modelId stringlengths 4 81 | tags list | pipeline_tag stringclasses 17
values | config dict | downloads int64 0 59.7M | first_commit timestamp[ns, tz=UTC] | card stringlengths 51 438k | embedding list |
|---|---|---|---|---|---|---|---|
alexandrainst/da-hatespeech-classification-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 866 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hing-roberta-finetuned-code-mixed-DS
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and... | [
-0.01287646684795618,
-0.0013085475657135248,
0.005186006426811218,
0.022857239469885826,
0.02225416526198387,
0.034109581261873245,
-0.027136415243148804,
-0.01406795997172594,
-0.027064388617873192,
0.046843692660331726,
0.013244693167507648,
-0.021186769008636475,
0.012349963188171387,
... |
alexandrainst/da-hatespeech-detection-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 1,719 | null | ---
license: mit
---
### Transmutation Circles on Stable Diffusion
This is the `<tcircle>` 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_inferen... | [
-0.02474617399275303,
-0.0175336804240942,
-0.015750601887702942,
0.03518899530172348,
0.0072026606649160385,
0.023689929395914078,
-0.001384799019433558,
-0.014333829283714294,
-0.033369556069374084,
0.03418143466114998,
-0.0038479208014905453,
-0.007900810800492764,
0.026033269241452217,
... |
alexandrainst/da-ner-base | [
"pytorch",
"tf",
"bert",
"token-classification",
"da",
"dataset:dane",
"transformers",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat... | 78 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hing-roberta-finetuned-combined-DS
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and c... | [
-0.010745322331786156,
0.0035534370690584183,
0.004296588245779276,
0.023414000868797302,
0.018593842163681984,
0.02847578190267086,
-0.028328029438853264,
-0.015071660280227661,
-0.026199351996183395,
0.05046066641807556,
0.010042907670140266,
-0.022273952141404152,
0.018100446090102196,
... |
DavidAMcIntosh/DialoGPT-small-rick | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | Fined-tuned BERT trained on 6500 images with warmup, increased epoch and decreased learning rate | [
0.016357816755771637,
-0.002644522115588188,
-0.012274064123630524,
0.026195641607046127,
0.015002816915512085,
0.012542853131890297,
-0.011394314467906952,
-0.004442648962140083,
-0.007022171746939421,
0.026514265686273575,
0.006832917686551809,
-0.03165603429079056,
0.03145086392760277,
... |
DavidAMcIntosh/small-rick | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: mit
---
### Riker Doll on Stable Diffusion
This is the `<rikerdoll>` 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)... | [
-0.026161398738622665,
-0.02531673200428486,
-0.021903760731220245,
0.034108106046915054,
0.010741873644292355,
0.01648656837642193,
-0.009873990900814533,
-0.008833246305584908,
-0.036849796772003174,
0.04876890778541565,
0.005383068695664406,
-0.02580435201525688,
0.03999335691332817,
0.... |
Davlan/bert-base-multilingual-cased-finetuned-amharic | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 109 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-finetuned-basil
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. -->
# ber... | [
-0.019222082570195198,
0.006279607303440571,
-0.013984886929392815,
0.034219205379486084,
0.030398720875382423,
0.011255604214966297,
-0.008352832868695259,
-0.011220185086131096,
-0.030994301661849022,
0.04853253439068794,
0.025719208642840385,
-0.026692960411310196,
0.01547230314463377,
... |
Davlan/bert-base-multilingual-cased-finetuned-hausa | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 151 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->... | [
-0.018779942765831947,
-0.01356754545122385,
-0.02163601852953434,
0.04315792769193649,
0.04856978356838226,
0.02846054546535015,
-0.037619154900312424,
0.012097103521227837,
-0.02365630678832531,
0.03721647709608078,
0.035962749272584915,
-0.004658426623791456,
0.027971601113677025,
0.039... |
Davlan/bert-base-multilingual-cased-finetuned-luo | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 11 | 2022-09-10T13:59:36Z | ---
library_name: keras
---
## 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:
| Hyperparameters ... | [
-0.025874098762869835,
-0.030422525480389595,
-0.013933918438851833,
0.020916016772389412,
0.018605880439281464,
0.0007647927850484848,
-0.015272622928023338,
-0.018145183101296425,
-0.03657500818371773,
0.04730711504817009,
0.018930671736598015,
-0.012843259610235691,
0.021045437082648277,
... |
Davlan/distilbert-base-multilingual-cased-masakhaner | [
"pytorch",
"tf",
"distilbert",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 16 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/apesahoy-dril_gpt2-stefgotbooted/1662822110359/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; m... | [
0.017044054344296455,
-0.040877681225538254,
0.003337468486279249,
0.0441175140440464,
0.041346997022628784,
0.01199498400092125,
-0.017062870785593987,
0.0011368095874786377,
-0.04159333556890488,
0.043243929743766785,
0.008864033967256546,
-0.015906432643532753,
-0.007185742259025574,
0.... |
Davlan/distilbert-base-multilingual-cased-ner-hrl | [
"pytorch",
"tf",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible",
"has_space"
] | token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 123,856 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/altgazza-apesahoy-stefgotbooted/1662823067384/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; ma... | [
0.013633220456540585,
-0.04473951831459999,
0.004678711295127869,
0.04522775858640671,
0.0412679947912693,
0.008705523796379566,
-0.012798679061233997,
0.0017817250918596983,
-0.04196102172136307,
0.04211162403225899,
0.009525024332106113,
-0.01562957465648651,
-0.006469357293099165,
0.024... |
Davlan/m2m100_418M-eng-yor-mt | [
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"M2M100ForConditionalGeneration"
],
"model_type": "m2m_100",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no... | 9 | null | ---
title: Daimond Price
emoji: 💩
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.10.0
app_file: app.py
pinned: false
license: cc-by-3.0
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
| [
-0.033189501613378525,
0.0002699286269489676,
-0.0038366899825632572,
0.01790456473827362,
0.04854483902454376,
-0.00557632464915514,
-0.010138186626136303,
-0.011936158873140812,
-0.03240283951163292,
0.043785206973552704,
0.013319535180926323,
0.029831836000084877,
0.06990766525268555,
0... |
Davlan/m2m100_418M-yor-eng-mt | [
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"M2M100ForConditionalGeneration"
],
"model_type": "m2m_100",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no... | 6 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4... | [
0.006226216908544302,
-0.0359017513692379,
0.0009103385964408517,
0.03353412449359894,
0.048418328166007996,
0.015130430459976196,
-0.02601616270840168,
-0.006104547064751387,
-0.030394522473216057,
0.03800749406218529,
-0.002233284991234541,
-0.009492631070315838,
0.00325158447958529,
0.0... |
Davlan/xlm-roberta-base-finetuned-shona | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repe... | 5 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- davanstrien/autotrain-data-encyclopedia_britannica
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
exa... | [
-0.007910935208201408,
-0.011989034712314606,
0.014587108977138996,
0.05181531235575676,
0.04441608488559723,
-0.005327607970684767,
-0.010459848679602146,
0.0014983394648879766,
-0.042185526341199875,
0.06064502149820328,
-0.0012521123280748725,
0.003828135784715414,
0.0014745715307071805,
... |
DecafNosebleed/scarabot-model | [
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 6 | null | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: turkish-poem-generation
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# turkish-poem-gene... | [
-0.012842790223658085,
-0.02124466747045517,
-0.0008251729304902256,
0.03811490163207054,
0.032563503831624985,
0.006036640610545874,
-0.009375404566526413,
-0.0153450733050704,
-0.040794648230075836,
0.07198596745729446,
0.018669437617063522,
-0.025834573432803154,
0.02213590405881405,
0.... |
Declan/Breitbart_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 9 | null | ---
tags:
- image-classification
- keras
- tf
metrics:
- accuracy
license: cc-by-sa-4.0
---
Model for MNIST on TensorFlow. | [
-0.028958864510059357,
-0.020324958488345146,
0.005854399874806404,
0.018667424097657204,
0.023842619732022285,
0.007915488444268703,
-0.0031158023048192263,
0.009586433880031109,
-0.02441152185201645,
0.04077332839369774,
0.017447607591748238,
0.015271102078258991,
0.01378639042377472,
0.... |
Declan/Breitbart_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3 | null | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this com... | [
-0.03126215189695358,
-0.010328886099159718,
-0.0070562176406383514,
0.036757759749889374,
0.017762772738933563,
0.011539244093000889,
0.008735360577702522,
-0.005180069245398045,
-0.00897044874727726,
0.05612855777144432,
0.007924782112240791,
-0.02346443384885788,
0.004798386245965958,
0... |
Declan/Breitbart_model_v7 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 5 | null | ---
license: mit
---
### disquieting muses on Stable Diffusion
This is the `<muses>` 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.ipy... | [
-0.024389056488871574,
-0.023077821359038353,
-0.026724448427557945,
0.04701969027519226,
0.008754236623644829,
0.039405595511198044,
0.012881546281278133,
-0.0005276232259348035,
-0.03178844973444939,
0.05757492035627365,
-0.005494067911058664,
-0.011403366923332214,
0.03680318593978882,
... |
Declan/Breitbart_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3 | null | ---
license: mit
---
### ned-flanders on Stable Diffusion
This is the `<flanders>` 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... | [
-0.01672794297337532,
-0.017949599772691727,
-0.04485587030649185,
0.03530961275100708,
0.008692468516528606,
0.019624991342425346,
0.0018433713121339679,
-0.015436900779604912,
-0.03651417791843414,
0.04892338439822197,
-0.0007379603921435773,
-0.014196529053151608,
0.03392866626381874,
0... |
Declan/Breitbart_modelv7 | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: mit
---
### Fluid_acrylic_Jellyfish_creatures_style_of_Carl_Ingram_art on Stable Diffusion
This is the `<jelly-core>` 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/mai... | [
-0.015578120946884155,
-0.027221018448472023,
-0.040716301649808884,
0.027627022936940193,
0.03628832474350929,
0.019526246935129166,
-0.005330827087163925,
-0.007843147031962872,
-0.0029483612161129713,
0.047986648976802826,
0.012035179883241653,
-0.024183977395296097,
0.00990542396903038,
... |
Declan/CNN_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 7 | 2022-09-10T22:07:33Z | ---
library_name: stable-baselines3
tags:
- HalfCheetahBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 747.07 +/- 1132.58
name: mean_reward
task:
type: reinforcement-learning
... | [
-0.04388038441538811,
0.000970002613030374,
-0.01081451028585434,
0.028145698830485344,
0.03744107484817505,
0.012794424779713154,
-0.02124609984457493,
-0.023392608389258385,
-0.04809059202671051,
0.07289472222328186,
0.012247388251125813,
-0.0003143140347674489,
0.02178449183702469,
0.01... |
Declan/ChicagoTribune_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3 | null | ---
tags:
- spacy
language:
- en
model-index:
- name: en_stonk_pipeline
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8565043157
- name: NER Recall
type: recall
value: 0.8348858173
- name: NER F Score... | [
0.006457516457885504,
-0.0052386014722287655,
-0.02497713640332222,
0.027828682214021683,
0.060234181582927704,
0.01936217024922371,
-0.0158491563051939,
0.015932243317365646,
-0.04950271174311638,
0.07679757475852966,
0.05229992792010307,
-0.010757004842162132,
0.02888968028128147,
0.0496... |
Declan/ChicagoTribune_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default... | [
-0.009319703094661236,
0.009262018837034702,
-0.029228778555989265,
0.03751998022198677,
0.06013989448547363,
0.03342858701944351,
-0.02396792732179165,
-0.03593872860074043,
-0.03378558158874512,
0.055754322558641434,
0.019556690007448196,
-0.0468403697013855,
0.0349782332777977,
0.043040... |
Declan/ChicagoTribune_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 7 | null | ---
license: mit
---
### klance on Stable Diffusion
This is the `<klance>` 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) notebo... | [
-0.024028778076171875,
-0.02345387637615204,
-0.02800370752811432,
0.03970882296562195,
0.010050518438220024,
0.013317622244358063,
-0.0011493376223370433,
0.0003734472848009318,
-0.03666409105062485,
0.0442899726331234,
-0.009553291834890842,
-0.015241360291838646,
0.03573185205459595,
0.... |
Declan/NPR_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: defau... | [
-0.010331597179174423,
0.010683259926736355,
-0.029260555282235146,
0.037548698484897614,
0.06072413921356201,
0.03272533044219017,
-0.022846896201372147,
-0.035877905786037445,
-0.0337616503238678,
0.055925995111465454,
0.018339550122618675,
-0.045406896620988846,
0.03377103805541992,
0.0... |
Declan/NewYorkTimes_model_v3 | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- conversational
---
# Hermione DialoGPT Model | [
-0.041167814284563065,
0.0011867397697642446,
0.012949300929903984,
0.029797429218888283,
-0.003561275079846382,
0.016477225348353386,
-0.0014331202255561948,
0.025448475033044815,
-0.02912418730556965,
0.01666002720594406,
0.023176761344075203,
-0.026266615837812424,
0.01640075072646141,
... |
Declan/NewYorkTimes_model_v4 | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 200.50 +/- 30.64
name: mean_reward
task:
type: reinforcement-learning
name: re... | [
-0.039214227348566055,
-0.00481741176918149,
-0.006166951730847359,
0.02650432102382183,
0.04389696195721626,
-0.01647108979523182,
-0.007763146422803402,
-0.025900758802890778,
-0.03630676120519638,
0.06575406342744827,
0.03039453737437725,
-0.02260540798306465,
0.02316426672041416,
0.002... |
Declan/NewYorkTimes_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 5 | null | ---
license: mit
---
### unfinished building on Stable Diffusion
This is the `<unfinished-building>` 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_conceptualiz... | [
-0.03420146554708481,
-0.013009054586291313,
-0.031889185309410095,
0.03635826334357262,
0.01458851620554924,
0.01973464898765087,
0.003822669619694352,
-0.002580453408882022,
-0.03938553109765053,
0.043868400156497955,
0.012909593060612679,
0.0005057859816588461,
0.02463625930249691,
0.04... |
Declan/NewYorkTimes_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3 | null | ---
license: mit
---
### Teelip-IR-Landscape on Stable Diffusion
This is the `<teelip-ir-landscape>` 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_conceptualiz... | [
-0.014622912742197514,
-0.02160600945353508,
-0.040720317512750626,
0.046693865209817886,
0.02421741373836994,
0.006977189797908068,
0.000897302757948637,
0.0017215776024386287,
-0.03327019512653351,
0.05657368525862694,
-0.013298439793288708,
-0.02193532884120941,
0.034774038940668106,
0.... |
Declan/Politico_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 5 | null | ---
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: Frozen... | [
-0.02147405594587326,
-0.01817850023508072,
-0.004122815560549498,
0.03336256742477417,
0.048395466059446335,
-0.019064489752054214,
-0.012045438401401043,
-0.013397568836808205,
-0.06080019474029541,
0.05524197965860367,
-0.006505110766738653,
-0.011016963981091976,
0.023376692086458206,
... |
Declan/Politico_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 5 | null | ---
license: mit
---
### Road to Ruin on Stable Diffusion
This is the `<RtoR>` 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) no... | [
-0.018852723762392998,
-0.023883285000920296,
-0.030248140916228294,
0.028558963909745216,
0.007713839411735535,
0.018610497936606407,
0.010151350870728493,
0.002304800320416689,
-0.0466047041118145,
0.041010838001966476,
-0.00394442118704319,
-0.010133113712072372,
0.028926393017172813,
0... |
Declan/Politico_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 9 | null | ---
license: mit
---
### Piotr Jablonski on Stable Diffusion
This is the `<piotr-jablonski>` 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_infer... | [
-0.025291990488767624,
-0.019048405811190605,
-0.03656541183590889,
0.04557237774133682,
0.01874661259353161,
0.009931770153343678,
0.015046663582324982,
0.00340909231454134,
-0.039948318153619766,
0.04275677725672722,
0.0013354268157854676,
-0.020194390788674355,
0.02775302715599537,
0.04... |
DeepBasak/Slack | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: mit
---
### nixeu on Stable Diffusion
This is the `<nixeu>` 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... | [
-0.024489106610417366,
-0.025259727612137794,
-0.02732219733297825,
0.03893283009529114,
0.011031204834580421,
0.02031315676867962,
0.0006512991967611015,
-0.005439814645797014,
-0.036416761577129364,
0.04239904507994652,
-0.005297367926687002,
-0.013279793784022331,
0.034227754920721054,
... |
DeepChem/ChemBERTa-5M-MLM | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngra... | 29 | null | ---
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: 7.56 +/- 2.71... | [
-0.021371711045503616,
-0.015353414230048656,
-0.00783317070454359,
0.03018980659544468,
0.04725225642323494,
-0.0011821414809674025,
-0.019167011603713036,
0.0019576638005673885,
-0.0412268228828907,
0.056590717285871506,
0.011813647113740444,
-0.011424345895648003,
0.010600246489048004,
... |
DeividasM/wav2vec2-large-xlsr-53-lithuanian | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"lt",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 7 | null | ---
license: mit
---
### leica on Stable Diffusion
This is the `<leica>` 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... | [
-0.019164644181728363,
-0.02242913469672203,
-0.02323301136493683,
0.028119154274463654,
0.011604408733546734,
0.007273785769939423,
-0.0033627180382609367,
0.005899372976273298,
-0.03823324292898178,
0.04021269455552101,
-0.0040192436426877975,
-0.019701218232512474,
0.03607220947742462,
... |
Deniskin/emailer_medium_300 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 14 | null | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: full
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128... | [
-0.025953400880098343,
-0.009528378956019878,
-0.005269064102321863,
0.03332878276705742,
0.018340284004807472,
0.012809474021196365,
0.01155755203217268,
0.00021413766080513597,
-0.016590183600783348,
0.061488792300224304,
0.0151669317856431,
-0.023159459233283997,
0.016882164403796196,
0... |
DeskDown/MarianMixFT_en-hi | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-roberta-base-finetuned-mbti-0911
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. -->... | [
-0.04382884502410889,
-0.007680135779082775,
-0.0037283797282725573,
0.01729419268667698,
0.025139570236206055,
0.03874083608388901,
-0.01236015185713768,
-0.0029314470011740923,
-0.045828040689229965,
0.046913690865039825,
0.029493387788534164,
-0.03659400716423988,
0.02468125894665718,
0... |
DeskDown/MarianMixFT_en-id | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3 | null | ---
language: en
pipeline_tag: fill-mask
tags:
- legal
license: mit
---
### InLegalBERT
Model and tokenizer files for the InLegalBERT model from the paper [Pre-training Transformers on Indian Legal Text](https://arxiv.org/abs/2209.06049).
### Training Data
For building the pre-training corpus of Indian legal text, ... | [
-0.005416124127805233,
-0.023484500125050545,
-0.019060933962464333,
0.05024779215455055,
0.022973744198679924,
0.048927176743745804,
-0.01671406626701355,
-0.016176996752619743,
-0.03145206719636917,
0.07720331102609634,
0.03408249840140343,
-0.016459070146083832,
0.02673342265188694,
0.0... |
DeskDown/MarianMixFT_en-ms | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 5 | 2022-09-11T12:31:09Z | The ELECTRA-large model, fine-tuned on the CoLA subset of the GLUE benchmark. | [
-0.04468589276075363,
0.0033090270590037107,
0.031589675694704056,
-0.008431944064795971,
0.05607583001255989,
0.0073718237690627575,
-0.014350753277540207,
0.012917027808725834,
-0.026400400325655937,
0.031277529895305634,
0.06079772487282753,
-0.014580127783119678,
0.03806031867861748,
0... |
Despin89/test | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name:... | [
-0.02285177633166313,
-0.0029209493659436703,
0.006041921209543943,
0.020610405132174492,
0.029006201773881912,
0.025830013677477837,
-0.023114826530218124,
-0.009819703176617622,
-0.025708450004458427,
0.04948452487587929,
0.021983342245221138,
-0.04665680602192879,
0.009086270816624165,
... |
Dev-DGT/food-dbert-multiling | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 17 | null | ---
language:
- uk
tags:
- automatic-speech-recognition
- audio
license: cc-by-nc-sa-4.0
datasets:
- https://github.com/egorsmkv/speech-recognition-uk
- mozilla-foundation/common_voice_6_1
metrics:
- wer
model-index:
- name: Ukrainian pruned_transducer_stateless5 v1.0.0
results:
- task:
name: Speech Recog... | [
-0.03485462814569473,
-0.015294034034013748,
-0.003955300431698561,
0.07018469274044037,
0.06339868158102036,
0.010218141600489616,
-0.012841559015214443,
-0.012878027744591236,
-0.0695270225405693,
0.0711604431271553,
0.0060714781284332275,
-0.012555458582937717,
-0.005516361445188522,
0.... |
Devmapall/paraphrase-quora | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_s... | 3 | null | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-vi
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: ... | [
-0.028123507276177406,
-0.0034949614200741053,
-0.001226512249559164,
0.044834546744823456,
0.026506245136260986,
0.008599923923611641,
-0.000767235760577023,
-0.016048956662416458,
-0.03394835442304611,
0.05329975485801697,
0.017126254737377167,
-0.02073024772107601,
0.0025689229369163513,
... |
Dilmk2/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 13 | 2022-09-11T14:51:35Z | ---
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:... | [
-0.02783862315118313,
0.019472815096378326,
0.004950824659317732,
0.009457261301577091,
0.04062443599104881,
-0.018566861748695374,
-0.024565398693084717,
-0.014546120539307594,
-0.028836006298661232,
0.08451080322265625,
0.015523530542850494,
-0.004719236399978399,
0.014365232549607754,
0... |
DimaOrekhov/transformer-method-name | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 8 | null | ---
license: mit
---
### cornell box on Stable Diffusion
This is the `<cornell-box>` 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.ipy... | [
-0.029897380620241165,
-0.02141175977885723,
-0.02845679596066475,
0.03763183206319809,
0.0053833224810659885,
0.02160528115928173,
-0.0023597583640366793,
-0.008861741051077843,
-0.033046573400497437,
0.03976396471261978,
-0.0061013889499008656,
-0.006884264759719372,
0.031818993389606476,
... |
DingleyMaillotUrgell/homer-bot | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 12 | null | ---
pipeline_tag: token-classification
datasets:
- conll2003
metrics:
- overall_precision
- overall_recall
- overall_f1
- overall_accuracy
- total_time_in_seconds
- samples_per_second
- latency_in_seconds
tags:
- distilbert
---
**task**: `token-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{... | [
-0.00643574632704258,
-0.005028290208429098,
-0.005684370640665293,
0.023883940652012825,
0.060813672840595245,
0.009944354183971882,
-0.03508993983268738,
-0.0038782234769314528,
-0.04162703454494476,
0.07078591734170914,
0.03842295706272125,
-0.01033780351281166,
-0.013950034976005554,
0... |
DivyanshuSheth/T5-Seq2Seq-Final | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- conversational
---
#My discord server DialoGPT | [
-0.03194087743759155,
0.01457927841693163,
0.0050521306693553925,
0.003091030055657029,
0.03162005543708801,
0.013124272227287292,
0.0169663168489933,
0.025889521464705467,
-0.02764798328280449,
0.008308561518788338,
0.044616200029850006,
-0.011002691462635994,
0.011583554558455944,
0.0454... |
Dizoid/Lll | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: mit
---
### sculptural style on Stable Diffusion
This is the `<diaosu>` 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.ipy... | [
-0.030422544106841087,
-0.024470262229442596,
-0.021983571350574493,
0.043530143797397614,
0.006067442707717419,
0.014707058668136597,
-0.0023014836478978395,
-0.0012938627041876316,
-0.037694428116083145,
0.05120459944009781,
-0.02519289404153824,
-0.012177401222288609,
0.030354075133800507... |
Dkwkk/W | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- nuts/autotrain-data-human_art_or_not
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Te... | [
-0.011422432959079742,
-0.015026187524199486,
0.019796404987573624,
0.05020441487431526,
0.04987442493438721,
-0.0035535115748643875,
-0.020212804898619652,
-0.002300190506502986,
-0.028096817433834076,
0.06758865714073181,
-0.002854629186913371,
0.0021455956157296896,
-0.0004052839067298919... |
Dmitry12/sber | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: mit
pipeline_tag: question-answering
widget:
- context: "Пушкин родился 6 июля 1799 года"
- text: "Когда родился Пушкин?"
example_title: "test"
---
обученный rubert от cointegrated/rubert-tiny2.
размер выборки - 4.
Эпохи - 16.
```python
from transformers import pipeline
qa_pipeline = pipel... | [
-0.016616616398096085,
-0.030629698187112808,
-0.006417026277631521,
0.043451957404613495,
0.06332925707101822,
0.007152054458856583,
-0.011452593840658665,
0.007709301542490721,
-0.06842350214719772,
0.03256411477923393,
0.0451243557035923,
0.0169638954102993,
-0.003760098246857524,
0.043... |
DongHai/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 9 | null | ---
pipeline_tag: text-classification
datasets:
- glue
metrics:
- accuracy
- total_time_in_seconds
- samples_per_second
- latency_in_seconds
tags:
- distilbert
---
**task**: `text-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.m5.2xlarge', 'supported_instructions': 'avx5... | [
-0.004107949323952198,
-0.015944240614771843,
-0.008730325847864151,
0.04169466346502304,
0.07597982883453369,
0.020715344697237015,
-0.02123238705098629,
-0.006549572106450796,
-0.051795706152915955,
0.07245238870382309,
0.014640944078564644,
-0.010624217800796032,
0.0021249374840408564,
... |
Waynehillsdev/wav2vec2-base-timit-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 5 | null | ---
license: mit
---
### swamp-choe-2 on Stable Diffusion
This is the `<cat-toy>` 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)... | [
-0.034338660538196564,
-0.02019643969833851,
-0.03234701231122017,
0.02371620014309883,
0.023657865822315216,
0.016066119074821472,
0.012337353080511093,
0.002211690414696932,
-0.03262057900428772,
0.04657367244362831,
-0.0005434537888504565,
-0.0015197095926851034,
0.03994869068264961,
0.... |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-25 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 30 | null | ---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_file: skops-ken4gzoq.pkl
widget:
structuredData:
area error:
- 30.29
- 96.05
- 48.31
compactness error:
- 0.01911
- 0.01652
- 0.01484
concave points error:
- 0.01037
- 0.0137
- 0.01093
... | [
-0.03621594235301018,
-0.020884115248918533,
-0.01997307687997818,
0.022733721882104874,
0.04109920561313629,
-0.016821078956127167,
-0.01876099221408367,
0.011886528693139553,
-0.061296287924051285,
0.06733988970518112,
0.022616349160671234,
-0.015485245734453201,
0.011333130300045013,
0.... |
albert-base-v1 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 38,156 | 2022-09-11T19:39:08Z | ---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification-conceptnet-validated
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relat... | [
-0.000526751799043268,
-0.009337222203612328,
-0.024754228070378304,
0.052230969071388245,
0.045514531433582306,
0.020527908578515053,
-0.032451555132865906,
-0.008995676413178444,
-0.0656425729393959,
0.03145764023065567,
0.016848405823111534,
0.004455870948731899,
0.01912151649594307,
0.... |
albert-large-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 687 | 2022-09-11T19:53:37Z | ---
license: mit
---
### Eye of Agamotto on Stable Diffusion
This is the `<eye-aga>` 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.ipy... | [
-0.020157095044851303,
-0.024937713518738747,
-0.026997555047273636,
0.024853689596056938,
0.011468555778265,
0.010428729467093945,
0.0024472048971801996,
-0.01436067745089531,
-0.03475657477974892,
0.04612934589385986,
0.008578387089073658,
-0.005362838506698608,
0.03266013041138649,
0.03... |
albert-large-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 26,792 | 2022-09-11T20:03:41Z | ---
license: bigscience-bloom-rail-1.0
---
# Yelpy BERT
A bert-base-uncased fine-tuned on yelp reviews (https://www.yelp.com/dataset) | [
-0.019840894266963005,
0.006337107624858618,
-0.0011883947299793363,
0.02072351984679699,
-0.0007668012985959649,
-0.007823239080607891,
-0.023511797189712524,
0.0188178438693285,
-0.02677779272198677,
0.03184019401669502,
0.03339952975511551,
-0.0007136258645914495,
0.031613923609256744,
... |
albert-xlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 341 | 2022-09-11T20:03:46Z | ---
license: mit
---
### Freddy Fazbear on Stable Diffusion
This is the `<freddy-fazbear>` 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_inferen... | [
-0.02189691923558712,
-0.02577126771211624,
-0.03334615379571915,
0.03144783154129982,
0.011914532631635666,
0.023649249225854874,
-0.005292234476655722,
-0.019118424504995346,
-0.0328817255795002,
0.03504491597414017,
0.012649365700781345,
-0.002049728762358427,
0.02907000482082367,
0.045... |
albert-xxlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 7,091 | 2022-09-11T20:14:06Z | ---
license: mit
---
### glass pipe on Stable Diffusion
This is the `<glass-sherlock>` 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.i... | [
-0.025378981605172157,
-0.016050904989242554,
-0.028128579258918762,
0.04343646764755249,
0.006898974534124136,
0.02089175023138523,
-0.007587248459458351,
-0.00792256835848093,
-0.04207076132297516,
0.0466785691678524,
-0.0003797014069277793,
-0.004241297487169504,
0.03377338871359825,
0.... |
albert-xxlarge-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 42,640 | 2022-09-11T20:19:23Z | ---
license: bigscience-bloom-rail-1.0
---
# Senty BERT
A yelpy-bert fine-tuned as a ternary classification task (positive, negative, neutral labels) on:
- yelp reviews (https://yelp.com/dataset)
- the SST-3 dataset | [
-0.014182621613144875,
0.013596340082585812,
-0.012240535579621792,
0.03511751443147659,
0.040284354239702225,
0.015662120655179024,
-0.041528742760419846,
0.008878729306161404,
-0.027485577389597893,
0.032747186720371246,
0.02432851679623127,
0.009644828736782074,
0.03467092663049698,
0.0... |
bert-base-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8,621,271 | null | ---
datasets:
- coscan-speech2
license: apache-2.0
metrics:
- accuracy
model-index:
- name: wav2vec2-base-coscan-no-region
results:
- dataset:
name: Coscan Speech
type: NbAiLab/coscan-speech
metrics:
- name: Test Accuracy
type: accuracy
value: 0.5449342464872512
- name: Validatio... | [
-0.02266724221408367,
-0.01846352405846119,
-0.026303870603442192,
0.033963654190301895,
0.054142870008945465,
0.018982596695423126,
-0.025885194540023804,
-0.01641804538667202,
-0.035131555050611496,
0.056361354887485504,
0.03315470367670059,
-0.006843035575002432,
0.010957426391541958,
0... |
bert-base-chinese | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3,377,486 | 2022-09-11T21:44:48Z | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- te_dx_jp
model-index:
- name: t5-base-TEDxJP-0front-1body-0rear
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... | [
-0.022378908470273018,
-0.031979601830244064,
0.012981160543859005,
0.047179244458675385,
0.020910950377583504,
0.016262277960777283,
-0.014762924052774906,
-0.027935972437262535,
-0.03473687544465065,
0.04166768863797188,
0.0014768386026844382,
-0.04136960208415985,
0.020268823951482773,
... |
bert-base-german-dbmdz-uncased | [
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 68,305 | 2022-09-11T20:52:46Z | ---
license: cc-by-nc-sa-4.0
---
This repository contains KenLM models for the Ukrainian language
Metrics for the NEWS models (tested with an acoustic model of [wav2vec2-xls-r-300m model](https://huggingface.co/Yehor/wav2vec2-xls-r-300m-uk-with-small-lm)):
| Model | CER | WER |
|-|-|-|
| no LM | 0.0412 | 0.2206 |
| ... | [
-0.031694184988737106,
-0.0033801656682044268,
-0.004748714622110128,
0.04237814620137215,
0.04572801664471626,
0.003393674036487937,
0.011807024478912354,
0.00260877120308578,
-0.062241341918706894,
0.0632275715470314,
0.014762637205421925,
-0.0428897850215435,
0.013063551858067513,
0.017... |
bert-base-multilingual-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
... | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 4,749,504 | 2022-09-11T22:39:14Z | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- te_dx_jp
model-index:
- name: t5-base-TEDxJP-5front-1body-0rear
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... | [
-0.02113025076687336,
-0.03309764713048935,
0.013946245424449444,
0.0462118461728096,
0.021152468398213387,
0.01599876396358013,
-0.017254415899515152,
-0.02744125761091709,
-0.03393711522221565,
0.04025725647807121,
0.002812626538798213,
-0.04130452126264572,
0.016597876325249672,
0.04314... |
bert-base-multilingual-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
... | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 328,585 | 2022-09-11T23:09:56Z | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- te_dx_jp
model-index:
- name: t5-base-TEDxJP-10front-1body-0rear
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 remov... | [
-0.023473763838410378,
-0.031705889850854874,
0.016989555209875107,
0.046015139669179916,
0.020563265308737755,
0.01975361816585064,
-0.014980594627559185,
-0.024059759452939034,
-0.03482624143362045,
0.043156541883945465,
0.0022088789846748114,
-0.04054737463593483,
0.018509572371840477,
... |
bert-base-uncased | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 59,663,489 | 2022-09-11T22:27:22Z | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- te_dx_jp
model-index:
- name: t5-base-TEDxJP-3front-1body-0rear
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... | [
-0.021034646779298782,
-0.03498494625091553,
0.004766535479575396,
0.04362240806221962,
0.023512404412031174,
0.02006234973669052,
-0.016195062547922134,
-0.030353231355547905,
-0.03529693931341171,
0.04274730011820793,
0.003807161469012499,
-0.04217483475804329,
0.015499369241297245,
0.03... |
bert-large-cased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 8,214 | null | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- te_dx_jp
model-index:
- name: t5-base-TEDxJP-8front-1body-0rear
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... | [
-0.024121589958667755,
-0.031218044459819794,
0.011721326038241386,
0.04498393461108208,
0.02259458228945732,
0.016241800040006638,
-0.014904836192727089,
-0.030980708077549934,
-0.03384781628847122,
0.042739931493997574,
-0.00038312116521410644,
-0.03956611454486847,
0.022534627467393875,
... |
bert-large-uncased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 480,510 | 2022-09-11T21:20:31Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default... | [
-0.00895691104233265,
0.009299160912632942,
-0.029254455119371414,
0.03743705898523331,
0.06009279191493988,
0.03360002115368843,
-0.02408580482006073,
-0.035628993064165115,
-0.033940281718969345,
0.05553862452507019,
0.020297439768910408,
-0.0468706339597702,
0.03514256700873375,
0.04295... |
bert-large-uncased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 76,685 | 2022-09-11T21:20:42Z | ---
license: mit
---
### black-waifu on Stable Diffusion
This is the `<black-waifu>` 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.ipy... | [
-0.02526375651359558,
-0.031015075743198395,
-0.025289176031947136,
0.04317764937877655,
0.013518516905605793,
0.02033812552690506,
0.0029942644760012627,
0.0007167160511016846,
-0.03480535373091698,
0.04740967974066734,
0.015880728140473366,
-0.005634017288684845,
0.036628466099500656,
0.... |
distilbert-base-cased | [
"pytorch",
"tf",
"onnx",
"distilbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"has_space"
] | null | {
"architectures": null,
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"n... | 574,859 | 2022-09-11T21:34:13Z | ---
license: mit
---
### roy-lichtenstein on Stable Diffusion
This is the `<roy-lichtenstein>` 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_inf... | [
-0.01674755848944187,
-0.007782298605889082,
-0.029020262882113457,
0.027979884296655655,
0.01716459169983864,
0.014937092550098896,
0.0005023105768486857,
-0.005874332971870899,
-0.04334511607885361,
0.047922283411026,
0.0020127659663558006,
-0.011181170120835304,
0.031639792025089264,
0.... |
distilbert-base-multilingual-cased | [
"pytorch",
"tf",
"onnx",
"safetensors",
"distilbert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
... | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 8,339,633 | 2022-09-11T21:57:43Z | ---
license: cc-by-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hing-mbert-finetuned-ours-DS
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complet... | [
-0.013037120923399925,
-0.004169590305536985,
-0.007270350120961666,
0.03318686783313751,
0.025924231857061386,
0.02234978787600994,
-0.03384080529212952,
-0.011397074908018112,
-0.033273618668317795,
0.04765602946281433,
0.009438453242182732,
-0.02054814249277115,
0.032988741993904114,
0.... |
distilbert-base-uncased | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"distilbert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 10,887,471 | 2022-09-11T22:20:48Z | ---
pipeline_tag: question-answering
datasets:
- squad
metrics:
- exact_match
- f1
- total_time_in_seconds
- samples_per_second
- latency_in_seconds
tags:
- distilbert
---
**task**: `question-answering`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.m5.2xlarge', 'supported_instructions'... | [
0.0017729622777551413,
-0.0257856547832489,
-0.017593063414096832,
0.041552890092134476,
0.054667118936777115,
0.0019839052110910416,
-0.020804233849048615,
0.025911692529916763,
-0.04973648861050606,
0.035434551537036896,
0.038264475762844086,
0.010251808911561966,
-0.005253415089100599,
... |
IssakaAI/wav2vec2-large-xls-r-300m-turkish-colab | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
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: Frozen... | [
-0.017829641699790955,
-0.018037039786577225,
-0.006315172649919987,
0.03388984873890877,
0.04996241629123688,
-0.018427588045597076,
-0.013407062739133835,
-0.012188950553536415,
-0.06014036759734154,
0.05744457244873047,
-0.0054605621844530106,
-0.011650205589830875,
0.021277345716953278,
... |
ATGdev/ai_ironman | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2022-09-12T19:09:22Z | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- te_dx_jp
model-index:
- name: t5-base-TEDxJP-0front-1body-9rear
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... | [
-0.016943424940109253,
-0.031450871378183365,
0.010048897005617619,
0.05182097852230072,
0.020586799830198288,
0.017227010801434517,
-0.019805165007710457,
-0.027121618390083313,
-0.03370685502886772,
0.03790857642889023,
-0.001153598539531231,
-0.04012791067361832,
0.021383723244071007,
0... |
Pinwheel/wav2vec2-large-xls-r-1b-hi-v2 | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 9 | 2022-09-12T19:15:49Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit u... | [
-0.021706635132431984,
-0.033120911568403244,
0.0069284033961594105,
0.022917678579688072,
0.010587208904325962,
0.024608587846159935,
-0.02954155206680298,
-0.016725419089198112,
-0.028829533606767654,
0.032675109803676605,
0.03176262229681015,
0.010438955388963223,
0.04351669177412987,
0... |
Aero/Tsubomi-Haruno | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational",
"license:mit"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 13 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
datasets:
- embedding-data/QQP_triplets
---
# tekraj/avodamed-synonym-generator1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vect... | [
-0.018814774230122566,
-0.02240542694926262,
-0.018433334305882454,
0.060375429689884186,
0.03883793205022812,
0.03427906334400177,
-0.008758018724620342,
0.008860773406922817,
-0.0659349337220192,
0.07837206870317459,
0.023123886436223984,
0.010029938071966171,
0.0026028340216726065,
0.03... |
Aeroxas/Botroxas-small | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- te_dx_jp
model-index:
- name: t5-base-TEDxJP-5front-1body-5rear
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... | [
-0.02126518078148365,
-0.0330989807844162,
0.013857982121407986,
0.04813334345817566,
0.019629983231425285,
0.01523971650749445,
-0.01622053235769272,
-0.0247406717389822,
-0.03232773393392563,
0.0406496599316597,
0.0025113446172326803,
-0.043297138065099716,
0.015555532649159431,
0.040943... |
Aftabhussain/Tomato_Leaf_Classifier | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index",
"autotrain_compatible"
] | image-classification | {
"architectures": [
"ViTForImageClassification"
],
"model_type": "vit",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 50 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- yelp_review_full
metrics:
- accuracy
model-index:
- name: Bert_Classifier
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: yelp_review_full
type: yelp_review_full
config: yelp_revi... | [
-0.00717953173443675,
0.007798820734024048,
-0.004393092356622219,
0.04415535181760788,
0.02461484633386135,
0.0052662561647593975,
-0.029995132237672806,
-0.018142957240343094,
-0.02561243064701557,
0.05810103192925453,
0.016461672261357307,
-0.02176637016236782,
0.010129781439900398,
0.0... |
Ahda/M | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- yelp_review_full
metrics:
- accuracy
model-index:
- name: Bert_Classifier
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: yelp_review_full
type: yelp_review_full
config: yelp_revi... | [
-0.0069747185334563255,
0.008000527508556843,
-0.00477176159620285,
0.04512399807572365,
0.02425849623978138,
0.004913334269076586,
-0.030366122722625732,
-0.018048381432890892,
-0.025139689445495605,
0.05814892426133156,
0.01458157878369093,
-0.02182614989578724,
0.010589301586151123,
0.0... |
Ahmadvakili/A | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conl... | [
-0.0014736787416040897,
0.017967447638511658,
-0.03811177983880043,
0.03661035746335983,
0.050347525626420975,
0.018191754817962646,
-0.029960637912154198,
-0.041013460606336594,
-0.03859422728419304,
0.0634620413184166,
0.037397902458906174,
-0.023833246901631355,
0.013803365640342236,
0.... |
AimB/mT5-en-kr-aihub-netflix | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- te_dx_jp
model-index:
- name: t5-base-TEDxJP-1front-1body-1rear
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... | [
-0.021434035152196884,
-0.03259631618857384,
0.013443008065223694,
0.05026832967996597,
0.019402913749217987,
0.016911501064896584,
-0.014031125232577324,
-0.025256812572479248,
-0.03117370791733265,
0.040049999952316284,
0.00011029921734007075,
-0.045705948024988174,
0.01989918388426304,
... |
Akash7897/distilbert-base-uncased-finetuned-sst2 | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 31 | 2022-09-13T18:24:16Z | ---
license: mit
---
### Poutine Dish on Stable Diffusion
This is the `<poutine-qc>` 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.ipy... | [
-0.007441746070981026,
-0.004574494902044535,
-0.021268026903271675,
0.05163907632231712,
0.01335098221898079,
-0.0012440013233572245,
0.018839186057448387,
-0.0010233345674350858,
-0.03924814239144325,
0.03976326808333397,
-0.0029002188239246607,
-0.009420488961040974,
0.028758928179740906,... |
Akashpb13/Hausa_xlsr | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ha",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index",
"... | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 31 | 2022-09-13T18:49:55Z | ---
license: mit
---
### grifter on Stable Diffusion
This is the `<grifter>` 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) note... | [
-0.026825597509741783,
-0.02560780569911003,
-0.036218367516994476,
0.040457602590322495,
0.02562580071389675,
0.02588282898068428,
-0.00006389759801095352,
-0.007555863354355097,
-0.0382617749273777,
0.05079781264066696,
0.010269328020513058,
-0.006184410769492388,
0.026287386193871498,
0... |
Akashpb13/Swahili_xlsr | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"sw",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 10 | null | ---
license: mit
---
### Dog on Stable Diffusion
This is the `<Winston>` 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... | [
-0.035767000168561935,
-0.00890758540481329,
-0.03773612901568413,
0.044208019971847534,
0.018075505271553993,
0.026600264012813568,
-0.004626025911420584,
-0.007513748947530985,
-0.043127745389938354,
0.052981700748205185,
0.007080137729644775,
-0.020657094195485115,
0.025881411507725716,
... |
Akira-Yana/distilbert-base-uncased-finetuned-cola | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2022-09-13T19:49:11Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- slurp
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/slurp_slu_2pass_gt`
This model was trained by Siddhant using slurp recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESP... | [
-0.026485858485102654,
0.005876241251826286,
-0.04342964291572571,
0.04137209802865982,
0.06443231552839279,
0.020274506881833076,
-0.013020058162510395,
0.012415475212037563,
-0.0661594420671463,
0.06580071151256561,
0.016975829377770424,
0.004577367100864649,
-0.0013228139141574502,
0.01... |
AkshaySg/LanguageIdentification | [
"multilingual",
"dataset:VoxLingua107",
"LID",
"spoken language recognition",
"license:apache-2.0"
] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2022-09-13T20:30:22Z | ---
license: mit
---
### Chillpill on Stable Diffusion
This is the `<Chillpill>` 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) ... | [
-0.03915288299322128,
-0.015543809160590172,
-0.01361850369721651,
0.03629579022526741,
0.006045870017260313,
0.026180731132626534,
-0.0034384247846901417,
0.00732441945001483,
-0.033905383199453354,
0.03983423486351967,
0.013063979335129261,
-0.00797160342335701,
0.04195571318268776,
0.03... |
AkshaySg/gramCorrection | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_s... | 4 | 2022-09-13T20:40:21Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-model2-1309
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. -->
# b... | [
-0.016242017969489098,
-0.022354282438755035,
-0.0319422110915184,
0.041751664131879807,
0.038760628551244736,
0.02211340330541134,
-0.019038867205381393,
-0.016434811055660248,
-0.05664810165762901,
0.07015880942344666,
0.014698632061481476,
-0.0307554230093956,
0.015914103016257286,
0.03... |
AlErysvi/Erys | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2022-09-13T21:09:29Z | ---
license: mit
---
### looney anime on Stable Diffusion
This is the `<looney-anime>` 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.i... | [
-0.013040035963058472,
-0.017051508650183678,
-0.027164535596966743,
0.04226571321487427,
0.011745929718017578,
0.006858194712549448,
0.009746252559125423,
0.000300326180877164,
-0.030375847592949867,
0.0516347698867321,
-0.007042236626148224,
-0.008056612685322762,
0.0416700541973114,
0.0... |
Alaeddin/convbert-base-turkish-ner-cased | [
"pytorch",
"convbert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"ConvBertForTokenClassification"
],
"model_type": "convbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"n... | 9 | 2022-09-13T21:13:56Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 449.00 +/- 109.17
name: mean_reward
task:
type: reinforcement-learning
... | [
-0.03982513025403023,
-0.016647091135382652,
-0.016763636842370033,
0.03721141070127487,
0.052108049392700195,
-0.003941358998417854,
-0.013144695200026035,
-0.026033420115709305,
-0.035702768713235855,
0.05341154336929321,
0.02209368534386158,
-0.031155699864029884,
0.01903749629855156,
0... |
AlanDev/DallEMiniButBetter | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2022-09-13T21:16:01Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-burak-new-300
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 com... | [
-0.02949930913746357,
-0.013545543886721134,
-0.023353032767772675,
0.03287279233336449,
0.04774138703942299,
0.028225209563970566,
0.0031789171043783426,
-0.000781249429564923,
-0.023555157706141472,
0.05229915678501129,
0.044780854135751724,
-0.02908722124993801,
0.005569884087890387,
0.... |
AlbertHSU/ChineseFoodBert | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 15 | 2022-09-13T21:55:28Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: distilbert-legal-chunk
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... | [
-0.0037182727828621864,
0.005499608349055052,
-0.04107614606618881,
0.03680754080414772,
0.05024527385830879,
0.02062525786459446,
-0.009761473163962364,
-0.01339040044695139,
-0.04454438388347626,
0.08959311991930008,
0.03489582613110542,
-0.02069685608148575,
0.019352097064256668,
0.0241... |
Aleenbo/Arcane | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2022-09-13T22:44:47Z | ---
license: mit
---
### green-tent on Stable Diffusion
This is the `<green-tent>` 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... | [
-0.03658488392829895,
-0.019423004239797592,
-0.03569849207997322,
0.03767845034599304,
0.015255678445100784,
0.019048012793064117,
-0.005557238589972258,
-0.007023612502962351,
-0.04118317738175392,
0.044324565678834915,
0.0016402719775214791,
-0.011414328590035439,
0.03344890847802162,
0... |
Aleksandar/distilbert-srb-ner-setimes-lr | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2022-09-13T23:05:28Z | ---
language: en
thumbnail: http://www.huggingtweets.com/39daph/1663110357486/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 9... | [
0.003498571924865246,
-0.03462803736329079,
-0.0018577944720163941,
0.05344030261039734,
0.04913993552327156,
0.011824547313153744,
-0.015958603471517563,
-0.01063080970197916,
-0.044306814670562744,
0.03295933082699776,
0.009197830222547054,
-0.005389804020524025,
-0.0163826085627079,
0.0... |
Aleksandar/electra-srb-oscar | [
"pytorch",
"electra",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"ElectraForMaskedLM"
],
"model_type": "electra",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngra... | 6 | 2022-09-13T23:08:57Z | ---
license: mit
---
### dtv-pkmn on Stable Diffusion
This is the `<dtv-pkm2>` 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) no... | [
-0.017816176638007164,
-0.02531428262591362,
-0.023836398497223854,
0.03545621037483215,
0.03677321970462799,
0.004850875586271286,
0.009051746688783169,
-0.01577019691467285,
-0.021498845890164375,
0.04788227006793022,
0.006728635169565678,
-0.022926026955246925,
0.038153842091560364,
0.0... |
Aleksandar1932/gpt2-country | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 12 | 2022-09-13T23:12:04Z | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4... | [
0.0061182319186627865,
-0.03519666939973831,
0.0015940211014822125,
0.03255261108279228,
0.04744900017976761,
0.01337125338613987,
-0.025618789717555046,
-0.0058798943646252155,
-0.028826026245951653,
0.036953315138816833,
-0.002360339043661952,
-0.009611569344997406,
0.003927024081349373,
... |
Aleksandar1932/gpt2-hip-hop | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8 | 2022-09-13T23:12:24Z | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4... | [
0.012205913662910461,
-0.03372862935066223,
-0.0031081149354577065,
0.03651200234889984,
0.04958057776093483,
0.016458770260214806,
-0.030243756249547005,
-0.010167238302528858,
-0.02472073584794998,
0.03712567314505577,
0.008892105892300606,
-0.0073497965931892395,
-0.003037138609215617,
... |
Aleksandar1932/gpt2-pop | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8 | 2022-09-13T23:13:30Z | ---
language: en
thumbnail: http://www.huggingtweets.com/mariahcarey/1663110896270/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; wid... | [
-0.0004660144040826708,
-0.03555246442556381,
0.0020296236034482718,
0.05013781040906906,
0.04167165234684944,
0.016927026212215424,
-0.010560905560851097,
-0.005353881511837244,
-0.04616912826895714,
0.034658804535865784,
0.009143796749413013,
0.005422037094831467,
-0.011672968976199627,
... |
Aleksandar1932/gpt2-rock-124439808 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 11 | 2022-09-13T23:13:34Z | ---
language: en
thumbnail: http://www.huggingtweets.com/sanbenito/1663110946747/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width... | [
0.002908826805651188,
-0.03811396658420563,
-0.00008943438297137618,
0.055030275136232376,
0.04847533628344536,
0.013293551281094551,
-0.0190461166203022,
-0.008809791877865791,
-0.03958076983690262,
0.030306557193398476,
0.006658905651420355,
-0.0013693893561139703,
-0.016593925654888153,
... |
Aleksandar1932/gpt2-soul | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 10 | 2022-09-13T23:15:06Z | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4... | [
0.005240230821073055,
-0.03436854109168053,
-0.0006849293131381273,
0.03948426991701126,
0.04792061820626259,
0.017735475674271584,
-0.02165147475898266,
-0.005244588013738394,
-0.029923081398010254,
0.03753022849559784,
-0.00013616388605441898,
-0.0013738711131736636,
0.0007590760942548513,... |
Aleksandar1932/gpt2-spanish-classics | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 9 | 2022-09-13T23:17:28Z | ---
language: en
thumbnail: http://www.huggingtweets.com/burgerking/1663111083258/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; widt... | [
0.014217160642147064,
-0.03587658703327179,
0.0015634623123332858,
0.05001379922032356,
0.044937584549188614,
0.013668606989085674,
-0.017734713852405548,
-0.001378144370391965,
-0.048058442771434784,
0.032720837742090225,
0.009835733100771904,
-0.0032917188946157694,
-0.012280316092073917,
... |
Aleksandra/distilbert-base-uncased-finetuned-squad | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2022-09-13T23:18:07Z | ---
license: mit
---
### 8bit on Stable Diffusion
This is the `<8bit>` 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. ... | [
-0.03310421481728554,
-0.01755470037460327,
-0.033574428409338,
0.036794696003198624,
0.009333697147667408,
0.015716630965471268,
0.00712035596370697,
0.00206080568023026,
-0.029381675645709038,
0.04648240655660629,
-0.004643096122890711,
-0.007010665722191334,
0.041508786380290985,
0.0358... |
Aleksandra/herbert-base-cased-finetuned-squad | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 8 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4... | [
0.02020876295864582,
-0.03590942546725273,
0.007455663289874792,
0.026435667648911476,
0.036167558282613754,
0.011745757423341274,
-0.02660912461578846,
0.002682116813957691,
-0.02967008575797081,
0.04561139643192291,
-0.008973922580480576,
-0.019324881955981255,
0.012943360023200512,
0.02... |
adorkin/xlm-roberta-en-ru-emoji | [
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"en",
"ru",
"dataset:tweet_eval",
"transformers"
] | text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 31 | 2022-09-13T23:18:49Z | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4... | [
0.020515743643045425,
-0.036493703722953796,
0.008136334829032421,
0.02748672105371952,
0.03696730360388756,
0.012142784893512726,
-0.026421023532748222,
0.0023419817443937063,
-0.02948012575507164,
0.045571353286504745,
-0.008919388055801392,
-0.019678618758916855,
0.011974400840699673,
0... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.