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
|
|---|---|---|---|---|---|---|
Aleksandar/bert-srb-base-cased-oscar | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 7 | null | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: zh
datasets:
- magicdata
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/jiyangtang_magicdata_asr_conformer_lm_transformer`
This model was trained by Jiyang Tang using magicdata recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout 9d0f3b3e1be6650d38cc5008518f445308fe06d9
pip install -e .
cd egs2/magicdata/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/jiyangtang_magicdata_asr_conformer_lm_transformer
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Sep 21 01:11:58 EDT 2022`
- python version: `3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]`
- espnet version: `espnet 202207`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `9d0f3b3e1be6650d38cc5008518f445308fe06d9`
- Commit date: `Mon Sep 19 20:27:41 2022 -0400`
## asr_train_asr_raw_zh_char_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_rnn_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/test|24279|24286|84.4|15.6|0.0|0.0|15.6|15.6|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_rnn_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/test|24279|243325|96.4|1.7|2.0|0.1|3.7|15.6|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
## ASR config
<details><summary>expand</summary>
```
config: conf/train_asr.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_raw_zh_char_sp
ngpu: 0
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: null
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 20
patience: null
val_scheduler_criterion:
- valid
- acc
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 4
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 20000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_zh_char_sp/train/speech_shape
- exp/asr_stats_raw_zh_char_sp/train/text_shape.char
valid_shape_file:
- exp/asr_stats_raw_zh_char_sp/valid/speech_shape
- exp/asr_stats_raw_zh_char_sp/valid/text_shape.char
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_noeng_sp/wav.scp
- speech
- sound
- - dump/raw/train_noeng_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/wav.scp
- speech
- sound
- - dump/raw/dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.0005
scheduler: warmuplr
scheduler_conf:
warmup_steps: 30000
token_list:
- <blank>
- <unk>
- 的
- 我
- 一
- 歌
- 你
- 天
- 不
- 了
- 放
- 来
- 播
- 下
- 个
- 是
- 有
- 给
- 首
- 好
- 请
- 在
- 听
- 么
- 气
- 要
- 想
- 曲
- 上
- 吗
- 去
- 到
- 这
- 啊
- 点
- 那
- 没
- 就
- 说
- 大
- 唱
- 人
- 最
- 第
- 看
- 会
- 明
- 集
- 吧
- 音
- 还
- 乐
- 今
- 电
- 开
- 能
- 度
- 哪
- 里
- 多
- 打
- 十
- 可
- 怎
- 道
- 什
- 新
- 雨
- 以
- 家
- 回
- 话
- 儿
- 他
- 时
- 小
- 温
- 样
- 爱
- 都
- 吃
- 呢
- 知
- 谁
- 为
- 子
- 们
- 也
- 过
- 老
- 很
- 出
- 中
- 现
- 冷
- 和
- 情
- 行
- 心
- 发
- 专
- 几
- 视
- 张
- 事
- 二
- 辑
- 五
- 三
- 后
- 找
- 些
- 早
- 学
- 晚
- 车
- 别
- 演
- 手
- 呀
- 调
- 感
- 问
- 九
- 饭
- 快
- 风
- 得
- 如
- 自
- 生
- 少
- 地
- 用
- 叫
- 帮
- 机
- 台
- 班
- 欢
- 候
- 起
- 等
- 把
- 年
- 干
- 高
- 太
- 啦
- 方
- 提
- 面
- 八
- 四
- 信
- 意
- 王
- 真
- 求
- 热
- 喜
- 觉
- 周
- 近
- 名
- 做
- 公
- 告
- 关
- 六
- 字
- 安
- 再
- 变
- 间
- 国
- 分
- 着
- 哈
- 水
- 节
- 只
- 动
- 北
- 刚
- 空
- 月
- 玩
- 让
- 伤
- 东
- 谢
- 网
- 七
- 见
- 之
- 比
- 杰
- 又
- 买
- 对
- 始
- 无
- 查
- 声
- 文
- 经
- 醒
- 美
- 西
- 哦
- 走
- 两
- 海
- 妈
- 李
- 报
- 诉
- 接
- 定
- 午
- 外
- 才
- 流
- 长
- 宝
- 门
- 收
- 己
- 室
- 林
- 种
- 南
- 日
- 目
- 陈
- 许
- 词
- 服
- 设
- 记
- 频
- 琴
- 主
- 完
- 友
- 花
- 跟
- 钱
- 睡
- 像
- 嗯
- 何
- 京
- 所
- 预
- 边
- 带
- 作
- 零
- 头
- 号
- 果
- 嘛
- 路
- 办
- 吉
- 语
- 本
- 合
- 卫
- 影
- 市
- 摄
- 通
- 加
- 女
- 成
- 因
- 前
- 衣
- 然
- 档
- 位
- 聊
- 哥
- 载
- 原
- <space>
- 思
- 氏
- 同
- 题
- 但
- 红
- 火
- 她
- 亲
- 传
- 江
- 清
- 息
- 注
- 死
- 啥
- 州
- 片
- 朋
- 相
- 星
- 华
- 已
- 负
- 白
- 色
- 姐
- 春
- 转
- 半
- 换
- 黄
- 游
- 工
- 法
- 理
- 山
- 该
- 英
- 较
- 先
- 穿
- 推
- 直
- 力
- 当
- 冻
- 费
- 刘
- 男
- 写
- 场
- 呵
- 克
- 正
- 单
- 身
- 系
- 苏
- 婆
- 难
- 阳
- 光
- 重
- 荐
- 越
- 马
- 城
- 错
- 次
- 期
- 口
- 金
- 线
- 准
- 爸
- 忙
- 体
- 于
- 句
- 广
- 福
- 活
- 应
- 亮
- 黑
- 特
- 司
- 喝
- 式
- 飞
- 介
- 者
- 慢
- 静
- 百
- 平
- 绍
- 差
- 照
- 团
- 烦
- 便
- 师
- 站
- 德
- 短
- 远
- 需
- 谱
- 郑
- 化
- 或
- 器
- 急
- 钢
- 您
- 忘
- 店
- 妹
- 梦
- 青
- 适
- 总
- 每
- 业
- 夜
- 神
- 版
- 健
- 区
- 实
- 从
- 孩
- 奏
- 韩
- 伦
- 志
- 算
- 雪
- 世
- 认
- 眼
- 模
- 全
- 与
- 书
- 拿
- 送
- 结
- 其
- 解
- 格
- 洗
- 幸
- 舞
- 望
- 速
- 试
- 钟
- 内
- 联
- 停
- 丽
- 课
- 河
- 沙
- 笑
- 久
- 永
- 贝
- 民
- 址
- 超
- 教
- 代
- 件
- 降
- 脑
- 恋
- 常
- 交
- 低
- 伙
- 而
- 毛
- 阿
- 齐
- 习
- 量
- 段
- 选
- 欣
- 昨
- 进
- 闻
- 住
- 受
- 类
- 酒
- 背
- 藏
- 暴
- 摇
- 云
- 怕
- 考
- 咋
- 武
- 赶
- 孙
- 识
- 嵩
- 景
- 某
- 省
- 界
- 罗
- 任
- 坐
- 级
- 遇
- 麻
- 县
- 被
- 龙
- 品
- 蛋
- 湖
- 离
- 希
- 卖
- 轻
- 岁
- 香
- 赏
- 忆
- 答
- 滚
- 保
- 运
- 深
- 央
- 更
- 况
- 部
- ，
- 猪
- 休
- 校
- 留
- 嘿
- 弹
- 挺
- 院
- 泪
- 拉
- 懂
- 暖
- 讲
- 顺
- 底
- 卡
- 使
- 表
- 剧
- 包
- 故
- 导
- 凉
- 连
- 咱
- 制
- 蔡
- 容
- 向
- 物
- 微
- 步
- 切
- 搜
- 婚
- 童
- 约
- 芳
- 凯
- 复
- 未
- 陪
- 防
- 典
- 夏
- 万
- 备
- 指
- 冰
- 管
- 基
- 琪
- 宇
- 晓
- 房
- 良
- 戏
- 悲
- 牛
- 千
- 达
- 汉
- 拜
- 奇
- 梅
- 菜
- 满
- 徐
- 楼
- 询
- 图
- 改
- 练
- 敬
- 票
- 吴
- 络
- 码
- 整
- 简
- 队
- 购
- 普
- 附
- 响
- 胡
- 装
- 暑
- 非
- 喂
- 消
- 浪
- 凤
- 愿
- 累
- 球
- 聚
- 启
- 假
- 潮
- 弟
- 玉
- 绿
- 康
- 拍
- 失
- 哭
- 易
- 木
- 斯
- 跳
- 军
- 处
- 搞
- 升
- 除
- 傻
- 骗
- 证
- 杨
- 园
- 茹
- 赵
- 标
- 窗
- 庆
- 惠
- 够
- 烟
- 俊
- 掉
- 建
- 呗
- 插
- 座
- 害
- 智
- 贵
- 左
- 落
- 计
- 客
- 宁
- 梁
- 舒
- 取
- 往
- 漫
- 兰
- 战
- 随
- 晴
- 条
- 入
- 叶
- 强
- 伟
- 雅
- 尔
- 树
- 余
- 弄
- 季
- 排
- 伍
- 吹
- 宏
- 商
- 柔
- 郊
- 铁
- 遍
- 确
- 闭
- 雄
- 似
- 冒
- 待
- 尘
- 群
- 病
- 退
- 务
- 育
- 坏
- 娘
- 莫
- 资
- 楚
- 辛
- 索
- 利
- 数
- 秦
- 燕
- 且
- 录
- 姑
- 念
- 痛
- 冬
- 尾
- 共
- 初
- 粤
- 哎
- 印
- 示
- 抱
- 终
- 泉
- 货
- 肯
- 它
- 伞
- 性
- 古
- 跑
- 腾
- 鱼
- 曾
- 源
- 银
- 读
- 油
- 川
- 言
- 倩
- 峰
- 激
- 置
- 灯
- 独
- 命
- 谈
- 苦
- 限
- 乡
- 菲
- 伴
- 将
- 震
- 炎
- 散
- 依
- 米
- 及
- 贞
- 兴
- 湿
- 寒
- 敏
- 否
- 俩
- 祝
- 慧
- 精
- 律
- 功
- 托
- 洋
- 敢
- 街
- 铃
- 必
- 弦
- 寻
- 涵
- 突
- 皮
- 反
- 烧
- 秋
- 刮
- 末
- 双
- 细
- 范
- 由
- 君
- 款
- 邮
- 醉
- 紧
- 哲
- 缘
- 岛
- 疼
- 阴
- 旋
- 怪
- 草
- 持
- 狼
- 具
- 至
- 汪
- 鸡
- 医
- 邓
- 份
- 右
- 密
- 士
- 修
- 亚
- 画
- 灵
- 妇
- 甜
- 靠
- 荣
- 程
- 莲
- 魂
- 此
- 户
- 属
- 贤
- 充
- 萧
- 血
- 逼
- 闹
- 吸
- 娜
- 肉
- 抒
- 价
- 桥
- 剑
- 巴
- 暗
- 豆
- 迪
- 戴
- 迅
- 朝
- 艺
- 谭
- 治
- 祥
- 尽
- 闷
- 宫
- 艳
- 父
- 存
- 媳
- 跪
- 雾
- 杜
- 味
- 奕
- 兵
- 脸
- 炫
- 兄
- 妮
- 优
- 熊
- 床
- 般
- 净
- 航
- 帝
- 刻
- 孤
- 轩
- 村
- 支
- 玮
- 狗
- 纯
- 楠
- 呐
- 冠
- 元
- 盛
- 决
- 诗
- 爷
- 堵
- 陶
- 乖
- 迷
- 羽
- 忧
- 倒
- 蜜
- 晒
- 仔
- 却
- 姜
- 哟
- 餐
- 雷
- 鸟
- 馆
- 韶
- 箱
- 操
- 乌
- 借
- 恒
- 舍
- 药
- 块
- 澡
- 石
- 软
- 奶
- 笨
- 夫
- 朴
- 义
- 派
- 晨
- 佳
- 科
- 姿
- 显
- 咏
- 饿
- 付
- 宗
- 键
- 止
- 员
- 磊
- 勤
- 崔
- 偏
- 额
- 免
- 乱
- 怀
- 侠
- 岳
- 斌
- 助
- 征
- 概
- 吕
- 彩
- 板
- 松
- 各
- 组
- 历
- 济
- 象
- 茶
- 领
- 按
- 创
- 镇
- 翻
- 配
- 宿
- 咯
- 帅
- 型
- 估
- 佩
- 惜
- 详
- 续
- 蓝
- 麟
- 珠
- 颜
- 彦
- 农
- 盘
- 母
- 鞋
- 账
- 博
- 礼
- 环
- 套
- 效
- 郭
- 居
- 佑
- 根
- 惊
- 圳
- 叔
- 若
- 逆
- 鸿
- 锁
- 食
- 芸
- 裤
- 娱
- 漂
- 野
- 麦
- 豫
- 顾
- 爽
- 族
- 仙
- 围
- 观
- 链
- 嗨
- 厅
- 巍
- 劲
- 极
- 呼
- 咖
- 淑
- 丝
- 昌
- 嘉
- 绝
- 史
- 击
- 承
- 蔚
- 堂
- 沉
- 笔
- 朵
- 凰
- 琥
- 匆
- 炜
- 输
- 须
- 娴
- 嘻
- 牌
- 田
- 杀
- 滴
- 鬼
- 桦
- 赛
- 玟
- 抽
- 案
- 轮
- 立
- 摆
- 屋
- 诺
- 丁
- 佰
- 蒙
- 澄
- 羊
- 添
- 质
- 波
- 萨
- 狂
- 丹
- 屁
- 角
- 章
- 产
- 宜
- 笛
- 严
- 维
- 测
- 娃
- 料
- 宋
- 洲
- 卦
- 猜
- 港
- 挂
- 淘
- 郁
- 统
- 断
- 锅
- 稍
- 绮
- 汗
- 辉
- 乎
- 破
- 钧
- 芹
- 择
- 胖
- 即
- 呜
- 旅
- 拨
- 紫
- 哇
- 默
- 论
- 朱
- 登
- 脚
- 订
- 秀
- ？
- 社
- 飘
- 尚
- 另
- 骂
- 并
- 恶
- 扫
- 裸
- 姨
- 苹
- 压
- 厌
- 汇
- 爆
- 局
- 睛
- 庄
- 唐
- 嘞
- 偶
- 乔
- 染
- 熟
- 喆
- 愉
- 虎
- 技
- 威
- 布
- 嘴
- 湾
- 术
- 讨
- 尼
- 诶
- 坊
- 删
- 桑
- 庾
- 斗
- 呃
- 仁
- 训
- 汤
- 脱
- 凡
- 例
- 唉
- 畅
- 参
- 晕
- 肥
- 营
- 鲁
- 减
- 琳
- 瑞
- 透
- 素
- 厉
- 追
- 扰
- 控
- 谣
- 足
- 检
- 扬
- 娇
- 耳
- 津
- 倾
- 淡
- 露
- 妞
- 熙
- 值
- 罪
- 浩
- 探
- 盐
- 列
- 券
- 潘
- 官
- 篇
- 纪
- 签
- 棒
- 丑
- 陆
- 养
- 佛
- 唯
- 芮
- 哒
- 榜
- 培
- 疯
- 财
- 卷
- 痴
- 凌
- 瓜
- 猫
- 泡
- 据
- 厦
- 辣
- 恩
- 土
- 补
- 递
- 伏
- 灰
- 糖
- 玛
- 黎
- 湘
- 遥
- 谅
- 桃
- 曼
- 招
- 勇
- 泰
- 杭
- 缓
- 朗
- 替
- 刷
- 封
- 骨
- 盖
- 眠
- 担
- 忽
- 蛮
- 蜗
- 肚
- 喽
- 懒
- 继
- 辈
- 魔
- 哼
- 顶
- 冲
- 番
- 释
- 形
- 页
- 渡
- 触
- 裂
- 逛
- 圆
- 迎
- 态
- 弃
- 洛
- 丰
- 困
- 展
- 束
- 巧
- 临
- 际
- 涛
- 酷
- 洁
- 毕
- 呆
- 励
- 臭
- 暂
- 评
- 沧
- 磨
- 洞
- 厂
- 吵
- 煮
- 旧
- 幽
- 寄
- 政
- 丫
- 闯
- 举
- 误
- 护
- 状
- 寂
- 牙
- 杯
- 议
- 眉
- 享
- 剩
- 秘
- 噢
- 耿
- 致
- 偷
- 丢
- 刀
- 销
- 盒
- 编
- 珍
- 葛
- 译
- 颗
- 括
- 奥
- 鲜
- 沈
- 婷
- 摩
- 炒
- 惯
- 啡
- 混
- 燥
- 扣
- 晶
- 柏
- 拥
- 旭
- 拾
- 验
- 嫁
- 铺
- 棉
- 划
- 虾
- 浙
- 寓
- 剪
- 贴
- 圣
- 颖
- 申
- 枝
- 艾
- 旁
- 溪
- '?'
- 厚
- 驶
- 燃
- 虽
- 途
- 祖
- 职
- 泽
- 腿
- 薇
- 阵
- 移
- 淋
- 灭
- 寞
- 森
- 延
- 孝
- 沥
- 迟
- 伪
- 催
- 投
- 伯
- 谓
- 诚
- 架
- 耶
- 项
- 撒
- 邦
- 善
- 鼻
- 芬
- 闲
- 增
- 卓
- 层
- 鹏
- 敲
- 镖
- 粉
- 欧
- 纸
- 甘
- 昆
- 哩
- 坚
- 苍
- 积
- 筝
- 擦
- 董
- 吻
- 折
- 欺
- 疆
- 勒
- 售
- 船
- 胜
- 甄
- 杂
- 骑
- 贱
- 饼
- 称
- 隆
- 竟
- 逃
- 啷
- 引
- 宾
- 莉
- 境
- 奖
- 救
- 讯
- 恰
- 垃
- 圾
- 宅
- 潜
- 皇
- 符
- 徽
- 造
- 翔
- 粥
- 桌
- 租
- 险
- 驾
- 祭
- 昂
- 牧
- 宣
- 综
- 谷
- 私
- 瓷
- 避
- 肖
- 闪
- 圈
- 喱
- 耀
- 悟
- 秒
- 篮
- 逗
- 蝶
- 趣
- 恨
- 恐
- 饺
- 碎
- 奔
- 幼
- 股
- 锦
- 锡
- 椅
- 玲
- 刑
- 嗓
- 喊
- 虑
- 俺
- 镜
- 耐
- 鹿
- 狄
- 兮
- 返
- 恭
- 含
- 傅
- 沟
- 莹
- 妃
- 忠
- 赤
- 喔
- 抓
- 迈
- 众
- 豪
- 祈
- 馨
- 嬛
- 庭
- 异
- 辰
- 琅
- 荷
- 匪
- 吐
- 警
- 虹
- 吓
- 聪
- 悔
- 归
- 富
- 陕
- 魏
- 欲
- 菊
- 雹
- 隐
- 涯
- 忍
- 芦
- 琊
- 酸
- 逊
- 亦
- 咪
- 瞎
- 滨
- 胸
- 采
- 穹
- 究
- 炊
- 痒
- 莎
- 柳
- 井
- 洪
- 胎
- 鼓
- 润
- 迁
- 玫
- 滩
- 傲
- 袁
- 赚
- 研
- 躺
- 烤
- 莱
- 搬
- 蒋
- 曹
- 孟
- 嫂
- 甲
- 瑰
- 窝
- 令
- 堆
- 废
- 掌
- 巡
- 妙
- 袋
- 争
- 萌
- 挑
- 册
- 饮
- 勋
- 珊
- 戒
- 绵
- 亡
- 劳
- 搭
- 甩
- 匙
- 彭
- 锋
- 钥
- 率
- 吟
- 鼠
- 纱
- 坡
- 潇
- 挣
- 逝
- 针
- 弱
- 妍
- 稳
- 怒
- 塘
- 卢
- 宵
- 悠
- 饱
- 披
- 瘦
- 浮
- 烂
- 壶
- 截
- 勿
- 序
- 委
- 兔
- 塔
- 执
- 墨
- 府
- 宙
- 欠
- 巨
- 帽
- 占
- 顿
- 权
- 坠
- 碰
- 著
- 硬
- 炮
- 骚
- 肃
- 规
- 厕
- 贾
- 葫
- 徒
- 瓶
- 辽
- 耍
- 赢
- 桂
- 浦
- 趟
- 柯
- 悉
- 恼
- 禁
- 殊
- 卧
- 赞
- 益
- 责
- 虚
- 姓
- 愁
- 舅
- 残
- 既
- 拖
- 棍
- 幻
- 库
- 骄
- 烈
- 尊
- 伊
- 缺
- 迹
- 疑
- 汽
- 郎
- 鸭
- 仪
- 盗
- 幺
- 萱
- 胃
- 脏
- 努
- 勉
- 池
- 咳
- 奋
- 批
- 蝴
- 监
- 犯
- 滑
- 牵
- 冯
- 败
- 毒
- 怖
- 绪
- 帐
- 协
- 韵
- 怜
- 薛
- 姚
- 副
- 塞
- 蕉
- 夹
- 萝
- 爹
- 貌
- 奈
- 乞
- 隔
- 澳
- 姥
- 妖
- 腰
- 纳
- 龄
- 材
- 旗
- 萤
- 俗
- 昼
- 坛
- 霍
- 怡
- 丐
- 咒
- 础
- 嘎
- 虫
- 枪
- 遗
- 献
- 陌
- 侣
- 。
- 昧
- 筒
- 袭
- 厨
- 爬
- 茂
- 媛
- 慰
- 填
- 霞
- 娟
- 摸
- 逍
- 赫
- 霾
- 泥
- 暧
- 翅
- 谦
- 夕
- 瑶
- 鑫
- 刺
- 袖
- 拒
- 玄
- 涂
- 溜
- 旬
- 鸣
- 泷
- 距
- 阻
- 绩
- 狠
- 宽
- 狐
- 赖
- 握
- 循
- 靓
- 述
- 糕
- 踏
- 侯
- 劵
- 壮
- 抄
- 苟
- 岗
- 供
- 湛
- 炼
- 烫
- 棋
- 糊
- 饶
- 悄
- 霸
- 竹
- 哀
- 拔
- 蓉
- 旦
- 晰
- 振
- 漠
- 苗
- 帘
- 糟
- 崇
- 踩
- 汕
- 寝
- 刹
- 蔬
- 旺
- 躁
- 守
- 液
- 疗
- 晋
- 坤
- 洒
- 串
- 屏
- 翠
- 鹅
- 腻
- 毅
- 蹈
- 党
- 咩
- 灿
- 哄
- 核
- 横
- 谎
- 忏
- 映
- 倔
- 则
- 肤
- 贺
- 潍
- 焦
- 渐
- 坑
- 瞄
- 融
- 琼
- 尤
- 逸
- 碧
- 葡
- 卜
- 察
- 邢
- 薄
- 亏
- 绒
- 萄
- 婉
- 闺
- 势
- 描
- 均
- 梨
- 椒
- 慕
- 污
- 弯
- 繁
- 炸
- 肿
- 阅
- 肺
- 席
- 呦
- 碟
- 耻
- 端
- 叹
- 庸
- 危
- 痘
- 峡
- 腐
- 霜
- 拳
- 昴
- 荡
- 屎
- 纠
- 夸
- 尿
- 钰
- 撼
- 嗽
- 雯
- 症
- 衡
- 互
- 孔
- 钻
- 萍
- 娄
- 斤
- 悦
- 谊
- 扯
- 驴
- 歉
- 扎
- 庐
- 蒲
- 吼
- 熬
- 鸳
- 蒸
- 驹
- 允
- 射
- 酱
- 鸯
- 企
- 馒
- 乘
- 葱
- 泳
- 莞
- 脆
- 寨
- 损
- 陀
- 膀
- 淮
- 侃
- 霉
- 施
- 橙
- 煲
- 妆
- 审
- 宠
- 穷
- 敌
- 堡
- 樱
- 诞
- 胆
- 彤
- 祷
- 渭
- 霆
- 亭
- 璐
- 邵
- 壁
- 禺
- 墙
- 葬
- 垫
- 吾
- 粒
- 爵
- 弘
- 妻
- 蕾
- 咨
- 固
- 幕
- 粗
- 抢
- 访
- 贸
- 挥
- 饰
- 硕
- 域
- 岸
- 咬
- 晗
- 姆
- 骤
- 抖
- 判
- 鄂
- 获
- 锻
- 郝
- 柜
- 醋
- 桐
- 泣
- 粘
- 革
- 脾
- 尸
- 侧
- 辆
- 埋
- 稻
- 肠
- 嫌
- 彬
- 庚
- 彼
- 龟
- 弥
- 籍
- 纽
- 喷
- 氛
- 币
- 蠢
- 磁
- 袜
- 柴
- 寸
- 韦
- 忐
- 忑
- 恢
- 缩
- 捷
- 绕
- 翼
- 琦
- 玻
- 驻
- 屈
- 岩
- 颂
- 仓
- 茜
- 璃
- 裙
- 僵
- 柿
- 稿
- 巾
- 撑
- 尹
- 嘟
- 牡
- 昏
- 歇
- 诵
- 丸
- 梯
- 挡
- 袄
- 逢
- 徙
- 渴
- 仰
- 跨
- 碗
- 阔
- 税
- 拼
- 宥
- 丞
- 凶
- 析
- 炖
- 舌
- 抗
- 脖
- 甚
- 豚
- 敷
- 瓦
- 织
- 邀
- 浏
- 猛
- 歪
- 阶
- 兽
- 俄
- 鹤
- 禹
- 纹
- 闽
- 惹
- 煤
- 患
- 岭
- 瑜
- 稀
- 拆
- 凄
- 崎
- 芝
- 摊
- 尺
- 彻
- 览
- 贷
- 珂
- 憋
- 径
- 抚
- 魅
- 悬
- 胶
- 倍
- 贯
- 籁
- 乃
- 哑
- 惑
- 撞
- 箫
- 绣
- 扁
- 苑
- 靖
- 漏
- 挤
- 轶
- 叮
- 烨
- 菇
- 砸
- 趁
- 媚
- 仅
- 藤
- 邱
- 陵
- 躲
- 滋
- 叛
- 捉
- 孕
- 铜
- 衫
- 寿
- 寺
- 枫
- 豹
- 伽
- 翡
- 蜂
- 丙
- 姗
- 羡
- 凑
- 鄙
- 庙
- 铭
- 宰
- 廖
- 肩
- 臣
- 抑
- 辅
- 誓
- 扇
- 啪
- 羞
- 诊
- 敦
- 跃
- 俞
- 肝
- 坦
- 贡
- 踢
- 齿
- 尧
- 淀
- 叉
- 浴
- 狮
- 昊
- 蟹
- 捏
- 略
- 禾
- 纲
- 赔
- 憾
- 赋
- 丘
- 尝
- 钓
- 涕
- 猴
- 鸽
- 纵
- 奉
- 涨
- 揍
- 怨
- 挨
- 兜
- 冈
- 凭
- 策
- 裴
- 摔
- 喵
- 佐
- 喉
- 膏
- 瑟
- 抬
- 纷
- 廊
- 贼
- 煎
- 熄
- 渝
- 缠
- 纶
- 岚
- 衬
- 遮
- 翰
- 誉
- 摘
- 勾
- 赣
- 姬
- 娅
- 撤
- 霖
- 泊
- 膝
- 耽
- 犹
- 仍
- 辞
- 溃
- 骏
- 弓
- 膜
- 诱
- 慌
- 惨
- 噪
- 涩
- 潭
- 幂
- 梓
- 植
- 罚
- 扮
- 涮
- 雁
- 兆
- 舟
- 咸
- 犀
- 炉
- 筋
- 陇
- 狸
- 帕
- 噶
- 茄
- 嗒
- 纬
- 障
- 聘
- 盼
- 盟
- 咧
- 灏
- 菠
- 巷
- 帖
- 慈
- 枕
- 唤
- 慨
- 呛
- 叽
- 砖
- 窍
- 瞒
- 龚
- 促
- 尖
- 螺
- 捞
- 盆
- 茫
- 屌
- 械
- 乳
- 啤
- 玺
- 廷
- 谐
- 吖
- 帆
- 蛇
- 琵
- 琶
- 扑
- 跌
- 崩
- 扭
- 扔
- 咿
- 菩
- 茉
- 攻
- 虐
- 甸
- 璇
- 驰
- 瞬
- 鸦
- 厢
- 囊
- 闫
- 届
- 墓
- 芒
- 栗
- 沫
- 违
- 缝
- 棵
- 杏
- 赌
- 灾
- 颤
- 沂
- 肇
- 桶
- 霄
- ！
- 咙
- 绥
- 仲
- 愈
- 竖
- 菌
- 捕
- 烘
- 阮
- 皆
- 咚
- 劫
- 揭
- 郸
- 庞
- 喇
- 拐
- 奴
- 咔
- 幅
- 偿
- 咦
- 召
- 薪
- 盯
- 黛
- 杉
- 辨
- 邯
- 枯
- 沃
- 吊
- 筷
- 陷
- 鹰
- 嗦
- 噻
- 屯
- 殇
- 抵
- 雕
- 辩
- 枣
- 捂
- 瘾
- 粮
- 巢
- 耗
- 储
- 殷
- 糯
- 轨
- 沾
- 淇
- 毁
- 沐
- 蚊
- 鉴
- 灌
- 玖
- 唔
- 芙
- 淳
- 昕
- 裹
- 茧
- 浑
- 睿
- 踪
- 邪
- 瘩
- 恺
- 斜
- 汰
- 逐
- 铮
- 毫
- 胞
- 昭
- 妥
- 筑
- 贪
- 蘑
- 皓
- 颐
- 疙
- 捡
- 泛
- 债
- 栎
- 棚
- 腹
- 构
- 蓬
- 宪
- 叭
- 愚
- 押
- 蜀
- 夷
- 娶
- 盾
- 倪
- 牟
- 抛
- 壳
- 衍
- 杆
- 撕
- 亿
- 纤
- 淹
- 翘
- 蔷
- 芊
- 罩
- 拯
- 嗷
- 浇
- 宴
- 遵
- 冥
- 祸
- 塑
- 沛
- 猎
- 携
- 噜
- 喘
- 缴
- 砍
- 唢
- 曦
- 遛
- 罢
- 峨
- 戚
- 稚
- 揉
- 堰
- 螃
- 薯
- 乙
- 矿
- 挽
- 弛
- 埃
- 淅
- 疲
- 窦
- 烛
- 媒
- 尬
- 汀
- 谨
- 罐
- 劣
- 伶
- 煜
- 栏
- 榆
- 矛
- 琐
- 槽
- 驼
- 渤
- 沒
- 泄
- 粑
- 匀
- 囧
- 茵
- 霹
- 澈
- 岑
- 乏
- 栋
- 拌
- 框
- 祁
- 叨
- 斋
- 玥
- 僧
- 疏
- 绳
- 晃
- 抹
- 授
- 蓄
- 檬
- 仇
- 毯
- 啵
- 泼
- 阁
- ','
- 邹
- 阎
- 渠
- 函
- 腊
- 割
- 绑
- 扶
- 肌
- 卑
- 匠
- 雳
- 绯
- 婧
- 煌
- 蒂
- 腔
- 仿
- 遭
- 阜
- 峻
- 劝
- 绎
- 黔
- 贫
- 剁
- 荆
- 樊
- 卸
- 锄
- 阕
- 狱
- 冉
- 鲍
- 荒
- 侄
- 唇
- 忌
- 掖
- 竞
- 匹
- 仗
- 锤
- 穆
- 践
- 冶
- 柱
- 聂
- 捧
- 唠
- 翁
- 掏
- 塌
- 沁
- 巩
- 沸
- 蜡
- 痕
- 削
- 晟
- 眯
- 灶
- 婴
- 啸
- 釜
- 兼
- 剂
- 氧
- 赐
- 铠
- 攀
- 扩
- 朦
- 胧
- 孽
- 挖
- 钞
- 碍
- 凝
- 鼎
- 屉
- 斑
- 抠
- 哗
- 哨
- 婶
- 劈
- 冕
- 霏
- 汾
- 雀
- 浚
- 屠
- 唰
- 疚
- 芽
- 惦
- 裕
- 仑
- 厘
- 烁
- 瞧
- 蚂
- 涿
- 尴
- 埔
- 橘
- 磕
- 苇
- 脂
- 臂
- 蛙
- 镁
- 绽
- 卿
- 荃
- 莺
- 迫
- 敖
- 呈
- 勃
- 碌
- 讶
- 赠
- 巫
- 篱
- 浓
- 攒
- 裁
- 嫣
- 彪
- 娣
- 坟
- 廉
- 聆
- 铉
- 瞌
- 葵
- 鞍
- 坎
- 畜
- 爪
- 锯
- 潼
- 矣
- 闸
- 俱
- 蹭
- 戈
- 扒
- 滤
- 撇
- 浅
- 唧
- 觅
- 婕
- 牢
- 堕
- 丈
- 滕
- 御
- 溢
- 阑
- 楞
- 伺
- 馋
- 禄
- 胳
- 措
- 伐
- 滔
- 沦
- 澎
- 谙
- 桢
- 肾
- 熏
- 炅
- 邻
- 吞
- 噔
- 哔
- 沿
- 竺
- 闵
- 妨
- 啰
- 儒
- 锈
- 虞
- 颠
- 脊
- 膊
- 搓
- 岐
- 浸
- 兹
- 吨
- 垂
- 晏
- 痹
- 哆
- 漆
- 叠
- 莓
- 嘀
- 挫
- 馈
- 愧
- 佟
- 疾
- 蒜
- 盈
- 侬
- 烊
- 炙
- 蜢
- 诡
- 莆
- 蛾
- 轴
- 妒
- 洱
- 擎
- 脉
- 飓
- 泫
- 浆
- 岔
- 蹦
- 愤
- 琛
- 趴
- 绘
- 忻
- 拽
- 牲
- 馅
- 鲨
- 靴
- 鳅
- 俐
- 罕
- 呕
- 凋
- 绫
- 蕊
- 圃
- 猥
- 氓
- 歧
- 秧
- 栈
- 梧
- 衷
- 巅
- 彝
- 嚎
- 菁
- 渔
- 茬
- 汐
- 拓
- 昔
- 囚
- 舜
- 搁
- 泸
- 涟
- 蚁
- 裳
- 鞭
- 辟
- 蝎
- 簧
- 予
- 倦
- 傍
- 荔
- 瞳
- 碑
- 桨
- 疫
- 骁
- 驿
- 柠
- 妾
- 隶
- 菏
- 煽
- 麒
- 奎
- 驯
- 飙
- 姻
- 沅
- 扉
- 斩
- 奢
- 蚌
- 掩
- 蹲
- 丧
- 辱
- 焉
- 佘
- 襄
- 芯
- 枉
- 谋
- 渊
- 哮
- 喀
- 朔
- 侏
- 姝
- 戎
- 磅
- 督
- 诛
- 奸
- 苞
- 庵
- 馄
- 聋
- 滁
- 垚
- 柬
- 猩
- 夺
- 啼
- 坝
- 竭
- 黏
- 衰
- 遂
- 潞
- 谜
- 蜻
- 蜓
- 瓣
- 秉
- 檐
- 楂
- 嗑
- 搅
- 嘚
- 倚
- 乒
- 宛
- 崽
- 恕
- 轰
- 淄
- 晞
- 酬
- 砂
- 筠
- 薰
- 蒿
- 瞅
- 勺
- 阙
- 伸
- 嚏
- 湄
- 咆
- 坂
- 役
- 掰
- 渣
- 魁
- 诅
- 浒
- 妓
- 珑
- 捎
- 焊
- 饲
- 脍
- 荫
- 堤
- 轿
- 乓
- 筹
- 撸
- 饨
- 渺
- 桓
- 旷
- 笙
- 晖
- 慎
- 埠
- 挪
- 汝
- 浊
- 仨
- 鳄
- 濮
- 汶
- 邰
- 钉
- 蔽
- 亨
- 屑
- 铅
- 喃
- 葩
- 哉
- 睁
- 骆
- 涉
- 汁
- 拦
- 痞
- 芜
- 俪
- 兑
- 梵
- 刊
- 缅
- 彰
- 俑
- 桔
- 堪
- 鸥
- 契
- 覆
- 拷
- 珞
- 诸
- 棱
- 忒
- 嫩
- 梶
- 贻
- 藕
- 愣
- 湃
- 趋
- 甭
- 嗖
- 怯
- 憧
- 珀
- 缸
- 蔓
- 稣
- 筱
- 杠
- 崖
- 凳
- 裆
- 隧
- 锣
- 嘣
- 瀑
- 漪
- 柄
- 凸
- 颁
- 迦
- 烙
- 岱
- 瑄
- 吭
- 肆
- 鳞
- 晾
- 憬
- 邑
- 甥
- 掀
- 褂
- 淫
- 瓢
- 暮
- 喧
- 祛
- 恙
- 禅
- 柚
- 樟
- 疮
- 嗡
- 懈
- 茨
- 矮
- 诠
- 侮
- 眨
- 羲
- 掐
- 琉
- 雍
- 晔
- 凹
- 怂
- 禧
- 蹬
- 绅
- 榄
- 箍
- 詹
- 溶
- 黯
- 啃
- 驸
- 朕
- 婺
- 援
- 铲
- 呻
- 犬
- 捣
- 眷
- 剃
- 惧
- 芷
- 叱
- 娥
- 钦
- 矫
- 憨
- 骊
- 坪
- 俏
- 炳
- 妲
- 冀
- 刁
- 馍
- 琢
- 扛
- 瞿
- 辙
- 茅
- 寡
- 絮
- 呷
- 哺
- 咕
- 驱
- 搂
- 圭
- 嫉
- 涓
- 茱
- '"'
- 笼
- 讽
- 涡
- 泓
- 弊
- 诀
- 璧
- 舔
- 嬅
- 亢
- 沪
- 绢
- 钙
- 喏
- 馥
- 怅
- 簿
- 薜
- 捶
- 冤
- 脐
- 岂
- 溺
- 蕙
- 铿
- 锵
- 锐
- 呸
- 砰
- 亩
- 漳
- 阪
- 栀
- 坞
- 跤
- 蓓
- 舰
- 缕
- 羁
- 芋
- 畔
- 衔
- 铝
- 盲
- 株
- 搏
- 曙
- 惩
- 逻
- 蹄
- 涤
- 宕
- 咤
- 尉
- 嘘
- 瀚
- 仃
- 稽
- 霑
- 飕
- 垮
- 酿
- 畏
- 鲸
- 梗
- 署
- 砒
- 雏
- 茗
- 恬
- 螂
- 拂
- 憔
- 悴
- 钗
- 棕
- 劭
- 歹
- 笠
- 厄
- 焖
- 拣
- 逮
- 蕴
- 淌
- 枸
- 杞
- 雇
- 漯
- 邂
- 逅
- ·
- 荟
- 塾
- 涌
- 挚
- 舱
- 惬
- 剖
- 榴
- 侦
- 摁
- 烹
- 烽
- 俘
- 麓
- 犊
- 酌
- 匿
- 梭
- 覃
- 隽
- 惆
- 掠
- 舵
- 艰
- 蟑
- 瘤
- 仆
- 穴
- 涅
- 衿
- 嚷
- 峪
- 榕
- 吒
- 酪
- 曝
- 帧
- 靶
- 嚣
- 踝
- 翊
- 陂
- 髓
- 瑚
- 裘
- 芍
- 炬
- 鲅
- 蚕
- 肢
- 颊
- 陛
- 籽
- 粟
- 滞
- 煞
- 乾
- 媞
- 刨
- 碾
- 瘫
- 盔
- 侈
- 徘
- 徊
- 熔
- 吆
- 褪
- 拟
- 廓
- 翟
- 俾
- 沽
- 垒
- 萎
- 僻
- 豌
- 卵
- 狡
- 篓
- 栽
- 崴
- 拧
- 颈
- 咐
- 胭
- 阱
- 鄱
- 漓
- 厥
- 烬
- 糙
- 褥
- 炕
- 恍
- 襟
- 韧
- 眸
- 毙
- 垢
- 叙
- 辜
- 酝
- 璋
- 荧
- 魇
- 皈
- 觞
- 喻
- 孺
- 匈
- 铛
- 诈
- 盏
- 淼
- 佣
- 苓
- 缚
- 洼
- 疡
- 猬
- 腑
- 阡
- 鲫
- 鹭
- 鹂
- 笆
- 埙
- 癌
- 璀
- 璨
- 疹
- 蓑
- 芭
- 嘶
- 桀
- 吩
- 泾
- 铂
- 倘
- 囗
- 璜
- 窃
- 癫
- 璞
- 墟
- 钩
- 粹
- 镐
- 韬
- 牺
- 寮
- 喳
- 鄞
- 笋
- 臧
- 疤
- 捐
- 腥
- 嬷
- 燮
- 濠
- 棠
- 夙
- 弑
- 乍
- 剔
- 嘈
- 钇
- 衅
- 挝
- 橡
- 矜
- 圩
- 恳
- 瑛
- 蔺
- 兖
- 焕
- 懿
- 钏
- 栾
- 筐
- 苒
- 碳
- 韭
- 箭
- 婵
- 迭
- 枷
- 孜
- 咽
- 悯
- 漉
- 噬
- 侍
- 蝉
- 涧
- 鹦
- 鹉
- 冼
- 竿
- …
- 袈
- 诏
- 锢
- 泠
- 匡
- 枚
- 坷
- 邝
- 癖
- 绷
- 皖
- 滦
- 滥
- 荨
- 虏
- 拈
- 浜
- 颓
- “
- ”
- 戳
- 钮
- 梳
- 溅
- 徨
- 旨
- 罂
- 蹉
- 腌
- 隙
- 侨
- 槟
- 泌
- 珈
- 芵
- 腮
- 晤
- 墩
- 鲤
- 扳
- 栓
- 窑
- 荏
- 饪
- 泵
- 猿
- 眀
- 嗝
- 禽
- 朽
- 偕
- 胀
- 谍
- 捅
- 蜉
- 蝣
- 蹋
- 拱
- 氯
- 噼
- 蚩
- 芥
- 蛟
- 貂
- 荚
- 痰
- 殿
- 遣
- 丛
- 碱
- 殖
- 炽
- 嚓
- 彗
- 窟
- 鳌
- 矶
- 镯
- 乜
- 髙
- 蛤
- 荤
- 坨
- 漱
- 惰
- 跎
- 萸
- 曰
- 亘
- 窘
- 厮
- 绐
- 黝
- 鞠
- 漩
- 蚱
- 垣
- 翩
- 嬴
- 彷
- 椰
- 砚
- 褐
- 黍
- 噗
- 耕
- 挠
- 妩
- 掂
- 峯
- 灸
- 晌
- 溧
- 鹃
- 屿
- 昙
- 廾
- 冢
- 龌
- 龊
- 瞪
- 刽
- 脓
- 壹
- 羱
- 奠
- 贰
- 佬
- 拙
- 颢
- 嘱
- 糗
- 昀
- 巳
- 辕
- 惫
- 黒
- 辐
- 窈
- 窕
- 拢
- 缪
- 逞
- 吝
- 裟
- 钝
- 寇
- 耙
- 隋
- 蝇
- 仟
- 铨
- 赊
- 皑
- 衢
- 胚
- 腺
- 啧
- 淤
- 妄
- 氢
- 寅
- 叻
- 嘲
- 叼
- 沮
- 磐
- 芈
- 饥
- 槿
- 卤
- 懵
- 惴
- 毋
- 箩
- 苔
- 峥
- 斥
- 矬
- 佚
- 肮
- 皎
- 憎
- 樨
- 讴
- 鳖
- 煦
- 焚
- 泗
- 皂
- 礁
- 睬
- 梢
- 妤
- 佗
- 蝌
- 蚪
- 渗
- 暇
- 卟
- 悼
- 瑨
- 伎
- 纺
- 耆
- 舶
- 礴
- 豺
- 涪
- 谬
- 赴
- 婪
- 吱
- 麽
- 犁
- 潸
- 鸪
- 鸢
- 鄯
- 讷
- 弶
- 橄
- 撬
- 赦
- 岷
- 垓
- 绞
- 虔
- 剥
- 澜
- 酗
- 谛
- 骥
- 撅
- 鱿
- 犷
- 讪
- 秃
- 卞
- 缆
- 蓦
- 庶
- 勐
- 笫
- 敛
- 弗
- 痱
- 啬
- 硚
- 昱
- 忿
- 撩
- 椿
- 侵
- 窄
- 邛
- 崃
- 涸
- 赈
- 狭
- 嵌
- 淖
- 瑙
- 踹
- 傈
- 僳
- 缭
- 睦
- 窜
- 嘅
- 樵
- 爰
- 侗
- 逑
- 弧
- 侑
- ：
- 娉
- 蝙
- 蝠
- 骅
- 饴
- 揣
- /
- 鲈
- 綦
- 拴
- 硝
- 梆
- 馗
- 夭
- 扼
- 鳃
- 惚
- 扈
- 矢
- 藁
- 飚
- 妊
- 踮
- 惟
- 痊
- 艇
- 偎
- 魄
- 篝
- 簸
- 擞
- 粽
- 缥
- 缈
- 跷
- 咁
- 悍
- 菀
- 陡
- 橱
- 遐
- 榨
- 渎
- 蹂
- 躏
- 舂
- 轼
- 枰
- 焰
- 幌
- 邸
- 捜
- 灼
- 茯
- 芎
- 穗
- 棘
- 碜
- 颉
- 鹧
- 啄
- 趾
- 茎
- 揽
- 靳
- 黜
- 惋
- 亥
- 铡
- 栅
- 挞
- 眈
- 膘
- 犍
- 珉
- 镪
- 昵
- 霓
- 圪
- 汲
- 惺
- 瑕
- 桩
- 洽
- 唏
- 耒
- 唻
- 豁
- 郓
- 纣
- 亊
- 鳝
- 蟆
- 癣
- 碚
- 踌
- 殁
- 缉
- 痔
- 頔
- 蔫
- ；
- 掺
- 愫
- 祟
- 拘
- 蜘
- 蛛
- 涎
- 耸
- 揪
- 芪
- 腕
- 袍
- 慵
- 绻
- 绛
- 螨
- 捌
- 墅
- 篷
- 啾
- 孪
- 唬
- 褛
- 跶
- 壤
- 慷
- 痧
- 懦
- 郯
- 莴
- 茴
- 嘬
- 铎
- 辫
- 绚
- 簇
- 墘
- 婿
- 咻
- 斡
- 沱
- 譬
- 羔
- 藓
- 肋
- 棂
- 赎
- 炭
- 徵
- 簌
- 艘
- 苪
- 眶
- 嘭
- 霎
- 馊
- 秽
- 仕
- 镶
- 纨
- 摧
- 蒨
- 闰
- 迩
- 篙
- 嚯
- 郫
- 陋
- 殒
- 邃
- 浔
- 瑾
- 鳟
- 祯
- 泻
- 氟
- 猾
- 酥
- 萦
- 郴
- 祀
- 涼
- 屡
- 摹
- 毡
- 妪
- 郡
- 柘
- 裱
- 囔
- 楷
- 鄄
- 蕲
- 偲
- 菘
- 姣
- 瞥
- 肪
- 饽
- 惭
- 胁
- 垄
- 榻
- 讼
- 旱
- 鬓
- 凇
- 钊
- 掣
- 浣
- 凃
- 蓥
- 臊
- 夔
- 脯
- 苛
- 阀
- 睫
- 腋
- 姊
- 躬
- 瘁
- 奄
- 靡
- 盂
- 柑
- 渑
- 恻
- 缱
- 拎
- 恤
- 缶
- 嵬
- 簋
- 囤
- 褴
- 蔼
- 沌
- 薏
- 鸵
- 跋
- 篪
- 罡
- 颇
- 嗄
- 胺
- 烯
- 酚
- 祠
- 迢
- 硖
- 眺
- 珏
- 怆
- 斧
- 痪
- 祺
- 嘤
- 谑
- 婊
- 滂
- 骇
- 帔
- 荼
- 硅
- 猖
- 皱
- 顽
- 榔
- 锌
- 蔻
- 滢
- 茸
- 捋
- 壥
- 孰
- 娩
- 锥
- 逾
- 诬
- 娠
- 厝
- 噎
- 秤
- 祢
- 嗳
- 嗜
- 滘
- 尅
- 悚
- 履
- 馕
- 簪
- 俭
- 摞
- 妗
- 蛎
- 暹
- 钾
- 膨
- 孚
- 驷
- 卯
- 猇
- 褚
- 町
- 骞
- －
- 芩
- 赁
- 粱
- 隼
- 掘
- 莽
- 郾
- 擒
- 叁
- 敕
- 镊
- 惘
- 蚤
- 邳
- 嗫
- 扪
- 瀛
- 凿
- 雎
- 啲
- 鲲
- 帼
- 枭
- 羹
- 驳
- 铆
- 肴
- 嫦
- 媲
- 鹳
- 秩
- 銮
- 饯
- 毽
- 珩
- 眩
- 仄
- 葳
- 撮
- 睇
- 塄
- 肘
- 钠
- 诓
- 呱
- 垅
- 菱
- 亍
- 戍
- 酯
- 袱
- 隘
- 蓟
- 暨
- 痣
- 辗
- 埵
- 殉
- 郏
- 孢
- 悳
- 讫
- 诲
- 髋
- 孑
- 睹
- 擅
- 嗮
- 慒
- 琰
- 濛
- 雌
- 恁
- 擀
- 娼
- 谕
- 撵
- 苯
- 聴
- 唛
- 撂
- 栖
- 拗
- 孬
- 怏
- 掇
- 肽
- 胰
- 沣
- 卅
- 箅
- 氨
- 浠
- 蠡
- 募
- 肛
- 岀
- 瞑
- 蛆
- 舀
- 蚝
- 歙
- 涔
- 诘
- 、
- 垡
- 涠
- 嘢
- 糸
- 胤
- 绊
- 柒
- 沓
- 粼
- 菖
- 犒
- 呒
- 唑
- 莘
- 莪
- 宸
- 睨
- \
- 鲶
- 蛐
- 溏
- 菈
- 蹩
- 焙
- 釆
- 瑗
- 睾
- 槐
- 榉
- 杷
- 鄢
- 僕
- 诽
- 嗲
- 蜃
- 戆
- 蘼
- 糜
- 霁
- 坻
- 硼
- 槛
- 枞
- 麸
- 谒
- 荀
- 邋
- 遢
- 锴
- 啶
- 粪
- 驭
- 筵
- 砌
- 莩
- 蹼
- 吔
- 缳
- 埭
- 隗
- 厶
- 丶
- "\x14"
- "\x17"
- 稼
- 铖
- 涣
- 亳
- 幢
- 沭
- 驮
- 奚
- 藐
- 颅
- 埤
- 愘
- 镲
- 窒
- 暄
- 诃
- 噘
- 歼
- 隅
- 爻
- 蘅
- 锹
- 锇
- 椎
- 琨
- 烩
- 枢
- 觧
- 萁
- 镂
- 龈
- 怠
- 阐
- 藉
- 凛
- 冽
- 珣
- 泘
- 抉
- 锭
- 蕃
- 蠃
- 毓
- 啐
- 栩
- 骷
- 髅
- 耷
- 寥
- 杵
- 蚬
- 窖
- 孛
- 舆
- 皿
- 柸
- 粳
- 钣
- 趸
- 叄
- 腚
- 杖
- 鸸
- 犲
- 浗
- 缮
- 哓
- 箧
- 攘
- 冇
- 钛
- 郗
- 囡
- 酆
- 姌
- 雉
- 胯
- 椭
- 埏
- 钵
- 绌
- 蝾
- 坼
- 濂
- ｗ
- ｏ
- ｒ
- ｄ
- 袒
- 峦
- 鹫
- 炯
- 悱
- 漕
- 莦
- 蔑
- 樽
- 牒
- 濡
- 嫯
- 陖
- 疸
- 桅
- 辖
- 僢
- 《
- 》
- 酣
- 遨
- 邬
- ':'
- 嫲
- 哌
- 锚
- 淙
- Ｑ
- 濑
- 熨
- 谴
- 筛
- 薹
- 磬
- 熠
- 腓
- 阉
- 钴
- 恂
- 溉
- 陨
- 螳
- 孵
- 瘠
- 嫡
- 哝
- 狙
- 怼
- 斟
- 甫
- 渌
- 卒
- 翕
- 沏
- 旮
- 旯
- 菡
- 變
- 狈
- 鳜
- 嵋
- 仞
- 鳕
- 噩
- 踟
- 躇
- 蛀
- 瘸
- 篡
- 锊
- 団
- 斐
- 蹍
- 冗
- "\uFEFF"
- 歆
- 圴
- 泯
- 伥
- 愎
- 坌
- 碘
- 赉
- 骧
- 矩
- 綽
- 秭
- 怵
- 麝
- 贩
- 溥
- 捆
- 腩
- 溴
- 卉
- 痦
- 荻
- 缇
- 秸
- 秆
- 捍
- 炀
- 阆
- 泞
- 懊
- 啕
- 蚶
- 衩
- 桜
- 旖
- 贬
- 酵
- 滟
- 纥
- 倭
- 赝
- 呶
- 哧
- 煸
- 劢
- 炝
- 僚
- 豇
- 阂
- 涝
- 骡
- 霭
- 窨
- 殴
- 竣
- 醇
- 擂
- 怦
- 怩
- 臾
- 搔
- 伱
- 啉
- 嫖
- 囝
- 糠
- 胥
- 酰
- 镫
- 蟒
- 荞
- 醪
- 颦
- 吏
- 颛
- 赳
- 贿
- 赂
- 痩
- 仂
- 颍
- 罔
- 猕
- 嚒
- 蘸
- 熹
- 捺
- 坜
- 郜
- 鉄
- 蒌
- 荑
- 藻
- 谌
- 钳
- 屮
- 疵
- 哞
- 琮
- 潴
- 讹
- 镭
- '3'
- 尕
- 倬
- 庇
- 侩
- 瘆
- 傀
- 儡
- 诧
- 葆
- 唾
- 皋
- 逄
- 诌
- 氦
- 彳
- 盅
- 曳
- 槲
- 挟
- 怿
- 顷
- 臃
- 衙
- 踵
- 霈
- 嗪
- 闩
- 锟
- 恿
- 抻
- 茁
- 惢
- 菅
- 迂
- 瞟
- 痉
- 挛
- 绦
- 晁
- 挢
- 蠕
- 洙
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
joint_net_conf: null
use_preprocessor: true
token_type: char
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_zh_char_sp/train/feats_stats.npz
model: espnet
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
output_size: 512
attention_heads: 8
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d
normalize_before: true
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
macaron_style: true
use_cnn_module: true
cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
required:
- output_dir
- token_list
version: '202207'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Aleksandar/bert-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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.7550595238095238
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5133689839572193
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.516320474777448
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5958866036687048
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.748
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4605263157894737
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5231481481481481
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9025161970769926
- name: F1 (macro)
type: f1_macro
value: 0.8979165451427438
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8328638497652581
- name: F1 (macro)
type: f1_macro
value: 0.6469572777603673
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6630552546045504
- name: F1 (macro)
type: f1_macro
value: 0.6493250582245075
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9562495652778744
- name: F1 (macro)
type: f1_macro
value: 0.8695137253747418
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8906298965841429
- name: F1 (macro)
type: f1_macro
value: 0.8885946595123109
---
# relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5133689839572193
- Accuracy on SAT: 0.516320474777448
- Accuracy on BATS: 0.5958866036687048
- Accuracy on U2: 0.4605263157894737
- Accuracy on U4: 0.5231481481481481
- Accuracy on Google: 0.748
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9025161970769926
- Micro F1 score on CogALexV: 0.8328638497652581
- Micro F1 score on EVALution: 0.6630552546045504
- Micro F1 score on K&H+N: 0.9562495652778744
- Micro F1 score on ROOT09: 0.8906298965841429
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.7550595238095238
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: average_no_mask
- data: relbert/semeval2012_relational_similarity
- split: train
- data_eval: relbert/conceptnet_high_confidence
- split_eval: full
- template_mode: manual
- template: I wasn’t aware of this relationship, but I just read in the encyclopedia that <obj> is <subj>’s <mask>
- loss_function: nce_logout
- classification_loss: True
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 30
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- exclude_relation_eval: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
Aleksandar/bert-srb-ner | [
"pytorch",
"bert",
"token-classification",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 4 | null | ---
tags:
- feature-extraction
pipeline_tag: feature-extraction
---
This model is the context encoder of the MS MARCO UniCOIL Lexical Model (Λ) from the SPAR paper:
[Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?](https://arxiv.org/abs/2110.06918)
<br>
Xilun Chen, Kushal Lakhotia, Barlas Oğuz, Anchit Gupta, Patrick Lewis, Stan Peshterliev, Yashar Mehdad, Sonal Gupta and Wen-tau Yih
<br>
**Meta AI**
The associated github repo is available here: https://github.com/facebookresearch/dpr-scale/tree/main/spar
This model is a BERT-base sized dense retriever trained on the MS MARCO corpus to imitate the behavior of [UniCOIL](https://github.com/castorini/pyserini/blob/master/docs/experiments-unicoil.md), a sparse retriever.
The following models are also available:
Pretrained Model | Corpus | Teacher | Architecture | Query Encoder Path | Context Encoder Path
|---|---|---|---|---|---
Wiki BM25 Λ | Wikipedia | BM25 | BERT-base | facebook/spar-wiki-bm25-lexmodel-query-encoder | facebook/spar-wiki-bm25-lexmodel-context-encoder
PAQ BM25 Λ | PAQ | BM25 | BERT-base | facebook/spar-paq-bm25-lexmodel-query-encoder | facebook/spar-paq-bm25-lexmodel-context-encoder
MARCO BM25 Λ | MS MARCO | BM25 | BERT-base | facebook/spar-marco-bm25-lexmodel-query-encoder | facebook/spar-marco-bm25-lexmodel-context-encoder
MARCO UniCOIL Λ | MS MARCO | UniCOIL | BERT-base | facebook/spar-marco-unicoil-lexmodel-query-encoder | facebook/spar-marco-unicoil-lexmodel-context-encoder
# Using the Lexical Model (Λ) Alone
This model should be used together with the associated query encoder, similar to the [DPR](https://huggingface.co/docs/transformers/v4.22.1/en/model_doc/dpr) model.
```
import torch
from transformers import AutoTokenizer, AutoModel
# The tokenizer is the same for the query and context encoder
tokenizer = AutoTokenizer.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder')
query_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder')
context_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-context-encoder')
query = "Where was Marie Curie born?"
contexts = [
"Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.",
"Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace."
]
# Apply tokenizer
query_input = tokenizer(query, return_tensors='pt')
ctx_input = tokenizer(contexts, padding=True, truncation=True, return_tensors='pt')
# Compute embeddings: take the last-layer hidden state of the [CLS] token
query_emb = query_encoder(**query_input).last_hidden_state[:, 0, :]
ctx_emb = context_encoder(**ctx_input).last_hidden_state[:, 0, :]
# Compute similarity scores using dot product
score1 = query_emb @ ctx_emb[0] # 341.3268
score2 = query_emb @ ctx_emb[1] # 340.1626
```
# Using the Lexical Model (Λ) with a Base Dense Retriever as in SPAR
As Λ learns lexical matching from a sparse teacher retriever, it can be used in combination with a standard dense retriever (e.g. [DPR](https://huggingface.co/docs/transformers/v4.22.1/en/model_doc/dpr#dpr), [Contriever](https://huggingface.co/facebook/contriever-msmarco)) to build a dense retriever that excels at both lexical and semantic matching.
In the following example, we show how to build the SPAR-Wiki model for Open-Domain Question Answering by concatenating the embeddings of DPR and the Wiki BM25 Λ.
```
import torch
from transformers import AutoTokenizer, AutoModel
from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer
from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
# DPR model
dpr_ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-multiset-base")
dpr_ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-multiset-base")
dpr_query_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained("facebook/dpr-question_encoder-multiset-base")
dpr_query_encoder = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-multiset-base")
# Wiki BM25 Λ model
lexmodel_tokenizer = AutoTokenizer.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder')
lexmodel_query_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder')
lexmodel_context_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-context-encoder')
query = "Where was Marie Curie born?"
contexts = [
"Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.",
"Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace."
]
# Compute DPR embeddings
dpr_query_input = dpr_query_tokenizer(query, return_tensors='pt')['input_ids']
dpr_query_emb = dpr_query_encoder(dpr_query_input).pooler_output
dpr_ctx_input = dpr_ctx_tokenizer(contexts, padding=True, truncation=True, return_tensors='pt')
dpr_ctx_emb = dpr_ctx_encoder(**dpr_ctx_input).pooler_output
# Compute Λ embeddings
lexmodel_query_input = lexmodel_tokenizer(query, return_tensors='pt')
lexmodel_query_emb = lexmodel_query_encoder(**query_input).last_hidden_state[:, 0, :]
lexmodel_ctx_input = lexmodel_tokenizer(contexts, padding=True, truncation=True, return_tensors='pt')
lexmodel_ctx_emb = lexmodel_context_encoder(**ctx_input).last_hidden_state[:, 0, :]
# Form SPAR embeddings via concatenation
# The concatenation weight is only applied to query embeddings
# Refer to the SPAR paper for details
concat_weight = 0.7
spar_query_emb = torch.cat(
[dpr_query_emb, concat_weight * lexmodel_query_emb],
dim=-1,
)
spar_ctx_emb = torch.cat(
[dpr_ctx_emb, lexmodel_ctx_emb],
dim=-1,
)
# Compute similarity scores
score1 = spar_query_emb @ spar_ctx_emb[0] # 317.6931
score2 = spar_query_emb @ spar_ctx_emb[1] # 314.6144
```
|
Aleksandar1932/distilgpt2-rock | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 11 | null | ---
license: mit
---
### million-live-akane-15k on Stable Diffusion
This is the `<akane>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 12 | null | ---
license: mit
---
### million-live-akane-3k on Stable Diffusion
This is the `<akane>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
AlekseyKulnevich/Pegasus-HeaderGeneration | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
"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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 8 | null | ---
tags:
- text-generation
---
# Model Card for bt-opt-1.3b
# Model Details
## Model Description
- **Developed by:** Opentensor
- **Shared by [Optional]:** Hugging Face and Meta
- **Model type:** Text Generation
- **Language(s) (NLP):** More information needed
- **License:** More information needed
- **Related Models:**
- **Parent Model:** OPT
- **Resources for more information:**
- [Associated Paper](https://arxiv.org/abs/2205.01068)
# Uses
## Direct Use
This model can be used for the task of Text Generation
## Downstream Use [Optional]
In addition, the model can be fine-tuned on a downstream task using the [CLM example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling)
## Out-of-Scope Use
The model should not be used to intentionally create hostile or alienating environments for people.
# Bias, Risks, and Limitations
As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of
unfiltered content from the internet, which is far from neutral the model is strongly biased :
> Like other large language models for which the diversity (or lack thereof) of training
> data induces downstream impact on the quality of our model, OPT-175B has limitations in terms
> of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and
> hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern
> large language models.
See model [facebook/opt-1.3b model card](https://huggingface.co/facebook/opt-1.3b) for example biased predictions
The model creators noted in the [associated paper](https://arxiv.org/pdf/2205.01068.pdf)
> we found OPT-175B does not work well with declarative instructions or point-blank interrogatives. Prompting with such instructions tends to produce a simulation of a dialogue beginning with such an instruction, rather than an execution of the instruction. Future work into instruction learning, in the vein of InstructGPT (Ouyang et al., 2022), may alleviate these limitations. OPT-175B also tends to be repetitive and can easily get stuck in a loop.
## Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
# Training Details
## Training Data
The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents:
- BookCorpus, which consists of more than 10K unpublished books,
- CC-Stories, which contains a subset of CommonCrawl data filtered to match the
story-like style of Winograd schemas,
- The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included.
- Pushshift.io Reddit dataset that was developed in Baumgartner et al. (2020) and processed in
Roller et al. (2021)
- CCNewsV2 containing an updated version of the English portion of the CommonCrawl News
dataset that was used in RoBERTa (Liu et al., 2019b)
The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally
to each dataset’s size in the pretraining corpus.
The dataset might contains offensive content as parts of the dataset are a subset of
public Common Crawl data, along with a subset of public Reddit data, which could contain sentences
that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety.
Alo see the dataset card in the [associated paper](https://arxiv.org/pdf/2205.01068.pdf).
## Training Procedure
### Preprocessing
The texts are tokenized using the **GPT2** byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens.
The 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training
### Speeds, Sizes, Times
More information needed
# Evaluation
## Testing Data, Factors & Metrics
### Testing Data
More information needed
### Factors
### Metrics
More information needed
## Results
More information needed
# Model Examination
More information needed
# Environmental Impact
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** 992 *80GB A100 GPUs
- **Hours used:** 792 (~33 dyas)
- **Cloud Provider:** More information needed
- **Compute Region:** More information needed
- **Carbon Emitted:** More information needed
# Technical Specifications [optional]
## Model Architecture and Objective
OPTForCausalLM
## Compute Infrastructure
More information needed
### Hardware
More information needed
### Software
Transformers_version: 4.22.1
# Citation
**BibTeX:**
```bibtex
@misc{zhang2022opt,
title={OPT: Open Pre-trained Transformer Language Models},
author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer},
year={2022},
eprint={2205.01068},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
# Glossary [optional]
More information needed
# More Information [optional]
More information needed
# Model Card Authors [optional]
Opentensor in collaboration with Ezi Ozoani and the Hugging Face team
# Model Card Contact
More information needed
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("opentensor/bt-opt-1.3b")
model = AutoModelForCausalLM.from_pretrained("opentensor/bt-opt-1.3b")
```
</details>
|
AliReza/distilbert-emotion | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: cc0-1.0
language: en
widget:
- text: "thismodelcanperformwordsegmentation"
- text: "sometimesitdoesntworkquitewell"
- text: "expertsexchange"
---
|
Alicanke/Wyau | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: cc0-1.0
language: pl
widget:
- text: "modelpodzielitentekstnasłowa"
- text: "czasamijednaknieidziemutozbytdobrze"
---
|
Alireza1044/albert-base-v2-mnli | [
"pytorch",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"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_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 235 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-small-finetuned-scan_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-scan_v2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Tokenizers 0.12.1
|
Alireza1044/dwight_bert_lm | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 14 | null | ---
license: mit
---
### yinit on Stable Diffusion
This is the `yinit-dropcap` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
Alireza1044/michael_bert_lm | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 10 | null | ---
license: mit
---
### BEE on Stable Diffusion
This is the `<b-e-e>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
AllwynJ/HarryBoy | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 12 | null | ---
license: mit
---
### pixel-mania on Stable Diffusion
This is the `<pixel-mania>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
|
Allybaby21/Allysai | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | data: https://github.com/BigSalmon2/InformalToFormalDataset
Text Generation Informal Formal
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln80Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln80Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
```
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
- penny has practically no value
- should be taken out of circulation
- just as other coins have been in us history
- lost use
- value not enough
- to make environmental consequences worthy
text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
```
first: ( was complicit in / was involved in ).
antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ).
***
first: ( have no qualms about / see no issue with ).
antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ).
***
first: ( do not see eye to eye / disagree often ).
antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ).
***
first:
```
```
stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground.
***
languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo.
***
dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia.
***
embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons.
```
Infill / Infilling / Masking / Phrase Masking (Works pretty decently actually, especially when you use logprobs code from above):
```
his contention [blank] by the evidence [sep] was refuted [answer]
***
few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
the joy of sport is that no two games are alike. for every exhilarating experience, however, there is an interminable one. the national pastime, unfortunately, has a penchant for the latter. what begins as a summer evening at the ballpark can quickly devolve into a game of tedium. the primary culprit is the [blank] of play. from batters readjusting their gloves to fielders spitting on their mitts, the action is [blank] unnecessary interruptions. the sport's future is [blank] if these tendencies are not addressed [sep] plodding pace [answer] riddled with [answer] bleak [answer]
***
microsoft word's [blank] pricing [blank] competition [sep] unconscionable [answer] invites [answer]
***
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
Backwards
```
Essay Intro (National Parks):
text: tourists are at ease in the national parks, ( swept up in the beauty of their natural splendor ).
***
Essay Intro (D.C. Statehood):
washington, d.c. is a city of outsize significance, ( ground zero for the nation's political life / center stage for the nation's political machinations ).
```
```
topic: the Golden State Warriors.
characterization 1: the reigning kings of the NBA.
characterization 2: possessed of a remarkable cohesion.
characterization 3: helmed by superstar Stephen Curry.
characterization 4: perched atop the league’s hierarchy.
characterization 5: boasting a litany of hall-of-famers.
***
topic: emojis.
characterization 1: shorthand for a digital generation.
characterization 2: more versatile than words.
characterization 3: the latest frontier in language.
characterization 4: a form of self-expression.
characterization 5: quintessentially millennial.
characterization 6: reflective of a tech-centric world.
***
topic:
```
```
regular: illinois went against the census' population-loss prediction by getting more residents.
VBG: defying the census' prediction of population loss, illinois experienced growth.
***
regular: microsoft word’s high pricing increases the likelihood of competition.
VBG: extortionately priced, microsoft word is inviting competition.
***
regular:
```
```
source: badminton should be more popular in the US.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) games played with racquets are popular, (2) just look at tennis and ping pong, (3) but badminton underappreciated, (4) fun, fast-paced, competitive, (5) needs to be marketed more
text: the sporting arena is dominated by games that are played with racquets. tennis and ping pong, in particular, are immensely popular. somewhat curiously, however, badminton is absent from this pantheon. exciting, fast-paced, and competitive, it is an underappreciated pastime. all that it lacks is more effective marketing.
***
source: movies in theaters should be free.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) movies provide vital life lessons, (2) many venues charge admission, (3) those without much money
text: the lessons that movies impart are far from trivial. the vast catalogue of cinematic classics is replete with inspiring sagas of friendship, bravery, and tenacity. it is regrettable, then, that admission to theaters is not free. in their current form, the doors of this most vital of institutions are closed to those who lack the means to pay.
***
source:
```
```
in the private sector, { transparency } is vital to the business’s credibility. the { disclosure of information } can be the difference between success and failure.
***
the labor market is changing, with { remote work } now the norm. this { flexible employment } allows the individual to design their own schedule.
***
the { cubicle } is the locus of countless grievances. many complain that the { enclosed workspace } restricts their freedom of movement.
***
```
```
it would be natural to assume that americans, as a people whose ancestors { immigrated to this country }, would be sympathetic to those seeking to do likewise.
question: what does “do likewise” mean in the above context?
(a) make the same journey
(b) share in the promise of the american dream
(c) start anew in the land of opportunity
(d) make landfall on the united states
***
in the private sector, { transparency } is vital to the business’s credibility. this orientation can be the difference between success and failure.
question: what does “this orientation” mean in the above context?
(a) visible business practices
(b) candor with the public
(c) open, honest communication
(d) culture of accountability
```
```
example: suppose you are a teacher. further suppose you want to tell an accurate telling of history. then suppose a parent takes offense. they do so in the name of name of their kid. this happens a lot.
text: educators' responsibility to remain true to the historical record often clashes with the parent's desire to shelter their child from uncomfortable realities.
***
example: suppose you are a student at college. now suppose you have to buy textbooks. that is going to be worth hundreds of dollars. given how much you already spend on tuition, that is going to hard cost to bear.
text: the exorbitant cost of textbooks, which often reaches hundreds of dollars, imposes a sizable financial burden on the already-strapped college student.
```
```
<Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle>
***
<Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle>
```
```
accustomed to having its name uttered ______, harvard university is weathering a rare spell of reputational tumult
(a) in reverential tones
(b) with great affection
(c) in adulatory fashion
(d) in glowing terms
``` |
AnaRhisT/bert_sequence_cs_validation | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: bsd-3-clause
---
# hu-text-metrics
Metrics for Hungarian text evaluation.
## Features
* **unique_words**: Number of unique words in a sentence, not including stopwords.
* **grammar_error**: Number of grammar errors in a sentence. _(Unimplemented)_
## Usage
For example usages, see the `_example.py` file.
|
Andrey1989/bert-multilingual-finetuned-ner | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
tags:
- autotrain
- tabular
- classification
- tabular-classification
datasets:
- Alexei1/autotrain-data-imdb-sentiment-analysis
co2_eq_emissions:
emissions: 0.018564765189754893
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1530155186
- CO2 Emissions (in grams): 0.0186
## Validation Metrics
- Loss: 0.694
- Accuracy: 0.487
- Macro F1: 0.218
- Micro F1: 0.487
- Weighted F1: 0.319
- Macro Precision: 0.162
- Micro Precision: 0.487
- Weighted Precision: 0.237
- Macro Recall: 0.333
- Micro Recall: 0.487
- Weighted Recall: 0.487
## Usage
```python
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
data.columns = ["feat_" + str(col) for col in data.columns]
predictions = model.predict(data) # or model.predict_proba(data)
``` |
Andrey1989/mt5-small-finetuned-mlsum-es | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-roberta-base-TF-weight1-epoch5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-roberta-base-TF-weight1-epoch5
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Andrey1989/mt5-small-finetuned-mlsum-fr | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-roberta-base-TF-weight1-epoch10
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-roberta-base-TF-weight1-epoch10
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Andrija/M-bert-NER | [
"pytorch",
"bert",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"transformers",
"license:apache-2.0",
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 8 | null | ---
tags:
- spacy
- token-classification
widget:
- text: "Section 319 Cr.P.C. contemplates a situation where the evidence adduced by the prosecution for Respondent No.3-G. Sambiah on 20th June 1984"
- text: "In The High Court Of Kerala At Ernakulam\n\nCrl Mc No. 1622 of 2006()\n\n\n1. T.R.Ajayan, S/O. O.Raman,\n ... Petitioner\n\n Vs\n\n\n\n1. M.Ravindran,\n ... Respondent\n\n2. Mrs. Nirmala Dinesh, W/O. Dinesh,\n\n For Petitioner :Sri.A.Kumar\n\n For Respondent :Smt.M.K.Pushpalatha\n\nThe Hon'ble Mr. Justice P.R.Raman\nThe Hon'ble Mr. Justice V.K.Mohanan\n\n Dated :07/01/2008\n\n O R D E R\n"
language:
- en
license: mit
model-index:
- name: en_legal_ner_trf
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
type: Named Entity Recognition
name: InLegalNER
split: Test
metrics:
- type: F1-Score
value: 91.076
name: Test F1-Score
---
# To Update
[AUTHORS] "[PAPER NAME]". [PAPER DETAILS] [PAPER LINK]
---
Indian Legal Named Entity Recognition(NER): Identifying relevant named entities in an Indian legal judgement using legal NER trained on [spacy](https://github.com/explosion/spaCy).
### Scores
| Type | Score |
| --- | --- |
| **F1-Score** | **91.076** |
| `Precision` | 91.979 |
| `Recall` | 90.19 |
| Feature | Description |
| --- | --- |
| **Name** | `en_legal_ner_trf` |
| **Version** | `3.2.0` |
| **spaCy** | `>=3.2.2,<3.3.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [InLegalNER Train Data](https://storage.googleapis.com/indianlegalbert/OPEN_SOURCED_FILES/NER/NER_TRAIN.zip) [GitHub](https://github.com/Legal-NLP-EkStep/legal_NER)|
| **License** | `MIT` |
| **Author** | [Aman Tiwari](https://www.linkedin.com/in/amant555/) |
## Load Pretrained Model
Install the model using pip
```sh
pip install https://huggingface.co/opennyaiorg/en_legal_ner_trf/resolve/main/en_legal_ner_trf-any-py3-none-any.whl
```
Using pretrained NER model
```python
# Using spacy.load().
import spacy
nlp = spacy.load("en_legal_ner_trf")
text = "Section 319 Cr.P.C. contemplates a situation where the evidence adduced by the prosecution for Respondent No.3-G. Sambiah on 20th June 1984"
doc = nlp(text)
# Print indentified entites
for ent in doc.ents:
print(ent,ent.label_)
##OUTPUT
#Section 319 PROVISION
#Cr.P.C. STATUTE
#G. Sambiah RESPONDENT
#20th June 1984 DATE
```
### Label Scheme
<details>
<summary>View label scheme (14 labels for 1 components)</summary>
| ENTITY | BELONGS TO |
| --- | --- |
| `LAWYER` | PREAMBLE |
| `COURT` | PREAMBLE, JUDGEMENT |
| `JUDGE` | PREAMBLE, JUDGEMENT |
| `PETITIONER` | PREAMBLE, JUDGEMENT |
| `RESPONDENT` | PREAMBLE, JUDGEMENT |
| `CASE_NUMBER` | JUDGEMENT |
| `GPE` | JUDGEMENT |
| `DATE` | JUDGEMENT |
| `ORG` | JUDGEMENT |
| `STATUTE` | JUDGEMENT |
| `WITNESS` | JUDGEMENT |
| `PRECEDENT` | JUDGEMENT |
| `PROVISION` | JUDGEMENT |
| `OTHER_PERSON` | JUDGEMENT |
</details>
## Author - Publication
```
[CITATION DETAILS]
``` |
Andrija/RobertaFastBPE | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 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: 194.40 +/- 31.46
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/SR_rule_based_only_classfn_epochs_1_shard_1 | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 6 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 2166 with parameters:
```
{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 216,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 1e-06
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 2166,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
AnonymousSub/SR_rule_based_roberta_bert_triplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 2 | null | ---
library_name: fastai
tags:
- image-classification
---
# 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 using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
AnonymousSub/SR_rule_based_roberta_bert_triplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 2 | null | ---
language:
- en
library_name: nemo
datasets:
- librispeech_asr
- fisher_corpus
- Switchboard-1
- WSJ-0
- WSJ-1
- National-Singapore-Corpus-Part-1
- National-Singapore-Corpus-Part-6
- vctk
- VoxPopuli-(EN)
- Europarl-ASR-(EN)
- Multilingual-LibriSpeech-(2000-hours)
- mozilla-foundation/common_voice_8_0
- MLCommons/peoples_speech
thumbnail: null
tags:
- automatic-speech-recognition
- speech
- audio
- Transducer
- Conformer
- Transformer
- pytorch
- NeMo
- hf-asr-leaderboard
license: cc-by-4.0
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
model-index:
- name: stt_en_conformer_transducer_large
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (clean)
type: librispeech_asr
config: clean
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 1.7
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: LibriSpeech (other)
type: librispeech_asr
config: other
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 3.7
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: Multilingual LibriSpeech
type: facebook/multilingual_librispeech
config: english
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 5.8
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: Mozilla Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
config: en
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 7.8
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: Wall Street Journal 92
type: wsj_0
args:
language: en
metrics:
- name: Test WER
type: wer
value: 1.5
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: Wall Street Journal 93
type: wsj_1
args:
language: en
metrics:
- name: Test WER
type: wer
value: 2.1
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: National Singapore Corpus
type: nsc_part_1
args:
language: en
metrics:
- name: Test WER
type: wer
value: 5.9
---
# NVIDIA Conformer-Transducer Large (en-US)
<style>
img {
display: inline;
}
</style>
| [](#model-architecture)
| [](#model-architecture)
| [](#datasets)
This model transcribes speech in lower case English alphabet along with spaces and apostrophes.
It is a large version of Conformer-Transducer (around 120M parameters) model.
See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-transducer) for complete architecture details.
## NVIDIA NeMo: Training
To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version.
```
pip install nemo_toolkit['all']
```
## How to Use this Model
The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
### Automatically instantiate the model
```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_en_conformer_transducer_large")
```
### Transcribing using Python
First, let's get a sample
```
wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
```
Then simply do:
```
asr_model.transcribe(['2086-149220-0033.wav'])
```
### Transcribing many audio files
```shell
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py
pretrained_name="nvidia/stt_en_conformer_transducer_large"
audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
```
### Input
This model accepts 16000 KHz Mono-channel Audio (wav files) as input.
### Output
This model provides transcribed speech as a string for a given audio sample.
## Model Architecture
Conformer-Transducer model is an autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses Transducer loss/decoding instead of CTC Loss. You may find more info on the detail of this model here: [Conformer-Transducer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html).
## Training
The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_transducer_bpe.yaml).
The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
### Datasets
All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of several thousand hours of English speech:
- Librispeech 960 hours of English speech
- Fisher Corpus
- Switchboard-1 Dataset
- WSJ-0 and WSJ-1
- National Speech Corpus (Part 1, Part 6)
- VCTK
- VoxPopuli (EN)
- Europarl-ASR (EN)
- Multilingual Librispeech (MLS EN) - 2,000 hrs subset
- Mozilla Common Voice (v8.0)
- People's Speech - 12,000 hrs subset
Note: older versions of the model may have trained on smaller set of datasets.
## Performance
The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.
| Version | Tokenizer | Vocabulary Size | LS test-other | LS test-clean | WSJ Eval92 | WSJ Dev93 | NSC Part 1 | MLS Test | MCV Test 6.1 | MCV Test 8.0 | Train Dataset |
|---------|-----------------------|-----------------|---------------|---------------|------------|-----------|-----|-------|------|----|------|
| 1.10.0 | SentencePiece Unigram | 1024 | 3.7 | 1.7 | 1.5 | 2.1 | 5.9 | 5.8 | 6.5 | 7.8 | NeMo ASRSET 3.0 |
## Limitations
Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
## NVIDIA Riva: Deployment
[NVIDIA Riva](https://developer.nvidia.com/riva), is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded.
Additionally, Riva provides:
* World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours
* Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization
* Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support
Although this model isn’t supported yet by Riva, the [list of supported models is here](https://huggingface.co/models?other=Riva).
Check out [Riva live demo](https://developer.nvidia.com/riva#demos).
## References
[1] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)
[2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)
[3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
## Licence
License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license. |
AnonymousSub/SR_rule_based_roberta_hier_triplet_epochs_1_shard_1_wikiqa_copy | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 2 | null | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: VietAI-NLP-ITN
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. -->
# VietAI-NLP-ITN
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4378
- Bleu: 81.8571
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:-----:|:------:|:---------------:|:-------:|
| 0.6529 | 1.0 | 31250 | 0.5660 | 78.7315 |
| 0.5125 | 2.0 | 62500 | 0.4770 | 81.3979 |
| 0.4798 | 3.0 | 93750 | 0.4554 | 81.6720 |
| 0.4568 | 4.0 | 125000 | 0.4435 | 81.7753 |
| 0.4387 | 5.0 | 156250 | 0.4378 | 81.8571 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
AnonymousSub/SR_rule_based_roberta_twostagetriplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 4 | 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: 169.43 +/- 77.42
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/SR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 4 | null | Access to model FrankKomes/DialoGPT-medium-cherry is restricted and you are not in the authorized list. Visit https://huggingface.co/FrankKomes/DialoGPT-medium-cherry to ask for access. |
AnonymousSub/SR_rule_based_twostage_quadruplet_epochs_1_shard_1 | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: new_classifer_epoch10
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. -->
# new_classifer_epoch10
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0837
- Accuracy: 0.9867
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0524 | 1.0 | 4248 | 0.0628 | 0.9790 |
| 0.0251 | 2.0 | 8496 | 0.0496 | 0.9848 |
| 0.0153 | 3.0 | 12744 | 0.0857 | 0.9837 |
| 0.0049 | 4.0 | 16992 | 0.1030 | 0.9849 |
| 0.0038 | 5.0 | 21240 | 0.0837 | 0.9867 |
| 0.003 | 6.0 | 25488 | 0.1165 | 0.9856 |
| 0.0026 | 7.0 | 29736 | 0.1143 | 0.9853 |
| 0.0004 | 8.0 | 33984 | 0.1475 | 0.9856 |
| 0.0004 | 9.0 | 38232 | 0.1328 | 0.9861 |
| 0.0 | 10.0 | 42480 | 0.1349 | 0.9862 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
AnonymousSub/bert-base-uncased_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 3 | null | ---
license: unlicense
language: "en"
widget:
- text: "FileZilla Project FileZilla Client 3.5.1."
- text: "Google Chrome 56.0.2924.87."
---
|
AnonymousSub/bert_mean_diff_epochs_1_shard_1 | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 6 | null | ---
language:
- ko
tags:
- bart
license: mit
---
# koBART Review Summarization
## finetuning BASE
https://huggingface.co/gogamza/kobart-summarization
# dataset
https://github.com/dnrso/dnrso.github.io
# Demo Space
https://huggingface.co/spaces/dnrso/koBART_Sum_Review_finetuning |
AnonymousSub/bert_triplet_epochs_1_shard_1 | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 2 | null | ---
license: afl-3.0
---
https://huggingface.co/julien-c/DPRNNTasNet-ks16_WHAM_sepclean
|
AnonymousSub/declutr-techqa | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 5 | 2022-09-23T04:40:11Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
metrics:
- rouge
model-index:
- name: t5-base-finetuned-eli-5
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: eli5
type: eli5
config: LFQA_reddit
split: train_eli5
args: LFQA_reddit
metrics:
- name: Rouge1
type: rouge
value: 13.4
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetuned-eli-5
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the eli5 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4557
- Rouge1: 13.4
- Rouge2: 1.9415
- Rougel: 10.4671
- Rougelsum: 12.0693
- Gen Len: 18.9529
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:---------:|:-------:|
| 3.6754 | 1.0 | 8520 | 3.4557 | 13.4 | 1.9415 | 10.4671 | 12.0693 | 18.9529 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
AnonymousSub/dummy_1 | [
"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_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 33 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: vit_classification_huggingface
results:
- task:
name: Animal-10 Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.980894148349762
---
# vit_classification_huggingface
Animal-10 dataset classification using Vision Transformer with Hugging Face.
## Example Images
#### cane
#### cavallo
#### elefante
#### farfalla
#### gallina
#### gatto
#### mucca
#### pecora
#### ragno
#### scoiattolo
 |
AnonymousSub/dummy_2 | [
"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_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 39 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.6976744186046512
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4068
- F1: 0.6977
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9585 | 1.0 | 99 | 0.5474 | 0.5651 |
| 0.4522 | 2.0 | 198 | 0.3921 | 0.6903 |
| 0.3243 | 3.0 | 297 | 0.4068 | 0.6977 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AnonymousSub/rule_based_bert_hier_diff_equal_wts_epochs_1_shard_10 | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-korean-g
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-korean-g
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9226
- Cer: 0.1638
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.8333 | 3.25 | 500 | 3.4624 | 0.9560 |
| 1.243 | 6.49 | 1000 | 1.0049 | 0.2488 |
| 0.3657 | 9.74 | 1500 | 0.8749 | 0.2087 |
| 0.2104 | 12.99 | 2000 | 0.8799 | 0.1909 |
| 0.1508 | 16.23 | 2500 | 0.9321 | 0.1845 |
| 0.1245 | 19.48 | 3000 | 0.8778 | 0.1744 |
| 0.1048 | 22.73 | 3500 | 0.9793 | 0.1808 |
| 0.0922 | 25.97 | 4000 | 0.9464 | 0.1697 |
| 0.0801 | 29.22 | 4500 | 0.9226 | 0.1638 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.13.0
|
AnonymousSub/rule_based_bert_mean_diff_epochs_1_shard_10 | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 4 | null | Indonesian BERT Base Sentiment Classifier is a sentiment-text-classification model. The model was originally the pre-trained [IndoBERT Base Model (phase1 - uncased)](https://huggingface.co/indobenchmark/indobert-base-p1) model using dataset crawling from social media Youtube with topic about "Pemakaian Behel/Kawat Gigi"
## How to Use
### As Text Classifier
```python
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification
pretrained= "liandarizkia/SA01-IndoBert"
model = AutoModelForSequenceClassification.from_pretrained(pretrained)
tokenizer = AutoTokenizer.from_pretrained(pretrained)
sentiment_analysis = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
label_index = {'LABEL_0': 'negative', 'LABEL_1': 'positive', 'LABEL_2': 'neutral'}
text = """Aku baru sebulan udah pengen lepas rasanya. Udah gak peduli uang yang keluar sayang. Pokoknya gak nyaman, setiap hari sedih terus. Akhirnya aku cerita ke dokterku kalau aku dah gak kuat aku bilang kalau bakal bertahan 2 atau 3 bulan dari pemasangan behel. Setelah itu aku minta buat beneran lepas aja. Pokoknya jangan ragu buat cerita ke dokter"""
result = sentiment_analysis(text)
status = label_index[result[0]['label']]
score = result[0]['score']
print(f'Text: {text} | Label : {status} ({score * 100:.3f}%)')
``` |
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb1
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. -->
# distilbert-base-uncased-finetuned-imdb1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2888
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.023 | 1.0 | 313 | 2.3080 |
| 2.1325 | 2.0 | 626 | 2.2527 |
| 2.2656 | 3.0 | 939 | 2.2888 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.10.0
- Datasets 2.5.1
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_roberta_hier_triplet_0.1_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: asr_test
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. -->
# asr_test
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
AnonymousSub/rule_based_roberta_hier_triplet_epochs_1_shard_1_wikiqa_copy | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4566
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 4.7239 | 1.0 | 41495 | 3.9360 |
| 3.7732 | 2.0 | 82990 | 3.5599 |
| 3.4792 | 3.0 | 124485 | 3.4566 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_roberta_only_classfn_twostage_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 10 | null |
---
language: en
---
<p align="center">
<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: recognition
https://github.com/mindee/doctr
### Example usage:
```python
>>> from doctr.io import DocumentFile
>>> from doctr.models import ocr_predictor, from_hub
>>> img = DocumentFile.from_images(['<image_path>'])
>>> # Load your model from the hub
>>> model = from_hub('mindee/my-model')
>>> # Pass it to the predictor
>>> # If your model is a recognition model:
>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
>>> reco_arch=model,
>>> pretrained=True)
>>> # If your model is a detection model:
>>> predictor = ocr_predictor(det_arch=model,
>>> reco_arch='crnn_mobilenet_v3_small',
>>> pretrained=True)
>>> # Get your predictions
>>> res = predictor(img)
```
|
AnonymousSub/rule_based_roberta_only_classfn_twostage_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 2 | null |
---
language: en
---
<p align="center">
<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: classification
https://github.com/mindee/doctr
### Example usage:
```python
>>> from doctr.io import DocumentFile
>>> from doctr.models import ocr_predictor, from_hub
>>> img = DocumentFile.from_images(['<image_path>'])
>>> # Load your model from the hub
>>> model = from_hub('mindee/my-model')
>>> # Pass it to the predictor
>>> # If your model is a recognition model:
>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
>>> reco_arch=model,
>>> pretrained=True)
>>> # If your model is a detection model:
>>> predictor = ocr_predictor(det_arch=model,
>>> reco_arch='crnn_mobilenet_v3_small',
>>> pretrained=True)
>>> # Get your predictions
>>> res = predictor(img)
```
|
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 24 | null |
---
language: en
---
<p align="center">
<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: recognition
https://github.com/mindee/doctr
### Example usage:
```python
>>> from doctr.io import DocumentFile
>>> from doctr.models import ocr_predictor, from_hub
>>> img = DocumentFile.from_images(['<image_path>'])
>>> # Load your model from the hub
>>> model = from_hub('mindee/my-model')
>>> # Pass it to the predictor
>>> # If your model is a recognition model:
>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
>>> reco_arch=model,
>>> pretrained=True)
>>> # If your model is a detection model:
>>> predictor = ocr_predictor(det_arch=model,
>>> reco_arch='crnn_mobilenet_v3_small',
>>> pretrained=True)
>>> # Get your predictions
>>> res = predictor(img)
```
|
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 7 | null | language:
- en
tags:
- translation
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
- sacrebleu |
AnonymousSub/rule_based_roberta_twostagetriplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 1 | null | ---
language:
- en
- it
- multilingual
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ccmatrix
model-index:
- name: t5-small-finetuned-en-to-it
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-en-to-it
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the ccmatrix dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0188
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.5524 | 1.0 | 750 | 2.2315 |
| 2.4839 | 2.0 | 1500 | 2.1932 |
| 2.4654 | 3.0 | 2250 | 2.1637 |
| 2.4001 | 4.0 | 3000 | 2.1352 |
| 2.3966 | 5.0 | 3750 | 2.1122 |
| 2.3537 | 6.0 | 4500 | 2.0921 |
| 2.3427 | 7.0 | 5250 | 2.0746 |
| 2.316 | 8.0 | 6000 | 2.0614 |
| 2.301 | 9.0 | 6750 | 2.0488 |
| 2.2813 | 10.0 | 7500 | 2.0403 |
| 2.2691 | 11.0 | 8250 | 2.0325 |
| 2.2561 | 12.0 | 9000 | 2.0265 |
| 2.258 | 13.0 | 9750 | 2.0217 |
| 2.2447 | 14.0 | 10500 | 2.0199 |
| 2.2432 | 15.0 | 11250 | 2.0188 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_twostage_quadruplet_epochs_1_shard_1 | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: convnext-tiny-224_finetuned
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. -->
# convnext-tiny-224_finetuned
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0895
- Precision: 0.9807
- Recall: 0.9608
- F1: 0.9702
- Accuracy: 0.9776
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 46 | 0.3080 | 0.9096 | 0.6852 | 0.7206 | 0.8365 |
| No log | 2.0 | 92 | 0.1644 | 0.9660 | 0.9176 | 0.9386 | 0.9551 |
| No log | 3.0 | 138 | 0.0974 | 0.9742 | 0.9586 | 0.9661 | 0.9744 |
| No log | 4.0 | 184 | 0.0795 | 0.9829 | 0.9670 | 0.9746 | 0.9808 |
| No log | 5.0 | 230 | 0.0838 | 0.9807 | 0.9608 | 0.9702 | 0.9776 |
| No log | 6.0 | 276 | 0.0838 | 0.9807 | 0.9608 | 0.9702 | 0.9776 |
| No log | 7.0 | 322 | 0.0803 | 0.9829 | 0.9670 | 0.9746 | 0.9808 |
| No log | 8.0 | 368 | 0.0869 | 0.9807 | 0.9608 | 0.9702 | 0.9776 |
| No log | 9.0 | 414 | 0.0897 | 0.9807 | 0.9608 | 0.9702 | 0.9776 |
| No log | 10.0 | 460 | 0.0895 | 0.9807 | 0.9608 | 0.9702 | 0.9776 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_twostagetriplet_epochs_1_shard_1 | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 10 | 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: 7.54 +/- 2.74
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="vivpavlov/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
AnonymousSub/specter-bert-model | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
model-index:
- name: bert-base-Massive-intent
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: massive
type: massive
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.8858829316281358
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-Massive-intent
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6707
- Accuracy: 0.8859
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6844 | 1.0 | 720 | 0.7190 | 0.8387 |
| 0.4713 | 2.0 | 1440 | 0.5449 | 0.8726 |
| 0.2459 | 3.0 | 2160 | 0.5893 | 0.8790 |
| 0.1469 | 4.0 | 2880 | 0.6631 | 0.8795 |
| 0.0874 | 5.0 | 3600 | 0.6707 | 0.8859 |
| 0.0507 | 6.0 | 4320 | 0.7189 | 0.8844 |
| 0.0344 | 7.0 | 5040 | 0.7480 | 0.8854 |
| 0.0225 | 8.0 | 5760 | 0.7956 | 0.8844 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
AnonymousSub/specter-bert-model_copy | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 2 | null | ---
license: apache-2.0
widget:
- text: 横浜国立大学は日本の[MASK]県にある。
---
This is RoBERTa model pretrained on texts in the Japanese language.
3.45GB wikipedia text
trained 1.65M step
use the sentencepiece tokenizer.
If you want to fine-tune model. Please use
```python
from transformers import BertTokenizer, RobertaModel
BertTokenizer.from_pretrained('')
RoBERTModel.from_pretrained('')
```
The accuracy in JGLUE-marc_ja-v1.0 binary sentiment classification 95.4%
Contribute by Yokohama Nationaly University Mori Lab
@article{liu2019roberta,
title={Roberta: A robustly optimized bert pretraining approach},
author={Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov,
Veselin},
journal={arXiv preprint arXiv:1907.11692},
year={2019}
} |
AnonymousSub/specter-bert-model_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 1 | null | ---
tags:
- generated_from_trainer
model-index:
- name: resultsb
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. -->
# resultsb
This model is a fine-tuned version of [bhumikak/resultsa](https://huggingface.co/bhumikak/resultsa) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8957
- Rouge2 Precision: 0.2127
- Rouge2 Recall: 0.2605
- Rouge2 Fmeasure: 0.2167
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 50
- label_smoothing_factor: 0.1
### Training results
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
ArBert/albert-base-v2-finetuned-ner-gmm | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"AlbertForTokenClassification"
],
"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_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-cased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-imdb
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3367
- Accuracy: 0.625
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.687 | 1.0 | 20 | 1.4339 | 0.625 |
| 1.4117 | 2.0 | 40 | 1.3367 | 0.625 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
ArcQ/gpt-experiments | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | 2022-09-23T19:36:22Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
model-index:
- name: bert-tiny-Massive-intent-KD-distilBERT
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: massive
type: massive
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.8396458435809149
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-tiny-Massive-intent-KD-distilBERT
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6612
- Accuracy: 0.8396
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 10.9795 | 1.0 | 720 | 9.3236 | 0.2917 |
| 9.4239 | 2.0 | 1440 | 7.9792 | 0.4092 |
| 8.2632 | 3.0 | 2160 | 6.9824 | 0.4811 |
| 7.3425 | 4.0 | 2880 | 6.1545 | 0.5514 |
| 6.56 | 5.0 | 3600 | 5.4829 | 0.6060 |
| 5.9032 | 6.0 | 4320 | 4.8994 | 0.6463 |
| 5.3078 | 7.0 | 5040 | 4.4129 | 0.6911 |
| 4.819 | 8.0 | 5760 | 4.0152 | 0.7073 |
| 4.3866 | 9.0 | 6480 | 3.6734 | 0.7324 |
| 3.9954 | 10.0 | 7200 | 3.3729 | 0.7516 |
| 3.6764 | 11.0 | 7920 | 3.1251 | 0.7600 |
| 3.3712 | 12.0 | 8640 | 2.9077 | 0.7752 |
| 3.1037 | 13.0 | 9360 | 2.7361 | 0.7787 |
| 2.8617 | 14.0 | 10080 | 2.5791 | 0.7860 |
| 2.6667 | 15.0 | 10800 | 2.4383 | 0.7944 |
| 2.476 | 16.0 | 11520 | 2.3301 | 0.7944 |
| 2.3203 | 17.0 | 12240 | 2.2099 | 0.8052 |
| 2.1698 | 18.0 | 12960 | 2.1351 | 0.8101 |
| 2.0563 | 19.0 | 13680 | 2.0554 | 0.8111 |
| 1.9294 | 20.0 | 14400 | 2.0100 | 0.8190 |
| 1.8304 | 21.0 | 15120 | 1.9566 | 0.8210 |
| 1.7315 | 22.0 | 15840 | 1.9076 | 0.8224 |
| 1.6587 | 23.0 | 16560 | 1.8511 | 0.8283 |
| 1.5876 | 24.0 | 17280 | 1.8230 | 0.8298 |
| 1.5173 | 25.0 | 18000 | 1.8002 | 0.8259 |
| 1.4676 | 26.0 | 18720 | 1.7667 | 0.8278 |
| 1.3956 | 27.0 | 19440 | 1.7512 | 0.8313 |
| 1.3436 | 28.0 | 20160 | 1.7233 | 0.8298 |
| 1.3031 | 29.0 | 20880 | 1.6802 | 0.8318 |
| 1.2584 | 30.0 | 21600 | 1.6768 | 0.8328 |
| 1.2233 | 31.0 | 22320 | 1.6612 | 0.8396 |
| 1.1884 | 32.0 | 23040 | 1.6608 | 0.8352 |
| 1.1374 | 33.0 | 23760 | 1.6195 | 0.8387 |
| 1.1299 | 34.0 | 24480 | 1.5969 | 0.8377 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Arcanos/1 | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | Access to model thet-system/en_core_sci_md is restricted and you are not in the authorized list. Visit https://huggingface.co/thet-system/en_core_sci_md to ask for access. |
Arcktosh/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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 8 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: stbl_clinical_bert_ft_rs4
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. -->
# stbl_clinical_bert_ft_rs4
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1088
- F1: 0.9076
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2994 | 1.0 | 101 | 0.0977 | 0.8416 |
| 0.0639 | 2.0 | 202 | 0.0846 | 0.8689 |
| 0.0318 | 3.0 | 303 | 0.0781 | 0.8879 |
| 0.0173 | 4.0 | 404 | 0.0770 | 0.8934 |
| 0.0099 | 5.0 | 505 | 0.0905 | 0.9021 |
| 0.005 | 6.0 | 606 | 0.0963 | 0.9020 |
| 0.0031 | 7.0 | 707 | 0.1024 | 0.9095 |
| 0.002 | 8.0 | 808 | 0.1063 | 0.9057 |
| 0.0017 | 9.0 | 909 | 0.1072 | 0.9076 |
| 0.0014 | 10.0 | 1010 | 0.1103 | 0.9089 |
| 0.0013 | 11.0 | 1111 | 0.1093 | 0.9087 |
| 0.0008 | 12.0 | 1212 | 0.1088 | 0.9076 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Ashim/dga-transformer | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/rossimiano/1664256351634/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: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1550158420988153856/OUoCVt_b_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ross Massimiano, DVM</div>
<div style="text-align: center; font-size: 14px;">@rossimiano</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Ross Massimiano, DVM.
| Data | Ross Massimiano, DVM |
| --- | --- |
| Tweets downloaded | 1324 |
| Retweets | 203 |
| Short tweets | 130 |
| Tweets kept | 991 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/312h1q2v/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @rossimiano's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1vljawam) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1vljawam/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/rossimiano')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Ayham/roberta_gpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"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_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 31 | null | ---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: rlt_2409_1450
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. -->
# rlt_2409_1450
This model is a fine-tuned version of [svalabs/gbert-large-zeroshot-nli](https://huggingface.co/svalabs/gbert-large-zeroshot-nli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0518
- F1: 0.9826
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.99 | 36 | 0.5165 | 0.8542 |
| No log | 1.99 | 72 | 0.1459 | 0.9599 |
| No log | 2.99 | 108 | 0.0733 | 0.9882 |
| No log | 3.99 | 144 | 0.1385 | 0.9502 |
| No log | 4.99 | 180 | 0.0948 | 0.9806 |
| No log | 5.99 | 216 | 0.0699 | 0.9822 |
| No log | 6.99 | 252 | 0.0582 | 0.9859 |
| No log | 7.99 | 288 | 0.0340 | 0.9933 |
| No log | 8.99 | 324 | 0.0475 | 0.9826 |
| No log | 9.99 | 360 | 0.0518 | 0.9826 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Ayham/xlnet_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"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_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 13 | null | ---
license: mit
---
### paolo bonolis on Stable Diffusion
This is the `<paolo-bonolis>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
Ayham/xlnet_gpt_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"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_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 11 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: BERT-tiny-emotion-intent
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.91
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT-tiny-emotion-intent
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3620
- Accuracy: 0.91
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2603 | 1.0 | 1000 | 0.7766 | 0.7815 |
| 0.5919 | 2.0 | 2000 | 0.4117 | 0.884 |
| 0.367 | 3.0 | 3000 | 0.3188 | 0.8995 |
| 0.2848 | 4.0 | 4000 | 0.2928 | 0.8985 |
| 0.2395 | 5.0 | 5000 | 0.2906 | 0.898 |
| 0.2094 | 6.0 | 6000 | 0.2887 | 0.907 |
| 0.1884 | 7.0 | 7000 | 0.2831 | 0.9065 |
| 0.1603 | 8.0 | 8000 | 0.3044 | 0.9065 |
| 0.1519 | 9.0 | 9000 | 0.3124 | 0.9095 |
| 0.1291 | 10.0 | 10000 | 0.3256 | 0.9065 |
| 0.1179 | 11.0 | 11000 | 0.3651 | 0.9035 |
| 0.1091 | 12.0 | 12000 | 0.3620 | 0.91 |
| 0.0977 | 13.0 | 13000 | 0.3992 | 0.907 |
| 0.0914 | 14.0 | 14000 | 0.4285 | 0.908 |
| 0.0876 | 15.0 | 15000 | 0.4268 | 0.9055 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Ayran/DialoGPT-small-gandalf | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 11 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
AyushPJ/ai-club-inductions-21-nlp-ALBERT | [
"pytorch",
"albert",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"AlbertForQuestionAnswering"
],
"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_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 8 | null | This is a test model, so the results are not really good.
The team is continuing to grow.
If you like it, Click like above to support the author. 🤗 |
Azaghast/GPT2-SCP-Descriptions | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_10_0
model-index:
- name: wav2vec2-large-xls-r-300m-j-phoneme-common-test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-j-phoneme-common-test
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_10_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Wer: 0.0001
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.1488 | 7.14 | 2000 | 0.0788 | 0.0919 |
| 0.0308 | 14.28 | 4000 | 0.0155 | 0.0271 |
| 0.0121 | 21.43 | 6000 | 0.0070 | 0.0103 |
| 0.0067 | 28.57 | 8000 | 0.0059 | 0.0067 |
| 0.0025 | 35.71 | 10000 | 0.0143 | 0.0180 |
| 0.0001 | 42.85 | 12000 | 0.0000 | 0.0001 |
| 0.0 | 50.0 | 14000 | 0.0000 | 0.0001 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.10.0+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
BSC-LT/roberta-base-bne-sqac | [
"pytorch",
"roberta",
"question-answering",
"es",
"dataset:BSC-TeMU/SQAC",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"qa",
"question answering",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 10 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: sst2
split: train
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.9036697247706422
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-sst2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3735
- Accuracy: 0.9037
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.243 | 1.0 | 4210 | 0.3735 | 0.9037 |
| 0.1557 | 2.0 | 8420 | 0.3907 | 0.8922 |
| 0.1248 | 3.0 | 12630 | 0.3690 | 0.8945 |
| 0.1017 | 4.0 | 16840 | 0.5466 | 0.8830 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Babelscape/rebel-large | [
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"en",
"dataset:Babelscape/rebel-dataset",
"transformers",
"seq2seq",
"relation-extraction",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"has_space"
] | text2text-generation | {
"architectures": [
"BartForConditionalGeneration"
],
"model_type": "bart",
"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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 9,458 | null | ---
license: bigscience-bloom-rail-1.0
---
Uses the Waifu Diffusion model as a base, linked here: https://huggingface.co/hakurei/waifu-diffusion
Custom Dreambooth model based off of the likeness of Emilia from Re:Zero. Dataset was 16 training images, and 11 regularization images. Trained for 3000 steps.
To use the model, simply insert the name 'Emilia' into your prompts. The class token used was 'white_hair_girl_violet_eyes'.
Append the class token after Emilia for stronger results.
EX: "A photo of Emilia white_hair_girl_violet_eyes" |
Babelscape/wikineural-multilingual-ner | [
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"de",
"en",
"es",
"fr",
"it",
"nl",
"pl",
"pt",
"ru",
"multilingual",
"dataset:Babelscape/wikineural",
"transformers",
"named-entity-recognition",
"sequence-tagger-model",
"license:cc-by-nc-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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 41,608 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 19.80 +/- 13.74
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
Badr/model1 | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: mit
---
### Thorneworks on Stable Diffusion
This is the `<Thorneworks>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
Bagus/SER-LSSED | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | 2022-09-24T18:26:10Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: shopinspo_demo
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.5267857313156128
---
# shopinspo_demo
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### womens dress
#### womens pants
#### womens shorts
#### womens skirt
#### womens top
 |
Bagus/ser-japanese | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_8_0
model-index:
- name: XLSR_Fine_Tuned_Urdu_V2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLSR_Fine_Tuned_Urdu_V2
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_8_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8023
- Wer: 0.4382
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.424 | 3.25 | 1000 | 2.9777 | 1.0 |
| 1.4315 | 6.49 | 2000 | 0.8493 | 0.5896 |
| 0.6938 | 9.74 | 3000 | 0.7438 | 0.4978 |
| 0.5129 | 12.99 | 4000 | 0.7480 | 0.4785 |
| 0.4133 | 16.23 | 5000 | 0.7568 | 0.4600 |
| 0.3496 | 19.48 | 6000 | 0.7387 | 0.4471 |
| 0.3133 | 22.73 | 7000 | 0.7655 | 0.4426 |
| 0.2767 | 25.97 | 8000 | 0.8081 | 0.4530 |
| 0.2581 | 29.22 | 9000 | 0.8023 | 0.4382 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Bagus/wav2vec2-large-xlsr-bahasa-indonesia | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"el",
"dataset:common_voice_id_6.1",
"transformers",
"audio",
"speech",
"bahasa-indonesia",
"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_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 12 | null | Jiangstyle on Stable Diffusion
This is the <Jiangstyle> concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the Stable Conceptualizer notebook. You can also train your own concepts and load them into the concept libraries using this notebook.
Here is the new concept you will be able to use as a style: |
Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition | [
"pytorch",
"tensorboard",
"wav2vec2",
"el",
"dataset:aesdd",
"transformers",
"audio",
"audio-classification",
"speech",
"license:apache-2.0"
] | audio-classification | {
"architectures": [
"Wav2Vec2ForSpeechClassification"
],
"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_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 21 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
model-index:
- name: bert-tiny-Massive-intent-KD-BERT_and_distilBERT
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: massive
type: massive
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.8470241023118544
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-tiny-Massive-intent-KD-BERT_and_distilBERT
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3729
- Accuracy: 0.8470
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 15.1159 | 1.0 | 720 | 12.8257 | 0.2253 |
| 12.9949 | 2.0 | 1440 | 10.9891 | 0.4304 |
| 11.3865 | 3.0 | 2160 | 9.5622 | 0.5032 |
| 10.0553 | 4.0 | 2880 | 8.3700 | 0.5539 |
| 8.9431 | 5.0 | 3600 | 7.4127 | 0.6104 |
| 8.0135 | 6.0 | 4320 | 6.6185 | 0.6286 |
| 7.1987 | 7.0 | 5040 | 5.9517 | 0.6818 |
| 6.5168 | 8.0 | 5760 | 5.3879 | 0.7118 |
| 5.9352 | 9.0 | 6480 | 4.9426 | 0.7275 |
| 5.4299 | 10.0 | 7200 | 4.5637 | 0.7413 |
| 5.0017 | 11.0 | 7920 | 4.2379 | 0.7585 |
| 4.5951 | 12.0 | 8640 | 3.9699 | 0.7678 |
| 4.2849 | 13.0 | 9360 | 3.7416 | 0.7737 |
| 3.991 | 14.0 | 10080 | 3.5502 | 0.7865 |
| 3.7455 | 15.0 | 10800 | 3.4090 | 0.7900 |
| 3.5315 | 16.0 | 11520 | 3.3053 | 0.7914 |
| 3.345 | 17.0 | 12240 | 3.1670 | 0.8003 |
| 3.1767 | 18.0 | 12960 | 3.0739 | 0.8013 |
| 3.0322 | 19.0 | 13680 | 2.9927 | 0.8047 |
| 2.8864 | 20.0 | 14400 | 2.9366 | 0.8037 |
| 2.7728 | 21.0 | 15120 | 2.8666 | 0.8091 |
| 2.6732 | 22.0 | 15840 | 2.8146 | 0.8126 |
| 2.5726 | 23.0 | 16560 | 2.7588 | 0.8195 |
| 2.493 | 24.0 | 17280 | 2.7319 | 0.8273 |
| 2.4183 | 25.0 | 18000 | 2.6847 | 0.8249 |
| 2.3526 | 26.0 | 18720 | 2.6317 | 0.8323 |
| 2.2709 | 27.0 | 19440 | 2.6071 | 0.8288 |
| 2.2125 | 28.0 | 20160 | 2.5982 | 0.8323 |
| 2.1556 | 29.0 | 20880 | 2.5546 | 0.8337 |
| 2.1042 | 30.0 | 21600 | 2.5278 | 0.8318 |
| 2.054 | 31.0 | 22320 | 2.5005 | 0.8411 |
| 2.0154 | 32.0 | 23040 | 2.4891 | 0.8347 |
| 1.9785 | 33.0 | 23760 | 2.4633 | 0.8367 |
| 1.9521 | 34.0 | 24480 | 2.4451 | 0.8421 |
| 1.9247 | 35.0 | 25200 | 2.4370 | 0.8416 |
| 1.8741 | 36.0 | 25920 | 2.4197 | 0.8446 |
| 1.8659 | 37.0 | 26640 | 2.4081 | 0.8406 |
| 1.8367 | 38.0 | 27360 | 2.3979 | 0.8426 |
| 1.8153 | 39.0 | 28080 | 2.3758 | 0.8451 |
| 1.7641 | 40.0 | 28800 | 2.3729 | 0.8470 |
| 1.7608 | 41.0 | 29520 | 2.3683 | 0.8460 |
| 1.7647 | 42.0 | 30240 | 2.3628 | 0.8446 |
| 1.7656 | 43.0 | 30960 | 2.3492 | 0.8470 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Bagus/wav2vec2-xlsr-japanese-speech-emotion-recognition | [
"pytorch",
"wav2vec2",
"audio-classification",
"ja",
"dataset:jtes",
"transformers",
"audio",
"speech",
"speech-emotion-recognition",
"has_space"
] | audio-classification | {
"architectures": [
"HubertForSequenceClassification"
],
"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_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 26 | null | Access to model saidhr20/pubmed-xlnet-text-classification is restricted and you are not in the authorized list. Visit https://huggingface.co/saidhr20/pubmed-xlnet-text-classification to ask for access. |
Bakkes/BakkesModWiki | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | 2022-09-24T19:02:27Z | ---
license: mit
---
### kysa-v-style on Stable Diffusion
This is the `<kysa-v-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
Bala/model_name | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: bert-tiny-emotion-KD-BERT
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9175
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-tiny-emotion-KD-BERT
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4810
- Accuracy: 0.9175
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 3.8247 | 1.0 | 1000 | 2.5170 | 0.7745 |
| 1.9864 | 2.0 | 2000 | 1.3436 | 0.874 |
| 1.1126 | 3.0 | 3000 | 0.8299 | 0.894 |
| 0.6924 | 4.0 | 4000 | 0.6500 | 0.9025 |
| 0.5272 | 5.0 | 5000 | 0.6097 | 0.908 |
| 0.4298 | 6.0 | 6000 | 0.5913 | 0.904 |
| 0.3936 | 7.0 | 7000 | 0.5165 | 0.9135 |
| 0.3238 | 8.0 | 8000 | 0.5120 | 0.9075 |
| 0.3018 | 9.0 | 9000 | 0.4989 | 0.916 |
| 0.2605 | 10.0 | 10000 | 0.4810 | 0.9175 |
| 0.2512 | 11.0 | 11000 | 0.4757 | 0.9135 |
| 0.219 | 12.0 | 12000 | 0.4676 | 0.914 |
| 0.2046 | 13.0 | 13000 | 0.4794 | 0.911 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Banshee/dialoGPT-luke-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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-tiny-sst2-KD-BERT
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: sst2
split: train
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.8348623853211009
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-tiny-sst2-KD-BERT
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8257
- Accuracy: 0.8349
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7521 | 1.0 | 4210 | 0.7345 | 0.8234 |
| 0.4301 | 2.0 | 8420 | 0.7748 | 0.8303 |
| 0.3335 | 3.0 | 12630 | 0.8257 | 0.8349 |
| 0.2831 | 4.0 | 16840 | 0.9145 | 0.8188 |
| 0.2419 | 5.0 | 21050 | 0.9096 | 0.8177 |
| 0.2149 | 6.0 | 25260 | 0.8410 | 0.8234 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
BaptisteDoyen/camembert-base-xnli | [
"pytorch",
"tf",
"camembert",
"text-classification",
"fr",
"dataset:xnli",
"transformers",
"zero-shot-classification",
"xnli",
"nli",
"license:mit",
"has_space"
] | zero-shot-classification | {
"architectures": [
"CamembertForSequenceClassification"
],
"model_type": "camembert",
"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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 405,474 | 2022-09-24T19:36:26Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: bert-tiny-emotion-KD-BERT_and_distilBERT
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.918
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-tiny-emotion-KD-BERT_and_distilBERT
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8780
- Accuracy: 0.918
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 7.1848 | 1.0 | 1000 | 4.7404 | 0.774 |
| 3.856 | 2.0 | 2000 | 2.7317 | 0.8685 |
| 2.3973 | 3.0 | 3000 | 1.8329 | 0.8895 |
| 1.5273 | 4.0 | 4000 | 1.2938 | 0.898 |
| 1.113 | 5.0 | 5000 | 1.1298 | 0.8985 |
| 0.9099 | 6.0 | 6000 | 1.0746 | 0.907 |
| 0.831 | 7.0 | 7000 | 1.0071 | 0.907 |
| 0.6813 | 8.0 | 8000 | 0.9556 | 0.9115 |
| 0.6432 | 9.0 | 9000 | 0.9746 | 0.913 |
| 0.5745 | 10.0 | 10000 | 0.8780 | 0.918 |
| 0.5319 | 11.0 | 11000 | 0.9410 | 0.909 |
| 0.4787 | 12.0 | 12000 | 0.9103 | 0.913 |
| 0.4529 | 13.0 | 13000 | 0.8829 | 0.915 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Barbarameerr/Barbara | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | 2022-09-24T19:45:01Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-tiny-sst2-KD-distilBERT
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: sst2
split: train
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.8325688073394495
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-tiny-sst2-KD-distilBERT
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1035
- Accuracy: 0.8326
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2008 | 1.0 | 4210 | 1.1319 | 0.8177 |
| 0.6821 | 2.0 | 8420 | 1.1035 | 0.8326 |
| 0.5315 | 3.0 | 12630 | 1.2271 | 0.8245 |
| 0.4486 | 4.0 | 16840 | 1.4426 | 0.8177 |
| 0.3857 | 5.0 | 21050 | 1.4309 | 0.8303 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
BatuhanYilmaz/mt5-small-finetuned-amazonbooks-en-es | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distillbert-base-spanish-uncased-finetuned-suicidios
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. -->
# distillbert-base-spanish-uncased-finetuned-suicidios
This model is a fine-tuned version of [CenIA/distillbert-base-spanish-uncased](https://huggingface.co/CenIA/distillbert-base-spanish-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2970
- Accuracy: 0.9483
- F1: 0.9483
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.3543 | 1.0 | 9618 | 0.2688 | 0.9422 | 0.9422 |
| 0.1726 | 2.0 | 19236 | 0.2970 | 0.9483 | 0.9483 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
BeIR/sparta-msmarco-distilbert-base-v1 | [
"pytorch",
"distilbert",
"feature-extraction",
"arxiv:2009.13013",
"arxiv:2104.08663",
"transformers"
] | feature-extraction | {
"architectures": [
"DistilBertModel"
],
"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,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 106 | 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: 283.01 +/- 14.03
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BearThreat/distilbert-base-uncased-finetuned-cola | [
"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,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 30 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
model-index:
- name: t5-base-finetuned-eli5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetuned-eli5
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the eli5 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Begimay/Task | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: mit
---
### Mizkif on Stable Diffusion
This is the `<mizkif>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
<br>
<h3>here are some images i rendered with this model</h3>
<span>graffiti wall</span>
<img src="https://i.imgur.com/PIq7Y0w.png" alt="graffiti wall" width="200"/>
<span>stained glass</span>
<img src="https://i.imgur.com/QcwB5GF.png" alt="stained glass" width="200"/>
<br>
<h3>here are the images i used to train the model</h3>
|
BenGeorge/MyModel | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: mit
---
### Brittney-Williams-Art on Stable Diffusion
This is the `<Brittney_Williams>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
Bharathdamu/wav2vec2-large-xls-r-300m-hindi-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"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_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 4 | null | ---
license: mit
---
### wheelchair on Stable Diffusion
This is the `<wheelchair>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
Bharathdamu/wav2vec2-large-xls-r-300m-hindi3-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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | data: https://github.com/BigSalmon2/InformalToFormalDataset
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln80Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln80Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
```
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
- penny has practically no value
- should be taken out of circulation
- just as other coins have been in us history
- lost use
- value not enough
- to make environmental consequences worthy
text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
```
first: ( was complicit in / was involved in ).
antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ).
***
first: ( have no qualms about / see no issue with ).
antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ).
***
first: ( do not see eye to eye / disagree often ).
antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ).
***
first:
```
```
stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground.
***
languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo.
***
dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia.
***
embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons.
```
Infill / Infilling / Masking / Phrase Masking (Works pretty decently actually, especially when you use logprobs code from above):
```
his contention [blank] by the evidence [sep] was refuted [answer]
***
few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
the joy of sport is that no two games are alike. for every exhilarating experience, however, there is an interminable one. the national pastime, unfortunately, has a penchant for the latter. what begins as a summer evening at the ballpark can quickly devolve into a game of tedium. the primary culprit is the [blank] of play. from batters readjusting their gloves to fielders spitting on their mitts, the action is [blank] unnecessary interruptions. the sport's future is [blank] if these tendencies are not addressed [sep] plodding pace [answer] riddled with [answer] bleak [answer]
***
microsoft word's [blank] pricing [blank] competition [sep] unconscionable [answer] invites [answer]
***
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
Backwards
```
Essay Intro (National Parks):
text: tourists are at ease in the national parks, ( swept up in the beauty of their natural splendor ).
***
Essay Intro (D.C. Statehood):
washington, d.c. is a city of outsize significance, ( ground zero for the nation's political life / center stage for the nation's political machinations ).
```
```
topic: the Golden State Warriors.
characterization 1: the reigning kings of the NBA.
characterization 2: possessed of a remarkable cohesion.
characterization 3: helmed by superstar Stephen Curry.
characterization 4: perched atop the league’s hierarchy.
characterization 5: boasting a litany of hall-of-famers.
***
topic: emojis.
characterization 1: shorthand for a digital generation.
characterization 2: more versatile than words.
characterization 3: the latest frontier in language.
characterization 4: a form of self-expression.
characterization 5: quintessentially millennial.
characterization 6: reflective of a tech-centric world.
***
topic:
```
```
regular: illinois went against the census' population-loss prediction by getting more residents.
VBG: defying the census' prediction of population loss, illinois experienced growth.
***
regular: microsoft word’s high pricing increases the likelihood of competition.
VBG: extortionately priced, microsoft word is inviting competition.
***
regular:
```
```
source: badminton should be more popular in the US.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) games played with racquets are popular, (2) just look at tennis and ping pong, (3) but badminton underappreciated, (4) fun, fast-paced, competitive, (5) needs to be marketed more
text: the sporting arena is dominated by games that are played with racquets. tennis and ping pong, in particular, are immensely popular. somewhat curiously, however, badminton is absent from this pantheon. exciting, fast-paced, and competitive, it is an underappreciated pastime. all that it lacks is more effective marketing.
***
source: movies in theaters should be free.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) movies provide vital life lessons, (2) many venues charge admission, (3) those without much money
text: the lessons that movies impart are far from trivial. the vast catalogue of cinematic classics is replete with inspiring sagas of friendship, bravery, and tenacity. it is regrettable, then, that admission to theaters is not free. in their current form, the doors of this most vital of institutions are closed to those who lack the means to pay.
***
source:
```
```
in the private sector, { transparency } is vital to the business’s credibility. the { disclosure of information } can be the difference between success and failure.
***
the labor market is changing, with { remote work } now the norm. this { flexible employment } allows the individual to design their own schedule.
***
the { cubicle } is the locus of countless grievances. many complain that the { enclosed workspace } restricts their freedom of movement.
***
```
```
it would be natural to assume that americans, as a people whose ancestors { immigrated to this country }, would be sympathetic to those seeking to do likewise.
question: what does “do likewise” mean in the above context?
(a) make the same journey
(b) share in the promise of the american dream
(c) start anew in the land of opportunity
(d) make landfall on the united states
***
in the private sector, { transparency } is vital to the business’s credibility. this orientation can be the difference between success and failure.
question: what does “this orientation” mean in the above context?
(a) visible business practices
(b) candor with the public
(c) open, honest communication
(d) culture of accountability
```
```
example: suppose you are a teacher. further suppose you want to tell an accurate telling of history. then suppose a parent takes offense. they do so in the name of name of their kid. this happens a lot.
text: educators' responsibility to remain true to the historical record often clashes with the parent's desire to shelter their child from uncomfortable realities.
***
example: suppose you are a student at college. now suppose you have to buy textbooks. that is going to be worth hundreds of dollars. given how much you already spend on tuition, that is going to hard cost to bear.
text: the exorbitant cost of textbooks, which often reaches hundreds of dollars, imposes a sizable financial burden on the already-strapped college student.
```
```
accustomed to having its name uttered ______, harvard university is weathering a rare spell of reputational tumult
(a) in reverential tones
(b) with great affection
(c) in adulatory fashion
(d) in glowing terms
```
```
informal english: i reached out to accounts who had a lot of followers, helping to make people know about us.
resume english: i partnered with prominent influencers to build brand awareness.
***
``` |
Bharathdamu/wav2vec2-model-hindibhasha | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
metrics:
- rouge
model-index:
- name: t5-small-finetuned-eli5-neel-final-again
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: eli5
type: eli5
config: LFQA_reddit
split: train_eli5
args: LFQA_reddit
metrics:
- name: Rouge1
type: rouge
value: 15.1361
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-eli5-neel-final-again
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eli5 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5993
- Rouge1: 15.1361
- Rouge2: 2.1584
- Rougel: 12.7499
- Rougelsum: 13.989
- Gen Len: 18.9998
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 3.8014 | 1.0 | 17040 | 3.5993 | 15.1361 | 2.1584 | 12.7499 | 13.989 | 18.9998 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
BigBoy/model | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-1.0.0
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. -->
# mt5-small-finetuned-1.0.0
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8753
- Rouge1: 57.3754
- Rouge2: 52.6902
- Rougel: 56.5013
- Rougelsum: 56.9205
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 7.598 | 1.0 | 339 | 1.1360 | 57.9291 | 52.9851 | 56.8619 | 57.36 |
| 1.6607 | 2.0 | 678 | 0.9274 | 58.4006 | 53.715 | 57.3505 | 57.8747 |
| 1.3212 | 3.0 | 1017 | 0.8753 | 57.3754 | 52.6902 | 56.5013 | 56.9205 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
BigDaddyNe1L/Hhaa | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: other
widget:
- text: "Chapter 1."
example_title: "First Prompt used in video"
- text: "Chapter 1. Shadowclan"
example_title: "Second prompt used in video"
- text: "Fireheart"
example_title: "Fireheart"
inference:
parameters:
temperature: 0.4
repetition_penalty: 1.1
min_length: 64
max_length: 128
---
This represents an OPT-125M model trained on the "Warriors: The Prophecies Begin" book series.
To train this model, I ripped text directly from PDFs using PyMuPdf.
This is the model trained in this [video](https://youtu.be/BAloWD4FXIM)
Please check out my [YouTube channel.](https://www.youtube.com/channel/UCLXxfueCPZRZnyGFWJ07uqA)
|
BigSalmon/MrLincoln2 | [
"pytorch",
"tensorboard",
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 9 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
BigSalmon/T52 | [
"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_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-multilingual-cased-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-finetuned-ner
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0247
- Precision: 0.9269
- Recall: 0.9509
- F1: 0.9387
- Accuracy: 0.9945
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0744 | 1.0 | 843 | 0.0266 | 0.8945 | 0.9293 | 0.9116 | 0.9920 |
| 0.016 | 2.0 | 1686 | 0.0239 | 0.9279 | 0.9446 | 0.9362 | 0.9942 |
| 0.0075 | 3.0 | 2529 | 0.0247 | 0.9269 | 0.9509 | 0.9387 | 0.9945 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Bimal/my_bot_model | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 10 | null | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: fillmaskmodel
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. -->
# fillmaskmodel
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4400, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
### Framework versions
- Transformers 4.22.1
- TensorFlow 2.8.2
- Tokenizers 0.12.1
|
Blabla/Pipipopo | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | 2022-09-25T12:35:23Z | ---
tags:
- image-classification
- pytorch
metrics:
- accuracy
model-index:
- name: syn-oct-ViT-Large-4Epochs-run1
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9416666626930237
---
# syn-oct-ViT-Large-4Epochs-run1 |
Blaine-Mason/hackMIT-finetuned-sst2 | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer"
] | 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_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 36 | null | ---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_pipeline
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 1.0
- name: NER Recall
type: recall
value: 1.0
- name: NER F Score
type: f_score
value: 1.0
---
| Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.4.1,<3.5.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (7 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `CAUSE`, `HIGH_BILL`, `INSTALL_METER`, `ISSUE`, `METER_CHECK`, `NEW_SERVICE`, `SITE_CHECK` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 100.00 |
| `ENTS_P` | 100.00 |
| `ENTS_R` | 100.00 |
| `TRANSFORMER_LOSS` | 0.02 |
| `NER_LOSS` | 0.01 | |
Blerrrry/Kkk | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
tags:
- image-classification
- pytorch
metrics:
- accuracy
model-index:
- name: syn-oct-ViT-Base-4Epochs-run1
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9750000238418579
---
# syn-oct-ViT-Base-4Epochs-run1 |
BlightZz/DialoGPT-medium-Kurisu | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 19 | null | ---
language: ja
license: mit
datasets:
- wikipedia
---
# nagisa_bert
A BERT model for [nagisa](https://github.com/taishi-i/nagisa).
The model is available in [Transformers](https://github.com/huggingface/transformers) 🤗.
A tokenizer for nagisa_bert is available [here](https://github.com/taishi-i/nagisa_bert).
## Install
To use this model, the following python library must be installed.
You can install [*nagisa_bert*](https://github.com/taishi-i/nagisa_bert) by using the *pip* command.
Python 3.7+ on Linux or macOS is required.
```bash
$ pip install nagisa_bert
```
## Usage
This model is available in Transformer's pipeline method.
```python
>>> from transformers import pipeline
>>> from nagisa_bert import NagisaBertTokenizer
>>> text = "nagisaで[MASK]できるモデルです"
>>> tokenizer = NagisaBertTokenizer.from_pretrained("taishi-i/nagisa_bert")
>>> fill_mask = pipeline("fill-mask", model='taishi-i/nagisa_bert', tokenizer=tokenizer)
>>> print(fill_mask(text))
[{'score': 0.1385931372642517,
'sequence': 'nagisa で 使用 できる モデル です',
'token': 8092,
'token_str': '使 用'},
{'score': 0.11947669088840485,
'sequence': 'nagisa で 利用 できる モデル です',
'token': 8252,
'token_str': '利 用'},
{'score': 0.04910655692219734,
'sequence': 'nagisa で 作成 できる モデル です',
'token': 9559,
'token_str': '作 成'},
{'score': 0.03792576864361763,
'sequence': 'nagisa で 購入 できる モデル です',
'token': 9430,
'token_str': '購 入'},
{'score': 0.026893319562077522,
'sequence': 'nagisa で 入手 できる モデル です',
'token': 11273,
'token_str': '入 手'}]
```
Tokenization and vectorization.
```python
>>> from transformers import BertModel
>>> from nagisa_bert import NagisaBertTokenizer
>>> text = "nagisaで[MASK]できるモデルです"
>>> tokenizer = NagisaBertTokenizer.from_pretrained("taishi-i/nagisa_bert")
>>> tokens = tokenizer.tokenize(text)
>>> print(tokens)
['na', '##g', '##is', '##a', 'で', '[MASK]', 'できる', 'モデル', 'です']
>>> model = BertModel.from_pretrained("taishi-i/nagisa_bert")
>>> h = model(**tokenizer(text, return_tensors="pt")).last_hidden_state
>>> print(h)
tensor([[[-0.2912, -0.6818, -0.4097, ..., 0.0262, -0.3845, 0.5816],
[ 0.2504, 0.2143, 0.5809, ..., -0.5428, 1.1805, 1.8701],
[ 0.1890, -0.5816, -0.5469, ..., -1.2081, -0.2341, 1.0215],
...,
[-0.4360, -0.2546, -0.2824, ..., 0.7420, -0.2904, 0.3070],
[-0.6598, -0.7607, 0.0034, ..., 0.2982, 0.5126, 1.1403],
[-0.2505, -0.6574, -0.0523, ..., 0.9082, 0.5851, 1.2625]]],
grad_fn=<NativeLayerNormBackward0>)
```
## Model description
### Architecture
The model architecture is the same as [the BERT bert-base-uncased architecture](https://huggingface.co/bert-base-uncased) (12 layers, 768 dimensions of hidden states, and 12 attention heads).
### Training Data
The models is trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 8, 2022 with [make_corpus_wiki.py](https://github.com/cl-tohoku/bert-japanese/blob/main/make_corpus_wiki.py) and [create_pretraining_data.py](https://github.com/cl-tohoku/bert-japanese/blob/main/create_pretraining_data.py).
### Training
The model is trained with the default parameters of [transformers.BertConfig](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertConfig).
Due to GPU memory limitations, the batch size is set to small; 16 instances per batch, and 2M training steps.
## Tutorial
You can find here a list of the notebooks on Japanese NLP using pre-trained models and transformers.
| Notebook | Description | |
|:----------|:-------------|------:|
| [Fill-mask](https://github.com/taishi-i/nagisa_bert/blob/develop/notebooks/fill_mask-japanese_bert_models.ipynb) | How to use the pipeline function in transformers to fill in Japanese text. |[](https://colab.research.google.com/github/taishi-i/nagisa_bert/blob/develop/notebooks/fill_mask-japanese_bert_models.ipynb)|
| [Feature-extraction](https://github.com/taishi-i/nagisa_bert/blob/develop/notebooks/feature_extraction-japanese_bert_models.ipynb) | How to use the pipeline function in transformers to extract features from Japanese text. |[](https://colab.research.google.com/github/taishi-i/nagisa_bert/blob/develop/notebooks/feature_extraction-japanese_bert_models.ipynb)|
| [Embedding visualization](https://github.com/taishi-i/nagisa_bert/blob/develop/notebooks/embedding_visualization-japanese_bert_models.ipynb) | Show how to visualize embeddings from Japanese pre-trained models. |[](https://colab.research.google.com/github/taishi-i/nagisa_bert/blob/develop/notebooks/embedding_visualization_japanese_bert_models.ipynb)|
| [How to fine-tune a model on text classification](https://github.com/taishi-i/nagisa_bert/blob/develop/notebooks/text_classification-amazon_reviews_ja.ipynb) | Show how to fine-tune a pretrained model on a Japanese text classification task. |[](https://colab.research.google.com/github/taishi-i/nagisa_bert/blob/develop/notebooks/text_classification-amazon_reviews_ja.ipynb)|
| [How to fine-tune a model on text classification with csv files](https://github.com/taishi-i/nagisa_bert/blob/develop/notebooks/text_classification-csv_files.ipynb) | Show how to preprocess the data and fine-tune a pretrained model on a Japanese text classification task. |[](https://colab.research.google.com/github/taishi-i/nagisa_bert/blob/develop/notebooks/text_classification-csv_files.ipynb)| |
BlightZz/MakiseKurisu | [
"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": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 14 | null | ---
tags:
- image-classification
- pytorch
metrics:
- accuracy
model-index:
- name: syn-oct-ViT-Large-8Epochs-run1
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9666666388511658
---
# syn-oct-ViT-Large-8Epochs-run1 |
BogdanKuloren/continual-learning-paper-embeddings-model | [
"pytorch",
"mpnet",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"MPNetModel"
],
"model_type": "mpnet",
"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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 11 | 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: 226.89 +/- 17.19
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Boondong/Wandee | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: testarenz
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. -->
# testarenz
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2153
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2806 | 1.0 | 5533 | 1.2153 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Brona/poc_de | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: cc-by-sa-3.0
---
Japanese RoBERTa base size trained by wikipedia dump 20220905 using fairseq.
Tokenizer is [japanese_roberta_tokenizer](https://github.com/k141303/japanese_roberta_tokenizer). |
Brykee/BrykeeBot | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: other
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.92
- name: F1
type: f1
value: 0.9205298013245033
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [Tianyi98/opt-350m-finetuned-cola](https://huggingface.co/Tianyi98/opt-350m-finetuned-cola) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4133
- Accuracy: 0.92
- F1: 0.9205
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.22.1
- Pytorch 1.10.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Bryson575x/riceboi | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: mit
---
**BIH (BERT Imitates Human) Model**
This is finetuned model based on pretrained klue/roberta-large
BIH learns the examples evaluated by native Korean speakers on the 'fit for commonsense'
**How to use**
Please check this git link [J-Seo/SRLev-BIH](https://github.com/J-Seo/SRLev-BIH)
**BibTeX entry and citation info**
```
@inproceedings{jay2022SRLev-BIH,
title={SRLev-BIH: An Evaluation Metric for Korean Generative Commonsense Reasoning},
author={Jaehyung Seo, Yoonna Jang, Jaewook Lee, Hyeonseok Moon, Sugyeong Eo, Chanjun Park, Aram So, and Heuiseok Lim},
booktitle={Proceedings of the 34th Annual Conference on Human & Cognitive Language Technology},
affilation={Korea University, NLP & AI},
month={October},
year={2022}
}
``` |
BumBelDumBel/ZORK_AI_FANTASY | [] | 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": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-multilingual-cased-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. -->
# distilbert-base-multilingual-cased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1954
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2983 | 1.0 | 5555 | 1.2202 |
| 1.0252 | 2.0 | 11110 | 1.1583 |
| 0.8078 | 3.0 | 16665 | 1.1954 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-ner | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 85 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
model-index:
- name: t5-small-t5-base
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-t5-base
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eli5 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-egy | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 16,451 | null | This is a `microsoft/codebert-base-mlm` model, trained for 1,000,000 steps (with `batch_size=32`) on **C** code from the `codeparrot/github-code-clean` dataset, on the masked-language-modeling task.
It is intended to be used in CodeBERTScore: [https://github.com/neulab/code-bert-score](https://github.com/neulab/code-bert-score), but can be used for any other model or task.
For more information, see: [https://github.com/neulab/code-bert-score](https://github.com/neulab/code-bert-score)
## Citation
If you use this model for research, please cite:
```
@article{zhou2023codebertscore,
url = {https://arxiv.org/abs/2302.05527},
author = {Zhou, Shuyan and Alon, Uri and Agarwal, Sumit and Neubig, Graham},
title = {CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code},
publisher = {arXiv},
year = {2023},
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-msa | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"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_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 71 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: t5-small-finetuned-xsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0.01
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 0.01 | 128 | 3.0141 | 18.0313 | 2.7105 | 14.1325 | 14.3393 | 18.8882 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
CAMeL-Lab/bert-base-arabic-camelbert-da-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | 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_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 37 | null | ---
license: bigscience-bloom-rail-1.0
widget :
- text: "அகத்தின் அழகு"
example_title: "அகத்தின் அழகு"
- text : "கடுகு சிறுத்தாலும்"
example_title: "கடுகு சிறுத்தாலும்"
- text : "யானைக்கும் அடி"
example_title : "யானைக்கும் அடி"
---
# GPT2-Tamil
## Model description
GPT2-Tamil is a GPT-2 transformer model fine Tuned on a large corpus of Tamil data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token i only uses the inputs from 1 to i but not the future tokens.
This way, the model learns an inner representation of the Tamil language that can then be used to extract features useful for downstream tasks.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task. See the
[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
## Usage
You can use this model for Tamil text generation:
```python
>>> from transformers import TFGPT2LMHeadModel, GPT2Tokenizer
>>> tokenizer = GPT2Tokenizer.from_pretrained("Lagstill/GPT-2-Tamil")
>>> model = TFGPT2LMHeadModel.from_pretrained("Lagstill/GPT-2-Tamil")
>>> text = "அகத்தின் அழகு"
>>> encoded_text = tokenizer.encode(text, return_tensors='tf')
>>> beam_output = model.generate(
encoded_text,
max_length=100,
num_beams=5,
temperature=0.7,
no_repeat_ngram_size=2,
num_return_sequences=5
)
>>> print(tokenizer.decode(beam_output[0], skip_special_tokens=True))
```
---
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.