bug fixes
This commit is contained in:
ritchie46
@@ -22,7 +22,7 @@
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"import os\n",
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"from tensorboardX import SummaryWriter\n",
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"\n",
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"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
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"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
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"print(device)"
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]
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},
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@@ -30,39 +30,7 @@
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" % Total % Received % Xferd Average Speed Time Time Time Current\n",
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" Dload Upload Total Spent Left Speed\n",
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"100 2814k 100 2814k 0 0 507k 0 0:00:05 0:00:05 --:--:-- 631k\n",
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"Archive: data.zip\n",
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" creating: data/\n",
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" inflating: data/eng-fra.txt \n",
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" creating: data/names/\n",
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" inflating: data/names/Arabic.txt \n",
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" inflating: data/names/Chinese.txt \n",
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" inflating: data/names/Czech.txt \n",
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" inflating: data/names/Dutch.txt \n",
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" inflating: data/names/English.txt \n",
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" inflating: data/names/French.txt \n",
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" inflating: data/names/German.txt \n",
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" inflating: data/names/Greek.txt \n",
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" inflating: data/names/Irish.txt \n",
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" inflating: data/names/Italian.txt \n",
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" inflating: data/names/Japanese.txt \n",
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" inflating: data/names/Korean.txt \n",
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" inflating: data/names/Polish.txt \n",
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" inflating: data/names/Portuguese.txt \n",
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" inflating: data/names/Russian.txt \n",
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" inflating: data/names/Scottish.txt \n",
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" inflating: data/names/Spanish.txt \n",
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" inflating: data/names/Vietnamese.txt \n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# download the needed data\n",
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"if not os.path.isfile('data.zip'):\n",
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@@ -71,7 +39,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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@@ -100,7 +68,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -185,7 +153,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -216,7 +184,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -239,12 +207,13 @@
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" np.random.shuffle(self.shuffle_idx)\n",
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" self.pairs = self.pairs[self.shuffle_idx]\n",
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" self.idx_pairs = self.idx_pairs[self.shuffle_idx] \n",
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" "
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" \n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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@@ -266,7 +235,7 @@
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"array(['we are even EOS', 'nous sommes a egalite EOS'], dtype='<U60')"
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]
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},
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"execution_count": 13,
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -313,7 +282,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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@@ -322,7 +291,7 @@
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"torch.Size([5, 1, 2])"
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]
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},
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"execution_count": 20,
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -377,24 +346,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"torch.Size([1, 10])\n",
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"torch.Size([1, 1, 10])\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"tensor(-23351.8633, grad_fn=<SumBackward0>)"
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"tensor(-23351.4941, grad_fn=<SumBackward0>)"
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]
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},
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"execution_count": 24,
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -444,16 +405,23 @@
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},
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{
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"cell_type": "code",
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"execution_count": 120,
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"torch.Size([3, 1, 256])\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"torch.Size([1, 1, 2])"
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]
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},
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"execution_count": 120,
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -534,7 +502,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 122,
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -559,7 +527,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 97,
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -571,14 +539,45 @@
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"encoder = Encoder(eng.n_words, embedding_size, context_vector_size, bidirectional)\n",
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"context_vector_size = context_vector_size * 2 if bidirectional else context_vector_size \n",
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"decoder = AttentionDecoder(embedding_size, context_vector_size, fra.n_words)\n",
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"# writer = SummaryWriter('tb/train')"
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"writer = SummaryWriter('tb/train3')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 61,
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'cuda'"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"decoder.device"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"epoch 0\n",
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"epoch 1\n",
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"epoch 2\n",
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"epoch 3\n"
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]
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}
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],
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"source": [
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"\n",
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"def train(encoder, decoder):\n",
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@@ -627,11 +626,267 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"def translate(start, end):\n",
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"m = AttentionDecoder"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"English scentence:\t we re all single\n",
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"French scentence:\t nous sommes toutes celibataires\n",
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"\n",
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"Model translation:\t nous sommes toutes celibataire \n",
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"\n",
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"\n",
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"English scentence:\t he is amusing himself by playing video games\n",
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"French scentence:\t il s amuse en jouant aux jeux videos\n",
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"\n",
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"Model translation:\t il est plein a des anglais \n",
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"\n",
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"\n",
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"English scentence:\t i m an artist\n",
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"French scentence:\t je suis une artiste\n",
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"\n",
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"Model translation:\t je suis un artiste \n",
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"\n",
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"\n",
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"English scentence:\t he s enjoying himself\n",
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"French scentence:\t il s amuse\n",
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"\n",
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"Model translation:\t il est est a \n",
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"\n",
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"\n",
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"English scentence:\t he is an american\n",
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"French scentence:\t il est americain\n",
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"\n",
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"Model translation:\t il est un \n",
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"\n",
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"\n",
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"English scentence:\t you re the oldest\n",
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"French scentence:\t tu es le plus vieux\n",
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"\n",
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"Model translation:\t vous etes le plus \n",
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"\n",
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"\n",
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"English scentence:\t he s about to leave\n",
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"French scentence:\t il est sur le point de partir\n",
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"\n",
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"Model translation:\t il est sur le point \n",
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"\n",
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"\n",
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"English scentence:\t i m really happy\n",
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"French scentence:\t je suis vraiment heureuse\n",
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"\n",
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"Model translation:\t je suis vraiment contente \n",
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"\n",
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"\n",
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"English scentence:\t we re all hungry\n",
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"French scentence:\t nous avons tous faim\n",
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"\n",
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"Model translation:\t nous avons toutes faim \n",
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"\n",
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"\n",
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"English scentence:\t i m not giving you any more money\n",
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"French scentence:\t je ne te donnerai pas davantage d argent\n",
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"\n",
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"Model translation:\t je ne te donnerai d argent d argent \n",
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"\n",
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"\n",
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"English scentence:\t he is independent of his parents\n",
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"French scentence:\t il est independant de ses parents\n",
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"\n",
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"Model translation:\t il est plein de ses parents \n",
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"\n",
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"\n",
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"English scentence:\t i am not a doctor but a teacher\n",
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"French scentence:\t je ne suis pas medecin mais professeur\n",
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"\n",
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"Model translation:\t je ne suis pas medecin mais enseignant \n",
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"\n",
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"\n",
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"English scentence:\t you are responsible for the result\n",
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"French scentence:\t vous etes responsable des resultats\n",
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"\n",
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"Model translation:\t vous es responsable de l \n",
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"\n",
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"\n",
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"English scentence:\t i m sympathetic\n",
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"French scentence:\t j eprouve de la compassion\n",
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"\n",
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"Model translation:\t je ne suis \n",
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"\n",
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"\n",
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"English scentence:\t you are drunk\n",
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"French scentence:\t tu es saoul\n",
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"\n",
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"Model translation:\t vous etes impressionnee \n",
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"\n",
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"\n",
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"English scentence:\t you aren t as short as i am\n",
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"French scentence:\t tu n es pas aussi petite que moi\n",
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"\n",
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"Model translation:\t vous n etes pas aussi petit que moi \n",
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"\n",
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"\n",
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"English scentence:\t i m resilient\n",
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"French scentence:\t je suis endurante\n",
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"\n",
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"Model translation:\t je suis endurant \n",
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"\n",
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"\n",
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"English scentence:\t you re wrong again\n",
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"French scentence:\t vous avez a nouveau tort\n",
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"\n",
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"Model translation:\t tu as tort \n",
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"\n",
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"\n",
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"English scentence:\t i am thinking of my vacation\n",
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"French scentence:\t je pense a mes vacances\n",
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"\n",
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"Model translation:\t je songe de mes enfants \n",
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"\n",
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"\n",
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"English scentence:\t she is able to sing very well\n",
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"French scentence:\t elle sait tres bien chanter\n",
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"\n",
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"Model translation:\t elle va bien bien bien bien \n",
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"\n",
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"\n",
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"English scentence:\t i m lazy\n",
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"French scentence:\t je suis faineant\n",
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"\n",
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"Model translation:\t je suis paresseux \n",
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"\n",
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"\n",
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"English scentence:\t he is a cruel person\n",
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"French scentence:\t c est un homme cruel\n",
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"\n",
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"Model translation:\t il est une personne \n",
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"\n",
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"\n",
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"English scentence:\t he is a poet\n",
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"French scentence:\t c est un poete\n",
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"\n",
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"Model translation:\t il est un \n",
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"\n",
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"\n",
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"English scentence:\t we re plastered\n",
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"French scentence:\t nous sommes bourres\n",
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"\n",
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"Model translation:\t nous sommes bourrees \n",
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"\n",
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"\n",
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"English scentence:\t she suddenly fell silent\n",
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"French scentence:\t elle se tut soudain\n",
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"\n",
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"Model translation:\t elle est deux deux \n",
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"\n",
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"\n",
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"English scentence:\t you re not making this easy\n",
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"French scentence:\t vous ne rendez pas ca facile\n",
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"\n",
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"Model translation:\t vous ne fais pas ca \n",
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"\n",
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"\n",
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"English scentence:\t she s very afraid of dogs\n",
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"French scentence:\t elle a une peur bleue des chiens\n",
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"\n",
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"Model translation:\t elle a tres peur des chiens \n",
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"\n",
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"\n",
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"English scentence:\t she is living abroad\n",
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"French scentence:\t elle vit actuellement a l etranger\n",
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"\n",
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"Model translation:\t elle est l a \n",
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"\n",
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"\n",
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"English scentence:\t i m going to my grandmother s\n",
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"French scentence:\t je vais chez ma grand mere\n",
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"\n",
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"Model translation:\t je vais me mon de la \n",
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"\n",
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"\n",
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"English scentence:\t they re not always right\n",
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"French scentence:\t elles n ont pas toujours raison\n",
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"\n",
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"Model translation:\t elles n ont pas toujours \n",
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"\n",
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"\n",
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"English scentence:\t you are very attractive in blue\n",
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"French scentence:\t le bleu vous va tres bien\n",
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"\n",
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"Model translation:\t vous avez vraiment beaucoup a \n",
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"\n",
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"\n",
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"English scentence:\t you re winning aren t you\n",
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"French scentence:\t vous gagnez n est ce pas\n",
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"\n",
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"Model translation:\t vous etes est n est ce \n",
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"\n",
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"\n",
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"English scentence:\t i m ticklish\n",
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"French scentence:\t je suis chatouilleuse\n",
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"\n",
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"Model translation:\t je suis mal \n",
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"\n",
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"\n",
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"English scentence:\t i m not going to name names\n",
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"French scentence:\t je ne vais pas citer de noms\n",
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"\n",
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"Model translation:\t je ne vais pas m a \n",
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"\n",
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"\n",
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"English scentence:\t they are at broadway avenue\n",
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"French scentence:\t ils sont au broadway avenue\n",
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"\n",
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"Model translation:\t ils sont a l des \n",
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"\n",
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"\n",
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"English scentence:\t i m sure\n",
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"French scentence:\t j en suis sure\n",
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"\n",
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"Model translation:\t je suis certain \n",
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"\n",
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"\n",
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"English scentence:\t she said that she was happy\n",
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"French scentence:\t elle a dit qu elle etait heureuse\n",
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"\n",
|
||||
"Model translation:\t elle dit qu a heureuse \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t i m all done\n",
|
||||
"French scentence:\t j ai tout fini\n",
|
||||
"\n",
|
||||
"Model translation:\t je ai tout termine \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t we re the problem\n",
|
||||
"French scentence:\t nous sommes le probleme\n",
|
||||
"\n",
|
||||
"Model translation:\t nous sommes le probleme \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t i m being serious\n",
|
||||
"French scentence:\t je suis serieux\n",
|
||||
"\n",
|
||||
"Model translation:\t je suis serieux \n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def translate( start, end):\n",
|
||||
" \n",
|
||||
" for i in range(start, end):\n",
|
||||
" \n",
|
||||
@@ -662,6 +917,15 @@
|
||||
" \n",
|
||||
"translate(20, 60)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"decoder"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
Reference in New Issue
Block a user