.co/openai/whisper-small) takes ~ 5hrs on A100 GPUs (1hr/1000 steps). Encountered `RuntimeError: The size of tensor a (504) must match the s... | 182be90a761c5ea6132e087f0b775f9c |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). In total roughly 100 hours were used primarily in US East/Asia Pacific (80%/20%), with AWS as the reference. Additional resources are available at [Our World in Data - CO2 Emissions](https://ourworldindata.org/co2-emissions) - __Hardware... | 0d975b1a0a8b9bf91b7a1c7d49785e5f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Citation - [Whisper - GITHUB](https://github.com/openai/whisper) - [Whisper - OpenAI - BLOG](https://openai.com/blog/whisper/) - [Model Card - HuggingFace Hub - GITHUB](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md) ```bibtex @misc{https://doi.org/10.485... | 70a84ae86c0a858b69af1346f3810af3 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xls-r-300m-th-v2 This model is a fine-tuned version of [Botnoi/wav2vec2-xls-r-300m-th-v1](https://huggingface.co/Botnoi/wav2vec2-xls-r-300m-th-v1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3630 - Wer: 0.3962 - Cer: 0.0942 - Clean Cer: 0.0767 | caf61fc6de9bcc419ee594adf1797fd6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.533e-08 - 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 - tr... | 6e0c8c6bfe1e63c721c0170a33144704 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Clean Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:| | 0.3323 | 0.68 | 1000 | 0.3635 | 0.3961 | 0.0942 | 0.0767 | | 0.3386 | 1.36 | 2000 | 0.3632 | 0.3962 ... | 079ec3684095d7161361e284f9946068 |
apache-2.0 | ['luke', 'named entity recognition', 'entity typing', 'relation classification', 'question answering'] | false | luke-japanese **luke-japanese** is the Japanese version of **LUKE** (**L**anguage **U**nderstanding with **K**nowledge-based **E**mbeddings), a pre-trained _knowledge-enhanced_ contextualized representation of words and entities. LUKE treats words and entities in a given text as independent tokens, and outputs contex... | 96cc952eb67244f40a81d6a95a67f95c |
apache-2.0 | ['luke', 'named entity recognition', 'entity typing', 'relation classification', 'question answering'] | false | Experimental results on JGLUE The experimental results evaluated on the dev set of [JGLUE](https://github.com/yahoojapan/JGLUE) are shown as follows: | Model | MARC-ja | JSTS | JNLI | JCommonsenseQA | | ---------------------- | --------- | ------------------- | --------- | ----... | 082190a60e4b9186077165de91494523 |
apache-2.0 | ['generated_from_keras_callback'] | false | evanz37/bert-finetuned-ard This model is a fine-tuned version of [evanz37/bert-finetuned-ner](https://huggingface.co/evanz37/bert-finetuned-ner) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0722 - Validation Loss: 0.0861 - Epoch: 2 | 76600a5e4cf4e6805e30d45c7f366a61 |
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': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 669, 'end_learning_rat... | 27af84af3fa7af1337462d9436f7ef92 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.3408 | 0.1290 | 0 | | 0.1065 | 0.0894 | 1 | | 0.0722 | 0.0861 | 2 | | eab8ae817b375b7675a439b6667821ff |
mit | ['deidentification', 'medical notes', 'ehr', 'phi'] | false | Model Description * A RoBERTa [[Liu et al., 2019]](https://arxiv.org/pdf/1907.11692.pdf) model fine-tuned for de-identification of medical notes. * Sequence Labeling (token classification): The model was trained to predict protected health information (PHI/PII) entities (spans). A list of protected health information... | 957f9e799e52ea36fb4b619d0ba87ea8 |
mit | ['deidentification', 'medical notes', 'ehr', 'phi'] | false | How to use * A demo on how the model works (using model predictions to de-identify a medical note) is on this space: [Medical-Note-Deidentification](https://huggingface.co/spaces/obi/Medical-Note-Deidentification). * Steps on how this model can be used to run a forward pass can be found here: [Forward Pass](https://g... | 8bf69dd40faedeb800444741562cb996 |
mit | ['deidentification', 'medical notes', 'ehr', 'phi'] | false | Dataset * The I2B2 2014 [[Stubbs and Uzuner, 2015]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4978170/) dataset was used to train this model. | | I2B2 | | I2B2 | | | --------- | --------------------- | ---------- | -------------------- | ---------- ... | fc6f2c7164594da2d0aed3921f752642 |
mit | ['deidentification', 'medical notes', 'ehr', 'phi'] | false | Training procedure * Steps on how this model was trained can be found here: [Training](https://github.com/obi-ml-public/ehr_deidentification/tree/master/steps/train). The "model_name_or_path" was set to: "roberta-large". * The dataset was sentencized with the en_core_sci_sm sentencizer from spacy. * The datas... | 8eb509d53ec14e7e4cf54b8ede89a780 |
creativeml-openrail-m | ['text-to-image'] | false | Messi-Ronaldo-v1.5 Dreambooth model trained by Fireman4740 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingfac... | 41e091bba9b8a11255a34b24313ed0c5 |
apache-2.0 | ['exbert'] | false | Model description [xaqren/sentiment_analysis] This is a fine-tuned downstream version of the bert-base-uncased model for sentiment analysis, this model is not intended for further downstream fine-tuning for any other tasks. This model is trained on a classified dataset for text-classification. | 58f4d35b94134075ba000607c6f115ee |
apache-2.0 | ['generated_from_trainer'] | false | presentation_irony_42 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: 0.9344 - F1: 0.6745 | 078b36145f74da2f237ff26c23ae96d5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.1637764704815665e-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: 4 | 1021e383bc6a2eb11bfdf95cbdc450f9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.6675 | 1.0 | 90 | 0.5988 | 0.6684 | | 0.5872 | 2.0 | 180 | 0.6039 | 0.6742 | | 0.3953 | 3.0 | 270 | 0.8549 | 0.6557 | |... | 126dc63a5d4029e5c35527ba7f758bea |
cc-by-4.0 | ['questions and answers generation'] | false | Model Card of `research-backup/t5-base-tweetqa-qag-np` This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question & answer pair generation task on the [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm... | b8d047413bedb070300ae4abbf1e2fbd |
cc-by-4.0 | ['questions and answers generation'] | false | Overview - **Language model:** [t5-base](https://huggingface.co/t5-base) - **Language:** en - **Training data:** [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-g... | 20224a0709378d96c0444448c7744ed1 |
cc-by-4.0 | ['questions and answers generation'] | false | model prediction question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-base-tweetqa-qag-np") output = pipe("Beyonce... | dfcaa4ce901934f9906e09c235b167e4 |
cc-by-4.0 | ['questions and answers generation'] | false | Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-base-tweetqa-qag-np/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_tweetqa.default.json) | | Score | Type | Dataset ... | 4898621ccb51864469528d3d1eab7e4d |
cc-by-4.0 | ['questions and answers generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_tweetqa - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: None - model: t5-base - max_length: 256 - max_length_output: 128 - epoch: 15 - batc... | a85f966e101019ba18aef2e512c2e0a5 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-vios-commonvoice-1 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: 0.8913 - Wer: 0.3621 | cd5cffbae00a7e1a63abd2a5cdee6c76 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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... | d2470a30b1aee049ff438a6204c68bd1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.4706 | 0.55 | 500 | 3.4725 | 1.0 | | 3.202 | 1.1 | 1000 | 2.7555 | 1.0008 | | 1.0507 | 1.66 | 1500 | 1.0481 | 0.619... | c4a95685a1b25013cce861f4a73d36a2 |
apache-2.0 | ['text-generation', 'text2text-generation'] | false | MTL-task-dialog The MTL-task-dialog model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen. The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](h... | ac1aaab6d893e375ef32b193ac8fa8dc |
apache-2.0 | ['text-generation', 'text2text-generation'] | false | Model Description MTL-task-dialog is supervised pre-trained using a mixture of labeled task-oriented system datasets. It is a variant (Single) of our main [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a standard Transformer encoder-decoder architecture. MTL-task-dialog is specially designed for task-or... | 3f6eb9fa575ae1e432d56ba1af607e36 |
apache-2.0 | ['text-generation', 'text2text-generation'] | false | Example ```python >>> from transformers import MvpTokenizer, MvpForConditionalGeneration >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp") >>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mtl-task-dialog") >>> inputs = tokenizer( ... "Given the task dialog: System response [X_SEP] I'm... | 80b20917c9b83c8d7ed82ca090bb66ef |
cc-by-4.0 | ['generated_from_trainer'] | false | electra-base-squad2-ta-qna-electra This model is a fine-tuned version of [deepset/electra-base-squad2](https://huggingface.co/deepset/electra-base-squad2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1644 | 4414057f14e589382ec96d35776927ca |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 | ffe4493c3b5520534d03d543123d3e1f |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 44 | 0.2352 | | No log | 2.0 | 88 | 0.1644 | | a24212f1edd5d0ae56547f853732b998 |
mit | [] | false | Grief Seed on Stable Diffusion This is the `grief seed` 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 also t... | 28e95603b2a7e98477a6cf833543eb48 |
apache-2.0 | [] | false | Example Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-large-caption2smiles", model_max_length=512) model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-large-caption2smiles') input_text = 'The molecule is a m... | 4e5540edc92ad62bf6a70ba9938a3fd2 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-SmithsModel This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3070 | c175603f027d9d4fd01898c1d506d86e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.6589 | 1.0 | 830 | 2.8652 | | 2.8362 | 2.0 | 1660 | 2.4309 | | 2.6291 | 3.0 | 2490 | 2.2826 | | 26d66508a52eddd6c601efa2731c0d42 |
apache-2.0 | ['generated_from_keras_callback'] | false | hsohn3/mayo-timebert-visit-uncased-wordlevel-block512-batch4-ep100 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8536 - Epoch: 99 | e47e269b2ef37b103c64d8bd35b3c6e0 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Epoch | |:----------:|:-----:| | 3.9508 | 0 | | 3.4063 | 1 | | 3.3682 | 2 | | 3.3468 | 3 | | 3.3330 | 4 | | 3.3308 | 5 | | 3.3225 | 6 | | 3.3106 | 7 | | 3.2518 | 8 | | 3.1859 | 9 | | 3.1373 | 10 | | ... | 1935c2a2cf8e8cb7451393169329eb98 |
apache-2.0 | ['generated_from_trainer'] | false | mnli_bert-base-uncased_144 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4509 - Accuracy: 0.8422 | 8fcde0620ab938146c83d0ef28ce7335 |
mit | ['conversational'] | false | DialoGPT Trained on the Speech of a Game Character This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script da... | 76357ad7738d0ab8ba92d1bd192b6c3f |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | wav2vec2-base-ks-padpt200 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 1.6540 - Accuracy: 0.6037 | 5cf1cc66844dc59f6cf5ea440b4db55d |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 256 - eval_batch_size: 256 - seed: 0 - gradient_accumulation_steps: 4 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_... | 6dbdd20aaa62bff0ec28926ca5774703 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2728 | 1.0 | 50 | 1.6540 | 0.6037 | | 0.8498 | 2.0 | 100 | 1.2559 | 0.6015 | | 0.7563 | 3.0 | 150 | 1.4192 | 0.... | fb9e7c5001a03d7ad2337f0c3321600d |
mit | ['text', 'MLM'] | false | BERT Medium for Luxembourgish Created from a dataset with 1M Luxembourgish sentences from Wikipedia. Corpus has approx. 16M words. The MLM objective was trained. The BERT model has parameters `L=8` and `H=512`. Vocabulary has 70K word pieces. Final loss scores, after 3 epochs: - Final train loss: 4.230 - Final tra... | 1a70fbe1e0e78df547a6fe9afd47d86d |
cc-by-sa-4.0 | ['asteroid', 'audio', 'ConvTasNet', 'audio-to-audio'] | false | Training config: ```yaml data: channels: 1 emb_model: 'no' metadata_path: metadata mixture: remix root_path: /fastdata/acp13gr/DAMP/DAMP-VSEP sample_rate: 16000 train_set: english_nonenglish filterbank: kernel_size: 20 n_filters: 256 stride: 10 main_args: exp_dir: exp/train_convtasnet_remix-no-0.... | 7075e85e4ab4acafbdb7a746ddb804ce |
cc-by-sa-4.0 | ['asteroid', 'audio', 'ConvTasNet', 'audio-to-audio'] | false | Results: ```yaml "si_sdr": 15.111802516750586, "si_sdr_imp": 15.178209807687663, "si_sdr_s0": 12.160261214703553, "si_sdr_s0_imp": 17.434593619085675, "si_sdr_s1": 18.063343818797623, "si_sdr_s1_imp": 12.92182599628965, "sdr": 15.959722569460281, "sdr_imp": 14.927002467087567, "sdr_s0": 13.270412028426595, "sdr_s0_imp... | 722410aeeab57373fd6d570d9fb22cbf |
cc-by-sa-4.0 | ['asteroid', 'audio', 'ConvTasNet', 'audio-to-audio'] | false | License notice: ** This is important, please fill it, if you need help, you can ask on Asteroid's slack.** This work "ConvTasNet_DAMPVSEP_EnglishNonEnglish_baseline" is a derivative of [DAMP-VSEP corpus](https://zenodo.org/record/3553059) by [Smule, Inc](https://www.smule.com/), used under [Restricted License](https... | cec2aee9ca1d898136d0504b2d898b8c |
mit | ['generated_from_trainer'] | false | finetuned_gpt2-medium_sst2_negation0.001_pretrainedTrue_epochs3 This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 3.0568 | 182456df3c32bf192a91216dacbad1a1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.2831 | 1.0 | 1322 | 2.8944 | | 1.971 | 2.0 | 2644 | 2.9808 | | 1.8553 | 3.0 | 3966 | 3.0568 | | 893270f5af6b51b3dc86d0b02b7018e3 |
apache-2.0 | Text Classification | false | BatteryOnlyBERT-uncased for Battery Abstract Classification
**Language model:** batteryonlybert-uncased
**Language:** English
**Downstream-task:** Text Classification
**Training data:** training\_data.csv
**Eval data:** val\_data.csv
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infras... | 9c3a6825268336ad7a21980dabf39b90 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_vp-100k_age_teens-0_sixties-10_s666 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using t... | 020651a7e67d0ceea6927201d57a4f6c |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | t5-base-TEDxJP-11body-0context This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.8068 - Wer: 0.1976 - Mer: 0.1904 - Wil: 0.2816 - Wip: 0.7184 - Hits: 602335 - Sub... | cd32ee4fcbc6353b6adc608465d9d420 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 | a459291e129ffdfad8570cb4057dfa71 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:------:|:------:|:-------------:|:---------:|:----------:|:------:| | 0.8909 ... | 5f8ff888968cdc1212dc5e47e68cbdb5 |
mit | ['donut', 'image-to-text', 'vision'] | false | Donut (base-sized model, fine-tuned on DocVQA) Donut model fine-tuned on DocVQA. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut). Disclaimer: The team releasing D... | 16084cdc381148a61caabf1b95c27734 |
mit | ['donut', 'image-to-text', 'vision'] | false | Intended uses & limitations This model is fine-tuned on DocVQA, a document visual question answering dataset. We refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/donut) which includes code examples. | 59e84857a337282b8823fd0e212c0270 |
apache-2.0 | ['generated_from_trainer'] | false | twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-14_45_20 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6113... | 19bef3dc00cb72245f3111df0c3de7da |
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