upload models

config.json

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+{
+  "architectures": [
+    "LtgBertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "auto_map": {
+    "AutoConfig": "configuration_ltgbert.LtgBertConfig",
+    "AutoModelForMaskedLM": "modeling_ltgbert.LtgBertForMaskedLM",
+    "AutoModelForSequenceClassification": "modeling_ltgbert.LtgBertForSequenceClassification"
+  },
+  "classifier_dropout": 0.2,
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "intermediate_size": 1024,
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "model_type": "ltgbert",
+  "num_attention_heads": 6,
+  "num_hidden_layers": 12,
+  "output_all_encoded_layers": true,
+  "pad_token_id": 4,
+  "position_bucket_size": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.0",
+  "vocab_size": 6144
+}
+

configuration_ltgbert.py

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+# coding=utf-8
+# Copyright 2023 Language Technology Group from University of Oslo and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""" LTG-BERT configutation """
+
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+LTG_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "bnc-bert-span": "https://huggingface.co/ltg/bnc-bert-span",
+    "bnc-bert-span-2x": "https://huggingface.co/ltg/bnc-bert-span-2x",
+    "bnc-bert-span-0.5x": "https://huggingface.co/ltg/bnc-bert-span-0.5x",
+    "bnc-bert-span-0.25x": "https://huggingface.co/ltg/bnc-bert-span-0.25x",
+    "bnc-bert-span-order": "https://huggingface.co/ltg/bnc-bert-span-order",
+    "bnc-bert-span-document": "https://huggingface.co/ltg/bnc-bert-span-document",
+    "bnc-bert-span-word": "https://huggingface.co/ltg/bnc-bert-span-word",
+    "bnc-bert-span-subword": "https://huggingface.co/ltg/bnc-bert-span-subword",
+
+    "norbert3-xs": "https://huggingface.co/ltg/norbert3-xs/config.json",
+    "norbert3-small": "https://huggingface.co/ltg/norbert3-small/config.json",
+    "norbert3-base": "https://huggingface.co/ltg/norbert3-base/config.json",
+    "norbert3-large": "https://huggingface.co/ltg/norbert3-large/config.json",
+
+    "norbert3-oversampled-base": "https://huggingface.co/ltg/norbert3-oversampled-base/config.json",
+    "norbert3-ncc-base": "https://huggingface.co/ltg/norbert3-ncc-base/config.json",
+    "norbert3-nak-base": "https://huggingface.co/ltg/norbert3-nak-base/config.json",
+    "norbert3-nb-base": "https://huggingface.co/ltg/norbert3-nb-base/config.json",
+    "norbert3-wiki-base": "https://huggingface.co/ltg/norbert3-wiki-base/config.json",
+    "norbert3-c4-base": "https://huggingface.co/ltg/norbert3-c4-base/config.json"
+}
+
+
+class LtgBertConfig(PretrainedConfig):
+    """
+    This is the configuration class to store the configuration of a [`LtgBertModel`]. It is used to
+    instantiate an LTG-BERT model according to the specified arguments, defining the model architecture.
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+    Args:
+        vocab_size (`int`, *optional*, defaults to 16384):
+            Vocabulary size of the LTG-BERT model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`LtgBertModel`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 2048):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        max_position_embeddings (`int`, *optional*, defaults to 512):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        classifier_dropout (`float`, *optional*):
+            The dropout ratio for the classification head.
+    """
+    model_type = "ltgbert"
+    def __init__(
+        self,
+        vocab_size=6144,
+        attention_probs_dropout_prob=0.1,
+        hidden_dropout_prob=0.1,
+        hidden_size=384,
+        intermediate_size=1024,
+        max_position_embeddings=512,
+        position_bucket_size=32,
+        num_attention_heads=6,
+        num_hidden_layers=12,
+        layer_norm_eps=1.0e-7,
+        pad_token_id=4,
+        output_all_encoded_layers=True,
+        classifier_dropout=None,
+        **kwargs,
+    ):
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.output_all_encoded_layers = output_all_encoded_layers
+        self.position_bucket_size = position_bucket_size
+        self.layer_norm_eps = layer_norm_eps
+        self.classifier_dropout = classifier_dropout

modeling_ltgbert.py

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+# coding=utf-8
+# Copyright 2023 Language Technology Group from University of Oslo and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""" PyTorch LTG-BERT model."""
+
+
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils import checkpoint
+
+from .configuration_ltgbert import LtgBertConfig
+from transformers.modeling_utils import PreTrainedModel
+from transformers.activations import gelu_new
+from transformers.modeling_outputs import (
+    MaskedLMOutput,
+    MultipleChoiceModelOutput,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+    BaseModelOutput
+)
+from transformers.pytorch_utils import softmax_backward_data
+from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward
+
+
+_CHECKPOINT_FOR_DOC = "ltg/bnc-bert-span"
+_CONFIG_FOR_DOC = "LtgBertConfig"
+
+
+LTG_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "bnc-bert-span",
+    "bnc-bert-span-2x",
+    "bnc-bert-span-0.5x",
+    "bnc-bert-span-0.25x",
+    "bnc-bert-span-order",
+    "bnc-bert-span-document",
+    "bnc-bert-span-word",
+    "bnc-bert-span-subword",
+
+    "norbert3-xs",
+    "norbert3-small",
+    "norbert3-base",
+    "norbert3-large",
+
+    "norbert3-oversampled-base",
+    "norbert3-ncc-base",
+    "norbert3-nak-base",
+    "norbert3-nb-base",
+    "norbert3-wiki-base",
+    "norbert3-c4-base"
+]
+
+
+class Encoder(nn.Module):
+    def __init__(self, config, activation_checkpointing=False):
+        super().__init__()
+        self.layers = nn.ModuleList([EncoderLayer(config, i) for i in range(config.num_hidden_layers)])
+
+        for i, layer in enumerate(self.layers):
+            layer.mlp.mlp[1].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i)))
+            layer.mlp.mlp[-2].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i)))
+
+        self.activation_checkpointing = activation_checkpointing
+    
+    def forward(self, hidden_states, attention_mask, relative_embedding):
+        hidden_states, attention_probs = [hidden_states], []
+
+        for layer in self.layers:
+            if self.activation_checkpointing:
+                hidden_state, attention_p = checkpoint.checkpoint(layer, hidden_states, attention_mask, relative_embedding)
+            else:
+                hidden_state, attention_p = layer(hidden_states, attention_mask, relative_embedding)
+
+            hidden_states.append(hidden_state)
+            attention_probs.append(attention_p)
+
+        return hidden_states, attention_probs
+
+
+class MaskClassifier(nn.Module):
+    def __init__(self, config, subword_embedding):
+        super().__init__()
+        self.nonlinearity = nn.Sequential(
+            nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False),
+            nn.Linear(config.hidden_size, config.hidden_size),
+            nn.GELU(),
+            nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False),
+            nn.Dropout(config.hidden_dropout_prob),
+            nn.Linear(subword_embedding.size(1), subword_embedding.size(0))
+        )
+        self.initialize(config.hidden_size, subword_embedding)
+
+    def initialize(self, hidden_size, embedding):
+        std = math.sqrt(2.0 / (5.0 * hidden_size))
+        nn.init.trunc_normal_(self.nonlinearity[1].weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        self.nonlinearity[-1].weight = embedding
+        self.nonlinearity[1].bias.data.zero_()
+        self.nonlinearity[-1].bias.data.zero_()
+
+    def forward(self, x, masked_lm_labels=None):
+        if masked_lm_labels is not None:
+            x = torch.index_select(x.flatten(0, 1), 0, torch.nonzero(masked_lm_labels.flatten() != -100).squeeze())
+        x = self.nonlinearity(x)
+        return x
+
+
+class EncoderLayer(nn.Module):
+    def __init__(self, config, layer_num):
+        super().__init__()
+        self.attention = Attention(config)
+        self.mlp = FeedForward(config)
+        temp = torch.zeros(layer_num+1)
+        temp[-1] = 1
+        self.prev_layer_weights = nn.Parameter(temp)
+
+    def forward(self, hidden_states, padding_mask, relative_embedding):
+        prev_layer_weights = F.softmax(self.prev_layer_weights, dim=-1)
+        x = prev_layer_weights[0] * hidden_states[0]
+        for i, hidden_state in enumerate(hidden_states[1:]):
+            x = x + prev_layer_weights[i+1] * hidden_state
+        attention_output, attention_probs = self.attention(x, padding_mask, relative_embedding)
+        x = attention_output
+        x = x + self.mlp(x)
+        return x, attention_probs
+
+
+class GeGLU(nn.Module):
+    def forward(self, x):
+        x, gate = x.chunk(2, dim=-1)
+        x = x * gelu_new(gate)
+        return x
+
+
+class FeedForward(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False),
+            nn.Linear(config.hidden_size, 2*config.intermediate_size, bias=False),
+            GeGLU(),
+            nn.LayerNorm(config.intermediate_size, eps=config.layer_norm_eps, elementwise_affine=False),
+            nn.Linear(config.intermediate_size, config.hidden_size, bias=False),
+            nn.Dropout(config.hidden_dropout_prob)
+        )
+        self.initialize(config.hidden_size)
+
+    def initialize(self, hidden_size):
+        std = math.sqrt(2.0 / (5.0 * hidden_size))
+        nn.init.trunc_normal_(self.mlp[1].weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        nn.init.trunc_normal_(self.mlp[-2].weight, mean=0.0, std=std, a=-2*std, b=2*std)
+
+    def forward(self, x):
+        return self.mlp(x)
+
+
+class MaskedSoftmax(torch.autograd.Function):
+    @staticmethod
+    def forward(self, x, mask, dim):
+        self.dim = dim
+        x.masked_fill_(mask, float('-inf'))
+        x = torch.softmax(x, self.dim)
+        x.masked_fill_(mask, 0.0)
+        self.save_for_backward(x)
+        return x
+
+    @staticmethod
+    def backward(self, grad_output):
+        output, = self.saved_tensors
+        input_grad = softmax_backward_data(self, grad_output, output, self.dim, output)
+        return input_grad, None, None
+
+
+class Attention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+
+        self.config = config
+
+        if config.hidden_size % config.num_attention_heads != 0:
+            raise ValueError(f"The hidden size {config.hidden_size} is not a multiple of the number of attention heads {config.num_attention_heads}")
+
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_size = config.hidden_size // config.num_attention_heads
+
+        self.in_proj_qk = nn.Linear(config.hidden_size, 2*config.hidden_size, bias=True)
+        self.in_proj_v = nn.Linear(config.hidden_size, config.hidden_size, bias=True)
+        self.out_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=True)
+
+        self.pre_layer_norm = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False)
+        self.post_layer_norm = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True)
+
+        position_indices = torch.arange(config.max_position_embeddings, dtype=torch.long).unsqueeze(1) \
+            - torch.arange(config.max_position_embeddings, dtype=torch.long).unsqueeze(0)
+        position_indices = self.make_log_bucket_position(position_indices, config.position_bucket_size, config.max_position_embeddings)
+        position_indices = config.position_bucket_size - 1 + position_indices
+        self.register_buffer("position_indices", position_indices, persistent=True)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.scale = 1.0 / math.sqrt(3 * self.head_size)
+        self.initialize()
+
+    def make_log_bucket_position(self, relative_pos, bucket_size, max_position):
+        sign = torch.sign(relative_pos)
+        mid = bucket_size // 2
+        abs_pos = torch.where((relative_pos < mid) & (relative_pos > -mid), mid - 1, torch.abs(relative_pos).clamp(max=max_position - 1))
+        log_pos = torch.ceil(torch.log(abs_pos / mid) / math.log((max_position-1) / mid) * (mid - 1)).int() + mid
+        bucket_pos = torch.where(abs_pos <= mid, relative_pos, log_pos * sign).long()
+        return bucket_pos
+
+    def initialize(self):
+        std = math.sqrt(2.0 / (5.0 * self.hidden_size))
+        nn.init.trunc_normal_(self.in_proj_qk.weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        nn.init.trunc_normal_(self.in_proj_v.weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        nn.init.trunc_normal_(self.out_proj.weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        self.in_proj_qk.bias.data.zero_()
+        self.in_proj_v.bias.data.zero_()
+        self.out_proj.bias.data.zero_()
+
+    def compute_attention_scores(self, hidden_states, relative_embedding):
+        key_len, batch_size, _ = hidden_states.size()
+        query_len = key_len
+
+        if self.position_indices.size(0) < query_len:
+            position_indices = torch.arange(query_len, dtype=torch.long).unsqueeze(1) \
+                - torch.arange(query_len, dtype=torch.long).unsqueeze(0)
+            position_indices = self.make_log_bucket_position(position_indices, self.position_bucket_size, 512)
+            position_indices = self.position_bucket_size - 1 + position_indices
+            self.position_indices = position_indices.to(hidden_states.device)
+
+        hidden_states = self.pre_layer_norm(hidden_states)
+
+        query, key = self.in_proj_qk(hidden_states).chunk(2, dim=2)  # shape: [T, B, D]
+        value = self.in_proj_v(hidden_states)  # shape: [T, B, D]
+
+        query = query.reshape(query_len, batch_size * self.num_heads, self.head_size).transpose(0, 1)
+        key = key.reshape(key_len, batch_size * self.num_heads, self.head_size).transpose(0, 1)
+        value = value.view(key_len, batch_size * self.num_heads, self.head_size).transpose(0, 1)
+
+        attention_scores = torch.bmm(query, key.transpose(1, 2) * self.scale)
+
+        query_pos, key_pos = self.in_proj_qk(self.dropout(relative_embedding)).chunk(2, dim=-1)  # shape: [2T-1, D]
+        query_pos = query_pos.view(-1, self.num_heads, self.head_size)  # shape: [2T-1, H, D]
+        key_pos = key_pos.view(-1, self.num_heads, self.head_size)  # shape: [2T-1, H, D]
+
+        query = query.view(batch_size, self.num_heads, query_len, self.head_size)
+        key = key.view(batch_size, self.num_heads, query_len, self.head_size)
+
+        attention_c_p = torch.einsum("bhqd,khd->bhqk", query, key_pos.squeeze(1) * self.scale)
+        attention_p_c = torch.einsum("bhkd,qhd->bhqk", key * self.scale, query_pos.squeeze(1))
+
+        position_indices = self.position_indices[:query_len, :key_len].expand(batch_size, self.num_heads, -1, -1)
+        attention_c_p = attention_c_p.gather(3, position_indices)
+        attention_p_c = attention_p_c.gather(2, position_indices)
+
+        attention_scores = attention_scores.view(batch_size, self.num_heads, query_len, key_len)
+        attention_scores.add_(attention_c_p)
+        attention_scores.add_(attention_p_c)
+
+        return attention_scores, value
+
+    def compute_output(self, attention_probs, value):
+        attention_probs = self.dropout(attention_probs)
+        context = torch.bmm(attention_probs.flatten(0, 1), value)  # shape: [B*H, Q, D]
+        context = context.transpose(0, 1).reshape(context.size(1), -1, self.hidden_size)  # shape: [Q, B, H*D]
+        context = self.out_proj(context)
+        context = self.post_layer_norm(context)
+        context = self.dropout(context)
+        return context
+
+    def forward(self, hidden_states, attention_mask, relative_embedding):
+        attention_scores, value = self.compute_attention_scores(hidden_states, relative_embedding)
+        attention_probs = MaskedSoftmax.apply(attention_scores, attention_mask, -1)
+        return self.compute_output(attention_probs, value), attention_probs.detach()
+
+
+class Embedding(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.word_embedding = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.word_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        self.relative_embedding = nn.Parameter(torch.empty(2 * config.position_bucket_size - 1, config.hidden_size))
+        self.relative_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        self.initialize()
+
+    def initialize(self):
+        std = math.sqrt(2.0 / (5.0 * self.hidden_size))
+        nn.init.trunc_normal_(self.relative_embedding, mean=0.0, std=std, a=-2*std, b=2*std)
+        nn.init.trunc_normal_(self.word_embedding.weight, mean=0.0, std=std, a=-2*std, b=2*std)
+
+    def forward(self, input_ids):
+        word_embedding = self.dropout(self.word_layer_norm(self.word_embedding(input_ids)))
+        relative_embeddings = self.relative_layer_norm(self.relative_embedding)
+        return word_embedding, relative_embeddings
+
+
+#
+# HuggingFace wrappers
+#
+
+class LtgBertPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = LtgBertConfig
+    base_model_prefix = "bnc-bert"
+    supports_gradient_checkpointing = True
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, Encoder):
+            module.activation_checkpointing = value
+
+    def _init_weights(self, _):
+        pass  # everything is already initialized
+
+
+LTG_BERT_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+    Parameters:
+        config ([`LtgBertConfig`]): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+LTG_BERT_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `({0})`):
+            Indices of input sequence tokens in the vocabulary.
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+            [What are attention masks?](../glossary#attention-mask)
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare LTG-BERT transformer outputting raw hidden-states without any specific head on top.",
+    LTG_BERT_START_DOCSTRING,
+)
+class LtgBertModel(LtgBertPreTrainedModel):
+    def __init__(self, config, add_mlm_layer=False):
+        super().__init__(config)
+        self.config = config
+
+        self.embedding = Embedding(config)
+        self.transformer = Encoder(config, activation_checkpointing=False)
+        self.classifier = MaskClassifier(config, self.embedding.word_embedding.weight) if add_mlm_layer else None
+
+    def get_input_embeddings(self):
+        return self.embedding.word_embedding
+
+    def set_input_embeddings(self, value):
+        self.embedding.word_embedding = value
+
+    def get_contextualized_embeddings(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None
+    ) -> List[torch.Tensor]:
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            raise ValueError("You have to specify input_ids")
+
+        batch_size, seq_length = input_shape
+        device = input_ids.device
+
+        if attention_mask is None:
+            attention_mask = torch.zeros(batch_size, seq_length, dtype=torch.bool, device=device)
+        else:
+            attention_mask = ~attention_mask.bool()
+        attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+ 
+        static_embeddings, relative_embedding = self.embedding(input_ids.t())
+        contextualized_embeddings, attention_probs = self.transformer(static_embeddings, attention_mask, relative_embedding)
+        contextualized_embeddings = [e.transpose(0, 1) for e in contextualized_embeddings]
+        last_layer = contextualized_embeddings[-1]
+        contextualized_embeddings = [contextualized_embeddings[0]] + [
+            contextualized_embeddings[i] - contextualized_embeddings[i - 1]
+            for i in range(1, len(contextualized_embeddings))
+        ]
+        return last_layer, contextualized_embeddings, attention_probs
+
+    @add_start_docstrings_to_model_forward(LTG_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutput]:
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
+
+        if not return_dict:
+            return (
+                sequence_output,
+                *([contextualized_embeddings] if output_hidden_states else []),
+                *([attention_probs] if output_attentions else [])
+            )
+
+        return BaseModelOutput(
+            last_hidden_state=sequence_output,
+            hidden_states=contextualized_embeddings if output_hidden_states else None,
+            attentions=attention_probs if output_attentions else None
+        )
+
+
+@add_start_docstrings("""LTG-BERT model with a `language modeling` head on top.""", LTG_BERT_START_DOCSTRING)
+class LtgBertForMaskedLM(LtgBertModel):
+    _keys_to_ignore_on_load_unexpected = ["head"]
+
+    def __init__(self, config):
+        super().__init__(config, add_mlm_layer=True)
+
+    def get_output_embeddings(self):
+        return self.classifier.nonlinearity[-1].weight
+
+    def set_output_embeddings(self, new_embeddings):
+        self.classifier.nonlinearity[-1].weight = new_embeddings
+
+    @add_start_docstrings_to_model_forward(LTG_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
+        subword_prediction = self.classifier(sequence_output)
+
+        masked_lm_loss = None
+        if labels is not None:
+            masked_lm_loss = F.cross_entropy(subword_prediction.flatten(0, 1), labels.flatten())
+
+        if not return_dict:
+            output = (
+                subword_prediction,
+                *([contextualized_embeddings] if output_hidden_states else []),
+                *([attention_probs] if output_attentions else [])
+            )
+            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+        return MaskedLMOutput(
+            loss=masked_lm_loss,
+            logits=subword_prediction,
+            hidden_states=contextualized_embeddings if output_hidden_states else None,
+            attentions=attention_probs if output_attentions else None
+        )
+
+
+class Classifier(nn.Module):
+    def __init__(self, config, num_labels: int):
+        super().__init__()
+
+        drop_out = getattr(config, "classifier_dropout", config.hidden_dropout_prob)
+
+        self.nonlinearity = nn.Sequential(
+            nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False),
+            nn.Linear(config.hidden_size, config.hidden_size),
+            nn.GELU(),
+            nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False),
+            nn.Dropout(drop_out),
+            nn.Linear(config.hidden_size, num_labels)
+        )
+        self.initialize(config.hidden_size)
+
+    def initialize(self, hidden_size):
+        std = math.sqrt(2.0 / (5.0 * hidden_size))
+        nn.init.trunc_normal_(self.nonlinearity[1].weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        nn.init.trunc_normal_(self.nonlinearity[-1].weight, mean=0.0, std=std, a=-2*std, b=2*std)
+        self.nonlinearity[1].bias.data.zero_()
+        self.nonlinearity[-1].bias.data.zero_()
+
+    def forward(self, x):
+        x = self.nonlinearity(x)
+        return x
+
+
+@add_start_docstrings(
+    """
+    LTG-BERT model with a sequence classification/regression head on top (a linear layer on top of the pooled
+    output) e.g. for GLUE tasks.
+    """,
+    LTG_BERT_START_DOCSTRING,
+)
+class LtgBertForSequenceClassification(LtgBertModel):
+    _keys_to_ignore_on_load_unexpected = ["classifier"]
+    _keys_to_ignore_on_load_missing = ["head"]
+
+    def __init__(self, config):
+        super().__init__(config, add_mlm_layer=False)
+
+        self.num_labels = config.num_labels
+        self.head = Classifier(config, self.num_labels)
+
+    @add_start_docstrings_to_model_forward(LTG_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
+        logits = self.head(sequence_output[:, 0, :])
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = nn.MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = nn.CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = nn.BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (
+                logits,
+                *([contextualized_embeddings] if output_hidden_states else []),
+                *([attention_probs] if output_attentions else [])
+            )
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=contextualized_embeddings if output_hidden_states else None,
+            attentions=attention_probs if output_attentions else None
+        )
+
+
+@add_start_docstrings(
+    """
+    LTG-BERT model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
+    Named-Entity-Recognition (NER) tasks.
+    """,
+    LTG_BERT_START_DOCSTRING,
+)
+class LtgBertForTokenClassification(LtgBertModel):
+    _keys_to_ignore_on_load_unexpected = ["classifier"]
+    _keys_to_ignore_on_load_missing = ["head"]
+
+    def __init__(self, config):
+        super().__init__(config, add_mlm_layer=False)
+
+        self.num_labels = config.num_labels
+        self.head = Classifier(config, self.num_labels)
+
+    @add_start_docstrings_to_model_forward(LTG_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
+        logits = self.head(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (
+                logits,
+                *([contextualized_embeddings] if output_hidden_states else []),
+                *([attention_probs] if output_attentions else [])
+            )
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=contextualized_embeddings if output_hidden_states else None,
+            attentions=attention_probs if output_attentions else None
+        )
+
+
+@add_start_docstrings(
+    """
+    LTG-BERT model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
+    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
+    """,
+    LTG_BERT_START_DOCSTRING,
+)
+class LtgBertForQuestionAnswering(LtgBertModel):
+    _keys_to_ignore_on_load_unexpected = ["classifier"]
+    _keys_to_ignore_on_load_missing = ["head"]
+
+    def __init__(self, config):
+        super().__init__(config, add_mlm_layer=False)
+
+        self.num_labels = config.num_labels
+        self.head = Classifier(config, self.num_labels)
+
+    @add_start_docstrings_to_model_forward(LTG_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        start_positions: Optional[torch.Tensor] = None,
+        end_positions: Optional[torch.Tensor] = None
+    ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
+        logits = self.head(sequence_output)
+
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = nn.CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (
+                start_logits,
+                end_logits,
+                *([contextualized_embeddings] if output_hidden_states else []),
+                *([attention_probs] if output_attentions else [])
+            )
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=contextualized_embeddings if output_hidden_states else None,
+            attentions=attention_probs if output_attentions else None
+        )
+
+
+@add_start_docstrings(
+    """
+    LTG-BERT model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
+    softmax) e.g. for RocStories/SWAG tasks.
+    """,
+    LTG_BERT_START_DOCSTRING,
+)
+class LtgBertForMultipleChoice(LtgBertModel):
+    _keys_to_ignore_on_load_unexpected = ["classifier"]
+    _keys_to_ignore_on_load_missing = ["head"]
+
+    def __init__(self, config):
+        super().__init__(config, add_mlm_layer=False)
+
+        self.num_labels = getattr(config, "num_labels", 2)
+        self.head = Classifier(config, self.num_labels)
+
+    @add_start_docstrings_to_model_forward(LTG_BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None
+    ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        num_choices = input_ids.shape[1]
+
+        flat_input_ids = input_ids.view(-1, input_ids.size(-1))
+        flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+
+        sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(flat_input_ids, flat_attention_mask)
+        logits = self.head(sequence_output)
+        reshaped_logits = logits.view(-1, num_choices)
+
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(reshaped_logits, labels)
+
+        if not return_dict:
+            output = (
+                reshaped_logits,
+                *([contextualized_embeddings] if output_hidden_states else []),
+                *([attention_probs] if output_attentions else [])
+            )
+            return ((loss,) + output) if loss is not None else output
+
+        return MultipleChoiceModelOutput(
+            loss=loss,
+            logits=reshaped_logits,
+            hidden_states=contextualized_embeddings if output_hidden_states else None,
+            attentions=attention_probs if output_attentions else None
+        )

special_tokens_map.json

@@ -0,0 +1,10 @@
+{
+  "bos_token": "[BOS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[EOS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}
+

tokenizer_config.json

@@ -0,0 +1,5 @@
+{
+  "model_max_length": 1000000000000000019884624838656,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}
+

zeroshot/anaphor_agreement/eval_results.json

@@ -0,0 +1 @@
+{"eval_accuracy": 0.5506134969325154}

zeroshot/anaphor_agreement/predictions.txt

@@ -0,0 +1,1957 @@
+index	prediction
+0	Katherine can't help herself.
+1	Karla could listen to himself.
+2	Marie won't think about itself.
+3	Mark hasn't discussed itself.
+4	Stephen impressed himself.
+5	Christina boasts about herself.
+6	Suzanne would irritate itself.
+7	Rose couldn't reference herself.
+8	Paul should stun herself.
+9	Kristen praises himself.
+10	Tamara might work with himself.
+11	Valerie remembers itself.
+12	Kristen hasn't worked with herself.
+13	Kendra disturbed herself.
+14	Lawrence had boasted about himself.
+15	Jason wasn't listening to herself.
+16	Sabrina disturbs himself.
+17	Some essay doesn't disagree with itself.
+18	Kenneth wasn't admiring itself.
+19	Melanie had referenced herself.
+20	Larry reveals itself.
+21	Samantha isn't watching herself.
+22	Rachel should like herself.
+23	Heather has helped herself.
+24	Lori has found herself.
+25	Christina hadn't shocked herself.
+26	Theresa wasn't alarming herself.
+27	Jacqueline works with himself.
+28	Alan hires himself.
+29	April isn't complaining about herself.
+30	Scott had questioned herself.
+31	Wayne helps itself.
+32	Amelia heals herself.
+33	Debra is disgusting itself.
+34	Irene has admired herself.
+35	Brad distracted himself.
+36	Sherry wouldn't cure herself.
+37	Ronald upset himself.
+38	Catherine hasn't noticed himself.
+39	Susan shouldn't criticize himself.
+40	Dawn has described herself.
+41	Joseph describes herself.
+42	Catherine isn't hurting herself.
+43	Tracy alarmed herself.
+44	Heather has embarrassed himself.
+45	Sharon hurt herself.
+46	Marie might admire herself.
+47	Susan did love itself.
+48	Russell hasn't hired himself.
+49	Every man insulted itself.
+50	Marie had insulted himself.
+51	Steve wasn't describing itself.
+52	Roger had known himself.
+53	Larry boasts about herself.
+54	Kayla complains about himself.
+55	A bird had found herself.
+56	Ellen wasn't worrying herself.
+57	Debra wasn't investigating herself.
+58	Larry referenced herself.
+59	Joel does complain about himself.
+60	Gregory won't attack himself.
+61	Christine hasn't questioned herself.
+62	Natalie returned to herself.
+63	Becca was approaching herself.
+64	Sheila saw himself.
+65	Jacqueline likes himself.
+66	Douglas healed himself.
+67	Colleen wasn't concealing herself.
+68	Catherine disturbed itself.
+69	Some company astounded herself.
+70	Todd likes himself.
+71	Tamara couldn't flee from himself.
+72	Rose praised herself.
+73	Joseph isn't annoying herself.
+74	Jodi liked herself.
+75	Ronald fled from himself.
+76	Tina fires herself.
+77	Michelle saw itself.
+78	Renee might admire herself.
+79	A girl couldn't reveal herself.
+80	Curtis won't describe himself.
+81	Patricia does respect himself.
+82	Valerie forgets itself.
+83	Deborah had annoyed herself.
+84	Clyde hasn't hated himself.
+85	Andrea does kiss itself.
+86	Irene isn't irritating itself.
+87	Stacy hadn't sounded like itself.
+88	Rose has shocked itself.
+89	George wasn't hurting himself.
+90	Amy would question himself.
+91	Tonya sees herself.
+92	Christina hasn't hired herself.
+93	Pamela did reveal himself.
+94	Diane had concealed himself.
+95	Melinda hasn't argued about herself.
+96	Deanna insults itself.
+97	Gregory isn't hurting himself.
+98	Rebecca should shock herself.
+99	Amanda stunned herself.
+100	That dancer wouldn't aggravate herself.
+101	Colleen talks to itself.
+102	Brian hasn't healed himself.
+103	Natalie is curing himself.
+104	Rhonda was upsetting herself.
+105	Peter wasn't disgusting herself.
+106	Kristen isn't aggravating herself.
+107	Victoria hid himself.
+108	Becca has seen himself.
+109	Sherry can't forget herself.
+110	Tracy had distracted itself.
+111	Stephanie is talking to himself.
+112	Carl describes herself.
+113	Jeffrey didn't describe himself.
+114	Jacqueline hadn't concealed herself.
+115	Tracy isn't upsetting herself.
+116	Sonia hasn't discovered herself.
+117	Laura does find herself.
+118	Jodi is confusing herself.
+119	Florence disliked herself.
+120	Alexander complains about himself.
+121	Melissa complains about herself.
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+1514	These drivers questioned himself.
+1515	The girls discovered herself.
+1516	Cheryl couldn't care for themselves.
+1517	Most stories disagreed with themselves.
+1518	Walter could know himself.
+1519	Every library impresses themselves.
+1520	Michelle hasn't concealed herself.
+1521	Linda stunned herself.
+1522	Some men were approaching themselves.
+1523	All ladies work with themselves.
+1524	Those guys have concealed itself.
+1525	Homer can find himself.
+1526	Those pedestrians bored himself.
+1527	Sandra could upset herself.
+1528	Heidi has distracted themselves.
+1529	Peter isn't kissing himself.
+1530	These students aren't aggravating itself.
+1531	Guy bothered himself.
+1532	Tara wasn't concealing themselves.
+1533	Most teachers concealed himself.
+1534	The university can't astound itself.
+1535	Some waiters do visit itself.
+1536	Those cashiers have concealed himself.
+1537	The children can know themselves.
+1538	Jacqueline couldn't love herself.
+1539	All drivers would praise themselves.
+1540	Catherine does complain about themselves.
+1541	Beth had upset herself.
+1542	These senators had seen herself.
+1543	Those schools don't irritate themselves.
+1544	Waitresses irritate himself.
+1545	A lot of teenagers have visited himself.
+1546	All adults worked with herself.
+1547	The teenagers scared itself.
+1548	Valerie wasn't embarrassing herself.
+1549	Rhonda does flee from themselves.
+1550	All patients care for themselves.
+1551	Sara can visit themselves.
+1552	All doctors wouldn't distract herself.
+1553	Russell sounds like themselves.
+1554	All actresses approached themselves.
+1555	These people had revealed himself.
+1556	James isn't talking to himself.
+1557	Jennifer is concealing themselves.
+1558	Debra is discovering herself.
+1559	Brett thought about himself.
+1560	Jane had upset herself.
+1561	All doctors haven't respected themselves.
+1562	Marla won't hurt themselves.
+1563	These cashiers aren't concealing themselves.
+1564	The Impressionists haven't respected itself.
+1565	Teresa has remembered herself.
+1566	Jessica had fired herself.
+1567	Mark doesn't flee from themselves.
+1568	Some museum distracted itself.
+1569	Most children aren't upsetting herself.
+1570	The company upsets itself.
+1571	Noah would conceal themselves.
+1572	Kendra might shock herself.
+1573	The teachers weren't arguing about itself.
+1574	Most universities upset himself.
+1575	Some bank distracts itself.
+1576	These students don't boast about themselves.
+1577	Candice shouldn't know herself.
+1578	Melinda concealed herself.
+1579	Natalie wouldn't hurt herself.
+1580	Sandra fled from herself.
+1581	April kissed herself.
+1582	Travis approached themselves.
+1583	A lot of senators watched herself.
+1584	Grace cared for herself.
+1585	That company annoys itself.
+1586	Teenagers were working with itself.
+1587	Children haven't appreciated himself.
+1588	Candice isn't visiting herself.
+1589	Many libraries are stunning herself.
+1590	Matt doesn't find themselves.
+1591	The girls hadn't scared themselves.
+1592	Tiffany isn't criticizing herself.
+1593	Bill can reference themselves.
+1594	Spain worries itself.
+1595	Lawrence had thought about himself.
+1596	Connie isn't boasting about herself.
+1597	Todd isn't revealing himself.
+1598	Mark had talked about themselves.
+1599	The Borgias were escaping from themselves.
+1600	Phillip hasn't impressed themselves.
+1601	Beverly hated herself.
+1602	Students are working with themselves.
+1603	These men kiss themselves.
+1604	That play isn't disagreeing with themselves.
+1605	Guests haven't questioned herself.
+1606	James distracted himself.
+1607	Most girls knew himself.
+1608	All men can hug himself.
+1609	Those actors admired itself.
+1610	A documentary should disagree with themselves.
+1611	The guy had admired themselves.
+1612	Dancers sounded like themselves.
+1613	Some essay is disagreeing with themselves.
+1614	Susan wasn't talking about herself.
+1615	Amanda would stun themselves.
+1616	Melanie would reference herself.
+1617	The cashiers help herself.
+1618	The adults can return to itself.
+1619	Holly hadn't worked with herself.
+1620	Jessica won't impress herself.
+1621	A lot of waitresses astound itself.
+1622	A lot of actors might visit herself.
+1623	Actresses hadn't hidden himself.
+1624	Those adults hadn't irritated itself.
+1625	Some actresses discover herself.
+1626	Stephanie couldn't talk to herself.
+1627	Many universities haven't worried himself.
+1628	Dancers hated himself.
+1629	Homer hadn't remembered himself.
+1630	All actors wouldn't forget itself.
+1631	A lot of girls hadn't hidden himself.
+1632	All dancers discussed herself.
+1633	Dana impressed themselves.
+1634	Some waitress did discuss herself.
+1635	Heather wasn't healing herself.
+1636	Some ladies aren't worrying themselves.
+1637	A lot of drivers fired itself.
+1638	Most committees had astounded itself.
+1639	Most adults disgusted himself.
+1640	Many cashiers cured himself.
+1641	Debra was scaring herself.
+1642	Some universities aren't bothering themselves.
+1643	Vincent astounded himself.
+1644	Those students could return to herself.
+1645	Alice might confuse herself.
+1646	All cashiers aggravated itself.
+1647	Deborah can't conceal herself.
+1648	A lot of drivers return to itself.
+1649	Carlos had discussed themselves.
+1650	A lot of girls can watch herself.
+1651	Karla hadn't hurt herself.
+1652	Jessica escaped from herself.
+1653	The Impressionists weren't appreciating herself.
+1654	Carl can bother themselves.
+1655	Tamara hasn't kissed themselves.
+1656	Edward couldn't praise themselves.
+1657	Most guys might stun themselves.
+1658	A lot of teachers will worry itself.
+1659	Brenda wasn't concealing herself.
+1660	A lot of women would flee from themselves.
+1661	The Lutherans had scared themselves.
+1662	A lot of girls weren't shocking herself.
+1663	All adults thought about themselves.
+1664	This dancer shouldn't aggravate herself.
+1665	Most doctors are hurting themselves.
+1666	The drivers hadn't returned to themselves.
+1667	Nicole should complain about herself.
+1668	Heather remembered herself.
+1669	The school bothers itself.
+1670	The guy distracted themselves.
+1671	Walter stunned himself.
+1672	Some guy returned to himself.
+1673	Tonya helped herself.
+1674	Michael was hiding themselves.
+1675	Many ladies conceal herself.
+1676	Some cashiers talk about themselves.
+1677	All actors will dislike itself.
+1678	Margaret healed herself.
+1679	Most cashiers have distracted himself.
+1680	Adults worked with itself.
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+1682	Kendra impressed herself.
+1683	Melissa will approach herself.
+1684	Those cashiers had sounded like himself.
+1685	The teenagers annoyed herself.
+1686	Most guys were hiding themselves.
+1687	Many actors haven't listened to themselves.
+1688	Most pedestrians might observe themselves.
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+1690	Becca has embarrassed herself.
+1691	Some guests conceal himself.
+1692	Tina remembered themselves.
+1693	Thomas couldn't investigate himself.
+1694	Waiters impressed itself.
+1695	The Lutherans found itself.
+1696	Kristen shouldn't heal herself.
+1697	The Borgias hadn't listened to itself.
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+1699	All people loved themselves.
+1700	Some guests have fired themselves.
+1701	Elizabeth hadn't worried herself.
+1702	Most pedestrians have kissed herself.
+1703	These banks aggravate itself.
+1704	Claire wasn't talking to themselves.
+1705	Many cashiers hadn't worked with herself.
+1706	Alicia concealed herself.
+1707	Bradley isn't talking about themselves.
+1708	Steven has hurt himself.
+1709	Many men are impressing themselves.
+1710	Many boys were healing himself.
+1711	Jessica is scaring herself.
+1712	Carol does worry herself.
+1713	Dennis would watch themselves.
+1714	Samuel had concealed himself.
+1715	Most senators healed themselves.
+1716	A lot of girls could observe herself.
+1717	Gerald hasn't astounded themselves.
+1718	Elizabeth shouldn't remember herself.
+1719	Steve insulted himself.
+1720	Laurie was hiding herself.
+1721	The Lutherans distracted themselves.
+1722	Regina was upsetting herself.
+1723	Jacqueline disgusted herself.
+1724	Carolyn was concealing herself.
+1725	Senators haven't embarrassed itself.
+1726	Richard disgusted himself.
+1727	Kenneth had embarrassed himself.
+1728	Most girls conceal herself.
+1729	Paula watched herself.
+1730	The Impressionists escape from itself.
+1731	The actresses notice themselves.
+1732	Sharon wasn't disgusting herself.
+1733	This company aggravates themselves.
+1734	The fish found himself.
+1735	Teenagers hadn't complained about themselves.
+1736	Bill is discovering himself.
+1737	Most guys criticize themselves.
+1738	A lot of cashiers aren't working with himself.
+1739	Charles couldn't hate himself.
+1740	Joseph hasn't disturbed themselves.
+1741	Senators might talk about themselves.
+1742	Laura hasn't hugged herself.
+1743	The teenagers think about herself.
+1744	Carlos hasn't referenced themselves.
+1745	Steven had returned to himself.
+1746	A lot of waiters were listening to herself.
+1747	Steve is helping themselves.
+1748	Colleen will aggravate herself.
+1749	Noah shouldn't argue about himself.
+1750	Travis won't approach himself.
+1751	Candice hadn't thought about herself.
+1752	Every waitress boasted about herself.
+1753	Guests concealed herself.
+1754	Melissa had bored herself.
+1755	Meredith hasn't revealed herself.
+1756	The actors were observing herself.
+1757	Many teenagers haven't forgotten herself.
+1758	Those museums are stunning himself.
+1759	Most girls didn't aggravate themselves.
+1760	Kevin wasn't disturbing themselves.
+1761	Most patients have investigated itself.
+1762	Gina didn't see herself.
+1763	The man praised themselves.
+1764	Carolyn approached herself.
+1765	Gary was scaring themselves.
+1766	The Borgias were admiring themselves.
+1767	People can't approach themselves.
+1768	Those people should remember herself.
+1769	Most women weren't helping itself.
+1770	Most actors hug herself.
+1771	All banks had upset himself.
+1772	Kimberley had cured herself.
+1773	David is questioning themselves.
+1774	Most waitresses wouldn't sound like themselves.
+1775	Renee bothered themselves.
+1776	Homer fired himself.
+1777	The Impressionists haven't found themselves.
+1778	Andrea didn't dislike themselves.
+1779	Girls kiss himself.
+1780	These organizations stunned themselves.
+1781	Rebecca hired herself.
+1782	Patrick approached himself.
+1783	Mark hasn't referenced themselves.
+1784	Many adults have hired herself.
+1785	A lot of people escaped from itself.
+1786	Some students shouldn't remember themselves.
+1787	Those students listen to herself.
+1788	Stephanie will hurt themselves.
+1789	Sherry does observe themselves.
+1790	Kenneth questioned himself.
+1791	April sounded like themselves.
+1792	Tara hasn't cured themselves.
+1793	Sherry is hiring themselves.
+1794	The boy cured himself.
+1795	Rose loved herself.
+1796	Nina wasn't leaving herself.
+1797	Susan escaped from themselves.
+1798	Those students visit themselves.
+1799	These ladies weren't leaving himself.
+1800	Mitchell can't cure himself.
+1801	The man is firing themselves.
+1802	The girl is kissing herself.
+1803	Steve scared themselves.
+1804	Tamara has stunned herself.
+1805	Florence left herself.
+1806	Some teenagers had disgusted himself.
+1807	Mark can't find himself.
+1808	Edward hadn't discovered themselves.
+1809	Some teachers can't love themselves.
+1810	Doctors could annoy herself.
+1811	Women concealed herself.
+1812	Lisa was concealing herself.
+1813	These girls had respected himself.
+1814	A lot of associations shock herself.
+1815	Brett is astounding themselves.
+1816	Claire disliked herself.
+1817	Many waiters were listening to herself.
+1818	Aaron escaped from himself.
+1819	Jessica will fire herself.
+1820	Amelia will flee from herself.
+1821	Joseph isn't shocking himself.
+1822	The Impressionists don't question themselves.
+1823	Many students aren't aggravating themselves.
+1824	All drivers are attacking themselves.
+1825	Students had approached herself.
+1826	Mitchell has insulted himself.
+1827	Sheila is boasting about themselves.
+1828	The doctors are observing herself.
+1829	Suzanne has alarmed herself.
+1830	Stephen has helped himself.
+1831	Most doctors weren't observing himself.
+1832	David couldn't scare himself.
+1833	Many children escape from herself.
+1834	Many customers wouldn't bother himself.
+1835	That hospital upsets itself.
+1836	Guy described himself.
+1837	Carol scared herself.
+1838	Susan wasn't watching herself.
+1839	These children wouldn't admire herself.
+1840	Many banks confused herself.
+1841	Patricia disliked herself.
+1842	Those students complained about himself.
+1843	The Lutherans can't admire themselves.
+1844	Children returned to himself.
+1845	A lot of teachers had talked about herself.
+1846	Janet had hired herself.
+1847	Many senators investigate himself.
+1848	Some guys might annoy himself.
+1849	These guests praise themselves.
+1850	All actresses aren't firing themselves.
+1851	A lot of pedestrians won't forget itself.
+1852	Sarah wasn't approaching herself.
+1853	Guy talked to himself.
+1854	William hasn't hurt themselves.
+1855	Actors hadn't concealed itself.
+1856	Customers don't respect themselves.
+1857	Some children can visit themselves.
+1858	Many cashiers have questioned herself.
+1859	All senators haven't impressed themselves.
+1860	Dana investigated herself.
+1861	Lisa couldn't flee from themselves.
+1862	April isn't noticing herself.
+1863	Many schools irritated itself.
+1864	Emily had distracted herself.
+1865	Deborah criticized herself.
+1866	These adults have disgusted themselves.
+1867	Deborah wasn't helping herself.
+1868	Tonya thought about herself.
+1869	Those cashiers hadn't kissed itself.
+1870	Ann could think about herself.
+1871	Children can't escape from themselves.
+1872	Katherine shocked herself.
+1873	Those guys approach herself.
+1874	A hospital aggravates themselves.
+1875	Some women aren't helping themselves.
+1876	Susan hugged herself.
+1877	Many adults cured herself.
+1878	The ladies bore herself.
+1879	Gerald had concealed himself.
+1880	Amy had watched herself.
+1881	Bill should shock himself.
+1882	Sandra respected herself.
+1883	Melanie isn't talking about herself.
+1884	The Impressionists aren't criticizing herself.
+1885	Waiters were escaping from themselves.
+1886	Some guests astound themselves.
+1887	Marcus saw himself.
+1888	Robert talked about himself.
+1889	All girls forgot himself.
+1890	These ladies aren't shocking themselves.
+1891	April has cared for themselves.
+1892	Carolyn shouldn't hire themselves.
+1893	Ellen knew herself.
+1894	Some girls help themselves.
+1895	The boys weren't referencing themselves.
+1896	Donald does cure himself.
+1897	Robert has worried himself.
+1898	Many waitresses attacked themselves.
+1899	Children appreciate himself.
+1900	Benjamin had argued about themselves.
+1901	Most guests won't flee from themselves.
+1902	Most dancers distract himself.
+1903	The Borgias haven't approached herself.
+1904	Those pedestrians aren't healing herself.
+1905	Boys hurt herself.
+1906	Douglas hasn't bothered themselves.
+1907	These women observed herself.
+1908	Cheryl hasn't criticized themselves.
+1909	Sara shocked herself.
+1910	Maria thought about themselves.
+1911	Donald respected himself.
+1912	All actors aren't noticing themselves.
+1913	Most pedestrians had irritated themselves.
+1914	Jessica watched herself.
+1915	All actresses couldn't reveal herself.
+1916	Every organization aggravates themselves.
+1917	The Borgias are admiring itself.
+1918	John stunned himself.
+1919	Irene wasn't concealing herself.
+1920	The governments hadn't alarmed itself.
+1921	Many waitresses were scaring itself.
+1922	A story disagreed with themselves.
+1923	Craig will annoy himself.
+1924	Martha shocked herself.
+1925	Tara wasn't describing themselves.
+1926	The teachers disturb themselves.
+1927	Some ladies disagree with themselves.
+1928	Most adults didn't help himself.
+1929	Some people are talking about themselves.
+1930	Alicia can heal herself.
+1931	Alan escaped from themselves.
+1932	Raymond fled from themselves.
+1933	Benjamin doesn't distract themselves.
+1934	Most pedestrians didn't visit itself.
+1935	Melanie hasn't visited themselves.
+1936	Ella shouldn't listen to herself.
+1937	Most pedestrians hadn't disgusted himself.
+1938	The dancers were attacking himself.
+1939	Ronald hurt himself.
+1940	The waiters couldn't reveal herself.
+1941	Lisa did find herself.
+1942	Christina can't respect herself.
+1943	Donald hadn't kissed themselves.
+1944	Heidi isn't questioning herself.
+1945	Those boys wouldn't heal themselves.
+1946	Paul isn't stunning themselves.
+1947	George had bothered himself.
+1948	Those patients did disgust themselves.
+1949	The Lutherans couldn't visit itself.
+1950	Some customers shouldn't hire himself.
+1951	Those guys cured itself.
+1952	Tara wasn't referencing themselves.
+1953	Most students attacked itself.
+1954	A lot of actors upset herself.
+1955	Keith could respect himself.

zeroshot/argument_structure/eval_results.json

@@ -0,0 +1 @@
+{"eval_accuracy": 0.5603782735208536}

zeroshot/binding/eval_results.json

@@ -0,0 +1 @@
+{"eval_accuracy": 0.4749183734045711}

zeroshot/control_raising/eval_results.json

@@ -0,0 +1 @@
+{"eval_accuracy": 0.5859478568272205}

zeroshot/determiner_noun_agreement/eval_results.json

@@ -0,0 +1 @@
+{"eval_accuracy": 0.4969504110315566}

zeroshot/ellipsis/eval_results.json

@@ -0,0 +1 @@
+{"eval_accuracy": 0.39838337182448036}

zeroshot/filler_gap/eval_results.json

@@ -0,0 +1 @@
+{"eval_accuracy": 0.4548708372237784}

zeroshot/hypernym/eval_results.json

@@ -0,0 +1 @@
+{"eval_accuracy": 0.49186046511627907}

zeroshot/hypernym/predictions.txt

@@ -0,0 +1,861 @@
+index	prediction
+0	If she has a dog, it must be the case that she has a mammal.
+1	She doesn't have a chihuahua, so she doesn't have a dog.
+2	She doesn't have a dog, so she doesn't have a mammal.
+3	If she has a chihuahua, it must be the case that she has a dog.
+4	If she has a chihuahua, then she has a cat.
+5	If she has a chihuahua, then she doesn't have a cat.
+6	The girl has a dog, therefore the girl has a poodle.
+7	If the girl doesn't have a dog, then the girl doesn't have a poodle.
+8	If the girl doesn't have a dog, it must be the case that the girl doesn't have a mammal.
+9	If the girl has a poodle, it must be the case that the girl has a dog.
+10	The girl has a poodle, therefore the girl has a dog.
+11	If the girl has a poodle, it must be the case that the girl doesn't have a cat.
+12	If David has a dog, that means David has a poodle.
+13	David doesn't have a poodle, so David doesn't have a dog.
+14	If David doesn't have a dog, then David doesn't have a poodle.
+15	David has a dog, so David has a poodle.
+16	If David has a poodle, that means David has a cat.
+17	If David has a poodle, that means David doesn't have a cat.
+18	He has a dog, therefore he has a mammal.
+19	If he doesn't have a dog, that means he doesn't have a poodle.
+20	He doesn't have a dog, therefore he doesn't have a mammal.
+21	If he has a dog, it must be the case that he has a poodle.
+22	If he has a poodle, that means he has a cat.
+23	He has a poodle, therefore he doesn't have a cat.
+24	He has a dog, therefore he has a pit bull.
+25	If he doesn't have a pit bull, then he doesn't have a dog.
+26	He doesn't have a dog, therefore he doesn't have a pit bull.
+27	If he has a dog, that means he has a pit bull.
+28	If he has a pit bull, then he has a cat.
+29	If he has a pit bull, then he doesn't have a cat.
+30	David has a dog, therefore David has a mammal.
+31	David doesn't have a dog, therefore David doesn't have a mammal.
+32	If she has a dog, it must be the case that she has a mammal.
+33	She doesn't have a dog, therefore she doesn't have a mammal.
+34	The boy has a dog, therefore the boy has a poodle.
+35	The boy doesn't have a dog, therefore the boy doesn't have a poodle.
+36	If the boy doesn't have a dog, it must be the case that the boy doesn't have a mammal.
+37	The boy has a dog, therefore the boy has a poodle.
+38	The boy has a poodle, so the boy has a dog.
+39	The boy has a poodle, so the boy doesn't have a cat.
+40	If David has a dog, that means David has a pit bull.
+41	David doesn't have a pit bull, therefore David doesn't have a dog.
+42	David doesn't have a dog, so David doesn't have a pit bull.
+43	If David has a pit bull, it must be the case that David has a dog.
+44	David has a pit bull, so David has a cat.
+45	David has a pit bull, therefore David doesn't have a cat.
+46	If he has a dog, that means he has a mammal.
+47	If he doesn't have a chihuahua, it must be the case that he doesn't have a dog.
+48	If he doesn't have a dog, that means he doesn't have a mammal.
+49	If he has a dog, it must be the case that he has a chihuahua.
+50	He has a chihuahua, so he has a cat.
+51	He has a chihuahua, so he doesn't have a cat.
+52	The girl has a dog, therefore the girl has a mammal.
+53	The girl doesn't have a dog, so the girl doesn't have a mammal.
+54	If David has a dog, it must be the case that David has a mammal.
+55	If David doesn't have a chihuahua, then David doesn't have a dog.
+56	If David doesn't have a dog, that means David doesn't have a mammal.
+57	If David has a dog, then David has a chihuahua.
+58	David has a chihuahua, so David has a cat.
+59	David has a chihuahua, therefore David doesn't have a cat.
+60	If he built a house, that means he built a building.
+61	If he didn't build a mansion, then he didn't build a house.
+62	If he didn't build a house, it must be the case that he didn't build a building.
+63	If he built a house, then he built a mansion.
+64	If he built a mansion, then he built a house.
+65	If he built a mansion, then he didn't build a church.
+66	If she built a house, then she built a building.
+67	She didn't build a house, so she didn't build a mansion.
+68	If she didn't build a house, it must be the case that she didn't build a building.
+69	If she built a mansion, that means she built a house.
+70	She built a mansion, so she built a church.
+71	If she built a mansion, it must be the case that she didn't build a house.
+72	The contractor built a house, therefore the contractor built a building.
+73	If the contractor didn't build a mansion, it must be the case that the contractor didn't build a house.
+74	The contractor didn't build a house, therefore the contractor didn't build a building.
+75	If the contractor built a mansion, then the contractor built a house.
+76	If the contractor built a mansion, then the contractor built a house.
+77	The contractor built a mansion, therefore the contractor didn't build a church.
+78	If the construction company built a house, that means the construction company built a building.
+79	If the construction company didn't build a house, then the construction company didn't build a mansion.
+80	If the construction company didn't build a house, then the construction company didn't build a building.
+81	If the construction company built a house, it must be the case that the construction company built a mansion.
+82	If the construction company built a mansion, that means the construction company built a church.
+83	The construction company built a mansion, so the construction company didn't build a church.
+84	If he built a house, it must be the case that he built a building.
+85	If he didn't build a cottage, then he didn't build a house.
+86	He didn't build a house, so he didn't build a building.
+87	If he built a house, that means he built a cottage.
+88	If he built a cottage, that means he built a church.
+89	If he built a cottage, that means he didn't build a church.
+90	If she built a house, it must be the case that she built a building.
+91	If she didn't build a cottage, it must be the case that she didn't build a house.
+92	If she didn't build a house, that means she didn't build a building.
+93	If she built a cottage, that means she built a house.
+94	She built a cottage, therefore she built a house.
+95	She built a cottage, so she didn't build a house.
+96	If the contractor built a house, it must be the case that the contractor built a building.
+97	If the contractor didn't build a cottage, that means the contractor didn't build a house.
+98	The contractor didn't build a house, so the contractor didn't build a building.
+99	If the contractor built a cottage, that means the contractor built a house.
+100	If the contractor built a cottage, that means the contractor built a church.
+101	The contractor built a cottage, so the contractor didn't build a church.
+102	If the construction company built a house, then the construction company built a building.
+103	The construction company didn't build a house, therefore the construction company didn't build a cottage.
+104	The construction company didn't build a house, so the construction company didn't build a building.
+105	If the construction company built a house, it must be the case that the construction company built a cottage.
+106	The construction company built a cottage, therefore the construction company built a house.
+107	The construction company built a cottage, so the construction company didn't build a house.
+108	If he is wearing pants, that means he is wearing clothing.
+109	He is wearing pants, so he is wearing jeans.
+110	If he is wearing jeans, then he is wearing socks.
+111	If she is wearing pants, it must be the case that she is wearing clothing.
+112	She is wearing pants, so she is wearing jeans.
+113	If she is wearing jeans, then she is wearing pants.
+114	The boy is wearing pants, so the boy is wearing clothing.
+115	If the boy is wearing pants, then the boy is wearing jeans.
+116	If the boy is wearing jeans, it must be the case that the boy is wearing pants.
+117	The girl is wearing pants, therefore the girl is wearing clothing.
+118	The girl is wearing jeans, so the girl is wearing pants.
+119	If the girl is wearing jeans, it must be the case that the girl is wearing pants.
+120	She is chopping a pepper, therefore she is chopping a chili.
+121	If she isn't chopping a pepper, it must be the case that she isn't chopping a chili.
+122	The chef is chopping a pepper, so the chef is chopping a chili.
+123	The chef isn't chopping a pepper, therefore the chef isn't chopping a chili.
+124	If the cook is chopping a pepper, that means the cook is chopping a chili.
+125	If the cook isn't chopping a pepper, that means the cook isn't chopping a chili.
+126	If he is chopping a pepper, that means he is chopping a chili.
+127	He isn't chopping a pepper, therefore he isn't chopping a chili.
+128	The speaker is playing a song, therefore the speaker is playing a ballad.
+129	The speaker isn't playing a ballad, so the speaker isn't playing a song.
+130	The speaker isn't playing a song, so the speaker isn't playing a ballad.
+131	If the speaker is playing a song, that means the speaker is playing a ballad.
+132	The speaker is playing a ballad, therefore the speaker is playing a song.
+133	The speaker is playing a ballad, so the speaker isn't playing a song.
+134	He is playing a song, so he is playing an anthem.
+135	If he isn't playing an anthem, it must be the case that he isn't playing a song.
+136	If he isn't playing a song, then he isn't playing an anthem.
+137	He is playing a song, therefore he is playing an anthem.
+138	He is playing an anthem, therefore he is playing a song.
+139	He is playing an anthem, therefore he isn't playing a song.
+140	She is playing a song, so she is playing an anthem.
+141	If she isn't playing an anthem, it must be the case that she isn't playing a song.
+142	If she isn't playing a song, it must be the case that she isn't playing an anthem.
+143	If she is playing a song, it must be the case that she is playing an anthem.
+144	If she is playing an anthem, it must be the case that she is playing a song.
+145	If she is playing an anthem, then she isn't playing a song.
+146	She is playing a song, therefore she is playing a ballad.
+147	She isn't playing a ballad, therefore she isn't playing a song.
+148	If she isn't playing a song, then she isn't playing a ballad.
+149	If she is playing a song, then she is playing a ballad.
+150	She is playing a ballad, therefore she is playing a song.
+151	She is playing a ballad, therefore she isn't playing a song.
+152	If the musician is playing a song, then the musician is playing an anthem.
+153	If the musician isn't playing an anthem, it must be the case that the musician isn't playing a song.
+154	If the musician isn't playing a song, it must be the case that the musician isn't playing an anthem.
+155	The musician is playing a song, therefore the musician is playing an anthem.
+156	If the musician is playing an anthem, then the musician is playing a song.
+157	If the musician is playing an anthem, it must be the case that the musician isn't playing a song.
+158	If he is playing a song, then he is playing a ballad.
+159	If he isn't playing a song, it must be the case that he isn't playing a ballad.
+160	He isn't playing a song, so he isn't playing a musical piece.
+161	He is playing a ballad, therefore he is playing a song.
+162	If he is playing a ballad, that means he is playing a song.
+163	If he is playing a ballad, then he isn't playing a song.
+164	If the musician is playing a song, that means the musician is playing a ballad.
+165	If the musician isn't playing a song, then the musician isn't playing a ballad.
+166	The musician isn't playing a song, so the musician isn't playing a ballad.
+167	If the musician is playing a song, that means the musician is playing a ballad.
+168	If the musician is playing a ballad, that means the musician is playing a song.
+169	If the musician is playing a ballad, then the musician isn't playing a song.
+170	The speaker is playing a song, therefore the speaker is playing an anthem.
+171	The speaker isn't playing an anthem, therefore the speaker isn't playing a song.
+172	If the speaker isn't playing a song, that means the speaker isn't playing an anthem.
+173	The speaker is playing an anthem, therefore the speaker is playing a song.
+174	The speaker is playing an anthem, therefore the speaker is playing a song.
+175	If the speaker is playing an anthem, then the speaker isn't playing a song.
+176	If he is growing herbs, then he is growing plants.
+177	If he isn't growing herbs, that means he isn't growing basil.
+178	If he isn't growing herbs, that means he isn't growing plants.
+179	If he is growing basil, that means he is growing herbs.
+180	If he is growing basil, then he is growing flowers.
+181	He is growing basil, therefore he isn't growing flowers.
+182	If the farm is growing herbs, it must be the case that the farm is growing plants.
+183	If the farm isn't growing basil, it must be the case that the farm isn't growing herbs.
+184	If the farm isn't growing herbs, that means the farm isn't growing plants.
+185	The farm is growing basil, so the farm is growing herbs.
+186	If the farm is growing basil, that means the farm is growing flowers.
+187	If the farm is growing basil, that means the farm isn't growing flowers.
+188	If she is growing herbs, then she is growing plants.
+189	If she isn't growing herbs, that means she isn't growing rosemary.
+190	She isn't growing herbs, so she isn't growing plants.
+191	If she is growing herbs, it must be the case that she is growing rosemary.
+192	She is growing rosemary, so she is growing flowers.
+193	If she is growing rosemary, then she isn't growing flowers.
+194	She is growing herbs, so she is growing plants.
+195	If she isn't growing herbs, it must be the case that she isn't growing basil.
+196	She isn't growing herbs, so she isn't growing plants.
+197	She is growing basil, therefore she is growing herbs.
+198	If she is growing basil, then she is growing flowers.
+199	If she is growing basil, then she isn't growing flowers.
+200	The farm is growing herbs, therefore the farm is growing plants.
+201	If the farm isn't growing rosemary, that means the farm isn't growing herbs.
+202	If the farm isn't growing herbs, that means the farm isn't growing plants.
+203	The farm is growing rosemary, therefore the farm is growing herbs.
+204	If the farm is growing rosemary, then the farm is growing flowers.
+205	If the farm is growing rosemary, then the farm isn't growing flowers.
+206	He is growing herbs, therefore he is growing plants.
+207	If he isn't growing herbs, it must be the case that he isn't growing rosemary.
+208	If he isn't growing herbs, then he isn't growing plants.
+209	He is growing herbs, therefore he is growing rosemary.
+210	If he is growing rosemary, then he is growing flowers.
+211	He is growing rosemary, therefore he isn't growing flowers.
+212	If the gardener is growing herbs, it must be the case that the gardener is growing plants.
+213	The gardener isn't growing basil, so the gardener isn't growing herbs.
+214	If the gardener isn't growing herbs, it must be the case that the gardener isn't growing plants.
+215	If the gardener is growing herbs, then the gardener is growing basil.
+216	The gardener is growing basil, therefore the gardener is growing flowers.
+217	If the gardener is growing basil, that means the gardener isn't growing flowers.
+218	If the gardener is growing herbs, then the gardener is growing plants.
+219	If the gardener isn't growing herbs, that means the gardener isn't growing rosemary.
+220	If the gardener isn't growing herbs, it must be the case that the gardener isn't growing plants.
+221	If the gardener is growing herbs, it must be the case that the gardener is growing rosemary.
+222	The gardener is growing rosemary, therefore the gardener is growing flowers.
+223	If the gardener is growing rosemary, that means the gardener isn't growing flowers.
+224	If he is hosting a party, it must be the case that he is hosting an event.
+225	He isn't hosting a party, therefore he isn't hosting a wedding.
+226	He isn't hosting a party, therefore he isn't hosting an event.
+227	He is hosting a party, so he is hosting a wedding.
+228	If he is hosting a wedding, it must be the case that he is hosting a party.
+229	If he is hosting a wedding, that means he isn't hosting a party.
+230	If she is hosting a party, then she is hosting a wedding.
+231	She isn't hosting a party, so she isn't hosting a wedding.
+232	If she isn't hosting a party, then she isn't hosting a wedding.
+233	If she is hosting a wedding, that means she is hosting a party.
+234	She is hosting a wedding, therefore she is hosting a party.
+235	She is hosting a wedding, so she isn't hosting a party.
+236	If the hotel is hosting a party, then the hotel is hosting an event.
+237	The hotel isn't hosting a wedding, so the hotel isn't hosting a party.
+238	If the hotel isn't hosting a party, then the hotel isn't hosting an event.
+239	The hotel is hosting a wedding, so the hotel is hosting a party.
+240	If the hotel is hosting a wedding, that means the hotel is hosting a party.
+241	If the hotel is hosting a wedding, then the hotel isn't hosting a party.
+242	If he is hosting a party, it must be the case that he is hosting an event.
+243	If he isn't hosting a party, then he isn't hosting a ball.
+244	He isn't hosting a party, so he isn't hosting an event.
+245	He is hosting a ball, so he is hosting a party.
+246	If he is hosting a ball, that means he is hosting a party.
+247	He is hosting a ball, therefore he isn't hosting a party.
+248	If she is hosting a party, it must be the case that she is hosting an event.
+249	If she isn't hosting a party, that means she isn't hosting a ball.
+250	If she isn't hosting a party, then she isn't hosting an event.
+251	If she is hosting a ball, then she is hosting a party.
+252	She is hosting a ball, therefore she is hosting a party.
+253	If she is hosting a ball, that means she isn't hosting a party.
+254	The hotel is hosting a party, so the hotel is hosting an event.
+255	If the hotel isn't hosting a ball, it must be the case that the hotel isn't hosting a party.
+256	If the hotel isn't hosting a party, then the hotel isn't hosting an event.
+257	The hotel is hosting a party, so the hotel is hosting a ball.
+258	If the hotel is hosting a ball, it must be the case that the hotel is hosting a party.
+259	If the hotel is hosting a ball, then the hotel isn't hosting a party.
+260	He is hosting a party, therefore he is hosting an event.
+261	If he isn't hosting a party, then he isn't hosting an event.
+262	If she is hosting a party, then she is hosting an event.
+263	If she isn't hosting a party, then she isn't hosting an event.
+264	If the hotel is hosting a party, that means the hotel is hosting an event.
+265	The hotel isn't hosting a party, therefore the hotel isn't hosting an event.
+266	The boy is donating an organ, so the boy is donating a kidney.
+267	If the boy isn't donating an organ, it must be the case that the boy isn't donating a kidney.
+268	If David is donating an organ, that means David is donating a kidney.
+269	David isn't donating an organ, therefore David isn't donating a kidney.
+270	If she is donating an organ, then she is donating a heart.
+271	If she isn't donating a heart, that means she isn't donating an organ.
+272	She isn't donating an organ, therefore she isn't donating a heart.
+273	She is donating an organ, therefore she is donating a heart.
+274	If she is donating a heart, it must be the case that she is donating an organ.
+275	If she is donating a heart, then she isn't donating an organ.
+276	If the girl is donating an organ, then the girl is donating a heart.
+277	If the girl isn't donating a heart, then the girl isn't donating an organ.
+278	If the girl isn't donating an organ, it must be the case that the girl isn't donating a heart.
+279	If the girl is donating an organ, it must be the case that the girl is donating a heart.
+280	The girl is donating a heart, so the girl is donating an organ.
+281	The girl is donating a heart, so the girl isn't donating an organ.
+282	If Sarah is donating an organ, that means Sarah is donating a liver.
+283	If Sarah isn't donating a liver, then Sarah isn't donating an organ.
+284	Sarah isn't donating an organ, so Sarah isn't donating a body part.
+285	Sarah is donating an organ, therefore Sarah is donating a liver.
+286	Sarah is donating a liver, so Sarah is donating an organ.
+287	Sarah is donating a liver, therefore Sarah isn't donating an organ.
+288	If she is donating an organ, that means she is donating a liver.
+289	If she isn't donating a liver, that means she isn't donating an organ.
+290	If she isn't donating an organ, that means she isn't donating a liver.
+291	If she is donating a liver, it must be the case that she is donating an organ.
+292	She is donating a liver, therefore she is donating an organ.
+293	If she is donating a liver, that means she isn't donating an organ.
+294	If David is donating an organ, it must be the case that David is donating a liver.
+295	If David isn't donating an organ, it must be the case that David isn't donating a liver.
+296	If David isn't donating an organ, then David isn't donating a liver.
+297	If David is donating an organ, it must be the case that David is donating a liver.
+298	If David is donating a liver, it must be the case that David is donating an organ.
+299	David is donating a liver, so David isn't donating an organ.
+300	If he is donating an organ, that means he is donating a heart.
+301	If he isn't donating an organ, then he isn't donating a heart.
+302	If he isn't donating an organ, that means he isn't donating a heart.
+303	He is donating an organ, therefore he is donating a heart.
+304	If he is donating a heart, that means he is donating an organ.
+305	If he is donating a heart, that means he isn't donating an organ.
+306	If the girl is donating an organ, it must be the case that the girl is donating a liver.
+307	If the girl isn't donating a liver, that means the girl isn't donating an organ.
+308	If the girl isn't donating an organ, then the girl isn't donating a liver.
+309	If the girl is donating a liver, then the girl is donating an organ.
+310	If the girl is donating a liver, then the girl is donating an organ.
+311	If the girl is donating a liver, then the girl isn't donating an organ.
+312	If he is donating an organ, that means he is donating a liver.
+313	If he isn't donating an organ, it must be the case that he isn't donating a liver.
+314	He isn't donating an organ, so he isn't donating a liver.
+315	If he is donating an organ, it must be the case that he is donating a liver.
+316	If he is donating a liver, that means he is donating an organ.
+317	If he is donating a liver, it must be the case that he isn't donating an organ.
+318	If she is donating an organ, it must be the case that she is donating a body part.
+319	If she isn't donating an organ, then she isn't donating a kidney.
+320	The girl is donating an organ, so the girl is donating a kidney.
+321	If the girl isn't donating an organ, then the girl isn't donating a kidney.
+322	If the writer is editing a manual, that means the writer is editing a novel.
+323	The writer is editing a manual, therefore the writer isn't writing a novel.
+324	The writer is writing a manual, so the writer is writing a novel.
+325	If the writer is writing a manual, it must be the case that the writer isn't writing a novel.
+326	The author is editing a manual, therefore the author is editing a novel.
+327	The author is editing a manual, therefore the author isn't writing a novel.
+328	If the writer is reading a manual, it must be the case that the writer is reading a novel.
+329	If the writer is reading a manual, it must be the case that the writer isn't writing a novel.
+330	He is writing a textbook, therefore he is writing a novel.
+331	If he is writing a textbook, that means he isn't writing a novel.
+332	She is writing a textbook, therefore she is writing a novel.
+333	If she is writing a textbook, that means she isn't writing a novel.
+334	He is editing a manual, so he is editing a novel.
+335	If he is editing a manual, then he isn't writing a novel.
+336	She is reading a memoir, therefore she is reading a novel.
+337	If she is reading a memoir, it must be the case that she isn't writing a novel.
+338	If the writer is writing a memoir, then the writer is writing a novel.
+339	If the writer is writing a memoir, then the writer isn't editing a novel.
+340	If the writer is writing a manual, then the writer is writing a novel.
+341	If the writer is writing a manual, then the writer isn't editing a novel.
+342	If she is editing a memoir, that means she is editing a novel.
+343	If she is editing a memoir, then she isn't editing a novel.
+344	If the writer is reading a manual, that means the writer is reading a novel.
+345	If the writer is reading a manual, then the writer isn't editing a novel.
+346	If he spotted a bird, it must be the case that he spotted an animal.
+347	He didn't spot an eagle, so he didn't spot a bird.
+348	If he didn't spot a bird, then he didn't spot an animal.
+349	If he spotted an eagle, that means he spotted a bird.
+350	If he spotted an eagle, that means he spotted a bird.
+351	He spotted an eagle, so he didn't spot a bird.
+352	The hiker spotted a bird, therefore the hiker spotted an animal.
+353	The hiker didn't spot a bird, so the hiker didn't spot a robin.
+354	If the hiker didn't spot a bird, it must be the case that the hiker didn't spot an animal.
+355	If the hiker spotted a bird, then the hiker spotted a robin.
+356	If the hiker spotted a robin, then the hiker spotted a bird.
+357	The hiker spotted a robin, therefore the hiker didn't spot a bird.
+358	The hiker spotted a bird, so the hiker spotted an animal.
+359	If the hiker didn't spot a bird, it must be the case that the hiker didn't spot an eagle.
+360	The hiker didn't spot a bird, therefore the hiker didn't spot an animal.
+361	If the hiker spotted an eagle, then the hiker spotted a bird.
+362	If the hiker spotted an eagle, that means the hiker spotted a bird.
+363	If the hiker spotted an eagle, then the hiker didn't spot a bird.
+364	If she spotted a bird, it must be the case that she spotted an animal.
+365	If she didn't spot a bird, that means she didn't spot a crow.
+366	If she didn't spot a bird, that means she didn't spot a crow.
+367	If she spotted a bird, it must be the case that she spotted a crow.
+368	If she spotted a crow, that means she spotted a bird.
+369	If she spotted a crow, then she didn't spot a bird.
+370	The biologist spotted a bird, so the biologist spotted an animal.
+371	The biologist didn't spot an eagle, so the biologist didn't spot a bird.
+372	If the biologist didn't spot a bird, then the biologist didn't spot an animal.
+373	If the biologist spotted an eagle, then the biologist spotted a bird.
+374	The biologist spotted an eagle, therefore the biologist spotted a bird.
+375	If the biologist spotted an eagle, it must be the case that the biologist didn't spot a bird.
+376	She spotted a bird, so she spotted an animal.
+377	She didn't spot an eagle, therefore she didn't spot a bird.
+378	She didn't spot a bird, so she didn't spot an animal.
+379	If she spotted a bird, it must be the case that she spotted an eagle.
+380	If she spotted an eagle, then she spotted a bird.
+381	If she spotted an eagle, it must be the case that she didn't spot a bird.
+382	The biologist spotted a bird, so the biologist spotted an animal.
+383	If the biologist didn't spot a bird, that means the biologist didn't spot a robin.
+384	If the biologist didn't spot a bird, that means the biologist didn't spot an animal.
+385	If the biologist spotted a robin, that means the biologist spotted a bird.
+386	The biologist spotted a robin, so the biologist spotted a bird.
+387	If the biologist spotted a robin, then the biologist didn't spot a bird.
+388	If the hiker spotted a bird, it must be the case that the hiker spotted an animal.
+389	If the hiker didn't spot a bird, that means the hiker didn't spot a crow.
+390	If the hiker didn't spot a bird, that means the hiker didn't spot an animal.
+391	If the hiker spotted a bird, that means the hiker spotted a crow.
+392	The hiker spotted a crow, therefore the hiker spotted a bird.
+393	The hiker spotted a crow, therefore the hiker didn't spot a bird.
+394	He spotted a bird, so he spotted an animal.
+395	He didn't spot a bird, therefore he didn't spot a crow.
+396	He didn't spot a bird, therefore he didn't spot an animal.
+397	He spotted a bird, therefore he spotted a crow.
+398	He spotted a crow, so he spotted a bird.
+399	He spotted a crow, therefore he didn't spot a bird.
+400	If the biologist spotted a bird, it must be the case that the biologist spotted an animal.
+401	If the biologist didn't spot a bird, that means the biologist didn't spot a crow.
+402	If the biologist didn't spot a bird, it must be the case that the biologist didn't spot an animal.
+403	The biologist spotted a bird, so the biologist spotted a crow.
+404	If the biologist spotted a crow, that means the biologist spotted a bird.
+405	If the biologist spotted a crow, that means the biologist didn't spot a bird.
+406	He spotted a bird, so he spotted an animal.
+407	If he didn't spot a robin, it must be the case that he didn't spot a bird.
+408	If he didn't spot a bird, then he didn't spot an animal.
+409	He spotted a bird, therefore he spotted a robin.
+410	If he spotted a robin, it must be the case that he spotted a bird.
+411	If he spotted a robin, that means he didn't spot a bird.
+412	If she spotted a bird, then she spotted an animal.
+413	She didn't spot a bird, so she didn't spot a robin.
+414	If she didn't spot a bird, it must be the case that she didn't spot an animal.
+415	She spotted a bird, therefore she spotted a robin.
+416	If she spotted a robin, then she spotted a bird.
+417	If she spotted a robin, it must be the case that she didn't spot a bird.
+418	If David drank a soda, that means David drank a drink.
+419	David isn't pouring a soda, therefore David isn't pouring a drink.
+420	The girl drank a soda, so the girl drank a drink.
+421	If the girl isn't pouring a soda, then the girl isn't pouring a drink.
+422	If he is pouring a soda, then he is pouring a drink.
+423	If he didn't drink a soda, it must be the case that he didn't drink a coke.
+424	If he didn't drink a soda, it must be the case that he didn't drink a drink.
+425	If he is pouring a soda, that means he is pouring a coke.
+426	If he is pouring a coke, then he is pouring a coffee.
+427	If he is pouring a coke, that means he didn't drink a coffee.
+428	If the boy is pouring a soda, that means the boy is pouring a drink.
+429	If the boy isn't pouring a soda, then the boy isn't pouring a drink.
+430	If he drank a soda, it must be the case that he drank a drink.
+431	He isn't pouring a coke, therefore he isn't pouring a soda.
+432	If he isn't pouring a soda, that means he isn't pouring a drink.
+433	If he drank a coke, then he drank a soda.
+434	He drank a coke, therefore he drank a coffee.
+435	He drank a coke, so he isn't pouring a coffee.
+436	If Sarah drank a soda, it must be the case that Sarah drank a drink.
+437	If Sarah isn't pouring a soda, then Sarah isn't pouring a drink.
+438	She drank a soda, therefore she drank a drink.
+439	She isn't pouring a coke, therefore she isn't pouring a soda.
+440	She isn't pouring a soda, therefore she isn't pouring a drink.
+441	She drank a soda, therefore she drank a coke.
+442	If she drank a coke, that means she drank a coffee.
+443	If she drank a coke, it must be the case that she isn't pouring a coffee.
+444	The girl is pouring a soda, therefore the girl is pouring a drink.
+445	The girl didn't drink a soda, therefore the girl didn't drink a drink.
+446	If he drank a soda, then he drank a drink.
+447	He isn't pouring a soda, so he isn't pouring a drink.
+448	If Sarah drank a soda, then Sarah drank a drink.
+449	Sarah didn't drink a soda, therefore Sarah didn't drink a coke.
+450	If Sarah didn't drink a soda, that means Sarah didn't drink a drink.
+451	Sarah drank a soda, so Sarah drank a coke.
+452	Sarah drank a coke, therefore Sarah drank a coffee.
+453	Sarah drank a coke, so Sarah didn't drink a coffee.
+454	If she is pouring a soda, it must be the case that she is pouring a drink.
+455	If she didn't drink a soda, it must be the case that she didn't drink a drink.
+456	The boy is pouring a soda, therefore the boy is pouring a drink.
+457	If the boy didn't drink a soda, then the boy didn't drink a coke.
+458	If the boy didn't drink a soda, it must be the case that the boy didn't drink a drink.
+459	The boy is pouring a soda, therefore the boy is pouring a coke.
+460	If the boy is pouring a coke, it must be the case that the boy is pouring a coffee.
+461	If the boy is pouring a coke, that means the boy didn't drink a coffee.
+462	If Sarah is cooking a meal, then Sarah is cooking food.
+463	If Sarah isn't cooking a meal, that means Sarah isn't cooking lunch.
+464	If Sarah isn't cooking a meal, that means Sarah isn't cooking food.
+465	If Sarah is cooking lunch, it must be the case that Sarah is cooking a meal.
+466	If Sarah is cooking lunch, that means Sarah is cooking a meal.
+467	If Sarah is cooking lunch, it must be the case that Sarah isn't cooking a snack.
+468	The girl is cooking a meal, therefore the girl is cooking food.
+469	If the girl isn't cooking dinner, then the girl isn't cooking a meal.
+470	If the girl isn't cooking a meal, it must be the case that the girl isn't cooking food.
+471	If the girl is cooking a meal, then the girl is cooking dinner.
+472	The girl is cooking dinner, therefore the girl is cooking a snack.
+473	If the girl is cooking dinner, it must be the case that the girl isn't cooking a snack.
+474	If Sarah is eating a meal, then Sarah is eating food.
+475	If Sarah isn't eating dinner, then Sarah isn't eating a meal.
+476	If Sarah isn't eating a meal, that means Sarah isn't eating dinner.
+477	If Sarah is eating a meal, it must be the case that Sarah is eating dinner.
+478	Sarah is eating dinner, so Sarah is eating a snack.
+479	If Sarah is eating dinner, that means Sarah isn't eating a snack.
+480	He is cooking a meal, therefore he is cooking breakfast.
+481	If he isn't cooking breakfast, that means he isn't cooking a meal.
+482	If he isn't cooking a meal, then he isn't cooking food.
+483	If he is cooking breakfast, it must be the case that he is cooking a meal.
+484	If he is cooking breakfast, that means he is cooking a meal.
+485	If he is cooking breakfast, then he isn't cooking a meal.
+486	The girl is cooking a meal, therefore the girl is cooking food.
+487	The girl isn't eating breakfast, therefore the girl isn't eating a meal.
+488	If the girl isn't eating a meal, then the girl isn't eating breakfast.
+489	The girl is cooking a meal, so the girl is cooking breakfast.
+490	If the girl is cooking breakfast, it must be the case that the girl is cooking a meal.
+491	The girl is cooking breakfast, therefore the girl isn't eating a meal.
+492	The boy is eating a meal, therefore the boy is eating food.
+493	If the boy isn't eating a meal, then the boy isn't eating dinner.
+494	If the boy isn't eating a meal, then the boy isn't eating dinner.
+495	The boy is eating dinner, therefore the boy is eating a meal.
+496	The boy is eating dinner, therefore the boy is eating a snack.
+497	If the boy is eating dinner, it must be the case that the boy isn't eating a snack.
+498	David is cooking a meal, therefore David is cooking food.
+499	David isn't eating lunch, so David isn't eating a meal.
+500	David isn't eating a meal, so David isn't eating lunch.
+501	If David is cooking lunch, it must be the case that David is cooking a meal.
+502	If David is cooking lunch, it must be the case that David is cooking a snack.
+503	David is cooking lunch, so David isn't eating a meal.
+504	She is cooking a meal, therefore she is cooking food.
+505	If she isn't eating a meal, then she isn't eating dinner.
+506	If she isn't eating a meal, then she isn't eating dinner.
+507	If she is cooking a meal, then she is cooking dinner.
+508	She is cooking dinner, so she is cooking a snack.
+509	If she is cooking dinner, that means she isn't eating a meal.
+510	If she is eating a meal, it must be the case that she is eating food.
+511	She isn't eating lunch, therefore she isn't eating a meal.
+512	She isn't eating a meal, so she isn't eating lunch.
+513	She is eating a meal, so she is eating lunch.
+514	She is eating lunch, therefore she is eating a snack.
+515	If she is eating lunch, it must be the case that she isn't eating a meal.
+516	If the girl is eating a meal, then the girl is eating food.
+517	If the girl isn't cooking dinner, then the girl isn't cooking a meal.
+518	If the girl isn't cooking a meal, then the girl isn't cooking dinner.
+519	If the girl is eating dinner, it must be the case that the girl is eating a meal.
+520	The girl is eating dinner, therefore the girl is eating a snack.
+521	The girl is eating dinner, therefore the girl isn't cooking a snack.
+522	If Sarah is eating a meal, then Sarah is eating food.
+523	If Sarah isn't eating a meal, that means Sarah isn't eating lunch.
+524	Sarah isn't eating a meal, therefore Sarah isn't eating lunch.
+525	Sarah is eating a meal, so Sarah is eating lunch.
+526	If Sarah is eating lunch, then Sarah is eating a snack.
+527	If Sarah is eating lunch, it must be the case that Sarah isn't eating a snack.
+528	David is cooking a meal, so David is cooking breakfast.
+529	If David isn't cooking a meal, it must be the case that David isn't cooking breakfast.
+530	David isn't cooking a meal, so David isn't cooking food.
+531	David is cooking a meal, so David is cooking breakfast.
+532	If David is cooking breakfast, then David is cooking a snack.
+533	David is cooking breakfast, so David isn't cooking a meal.
+534	She is addicted to a drug, therefore she is addicted to cocaine.
+535	If she isn't addicted to cocaine, then she isn't addicted to a drug.
+536	If she isn't addicted to a drug, then she isn't addicted to cocaine.
+537	If she is addicted to cocaine, then she is addicted to a drug.
+538	If she is addicted to cocaine, then she is addicted to water.
+539	If she is addicted to cocaine, it must be the case that she isn't addicted to water.
+540	David is consuming a drug, so David is consuming caffeine.
+541	If David isn't consuming a drug, that means David isn't consuming caffeine.
+542	David isn't consuming a drug, so David isn't consuming caffeine.
+543	If David is consuming caffeine, it must be the case that David is consuming a drug.
+544	David is consuming caffeine, so David is consuming water.
+545	David is consuming caffeine, so David isn't consuming water.
+546	If the boy is addicted to a drug, then the boy is addicted to cocaine.
+547	The boy isn't addicted to cocaine, so the boy isn't addicted to a drug.
+548	The boy isn't addicted to a drug, so the boy isn't addicted to cocaine.
+549	If the boy is addicted to a drug, then the boy is addicted to cocaine.
+550	If the boy is addicted to cocaine, then the boy is addicted to water.
+551	If the boy is addicted to cocaine, it must be the case that the boy isn't addicted to water.
+552	If the boy is consuming a drug, it must be the case that the boy is consuming caffeine.
+553	The boy isn't addicted to caffeine, so the boy isn't addicted to a drug.
+554	If the boy isn't addicted to a drug, then the boy isn't addicted to caffeine.
+555	If the boy is consuming a drug, it must be the case that the boy is consuming caffeine.
+556	If the boy is consuming caffeine, it must be the case that the boy is consuming water.
+557	If the boy is consuming caffeine, that means the boy isn't addicted to water.
+558	If the girl is addicted to a drug, then the girl is addicted to caffeine.
+559	If the girl isn't addicted to a drug, it must be the case that the girl isn't addicted to caffeine.
+560	If the girl isn't addicted to a drug, it must be the case that the girl isn't addicted to caffeine.
+561	The girl is addicted to a drug, so the girl is addicted to caffeine.
+562	If the girl is addicted to caffeine, it must be the case that the girl is addicted to water.
+563	If the girl is addicted to caffeine, it must be the case that the girl isn't addicted to water.
+564	He is consuming a drug, so he is consuming caffeine.
+565	He isn't consuming caffeine, so he isn't consuming a drug.
+566	If he isn't consuming a drug, then he isn't consuming caffeine.
+567	If he is consuming caffeine, then he is consuming a drug.
+568	If he is consuming caffeine, then he is consuming water.
+569	If he is consuming caffeine, then he isn't consuming water.
+570	If Sarah is consuming a drug, then Sarah is consuming caffeine.
+571	If Sarah isn't addicted to a drug, it must be the case that Sarah isn't addicted to caffeine.
+572	If Sarah isn't addicted to a drug, then Sarah isn't addicted to caffeine.
+573	Sarah is consuming caffeine, therefore Sarah is consuming a drug.
+574	Sarah is consuming caffeine, so Sarah is consuming water.
+575	If Sarah is consuming caffeine, it must be the case that Sarah isn't addicted to water.
+576	If he is addicted to a drug, it must be the case that he is addicted to caffeine.
+577	If he isn't consuming a drug, it must be the case that he isn't consuming caffeine.
+578	If he isn't consuming a drug, then he isn't consuming caffeine.
+579	He is addicted to a drug, so he is addicted to caffeine.
+580	If he is addicted to caffeine, it must be the case that he is addicted to water.
+581	He is addicted to caffeine, so he isn't consuming water.
+582	If the girl is addicted to a drug, that means the girl is addicted to cocaine.
+583	The girl isn't addicted to cocaine, so the girl isn't addicted to a drug.
+584	The girl isn't addicted to a drug, therefore the girl isn't addicted to cocaine.
+585	If the girl is addicted to a drug, it must be the case that the girl is addicted to cocaine.
+586	If the girl is addicted to cocaine, it must be the case that the girl is addicted to water.
+587	The girl is addicted to cocaine, so the girl isn't addicted to water.
+588	David is addicted to a drug, therefore David is addicted to cocaine.
+589	If David isn't consuming a drug, it must be the case that David isn't consuming cocaine.
+590	David isn't consuming a drug, so David isn't consuming cocaine.
+591	If David is addicted to cocaine, then David is addicted to a drug.
+592	If David is addicted to cocaine, that means David is addicted to water.
+593	If David is addicted to cocaine, that means David isn't consuming water.
+594	The farm produces parmesan, therefore the farm produces cheese.
+595	The farm produces parmesan, so the farm doesn't sell cheese.
+596	The farm sells cheese, therefore the farm sells brie.
+597	If the farm doesn't produce cheese, that means the farm doesn't produce brie.
+598	If the farm doesn't produce cheese, that means the farm doesn't produce brie.
+599	The farm sells brie, so the farm sells cheese.
+600	If the farm sells brie, that means the farm sells cheese.
+601	The farm sells brie, therefore the farm doesn't produce cheese.
+602	The farm sells parmesan, so the farm sells cheese.
+603	If the farm sells parmesan, then the farm doesn't sell cheese.
+604	If the farm sells parmesan, then the farm sells cheese.
+605	The farm sells parmesan, so the farm doesn't produce cheese.
+606	She sells parmesan, so she sells cheese.
+607	She sells parmesan, therefore she doesn't produce cheese.
+608	She sells cheese, therefore she sells brie.
+609	If she doesn't produce cheese, it must be the case that she doesn't produce brie.
+610	If she doesn't produce cheese, then she doesn't produce brie.
+611	If she sells cheese, then she sells brie.
+612	If she sells brie, then she sells cheese.
+613	If she sells brie, then she doesn't produce cheese.
+614	If he produces cheese, then he produces brie.
+615	He doesn't produce cheese, therefore he doesn't produce brie.
+616	If he doesn't produce cheese, that means he doesn't produce brie.
+617	He produces cheese, so he produces brie.
+618	He produces brie, therefore he produces cheese.
+619	If he produces brie, that means he doesn't produce cheese.
+620	If she produces parmesan, that means she produces cheese.
+621	If she produces parmesan, then she doesn't sell cheese.
+622	She is meeting a sibling, therefore she is meeting a brother.
+623	She isn't meeting a brother, therefore she isn't meeting a sibling.
+624	If she isn't meeting a sibling, it must be the case that she isn't meeting a brother.
+625	If she is meeting a brother, then she is meeting a sibling.
+626	If she is meeting a brother, that means she is meeting a cousin.
+627	If she is meeting a brother, that means she isn't meeting a cousin.
+628	The girl is helping a sibling, therefore the girl is helping a sister.
+629	The girl isn't helping a sister, therefore the girl isn't helping a sibling.
+630	The girl isn't helping a sibling, so the girl isn't helping a sister.
+631	If the girl is helping a sibling, that means the girl is helping a sister.
+632	The girl is helping a sister, therefore the girl is helping a cousin.
+633	The girl is helping a sister, therefore the girl isn't helping a cousin.
+634	If David is meeting a sibling, then David is meeting a brother.
+635	If David isn't fighting with a brother, it must be the case that David isn't fighting with a sibling.
+636	If David isn't fighting with a sibling, it must be the case that David isn't fighting with a brother.
+637	David is meeting a brother, so David is meeting a sibling.
+638	If David is meeting a brother, it must be the case that David is meeting a cousin.
+639	If David is meeting a brother, that means David isn't fighting with a cousin.
+640	If she is fighting with a sibling, it must be the case that she is fighting with a sister.
+641	She isn't helping a sister, so she isn't helping a sibling.
+642	She isn't helping a sibling, therefore she isn't helping a sister.
+643	If she is fighting with a sibling, then she is fighting with a sister.
+644	If she is fighting with a sister, it must be the case that she is fighting with a cousin.
+645	If she is fighting with a sister, then she isn't helping a cousin.
+646	He is helping a sibling, so he is helping a sister.
+647	If he isn't fighting with a sister, then he isn't fighting with a sibling.
+648	If he isn't fighting with a sibling, then he isn't fighting with a sister.
+649	If he is helping a sibling, it must be the case that he is helping a sister.
+650	If he is helping a sister, it must be the case that he is helping a cousin.
+651	If he is helping a sister, it must be the case that he isn't fighting with a cousin.
+652	The boy is meeting a sibling, therefore the boy is meeting a sister.
+653	The boy isn't helping a sister, therefore the boy isn't helping a sibling.
+654	If the boy isn't helping a sibling, it must be the case that the boy isn't helping a sister.
+655	The boy is meeting a sibling, therefore the boy is meeting a sister.
+656	The boy is meeting a sister, so the boy is meeting a cousin.
+657	If the boy is meeting a sister, then the boy isn't helping a cousin.
+658	The boy is helping a sibling, therefore the boy is helping a sister.
+659	The boy isn't helping a sister, so the boy isn't helping a sibling.
+660	If the boy isn't helping a sibling, it must be the case that the boy isn't helping a sister.
+661	If the boy is helping a sister, then the boy is helping a sibling.
+662	If the boy is helping a sister, then the boy is helping a cousin.
+663	If the boy is helping a sister, that means the boy isn't helping a cousin.
+664	The boy is helping a sibling, therefore the boy is helping a sister.
+665	If the boy isn't fighting with a sister, that means the boy isn't fighting with a sibling.
+666	The boy isn't fighting with a sibling, so the boy isn't fighting with a sister.
+667	If the boy is helping a sibling, it must be the case that the boy is helping a sister.
+668	If the boy is helping a sister, it must be the case that the boy is helping a cousin.
+669	If the boy is helping a sister, it must be the case that the boy isn't fighting with a cousin.
+670	If Sarah is helping a sibling, then Sarah is helping a brother.
+671	Sarah isn't helping a sibling, so Sarah isn't helping a brother.
+672	Sarah isn't helping a sibling, therefore Sarah isn't helping a brother.
+673	If Sarah is helping a sibling, that means Sarah is helping a brother.
+674	If Sarah is helping a brother, it must be the case that Sarah is helping a cousin.
+675	If Sarah is helping a brother, it must be the case that Sarah isn't helping a cousin.
+676	If she is fighting with a sibling, it must be the case that she is fighting with a brother.
+677	If she isn't fighting with a sibling, then she isn't fighting with a brother.
+678	If she isn't fighting with a sibling, then she isn't fighting with a brother.
+679	If she is fighting with a sibling, that means she is fighting with a brother.
+680	If she is fighting with a brother, then she is fighting with a cousin.
+681	If she is fighting with a brother, it must be the case that she isn't fighting with a cousin.
+682	If he is meeting a sibling, that means he is meeting a brother.
+683	If he isn't fighting with a brother, that means he isn't fighting with a sibling.
+684	He isn't fighting with a sibling, therefore he isn't fighting with a brother.
+685	If he is meeting a brother, that means he is meeting a sibling.
+686	He is meeting a brother, so he is meeting a cousin.
+687	If he is meeting a brother, then he isn't fighting with a cousin.
+688	David is meeting a sibling, therefore David is meeting a sister.
+689	David isn't fighting with a sibling, so David isn't fighting with a sister.
+690	David isn't fighting with a sibling, therefore David isn't fighting with a sister.
+691	David is meeting a sibling, so David is meeting a sister.
+692	If David is meeting a sister, then David is meeting a cousin.
+693	If David is meeting a sister, it must be the case that David isn't fighting with a cousin.
+694	If the boy bought a computer, that means the boy bought a pc.
+695	If the boy didn't break a pc, that means the boy didn't break a computer.
+696	The boy didn't break a computer, so the boy didn't break a pc.
+697	If the boy bought a pc, then the boy bought a computer.
+698	If the boy bought a pc, that means the boy bought a phone.
+699	If the boy bought a pc, it must be the case that the boy didn't break a phone.
+700	If David bought a computer, it must be the case that David bought a laptop.
+701	David didn't break a computer, therefore David didn't break a laptop.
+702	If David didn't break a computer, then David didn't break a laptop.
+703	David bought a computer, therefore David bought a laptop.
+704	If David bought a laptop, it must be the case that David bought a computer.
+705	If David bought a laptop, then David didn't break a computer.
+706	The girl bought a computer, therefore the girl bought a pc.
+707	If the girl didn't buy a pc, that means the girl didn't buy a computer.
+708	If the girl didn't buy a computer, that means the girl didn't buy a pc.
+709	If the girl bought a computer, it must be the case that the girl bought a pc.
+710	If the girl bought a pc, it must be the case that the girl bought a phone.
+711	If the girl bought a pc, it must be the case that the girl didn't buy a phone.
+712	If the boy is using a computer, then the boy is using a pc.
+713	The boy isn't using a computer, therefore the boy isn't using a pc.
+714	If the boy isn't using a computer, that means the boy isn't using a pc.
+715	The boy is using a pc, therefore the boy is using a computer.
+716	If the boy is using a pc, it must be the case that the boy is using a phone.
+717	The boy is using a pc, therefore the boy isn't using a phone.
+718	If she bought a computer, it must be the case that she bought a pc.
+719	If she didn't break a pc, that means she didn't break a computer.
+720	If she didn't break a computer, it must be the case that she didn't break a pc.
+721	She bought a pc, therefore she bought a computer.
+722	She bought a pc, therefore she bought a computer.
+723	If she bought a pc, it must be the case that she didn't break a phone.
+724	If she is using a computer, it must be the case that she is using a laptop.
+725	She didn't buy a computer, so she didn't buy a laptop.
+726	If she didn't buy a computer, it must be the case that she didn't buy a laptop.
+727	If she is using a laptop, then she is using a computer.
+728	She is using a laptop, so she is using a phone.
+729	If she is using a laptop, that means she didn't buy a phone.
+730	If she broke a computer, it must be the case that she broke a laptop.
+731	She didn't break a computer, therefore she didn't break a laptop.
+732	If she didn't break a computer, then she didn't break a laptop.
+733	She broke a computer, so she broke a laptop.
+734	She broke a laptop, so she broke a phone.
+735	She broke a laptop, therefore she didn't break a computer.
+736	If David is using a computer, that means David is using a pc.
+737	If David isn't using a computer, that means David isn't using a pc.
+738	David isn't using a computer, therefore David isn't using a pc.
+739	David is using a pc, therefore David is using a computer.
+740	David is using a pc, therefore David is using a phone.
+741	David is using a pc, therefore David isn't using a phone.
+742	The boy bought a computer, therefore the boy bought a laptop.
+743	If the boy didn't buy a laptop, that means the boy didn't buy a computer.
+744	The boy didn't buy a computer, therefore the boy didn't buy a laptop.
+745	The boy bought a laptop, therefore the boy bought a computer.
+746	The boy bought a laptop, therefore the boy bought a phone.
+747	If the boy bought a laptop, it must be the case that the boy didn't buy a phone.
+748	If Sarah bought a computer, that means Sarah bought a pc.
+749	Sarah didn't buy a computer, therefore Sarah didn't buy a pc.
+750	Sarah didn't buy a computer, so Sarah didn't buy a pc.
+751	Sarah bought a computer, so Sarah bought a pc.
+752	If Sarah bought a pc, it must be the case that Sarah bought a phone.
+753	Sarah bought a pc, so Sarah didn't buy a phone.
+754	If he bought a computer, it must be the case that he bought a pc.
+755	He didn't buy a computer, so he didn't buy a pc.
+756	If he didn't buy a computer, then he didn't buy a pc.
+757	If he bought a pc, then he bought a computer.
+758	If he bought a pc, that means he bought a phone.
+759	If he bought a pc, that means he didn't buy a phone.
+760	If the boy broke a computer, that means the boy broke a pc.
+761	The boy isn't using a pc, so the boy isn't using a computer.
+762	If the boy isn't using a computer, that means the boy isn't using a pc.
+763	If the boy broke a pc, then the boy broke a computer.
+764	The boy broke a pc, so the boy broke a phone.
+765	The boy broke a pc, so the boy isn't using a phone.
+766	If Sarah is playing a string instrument, that means Sarah is playing an instrument.
+767	If Sarah isn't learning to play a string instrument, it must be the case that Sarah isn't learning to play an instrument.
+768	If he is tuning a string instrument, then he is tuning the guitar.
+769	He isn't learning to play a string instrument, therefore he isn't learning to play the guitar.
+770	If he isn't learning to play a string instrument, that means he isn't learning to play the guitar.
+771	If he is tuning the guitar, it must be the case that he is tuning a string instrument.
+772	He is tuning the guitar, so he is tuning a wind instrument.
+773	If he is tuning the guitar, it must be the case that he isn't learning to play a wind instrument.
+774	If the boy is playing a string instrument, that means the boy is playing the violin.
+775	If the boy isn't tuning the violin, then the boy isn't tuning a string instrument.
+776	The boy isn't tuning a string instrument, so the boy isn't tuning the violin.
+777	The boy is playing the violin, so the boy is playing a string instrument.
+778	If the boy is playing the violin, that means the boy is playing a wind instrument.
+779	The boy is playing the violin, therefore the boy isn't tuning a wind instrument.
+780	The girl is tuning a string instrument, so the girl is tuning an instrument.
+781	The girl isn't playing a string instrument, therefore the girl isn't playing an instrument.
+782	If she is tuning a string instrument, it must be the case that she is tuning the guitar.
+783	If she isn't learning to play a string instrument, it must be the case that she isn't learning to play the guitar.
+784	If she isn't learning to play a string instrument, then she isn't learning to play the guitar.
+785	If she is tuning a string instrument, that means she is tuning the guitar.
+786	If she is tuning the guitar, then she is tuning a wind instrument.
+787	If she is tuning the guitar, then she isn't learning to play a wind instrument.
+788	If he is playing a string instrument, that means he is playing an instrument.
+789	If he isn't playing a string instrument, it must be the case that he isn't playing an instrument.
+790	If David is playing a string instrument, then David is playing the guitar.
+791	David isn't playing a string instrument, so David isn't playing the guitar.
+792	If David isn't playing a string instrument, that means David isn't playing the guitar.
+793	If David is playing a string instrument, that means David is playing the guitar.
+794	If David is playing the guitar, that means David is playing a wind instrument.
+795	David is playing the guitar, therefore David isn't playing a wind instrument.
+796	David is playing a string instrument, so David is playing the banjo.
+797	David isn't tuning a string instrument, so David isn't tuning an instrument.
+798	If David is learning to play a string instrument, that means David is learning to play the guitar.
+799	If David isn't playing a string instrument, then David isn't playing the guitar.
+800	David isn't playing a string instrument, therefore David isn't playing the guitar.
+801	If David is learning to play the guitar, that means David is learning to play a string instrument.
+802	David is learning to play the guitar, therefore David is learning to play a wind instrument.
+803	If David is learning to play the guitar, then David isn't playing a wind instrument.
+804	If Sarah is learning to play a string instrument, it must be the case that Sarah is learning to play the violin.
+805	Sarah isn't learning to play the violin, therefore Sarah isn't learning to play a string instrument.
+806	Sarah isn't learning to play a string instrument, so Sarah isn't learning to play an instrument.
+807	Sarah is learning to play a string instrument, so Sarah is learning to play the violin.
+808	If Sarah is learning to play the violin, that means Sarah is learning to play a wind instrument.
+809	If Sarah is learning to play the violin, then Sarah isn't learning to play a wind instrument.
+810	If Sarah is playing a string instrument, then Sarah is playing the banjo.
+811	If Sarah isn't playing a string instrument, then Sarah isn't playing an instrument.
+812	She is learning to play a string instrument, therefore she is learning to play the guitar.
+813	If she isn't learning to play a string instrument, it must be the case that she isn't learning to play the guitar.
+814	If she isn't learning to play a string instrument, then she isn't learning to play the guitar.
+815	If she is learning to play the guitar, then she is learning to play a string instrument.
+816	She is learning to play the guitar, so she is learning to play a wind instrument.
+817	She is learning to play the guitar, so she isn't learning to play a wind instrument.
+818	If the sofa is made of wood, then the sofa is made of oak.
+819	If the sofa isn't made of wood, it must be the case that the sofa isn't made of oak.
+820	The sofa isn't made of wood, therefore the sofa isn't made of oak.
+821	The sofa is made of wood, so the sofa is made of oak.
+822	If the sofa is made of oak, it must be the case that the sofa is made of wood.
+823	The sofa is made of oak, therefore the sofa isn't made of wood.
+824	The chair is made of wood, so the chair is made of oak.
+825	If the chair isn't made of oak, then the chair isn't made of wood.
+826	The chair isn't made of wood, so the chair isn't made of oak.
+827	If the chair is made of oak, that means the chair is made of wood.
+828	The chair is made of oak, so the chair is made of wood.
+829	The chair is made of oak, so the chair isn't made of wood.
+830	If the couch is made of wood, that means the couch is made of oak.
+831	The couch isn't made of wood, therefore the couch isn't made of oak.
+832	If the couch isn't made of wood, it must be the case that the couch isn't made of oak.
+833	The couch is made of wood, therefore the couch is made of oak.
+834	If the couch is made of oak, it must be the case that the couch is made of wood.
+835	If the couch is made of oak, that means the couch isn't made of wood.
+836	The sofa is made of maple, so the sofa is made of wood.
+837	If the sofa is made of maple, it must be the case that the sofa isn't made of wood.
+838	If the chair is made of maple, then the chair is made of wood.
+839	If the chair is made of maple, then the chair isn't made of wood.
+840	The couch is made of maple, therefore the couch is made of wood.
+841	If the couch is made of maple, that means the couch isn't made of wood.
+842	If the sofa is made of wood, that means the sofa is made of pine.
+843	The sofa isn't made of pine, therefore the sofa isn't made of wood.
+844	The sofa isn't made of wood, so the sofa isn't made of pine.
+845	If the sofa is made of wood, it must be the case that the sofa is made of pine.
+846	If the sofa is made of pine, then the sofa is made of wood.
+847	If the sofa is made of pine, that means the sofa isn't made of wood.
+848	The chair is made of wood, so the chair is made of pine.
+849	If the chair isn't made of wood, that means the chair isn't made of pine.
+850	The chair isn't made of wood, so the chair isn't made of pine.
+851	The chair is made of wood, therefore the chair is made of pine.
+852	If the chair is made of pine, it must be the case that the chair is made of wood.
+853	If the chair is made of pine, then the chair isn't made of wood.
+854	If the couch is made of wood, that means the couch is made of pine.
+855	The couch isn't made of wood, therefore the couch isn't made of pine.
+856	If the couch isn't made of wood, then the couch isn't made of pine.
+857	If the couch is made of pine, then the couch is made of wood.
+858	If the couch is made of pine, then the couch is made of wood.
+859	If the couch is made of pine, it must be the case that the couch isn't made of wood.

zeroshot/irregular_forms/eval_results.json

@@ -0,0 +1 @@
+{"eval_accuracy": 0.5221374045801527}

zeroshot/irregular_forms/predictions.txt

@@ -0,0 +1,1966 @@
+index	prediction
+0	The hid offspring aren't confident.
+1	The hidden bicycles weren't exposed.
+2	The hidden glass was big.
+3	The wore skirts aren't important.
+4	The wore hats aren't big.
+5	The broke mirrors were blue.
+6	The wore pants weren't big.
+7	The known children were nice.
+8	The forgot newspaper article was bad.
+9	The knew adult was exposed.
+10	The hid patients weren't busy.
+11	The wore scarf was popular.
+12	The took rabbit wasn't popular.
+13	The knew girl is important.
+14	The broke dishes weren't red.
+15	The wore sweaters weren't smooth.
+16	The forgotten children weren't hidden.
+17	The hidden plates aren't white.
+18	The forgotten doctors are young.
+19	The wore skirt was important.
+20	The taken cilantro wasn't yellow.
+21	The wore shawls aren't concealed.
+22	The hid fish wasn't important.
+23	The knew people aren't hidden.
+24	The worn glove is red.
+25	The known senators aren't serious.
+26	The broke couch isn't small.
+27	The hidden lady isn't popular.
+28	The worn scarf wasn't lost.
+29	The broke tooth wasn't whole.
+30	The broke forks are white.
+31	The hidden guest wasn't hidden.
+32	The hid cactus was out in the open.
+33	The broke skateboards are cheap.
+34	The broken computers weren't white.
+35	The worn shirt is gray.
+36	The knew waiter is scared.
+37	The worn shoe was exposed.
+38	The hid mouse wasn't brown.
+39	The wore glasses aren't small.
+40	The worn dress is red.
+41	The broke skateboards aren't bad.
+42	The hid fungus isn't black.
+43	The broken screen isn't small.
+44	The broke carriage is lost.
+45	The knew adults aren't lost.
+46	The worn jacket was smooth.
+47	The broke dishes weren't blue.
+48	The forgotten driver wasn't bad.
+49	The broke screen was new.
+50	The hid nuclei were purple.
+51	The knew man wasn't confident.
+52	The knew actress wasn't concealed.
+53	The knew student wasn't hidden.
+54	The broke computer was small.
+55	The knew guests are concealed.
+56	The took mountain was popular.
+57	The hidden screens were exposed.
+58	The knew customer wasn't hidden.
+59	The broke computers were black.
+60	The broke forks were old.
+61	The hidden drawings weren't popular.
+62	The broke bike wasn't lost.
+63	The broke cars aren't rough.
+64	The hidden hat wasn't cheap.
+65	The worn dresses aren't old.
+66	The hid mice were hidden.
+67	The worn glasses weren't dirty.
+68	The taken skateboards were small.
+69	The hid shirt wasn't hard.
+70	The broke glasses aren't whole.
+71	The broken bicycles are concealed.
+72	The knew teenagers weren't popular.
+73	The broke vase wasn't orange.
+74	The knew women are good.
+75	The broke foot is old.
+76	The took screen wasn't popular.
+77	The hid tomato was important.
+78	The known adults weren't uncertain.
+79	The forgot actresses were hopeful.
+80	The wore shirt was important.
+81	The worn coats weren't yellow.
+82	The took chair isn't dirty.
+83	The wore skirts weren't green.
+84	The broken bike is big.
+85	The broke couches weren't brown.
+86	The wore socks weren't small.
+87	The broke dishes aren't new.
+88	The broke vase is gray.
+89	The broke feet were red.
+90	The hidden movie theaters were white.
+91	The knew drivers weren't popular.
+92	The hid steak was bad.
+93	The took mountains weren't orange.
+94	The broke plate wasn't smooth.
+95	The hidden rivers aren't cheap.
+96	The hid customers aren't scared.
+97	The forgotten analysis wasn't good.
+98	The knew teenager was nice.
+99	The broken wheelbarrow is small.
+100	The knew adult was serious.
+101	The worn skirts weren't purple.
+102	The knew doctor isn't good.
+103	The wore sweaters are exposed.
+104	The knew guests are upset.
+105	The knew senator was fast.
+106	The broke windows aren't new.
+107	The broke car isn't gray.
+108	The forgotten lady is standing.
+109	The taken book is good.
+110	The forgot newspaper article isn't brown.
+111	The hidden ice cream was white.
+112	The hidden louse wasn't hidden.
+113	The forgotten pamphlets weren't concealed.
+114	The taken photograph wasn't gray.
+115	The hidden closet is new.
+116	The broke foot was big.
+117	The broke window is hidden.
+118	The hid children were happy.
+119	The forgotten customer is lost.
+120	The worn dress isn't popular.
+121	The worn coat isn't new.
+122	The wore scarves aren't concealed.
+123	The broke mirrors are purple.
+124	The knew pedestrians were unemployed.
+125	The hidden brochures were yellow.
+126	The hid teacher was scared.
+127	The wore blouses aren't big.
+128	The broke dish was good.
+129	The broke plane is good.
+130	The broke vases weren't concealed.
+131	The broke chair isn't red.
+132	The broke teeth aren't lost.
+133	The known patients are confident.
+134	The broke vase wasn't yellow.
+135	The forgotten senator isn't confident.
+136	The broke vases weren't brown.
+137	The wore shawls weren't important.
+138	The forgotten man wasn't serious.
+139	The worn shawl is lost.
+140	The took shirt was bad.
+141	The hid closets weren't hard.
+142	The hidden cafes are messy.
+143	The worn shirt was lost.
+144	The broken forks aren't gray.
+145	The worn coats aren't hard.
+146	The wore pants are lost.
+147	The worn skirt isn't white.
+148	The broke screens are exposed.
+149	The broken bicycle is whole.
+150	The wore scarves weren't white.
+151	The broke carriages were new.
+152	The knew cashiers weren't important.
+153	The broke window is cheap.
+154	The broke skateboard is lost.
+155	The wore shirt wasn't clean.
+156	The forgotten children weren't troubled.
+157	The known cashier isn't fast.
+158	The broke couch was black.
+159	The hid trucks weren't popular.
+160	The forgotten man is big.
+161	The worn skirts were big.
+162	The hid slopes are important.
+163	The worn dress was clean.
+164	The broke plane was yellow.
+165	The taken hair was purple.
+166	The broke carriages were important.
+167	The worn hat was yellow.
+168	The broke cup was new.
+169	The hidden mushroom isn't under ripe.
+170	The hidden drawing wasn't smooth.
+171	The knew people weren't bad.
+172	The worn hat wasn't big.
+173	The forgotten actresses were nice.
+174	The wore skirts were old.
+175	The wore dresses aren't cheap.
+176	The broke screen was yellow.
+177	The broken dish isn't small.
+178	The knew child wasn't serious.
+179	The hidden cart wasn't small.
+180	The taken glass was orange.
+181	The knew actresses aren't smart.
+182	The forgotten actors were upset.
+183	The hidden reports aren't yellow.
+184	The broke foot isn't concealed.
+185	The knew cashiers are out in the open.
+186	The broke feet weren't hidden.
+187	The broken bicycles were smooth.
+188	The worn shoe wasn't yellow.
+189	The worn dress isn't orange.
+190	The forgotten waiters were happy.
+191	The broke planes are white.
+192	The hid bird is standing.
+193	The known lady isn't smart.
+194	The broken screens aren't green.
+195	The broke couches weren't hard.
+196	The known pedestrian was busy.
+197	The hid theses weren't brown.
+198	The broke vase was broken.
+199	The known guest was concealed.
+200	The took grocery store wasn't concealed.
+201	The taken computers were old.
+202	The hid girl is hidden.
+203	The hidden public parks weren't rough.
+204	The wore gloves were new.
+205	The taken cactus was white.
+206	The broke fork is messy.
+207	The forgotten student is confident.
+208	The forgotten children weren't upset.
+209	The broken wheelbarrows are new.
+210	The broken couches aren't good.
+211	The hid pepper wasn't fresh.
+212	The took goose isn't purple.
+213	The taken planes were bad.
+214	The hid stimulus wasn't out in the open.
+215	The broke carriages are out in the open.
+216	The hidden glacier isn't blue.
+217	The knew adult isn't unlucky.
+218	The hidden dresses aren't bad.
+219	The broke couches were concealed.
+220	The broke planes aren't exposed.
+221	The broke bicycle isn't exposed.
+222	The wore sweaters are green.
+223	The broke cups were hard.
+224	The hid cup was cheap.
+225	The wore sock isn't big.
+226	The forgotten documentary is important.
+227	The hidden theses are lost.
+228	The broke cart is bad.
+229	The taken light is exposed.
+230	The broke window was important.
+231	The forgot women were small.
+232	The taken axes were exposed.
+233	The hidden peppers weren't important.
+234	The hid pasta isn't concealed.
+235	The taken wheelbarrows aren't yellow.
+236	The hid bike isn't good.
+237	The taken tooth is gray.
+238	The hid man isn't young.
+239	The wore sock wasn't black.
+240	The knew waiters aren't unsure.
+241	The broken computer was smooth.
+242	The known men aren't hidden.
+243	The hid ladder isn't white.
+244	The took bikes are orange.
+245	The hidden stimulus wasn't orange.
+246	The broke unicycle was important.
+247	The took icicle was hard.
+248	The took bread isn't black.
+249	The hid picture wasn't black.
+250	The forgotten boys aren't busy.
+251	The hid movie theaters are popular.
+252	The broke chairs aren't whole.
+253	The worn coat isn't hard.
+254	The broke couch wasn't smooth.
+255	The broke plates weren't new.
+256	The broke truck wasn't purple.
+257	The taken broccoli was bad.
+258	The worn shoes aren't purple.
+259	The broke window was big.
+260	The worn hat is smooth.
+261	The forgotten waiters are concealed.
+262	The broke cups aren't clean.
+263	The broken vase wasn't black.
+264	The taken sketch is green.
+265	The forgot books are hard.
+266	The hid pasta isn't black.
+267	The hidden pamphlet wasn't good.
+268	The knew drivers aren't young.
+269	The broke carts aren't good.
+270	The known teenager isn't excited.
+271	The took projector isn't brown.
+272	The hidden dogs aren't orange.
+273	The forgotten movies were long.
+274	The taken fish was red.
+275	The wore scarf wasn't white.
+276	The forgotten students are young.
+277	The forgotten doctor wasn't unemployed.
+278	The hid face was important.
+279	The broke carriages were brown.
+280	The forgotten men were unconvinced.
+281	The broken plate isn't small.
+282	The wore pants weren't cheap.
+283	The broke couches were brown.
+284	The broke couch was smooth.
+285	The hid teeth weren't green.
+286	The taken public parks were clean.
+287	The taken rabbits weren't lost.
+288	The took computers weren't popular.
+289	The wore sweater is big.
+290	The known boys weren't exposed.
+291	The broke cart is new.
+292	The broke plate is gray.
+293	The hid report wasn't long.
+294	The hid waiter was lucky.
+295	The broken chair isn't white.
+296	The broke windows are out in the open.
+297	The broken bicycle wasn't small.
+298	The forgot commentaries are important.
+299	The hidden dress was smooth.
+300	The broke computers are hidden.
+301	The hidden glass was broken.
+302	The knew cashiers are unconvinced.
+303	The worn jackets weren't out in the open.
+304	The known dancers are content.
+305	The known doctors weren't small.
+306	The wore gloves aren't messy.
+307	The broke trucks aren't bad.
+308	The hid ice is gray.
+309	The forgotten commentaries aren't bad.
+310	The forgotten teenager was unlucky.
+311	The taken carts aren't gray.
+312	The worn dress wasn't rough.
+313	The forgotten documentaries are long.
+314	The broke screen was broken.
+315	The hid men are scared.
+316	The wore sweater is rough.
+317	The worn coat wasn't red.
+318	The taken box was hidden.
+319	The forgotten commentaries aren't good.
+320	The forgotten drivers were hidden.
+321	The hid socks were big.
+322	The forgotten stories are bad.
+323	The forgotten pamphlets aren't black.
+324	The knew boys were hopeful.
+325	The broke windows weren't concealed.
+326	The forgot person isn't exposed.
+327	The hid jacket wasn't gray.
+328	The hid blouses weren't black.
+329	The forgot ladies were delighted.
+330	The hidden turtles were lost.
+331	The hid oasis is concealed.
+332	The taken cats aren't small.
+333	The broke dish was popular.
+334	The taken shoes aren't hidden.
+335	The broke cup is red.
+336	The broke plate is white.
+337	The forgotten man was hopeful.
+338	The broke screens were black.
+339	The broken cup isn't bad.
+340	The worn dresses were old.
+341	The hid fish isn't big.
+342	The took candles aren't big.
+343	The wore scarf isn't yellow.
+344	The knew doctor wasn't old.
+345	The broke glasses were purple.
+346	The knew men were exposed.
+347	The broke plate is rough.
+348	The broke computers were blue.
+349	The broken teeth are big.
+350	The broke feet aren't clean.
+351	The broke planes aren't hidden.
+352	The broke glass was broken.
+353	The broke glass was green.
+354	The worn blouse wasn't brown.
+355	The worn hat is hidden.
+356	The broke unicycle was good.
+357	The broke bikes were orange.
+358	The broken cars aren't small.
+359	The broke glass was small.
+360	The forgotten waiters weren't confident.
+361	The worn shirt isn't brown.
+362	The forgotten offspring wasn't unlucky.
+363	The wore sock wasn't cheap.
+364	The broke planes aren't brown.
+365	The worn shawls weren't hard.
+366	The took paintings aren't gray.
+367	The hid school was concealed.
+368	The forgot boys are exposed.
+369	The forgotten actor wasn't busy.
+370	The hid pies aren't blue.
+371	The wore blouses weren't rough.
+372	The forgotten girl wasn't happy.
+373	The broken cart wasn't lost.
+374	The taken steps were good.
+375	The broke carriages weren't yellow.
+376	The hidden school isn't bad.
+377	The hid library wasn't green.
+378	The broke carriages were bad.
+379	The wore hats weren't good.
+380	The worn shirt isn't concealed.
+381	The broke bike was whole.
+382	The hidden window is red.
+383	The broke plane isn't small.
+384	The worn jacket wasn't concealed.
+385	The hidden report isn't popular.
+386	The hid patient was scared.
+387	The worn hat isn't hidden.
+388	The wore sweaters aren't green.
+389	The broke plane is black.
+390	The broke foot isn't cheap.
+391	The forgotten commentaries were long.
+392	The forgotten customer isn't young.
+393	The forgotten offspring wasn't lost.
+394	The hid theses aren't gray.
+395	The wore shoes are clean.
+396	The broken bicycle isn't red.
+397	The broken couches are white.
+398	The knew students aren't good.
+399	The took bread isn't orange.
+400	The broke carriages are green.
+401	The knew cashiers are excited.
+402	The taken lights weren't orange.
+403	The forgotten analyses are bad.
+404	The broke chairs weren't important.
+405	The broke carriage isn't yellow.
+406	The known waitresses weren't clever.
+407	The broken vase was green.
+408	The broke dish was purple.
+409	The hidden candles are white.
+410	The worn scarves were rough.
+411	The worn glove is popular.
+412	The worn pants weren't messy.
+413	The taken movie theater is small.
+414	The hid wheelbarrow is out in the open.
+415	The worn shoe wasn't popular.
+416	The forgotten guys were employed.
+417	The wore socks are blue.
+418	The hid ladies were exposed.
+419	The worn glasses were small.
+420	The took sheep wasn't smooth.
+421	The wore shirt was yellow.
+422	The hidden rug was hidden.
+423	The broke computer was hard.
+424	The worn sweaters were important.
+425	The known actresses were employed.
+426	The worn skirt isn't cheap.
+427	The took forks weren't out in the open.
+428	The hid dress was bad.
+429	The knew senators were important.
+430	The wore sweater wasn't brown.
+431	The wore shoes are small.
+432	The taken carriage is white.
+433	The taken computers are good.
+434	The hidden candles are big.
+435	The broke chairs were popular.
+436	The hidden casserole isn't brown.
+437	The wore socks were smooth.
+438	The taken wheelbarrows are important.
+439	The taken lamps weren't red.
+440	The broke chair is new.
+441	The broken unicycle was brown.
+442	The hid college campus isn't hard.
+443	The hid sweater is small.
+444	The knew actress isn't exposed.
+445	The forgotten guests aren't serious.
+446	The taken pepper is big.
+447	The wore sweaters are black.
+448	The broke bike is important.
+449	The known children are determined.
+450	The forgotten synopsis was good.
+451	The broke glasses aren't hidden.
+452	The wore scarves were green.
+453	The broke bike was out in the open.
+454	The wore scarf isn't lost.
+455	The broken carts weren't lost.
+456	The forgotten dancers were bad.
+457	The broke window was small.
+458	The broke computers weren't important.
+459	The wore blouses are exposed.
+460	The known ladies weren't scared.
+461	The broke bicycles were cheap.
+462	The broke bicycle is good.
+463	The knew guests were employed.
+464	The hid women were serious.
+465	The broke fork was messy.
+466	The forgotten people aren't unemployed.
+467	The taken bike is black.
+468	The took pamphlet was small.
+469	The hid socks are old.
+470	The broken convertible isn't green.
+471	The worn shoe isn't white.
+472	The broke unicycle was red.
+473	The wore blouse was out in the open.
+474	The hid slope is hard.
+475	The broken couches were bad.
+476	The wore blouses were popular.
+477	The forgotten pamphlets are big.
+478	The broken wheelbarrow is out in the open.
+479	The worn skirts weren't important.
+480	The hid drivers were excited.
+481	The known people aren't determined.
+482	The broke skateboard was green.
+483	The broke foot wasn't rough.
+484	The forgotten men are upset.
+485	The broke screen was black.
+486	The hidden mirrors weren't out in the open.
+487	The broke carriage is new.
+488	The broken dish wasn't big.
+489	The broke couch is clean.
+490	The wore scarf was out in the open.
+491	The hid fork was blue.
+492	The wore shoes are new.
+493	The hidden cart is popular.
+494	The broke mirror is good.
+495	The forgot plays weren't bad.
+496	The hidden couches were rough.
+497	The broke couch wasn't lost.
+498	The took chair isn't green.
+499	The hidden sketches weren't blue.
+500	The broke bikes are good.
+501	The knew dancer is big.
+502	The broken wheelbarrows were rough.
+503	The broken wheelbarrow isn't black.
+504	The broke carts aren't gray.
+505	The hid bread is purple.
+506	The broken unicycle was orange.
+507	The known boys weren't ill.
+508	The took apple was fresh.
+509	The wore blouses aren't lost.
+510	The known lady isn't small.
+511	The wore skirt isn't new.
+512	The wore socks were gray.
+513	The broke carts are whole.
+514	The wore sock isn't smooth.
+515	The forgotten alumni aren't confident.
+516	The forgot alumni are unconvinced.
+517	The hid plane is cheap.
+518	The knew girls weren't delighted.
+519	The forgot teenager wasn't uncertain.
+520	The knew drivers aren't unemployed.
+521	The broke truck isn't small.
+522	The forgotten waitress isn't concealed.
+523	The hid scarf wasn't brown.
+524	The knew patients were hopeful.
+525	The hid photographs are blue.
+526	The broke chairs are gray.
+527	The broken bicycle is black.
+528	The hidden slope was yellow.
+529	The forgotten plays aren't popular.
+530	The knew boy is content.
+531	The broke bicycles are cheap.
+532	The took gloves weren't important.
+533	The wore skirt was bad.
+534	The hidden college campus isn't yellow.
+535	The knew customer isn't hopeful.
+536	The hid actor isn't smart.
+537	The hidden carriages were gray.
+538	The broke bicycles are broken.
+539	The knew girls are busy.
+540	The broke wheelbarrow isn't hard.
+541	The knew teenager isn't busy.
+542	The taken axes are brown.
+543	The broken computer isn't blue.
+544	The wore socks aren't important.
+545	The worn glove is rough.
+546	The forgotten teachers are small.
+547	The broke bike is brown.
+548	The hidden closet isn't hard.
+549	The known offspring aren't standing.
+550	The broke convertible is orange.
+551	The broke cups are new.
+552	The wore blouses were rough.
+553	The wore sweater was good.
+554	The forgot driver was displeased.
+555	The hidden rug was yellow.
+556	The broke bike wasn't new.
+557	The hid doctors were unsure.
+558	The forgotten movie was good.
+559	The knew men were troubled.
+560	The worn skirts are brown.
+561	The broke carriage is popular.
+562	The broke planes are blue.
+563	The forgot drivers weren't unconvinced.
+564	The wore shirt isn't new.
+565	The broken cart isn't green.
+566	The taken sweaters aren't important.
+567	The broke dishes were red.
+568	The wore scarves aren't bad.
+569	The known adult isn't small.
+570	The forgotten actresses are unconvinced.
+571	The known actress isn't standing.
+572	The hid senators aren't standing.
+573	The broke convertible wasn't red.
+574	The broke window is new.
+575	The worn shoe is important.
+576	The worn scarves were black.
+577	The taken axes were big.
+578	The knew man isn't happy.
+579	The known alumnus isn't lucky.
+580	The forgotten doctor wasn't smart.
+581	The knew waitresses were big.
+582	The forgotten analysis is good.
+583	The forgotten children are clever.
+584	The broke tooth was gray.
+585	The known actor isn't employed.
+586	The broke screens are black.
+587	The broke chairs aren't cheap.
+588	The broke trucks were brown.
+589	The knew alumni aren't uncertain.
+590	The worn hats are yellow.
+591	The took lamps aren't hard.
+592	The knew student is unconvinced.
+593	The taken chair is big.
+594	The known ladies were employed.
+595	The forgotten patients aren't good.
+596	The knew customer isn't exposed.
+597	The hidden deer wasn't yellow.
+598	The hidden mouths are yellow.
+599	The forgotten book wasn't gray.
+600	The broke glass wasn't whole.
+601	The worn glove was new.
+602	The wore sweaters were orange.
+603	The hid actor is troubled.
+604	The worn shawl wasn't gray.
+605	The broke windows were orange.
+606	The broken computer isn't cheap.
+607	The hid eye wasn't brown.
+608	The hid bike is broken.
+609	The broke mirrors are new.
+610	The broke cups were lost.
+611	The hidden men weren't exposed.
+612	The wore pants weren't important.
+613	The hid oxen weren't concealed.
+614	The broken cup wasn't important.
+615	The taken lakes are white.
+616	The broke tooth was rough.
+617	The hidden car wasn't orange.
+618	The broke carriages are black.
+619	The broke mirrors were purple.
+620	The forgotten thesis is black.
+621	The taken horses were brown.
+622	The broke fork is out in the open.
+623	The broke car isn't broken.
+624	The hid rice was white.
+625	The broke cart is gray.
+626	The forgot commentaries were popular.
+627	The took oasis is purple.
+628	The knew offspring isn't unsure.
+629	The forgotten documentaries are bad.
+630	The forgotten documentaries were good.
+631	The hid teenager is content.
+632	The broke carts are bad.
+633	The worn blouse isn't concealed.
+634	The forgotten woman is standing.
+635	The hid men weren't determined.
+636	The broke couch isn't messy.
+637	The hid pamphlet wasn't big.
+638	The forgotten waitresses are lost.
+639	The forgotten dancers aren't nice.
+640	The hidden actor isn't lost.
+641	The forgotten adult wasn't bad.
+642	The knew adults aren't unlucky.
+643	The forgotten story wasn't long.
+644	The forgotten actress is unconvinced.
+645	The hidden women weren't determined.
+646	The knew doctors are hidden.
+647	The hid hill is red.
+648	The broke plane was old.
+649	The knew cashier is uncertain.
+650	The broke couches are out in the open.
+651	The worn skirt wasn't gray.
+652	The took glaciers aren't rough.
+653	The known lady wasn't scared.
+654	The known adult was clever.
+655	The wore jackets were blue.
+656	The knew cashier is delighted.
+657	The broke mirror is small.
+658	The hid face isn't big.
+659	The knew patient wasn't young.
+660	The broke cup is hard.
+661	The hidden girls aren't fast.
+662	The broke dish was bad.
+663	The broken bicycle wasn't concealed.
+664	The broke plates weren't black.
+665	The broke couches weren't cheap.
+666	The hid offspring are out in the open.
+667	The wore shawls are green.
+668	The broken unicycle wasn't lost.
+669	The knew woman is serious.
+670	The forgot senator isn't out in the open.
+671	The forgotten women aren't nice.
+672	The hidden window wasn't rough.
+673	The forgotten doctor isn't employed.
+674	The knew actor wasn't employed.
+675	The broke skateboards are hidden.
+676	The broke window isn't out in the open.
+677	The took casserole wasn't popular.
+678	The hid cars are important.
+679	The wore gloves were green.
+680	The worn scarf isn't hard.
+681	The forgotten brochures were big.
+682	The worn shirt wasn't purple.
+683	The forgotten offspring weren't upset.
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+1366	Heather drove to that lake.
+1367	Many guys forgot Anna.
+1368	A lot of cashiers sung.
+1369	Women got a job.
+1370	Some actress broke that dish.
+1371	Every shirt shrank.
+1372	Some icicles frozen.
+1373	Kathleen sang.
+1374	Wendy worn this blouse.
+1375	Catherine forgot Bill.
+1376	Some girl broke a fork.
+1377	Wayne broken most bicycles.
+1378	Jackets shrank.
+1379	Ann known Helen.
+1380	Debra broke the bicycle.
+1381	Many gloves shrank.
+1382	Steve broke that couch.
+1383	Dana got fired.
+1384	Most fish broke this dish.
+1385	Carlos drunk.
+1386	Ruth known Alexander.
+1387	Grace hid.
+1388	Elaine came here.
+1389	Colleen hidden away.
+1390	Some mushroom gone bad.
+1391	Amanda drove to this college campus.
+1392	Sherry hidden the art gallery.
+1393	Omar hidden away.
+1394	A lot of fish broken some wheelbarrows.
+1395	Actresses written.
+1396	Benjamin wore some shawls.
+1397	Teresa known Guy.
+1398	Women spoke up.
+1399	Katherine knew Roger.
+1400	Women gone to this museum.
+1401	Jane went fishing.
+1402	Naomi drove to most art galleries.
+1403	Brett written.
+1404	Samuel drove to some cafe.
+1405	Cilantro gone bad.
+1406	Marla forgot Donna.
+1407	Christina worn some blouse.
+1408	Marcus went fishing.
+1409	Heather written.
+1410	Joseph went fishing.
+1411	Jodi hid.
+1412	Many actresses got fired.
+1413	Cheryl hid many lights.
+1414	Most students broke most dishes.
+1415	Chad went to all rivers.
+1416	Samantha went to many closets.
+1417	Samuel came here.
+1418	These patients drove to every cafe.
+1419	Julie drove to some schools.
+1420	Walter worn many blouses.
+1421	Most teenagers written.
+1422	Christina drove to banks.
+1423	Kathleen woken up.
+1424	Every sock shrank.
+1425	Cheryl forgot a boy.
+1426	Waitresses knew Vincent.
+1427	Mark wore every skirt.
+1428	Steven wore all shirts.
+1429	Richard wore every coat.
+1430	Joel hid away.
+1431	Sara hid away.
+1432	Diana drunk.
+1433	All pedestrians hidden Omar.
+1434	Rose forgot Vanessa.
+1435	John known Stephen.
+1436	Katherine hidden.
+1437	A waiter hidden that cucumber.
+1438	Emily hidden many windows.
+1439	Hamsters come here.
+1440	Sally worn that sock.
+1441	Nina drunk.
+1442	This lady went fishing.
+1443	William got fired.
+1444	Marie forgot Eric.
+1445	Dan went fishing.
+1446	Keith got a job.
+1447	Scott forgotten Jodi.
+1448	Judy forgotten Anna.
+1449	Allison knew Walter.
+1450	Some cashiers written.
+1451	Ice cream froze.
+1452	Katherine spoken up.
+1453	Dana broke this plane.
+1454	Many ladies drove to some closet.
+1455	Some doctors sang.
+1456	Irene went to that public park.
+1457	Many senators went to all closets.
+1458	That customer woken up.
+1459	Christine drunk.
+1460	Every scarf shrank.
+1461	Emily drunk.
+1462	Maria written.
+1463	Holly hid Tiffany.
+1464	These women taken the sketches.
+1465	Raymond went to that school.
+1466	Gregory spoke up.
+1467	Many governments knew a lot of guests.
+1468	Sabrina got a job.
+1469	The Borgias broke a screen.
+1470	Michelle forgot Linda.
+1471	Stacy broke some plates.
+1472	Samuel gone to the art galleries.
+1473	Holly broken the computer.
+1474	Michael broke some plate.
+1475	Connie broken the cart.
+1476	Those students knew Rachel.
+1477	Lisa forgotten Brian.
+1478	Some actor went to some library.
+1479	Marie got a job.
+1480	Carolyn broke many cups.
+1481	The Impressionists sung.
+1482	Some actor spoke up.
+1483	Tonya hid Derek.
+1484	All women hidden away.
+1485	Every museum taken a lot of turtles.
+1486	Regina hid.
+1487	Linda worn the shirts.
+1488	Eric got a job.
+1489	Claire wore these socks.
+1490	These girls hid Karen.
+1491	Every senator came here.
+1492	Some fish hid.
+1493	Many lakes froze.
+1494	Peter got a job.
+1495	Most icicles frozen.
+1496	Rodney ate.
+1497	Kirsten wore shawls.
+1498	Regina woken up.
+1499	A cashier took these boxes.
+1500	Maria got a job.
+1501	Michelle wore many skirts.
+1502	Janice wore that blouse.
+1503	Mark got a job.
+1504	Heather took the turtle.
+1505	Debra drove to most hospitals.
+1506	Catherine wore this shawl.
+1507	Heather drunk.
+1508	Edward forgotten Patricia.
+1509	A lot of peppers went bad.
+1510	Patricia drove to a restaurant.
+1511	Guests ate.
+1512	Brenda got fired.
+1513	Kendra knew this customer.
+1514	An actress went to these malls.
+1515	Catherine took a popsicle.
+1516	A lot of customers ate.
+1517	The drivers hid.
+1518	The Borgias hidden this snake.
+1519	Rhonda ate.
+1520	Adam knew Rodney.
+1521	A lot of ladies hid Todd.
+1522	Frank broke those planes.
+1523	The guests ate.
+1524	These patients wore socks.
+1525	Most hats shrank.
+1526	Douglas sang.
+1527	All horses came here.
+1528	Gerald gone to all libraries.
+1529	Tiffany went fishing.
+1530	Tina went to that hill.
+1531	The actor sang.
+1532	Nina knew Winston Churchill.
+1533	The drivers broke this unicycle.
+1534	Derek gone to those libraries.
+1535	Many teachers spoken up.
+1536	That cilantro went bad.
+1537	Nancy taken that skateboard.
+1538	Steven broke this mirror.
+1539	Barbara woken up.
+1540	The Lutherans wore many pants.
+1541	Adults broken this computer.
+1542	The Borgias got fired.
+1543	These socks shrank.
+1544	Noah drunk.
+1545	Julia forgot Raymond.
+1546	This ice cream frozen.
+1547	A lot of senators drunk.
+1548	Dennis knew Tracy.
+1549	Carla hid the book.
+1550	Brenda gone to every college campus.
+1551	Michael drunk.
+1552	Michael ate.
+1553	The Borgias broke the computers.
+1554	Janet broke those trucks.
+1555	Marcus broke some screens.
+1556	Teresa broke the trucks.
+1557	Daniel took that ladder.
+1558	Many blouses shrank.
+1559	Rachel went to a lot of hospitals.
+1560	Some cats broke many carriages.
+1561	Travis got a job.
+1562	Paula come here.
+1563	Roger forgot Christine.
+1564	Nina spoken up.
+1565	Patricia knew most students.
+1566	These pedestrians drove to a lot of libraries.
+1567	The water frozen.
+1568	Regina got a job.
+1569	Debra hid.
+1570	Beverly drove to every closet.
+1571	Jennifer written.
+1572	Those people written.
+1573	Sharon broke that glass.
+1574	Maria gone to this mountain.
+1575	Donald broke these cups.
+1576	A lot of pedestrians went to art galleries.
+1577	Many rabbits broke couches.
+1578	This dancer gone to the cafe.
+1579	Sally drove to every mountain.
+1580	Some girls forgotten Kristin.
+1581	The jackets shrank.
+1582	Erin came here.
+1583	Candice hidden.
+1584	The ladies drunk.
+1585	A lot of rivers frozen.
+1586	Monet known Stacy.
+1587	Cindy got fired.
+1588	Benjamin taken this dog.
+1589	Denise broke every skateboard.
+1590	Many icicles frozen.
+1591	Diane gone to every closet.
+1592	Derek broke every plane.
+1593	Catherine come here.
+1594	A child gone to those hills.
+1595	Some schools taken those scarves.
+1596	Some actors hid.
+1597	Matt ate.
+1598	Amanda sang.
+1599	That guy hid.
+1600	Cynthia come here.
+1601	Karla ate.
+1602	Karen went to a lot of grocery stores.
+1603	That actor drove to that closet.
+1604	Some shawls shrank.
+1605	Deborah broke these plates.
+1606	Catherine spoken up.
+1607	Doctors drove to a high school.
+1608	Some patients sang.
+1609	Those dancers took every nose.
+1610	Every student broke screens.
+1611	Michelle written.
+1612	Bradley hid.
+1613	Becca knew Steve.
+1614	Elaine got fired.
+1615	Chad woke up.
+1616	Helen drove to all cafes.
+1617	Theodore forgotten Bethany.
+1618	Melanie known Amelia.
+1619	A man hid away.
+1620	The glaciers frozen.
+1621	Russell drove to the college campuses.
+1622	Omar drove to these schools.
+1623	Some ladies drunk.
+1624	These guests worn some coat.
+1625	Tiffany forgotten Suzanne.
+1626	Mark drove to that mall.
+1627	Regina drove to all restaurants.
+1628	Kristin hid.
+1629	Karla drunk.
+1630	Every driver went fishing.
+1631	These customers hidden.
+1632	Ella ate.
+1633	Denise drove to some public park.
+1634	Jesus went fishing.
+1635	Most teachers broke that plate.
+1636	Some pepper went bad.
+1637	Every coat shrank.
+1638	Susan forgot Tracy.
+1639	Chad wore the sock.
+1640	Some banks forgotten Kendra.
+1641	Dan worn most glasses.
+1642	Wendy knew Carrie.
+1643	Clyde took most bananas.
+1644	That guest hidden away.
+1645	That senator got fired.
+1646	Teresa gone to some high schools.
+1647	Christopher broke the dishes.
+1648	Those gloves shrank.
+1649	Many adults went fishing.
+1650	That mushroom went bad.
+1651	Susan broke vases.
+1652	Homer broken a lot of screens.
+1653	Stacey forgotten all women.
+1654	Most pedestrians hid away.
+1655	Allison hidden away.
+1656	Timothy got a job.
+1657	Many shawls shrank.
+1658	Christopher hidden every convertible.
+1659	Alexander broke those bikes.
+1660	Carol hidden.
+1661	Many waiters got fired.
+1662	Edward come here.
+1663	The waitresses forgotten Michael.
+1664	Brett known Paula.
+1665	Sabrina taken some dog.
+1666	These doctors gone to glaciers.
+1667	Every driver broke a plate.
+1668	A lot of children hidden Cynthia.
+1669	Theodore wore every scarf.
+1670	Florence hid Noah.
+1671	Those patients broken this computer.
+1672	Carol took these sketches.
+1673	Those mushrooms went bad.
+1674	Vanessa taken all art galleries.
+1675	Patrick went fishing.
+1676	Tara hidden.
+1677	This eggplant went bad.
+1678	Wendy drunk.
+1679	Alexander woken up.
+1680	Carol come here.
+1681	Jeffrey took every candle.
+1682	Brad got fired.
+1683	Many associations forgotten Suzanne.
+1684	Laurie gone to some bank.
+1685	A skirt shrank.
+1686	Becky drove to that art gallery.
+1687	Girls went to the Great Lakes.
+1688	That jacket shrank.
+1689	Carlos forgot Steve.
+1690	Every driver worn these hats.
+1691	Tara got a job.
+1692	Naomi forgotten Gary.
+1693	Winston Churchill hid.
+1694	Michael hidden.
+1695	Some hat shrank.
+1696	Jesus hid.
+1697	Martha took a lot of bicycles.
+1698	Melissa came here.
+1699	Brenda hid away.
+1700	George went to all movie theaters.
+1701	Some doctors worn those scarves.
+1702	A teenager broke that bike.
+1703	Nicole forgotten Meredith.
+1704	Some bird woke up.
+1705	Andrew broken this fork.
+1706	Men spoken up.
+1707	Regina taken some bread.
+1708	These girls hidden Paul.
+1709	Gerald broke those carriages.
+1710	Thomas woken up.
+1711	Cheryl broke every bike.
+1712	Melanie went to every high school.
+1713	The shoe shrank.
+1714	Holly got fired.
+1715	Edward gone to that college campus.
+1716	Douglas woken up.
+1717	Jane woken up.
+1718	Alexander broke these cups.
+1719	Judy got fired.
+1720	Elaine sung.
+1721	Julia known Peter.
+1722	Jessica hidden a newspaper article.
+1723	Melanie written.
+1724	The waiters hid away.
+1725	Thomas hidden.
+1726	Every adult come here.
+1727	Erin ate.
+1728	Sabrina sang.
+1729	Stacey hidden Sabrina.
+1730	Christina went to every closet.
+1731	That actress spoke up.
+1732	Bruce hidden Sarah.
+1733	Most waiters broke a screen.
+1734	Christopher spoken up.
+1735	A woman broke a glass.
+1736	Alicia ate.
+1737	Aaron hidden away.
+1738	Those guys got a job.
+1739	Most waiters wore some shawls.
+1740	Most skirts shrank.
+1741	That teenager hid away.
+1742	Alice got fired.
+1743	Stephen got fired.
+1744	Every glacier frozen.
+1745	Monica known dancers.
+1746	Tamara worn blouses.
+1747	Karen hid that hamster.
+1748	Carrie sang.
+1749	Bill drunk.
+1750	These waitresses went to Spain.
+1751	Edward written.
+1752	Carlos came here.
+1753	These children drove to Spain.
+1754	Ronald got a job.
+1755	Peter went to most high schools.
+1756	All guys hidden Angela.
+1757	Sherry drove to the hospital.
+1758	The Impressionists woke up.
+1759	These cashiers hid Margaret.
+1760	Those children went to that movie theater.
+1761	Amelia gone fishing.
+1762	Jesus forgotten Denise.
+1763	This water froze.
+1764	Wendy went to a lot of public parks.
+1765	Most museums known Benjamin.
+1766	Tanya come here.
+1767	Rodney broke some plates.
+1768	Alexander drove to restaurants.
+1769	Every woman drove to college campuses.
+1770	Charles wore that coat.
+1771	This pedestrian ate.
+1772	These dancers sung.
+1773	Elizabeth got fired.
+1774	Thomas sang.
+1775	Some shoes shrank.
+1776	The Lutherans got a job.
+1777	Brian forgotten Sherry.
+1778	Jane wore the shirt.
+1779	This teacher broke a skateboard.
+1780	Jason took many boxes.
+1781	Rose drunk.
+1782	Lori broken glasses.
+1783	Theresa worn a lot of coats.
+1784	This patient went fishing.
+1785	Most students forgotten Jesus.
+1786	A mushroom went bad.
+1787	Libraries forgotten Wayne.
+1788	Aaron wore that shoe.
+1789	Girls sang.
+1790	Diana got a job.
+1791	Edward hid away.
+1792	Regina got fired.
+1793	Ella took rice.
+1794	Heidi wore some glove.
+1795	Frank broke a couch.
+1796	Dana hidden away.
+1797	Monet known Mary.
+1798	This senator broke these plates.
+1799	These students drove to a restaurant.
+1800	Walter went fishing.
+1801	Ann known Florence.
+1802	Broccoli went bad.
+1803	The association taken those bikes.
+1804	Aaron went to every art gallery.
+1805	The Impressionists drove to the mall.
+1806	Every icicle frozen.
+1807	Gerald hidden these apples.
+1808	Colleen went fishing.
+1809	All governments forgotten Kenneth.
+1810	Kristin hid away.
+1811	Some patients drunk.
+1812	The Lutherans drunk.
+1813	Stacy broken a lot of cars.
+1814	These senators drunk.
+1815	Ronald went fishing.
+1816	Denise knew Stacy.
+1817	Walter went to that school.
+1818	Most rivers froze.
+1819	Adults hid away.
+1820	The senators drunk.
+1821	Eva spoken up.
+1822	Martin wore some blouses.
+1823	Patricia drunk.
+1824	All senators gone to this hill.
+1825	The shawls shrank.
+1826	Angela written.
+1827	Carmen went fishing.
+1828	Stephanie came here.
+1829	Those waitresses written.
+1830	Teresa broke some mirror.
+1831	Clyde hid.
+1832	Connie ate.
+1833	Ronald broke that wheelbarrow.
+1834	Robert hidden.
+1835	Elizabeth wore all sweaters.
+1836	Daniel ate.
+1837	Many children drunk.
+1838	Winston Churchill broke most forks.
+1839	Snakes woke up.
+1840	Craig got a job.
+1841	These patients broken this cart.
+1842	Martha hidden away.
+1843	Ann written.
+1844	Tammy sang.
+1845	Jacqueline written.
+1846	The senator knew Carl.
+1847	Mary broke this unicycle.
+1848	Karen hid away.
+1849	Marla got a job.
+1850	Donald spoken up.
+1851	Clyde hidden those drawings.
+1852	Derek taken most mushrooms.
+1853	Claire wore most skirts.
+1854	Carol hid this scarf.
+1855	A jacket shrank.
+1856	Travis went fishing.
+1857	Hospitals forgot most actors.
+1858	A lot of jackets shrank.
+1859	Many patients come here.
+1860	Every adult got a job.
+1861	The bird broke some trucks.
+1862	Those children forgot Andrew.
+1863	Mark woken up.
+1864	The Borgias wrote.
+1865	Alexander hid away.
+1866	A lot of doctors broke a lot of couches.
+1867	Clyde sang.
+1868	These associations taken all skateboards.
+1869	A child known some driver.
+1870	Nancy drove to restaurants.
+1871	Adam got a job.
+1872	Steven worn this coat.
+1873	Gregory got a job.
+1874	Senators went to some closet.
+1875	Grace broke the carts.
+1876	A lot of teachers wore the gloves.
+1877	These glaciers froze.
+1878	Nancy went to these closets.
+1879	Most banks known Julie.
+1880	Christina hidden.
+1881	Eric forgotten Vanessa.
+1882	Those students came here.
+1883	Laura drove to some mountain.
+1884	Jodi drove to some hills.
+1885	Donald ate.
+1886	Margaret came here.
+1887	This woman got a job.
+1888	Every teenager forgot Sharon.
+1889	Vincent hid a dog.
+1890	Ruth went fishing.
+1891	Curtis worn these dresses.
+1892	A lake froze.
+1893	Karla come here.
+1894	Tracy drove to many restaurants.
+1895	Nina drove to some hill.
+1896	Lucille broke this unicycle.
+1897	Some dancers hidden dancers.
+1898	Carla wrote.
+1899	Carlos took a bird.
+1900	Kayla taken some window.
+1901	Some socks shrank.
+1902	Tracy known Kathleen.
+1903	Gary knew many women.
+1904	Patrick drove to a bank.
+1905	All students broke a lot of glasses.
+1906	Charles wrote.
+1907	Many patients hid Omar.
+1908	Clyde took all shirts.
+1909	The Impressionists drove to this high school.
+1910	Many students wore most shawls.
+1911	Florence worn most gloves.
+1912	Tamara got fired.
+1913	Jeffrey gone to libraries.
+1914	These dancers taken the art galleries.
+1915	Dawn written.
+1916	James written.
+1917	Maria worn some shirt.
+1918	This child knew a lot of men.
+1919	Jerry broke some carriage.
+1920	All snakes broke all bicycles.
+1921	The students knew Jason.
+1922	Those girls broke that wheelbarrow.
+1923	Timothy hidden Danielle.
+1924	Christopher hidden away.
+1925	Deborah got fired.
+1926	Anna hid away.
+1927	Richard got a job.
+1928	Theodore forgotten Janice.
+1929	The children got fired.
+1930	Every doctor drove to the college campuses.
+1931	Kirsten hidden the college campus.
+1932	The actresses known Scott.
+1933	Amelia forgotten George.
+1934	Paula went fishing.
+1935	Sally gone to every hill.
+1936	Gary taken photographs.
+1937	That girl hid away.
+1938	Tiffany ate.
+1939	Aaron broke these computers.
+1940	A pedestrian written.
+1941	Steven drunk.
+1942	Galileo known Richard.
+1943	Mary knew Bruce.
+1944	Winston Churchill forgotten Charles.
+1945	The girls went to all cafes.
+1946	Mitchell drunk.
+1947	Richard got fired.
+1948	Colleen broken this car.
+1949	Gerald wore this glove.
+1950	Guy drove to art galleries.
+1951	Karen hid.
+1952	Jason ate.
+1953	Alan took this bicycle.
+1954	Sonia took these newspaper articles.
+1955	Veronica hidden away.
+1956	The man broke some skateboard.
+1957	Charles went fishing.
+1958	Cynthia woken up.
+1959	Vanessa ate.
+1960	All teachers drunk.
+1961	Every cat broke that skateboard.
+1962	Every dancer worn that sweater.
+1963	Ann went to some river.
+1964	Deanna broke the cart.

zeroshot/island_effects/eval_results.json

@@ -0,0 +1 @@
+{"eval_accuracy": 0.5041106128550075}

zeroshot/npi_licensing/eval_results.json

@@ -0,0 +1 @@
+{"eval_accuracy": 0.3272092317036137}

zeroshot/qa_congruence_easy/eval_results.json

@@ -0,0 +1 @@
+{"eval_accuracy": 0.4375}

zeroshot/qa_congruence_easy/predictions.txt

@@ -0,0 +1,65 @@
+index	prediction
+0	What did you get?\nI got a teacher.
+1	A: What did you sell?\nB: A chair.
+2	David: What did you sell?\nSarah: I sold a chair.
+3	A: What did you get?\nB: I got a doctor.
+4	David: What did you get?\nSarah: I got eggs.
+5	A: What did you break?\nB: I broke a doctor.
+6	What did you make?\nA teacher.
+7	What did you make?\nA lamp.
+8	David: What did you make?\nSarah: A computer.
+9	David: What did you cook?\nSarah: A teacher.
+10	David: What did you eat?\nSarah: Sarah.
+11	A: What did you cook?\nB: I cooked chicken.
+12	What did you cook?\nSarah.
+13	What did you close?\nI closed the gate.
+14	What did you open?\nI opened the box.
+15	What did you lock?\nI locked the window.
+16	A: What did you close?\nB: David.
+17	A: What did you lock?\nB: David.
+18	David: What did you close?\nSarah: I closed the window.
+19	David: What did you close?\nSarah: David.
+20	A: Who did you see?\nB: A car.
+21	David: Who did you see?\nSarah: I saw a teacher.
+22	A: Who helped you?\nB: Milk.
+23	David: Who did you see?\nSarah: David.
+24	A: Who did you see?\nB: I saw eggs.
+25	Who is here?\nA car.
+26	Who did you see?\nI saw a doctor.
+27	Who did you meet?\nA doctor.
+28	David: Who did you meet?\nSarah: A teacher.
+29	David: Who kissed you?\nSarah: A chair kissed you.
+30	Who is talking to you?\nA lamp.
+31	Who kissed you?\nA teacher.
+32	David: Who shook your hand?\nSarah: A teacher.
+33	Who shook your hand?\nA doctor.
+34	A: Who kissed you?\nB: Sarah.
+35	David: Who kissed you?\nSarah: David.
+36	David: Who is sleeping?\nSarah: David is sleeping.
+37	Who is sleeping?\nEggs.
+38	David: Who is sleeping?\nSarah: A chair.
+39	A: Who is sleeping?\nB: A teacher.
+40	A: Who is playing the guitar?\nB: A doctor is playing the guitar.
+41	A: Who painted that picture?\nB: David.
+42	David: Who is playing the guitar?\nSarah: Sarah.
+43	Who is drawing?\nEggs.
+44	David: Who is drawing?\nSarah: A car is drawing.
+45	A: Where did you send the package?\nB: I sent the package a computer.
+46	Where did you send the package?\nI sent the package eggs.
+47	A: Where did you go?\nB: I went a book.
+48	David: Where did you go?\nSarah: Milk.
+49	A: Where did you travel to?\nB: A car.
+50	David: Where did you send the package?\nSarah: I sent the package across the country.
+51	Where did you send the package?\nTo california.
+52	A: Where are you headed?\nB: Eggs.
+53	A: Where did you drive to?\nB: A chair.
+54	Where are you headed?\nA computer.
+55	A: Where did you travel to?\nB: I traveled eggs.
+56	Where did you find it?\nI found it a car.
+57	A: Where did you put it?\nB: I put it on the table.
+58	David: Where did you put it?\nSarah: Eggs.
+59	A: Where did you leave it?\nB: A book.
+60	David: Where did you leave it?\nSarah: I left it a book.
+61	Where did you put it?\nI put it a piano.
+62	A: Where did you leave it?\nB: A table.
+63	Where did you find it?\nI found it eggs.

zeroshot/qa_congruence_tricky/eval_results.json

@@ -0,0 +1 @@
+{"eval_accuracy": 0.37575757575757573}

zeroshot/qa_congruence_tricky/predictions.txt

@@ -0,0 +1,166 @@
+index	prediction
+0	Who cleaned?\nDavid cleaned.
+1	A: Who cleaned?\nB: Sarah.
+2	A: Who cleaned?\nB: Sarah cleaned.
+3	David: Who wrote?\nSarah: A book wrote.
+4	A: Who wrote?\nB: Sarah.
+5	David: Who read?\nSarah: A book.
+6	David: Who wrote?\nSarah: David.
+7	A: Who wrote?\nB: A teacher wrote.
+8	A: Who read?\nB: A letter.
+9	Who read?\nA book read.
+10	A: Who read?\nB: A letter.
+11	David: Who wrote?\nSarah: David wrote.
+12	A: Who read?\nB: A teacher read.
+13	Who ate?\nA doctor.
+14	Who ate?\nDinner ate.
+15	Who ate?\nA teacher ate.
+16	A: Who cooked?\nB: David cooked.
+17	David: Who ate?\nSarah: Sarah.
+18	A: Who cooked?\nB: Sarah cooked.
+19	David: Who cooked?\nSarah: David.
+20	Who cooked?\nSarah.
+21	David: Who ate?\nSarah: Sarah ate.
+22	Who studies?\nScience studies.
+23	Who studies?\nA doctor studies.
+24	David: Who studies?\nSarah: History studies.
+25	A: Who studies?\nB: David.
+26	David: Who studies?\nSarah: David studies.
+27	Where were you reading?\nA book read.
+28	A: Where were you writing?\nB: A book.
+29	David: Where were you writing?\nSarah: A book wrote.
+30	David: Where were you writing?\nSarah: An essay.
+31	A: Where were you reading?\nB: An essay.
+32	David: Where were you writing?\nSarah: A book.
+33	A: Where were you reading?\nB: A letter read.
+34	A: Where were you writing?\nB: A letter wrote.
+35	David: Where were you reading?\nSarah: A letter.
+36	David: Where were you writing?\nSarah: An essay wrote.
+37	Where did you eat?\nPasta ate.
+38	David: Where did you eat?\nSarah: Dinner.
+39	David: Where did you cook?\nSarah: At home.
+40	Where did you cook?\nIn the kitchen cooked.
+41	Where did you cook?\nAt home cooked.
+42	A: Where did you cook?\nB: At home cooked.
+43	A: Where did you cook?\nB: At home.
+44	Where did you eat?\nIn the kitchen.
+45	Where did you eat?\nLunch.
+46	David: Where do you teach?\nSarah: Math teaches.
+47	A: Where do you teach?\nB: History.
+48	David: Where do you teach?\nSarah: History.
+49	Where do you study?\nScience studies.
+50	David: Where do you study?\nSarah: History.
+51	David: Where do you study?\nSarah: Math studies.
+52	A: Where do you teach?\nB: Science teaches.
+53	David: When did you clean?\nSarah: I cleaned yesterday.
+54	A: When did you clean?\nB: I cleaned yesterday.
+55	When were you writing?\nA letter wrote.
+56	David: When were you reading?\nSarah: A letter.
+57	When were you writing?\nA book wrote.
+58	David: When were you reading?\nSarah: A book read.
+59	When were you reading?\nA book read.
+60	When were you reading?\nA letter.
+61	When were you writing?\nA letter wrote.
+62	David: When were you reading?\nSarah: An essay.
+63	David: When were you reading?\nSarah: Last week.
+64	David: When were you writing?\nSarah: An hour ago wrote.
+65	When were you writing?\nYesterday wrote.
+66	When were you reading?\nA book.
+67	A: When did you cook?\nB: Yesterday.
+68	David: When did you eat?\nSarah: A sandwich.
+69	When did you eat?\nYesterday ate.
+70	When did you eat?\nThis morning.
+71	When did you eat?\nLunch.
+72	When did you eat?\nLunch ate.
+73	A: When did you cook?\nB: Dinner.
+74	When did you cook?\nThis morning.
+75	A: When did you eat?\nB: Dinner ate.
+76	When do you teach?\nMath.
+77	When do you study?\nMath studies.
+78	When do you teach?\nMath teaches.
+79	When do you study?\nMath.
+80	David: When do you study?\nSarah: History.
+81	A: When do you teach?\nB: History teaches.
+82	David: When do you study?\nSarah: Math studies.
+83	When do you study?\nScience.
+84	David: When do you study?\nSarah: Science studies.
+85	David: When do you teach?\nSarah: Science teaches.
+86	David: Why did you clean?\nSarah: I cleaned for fun.
+87	Why did you clean?\nI cleaned it was dirty.
+88	David: Why did you clean?\nSarah: The rug.
+89	David: Why were you reading?\nSarah: A book.
+90	A: Why were you reading?\nB: A letter.
+91	Why were you reading?\nA letter read.
+92	David: Why were you reading?\nSarah: A book.
+93	A: Why were you reading?\nB: A book read.
+94	David: Why were you writing?\nSarah: For work wrote.
+95	A: Why were you reading?\nB: A letter read.
+96	David: Why were you writing?\nSarah: A book.
+97	David: Why did you cook?\nSarah: Dinner cooked.
+98	David: Why did you eat?\nSarah: Lunch ate.
+99	A: Why did you eat?\nB: Just because ate.
+100	David: Why did you eat?\nSarah: Lunch ate.
+101	David: Why did you cook?\nSarah: Just because.
+102	Why did you eat?\nDinner.
+103	A: Why did you cook?\nB: Lunch cooked.
+104	David: Why did you cook?\nSarah: Dinner cooked.
+105	Why did you cook?\nA sandwich cooked.
+106	A: Why did you cook?\nB: A sandwich cooked.
+107	Why did you eat?\nDinner.
+108	A: Why do you study?\nB: Science studies.
+109	Why do you teach?\nJust because teaches.
+110	Why do you teach?\nJust because.
+111	A: Why do you study?\nB: Just because.
+112	Why do you teach?\nHistory teaches.
+113	A: Why do you teach?\nB: History.
+114	Why do you study?\nScience studies.
+115	A: Why do you study?\nB: Science studies.
+116	David: Why do you study?\nSarah: History studies.
+117	David: Why do you study?\nSarah: History.
+118	A: Why do you teach?\nB: For fun teaches.
+119	How many were you reading?\nA book.
+120	A: How many were you writing?\nB: Two.
+121	David: How many were you writing?\nSarah: A book wrote.
+122	How many were you writing?\nA lot wrote.
+123	How many were you reading?\nA couple read.
+124	A: How many did you cook?\nB: Two cooked.
+125	David: How many did you eat?\nSarah: Three ate.
+126	How many did you eat?\nOne.
+127	How many did you eat?\nA few.
+128	How many did you eat?\nOne.
+129	A: How many do you study?\nB: One studies.
+130	How many do you teach?\nA lot.
+131	David: How many do you study?\nSarah: A few.
+132	A: How many do you study?\nB: History.
+133	A: How many do you study?\nB: Science.
+134	David: How many do you teach?\nSarah: Science teaches.
+135	David: How many do you study?\nSarah: A couple.
+136	David: How many do you study?\nSarah: History studies.
+137	A: How many do you teach?\nB: History.
+138	How many did you clean?\nA lot.
+139	A: How many did you clean?\nB: Ten.
+140	How many did you clean?\nTen.
+141	A: How many did you clean?\nB: A lot.
+142	A: How were you writing?\nB: A letter.
+143	David: How were you reading?\nSarah: A book.
+144	How were you writing?\nAn essay.
+145	How were you writing?\nA letter wrote.
+146	How were you writing?\nAn essay wrote.
+147	How were you writing?\nCarefully wrote.
+148	A: How were you reading?\nB: A letter.
+149	A: How were you reading?\nB: A book read.
+150	David: How did you eat?\nSarah: Lunch.
+151	A: How did you cook?\nB: Lunch cooked.
+152	How did you eat?\nLunch.
+153	David: How did you cook?\nSarah: Lunch.
+154	David: How did you cook?\nSarah: With tongs cooked.
+155	How did you cook?\nPasta.
+156	How do you study?\nHistory.
+157	David: How do you study?\nSarah: History.
+158	A: How do you teach?\nB: Science.
+159	How do you teach?\nScience teaches.
+160	How do you teach?\nHistory.
+161	How do you teach?\nHistory.
+162	David: How do you teach?\nSarah: Science.
+163	A: How do you study?\nB: Math.
+164	A: How did you clean?\nB: I cleaned slowly.

zeroshot/quantifiers/eval_results.json

@@ -0,0 +1 @@
+{"eval_accuracy": 0.43225141679546625}

zeroshot/subject_aux_inversion/eval_results.json

@@ -0,0 +1 @@
+{"eval_accuracy": 0.628445962429861}

zeroshot/subject_verb_agreement/eval_results.json

@@ -0,0 +1 @@
+{"eval_accuracy": 0.4977416440831075}

zeroshot/turn_taking/eval_results.json

@@ -0,0 +1 @@
+{"eval_accuracy": 0.5178571428571429}

zeroshot/turn_taking/predictions.txt

@@ -0,0 +1,281 @@
+index	prediction
+0	David: Should she quit?\nSarah: No, I shouldn't.
+1	David: Should she meet him?\nSarah: Yes, I should.
+2	A: Did you think it was going to work?\nB: No, I didn't.
+3	A: Did you go somewhere after the movie was over?\nB: Yes, I did.
+4	A: Did you quit?\nB: Yes, I did.
+5	A: Did you meet him?\nB: Yes, I did.
+6	A: Should you consider asking for another option?\nB: No, I shouldn't.
+7	Should you meet him?\nNo, I shouldn't.
+8	Did you try to finish it on time or not?\nNo, you didn't.
+9	Did you arrive?\nNo, we didn't.
+10	David: Did you meet him?\nSarah: Yes, you did.
+11	David: Should you consider asking for another option?\nSarah: Yes, you should.
+12	David: Did you finish?\nSarah: No, you didn't.
+13	David: Did you go somewhere after the movie was over?\nSarah: Yes, you did.
+14	Should you quit?\nYes, you should.
+15	Should you meet him?\nNo, you shouldn't.
+16	A: Did you think it was going to work?\nB: Yes, you did.
+17	"Should they meet him?" he asked. "Yes, they should," she said.
+18	Did they try to finish it on time or not?\nNo, they didn't.
+19	David: Did they leave?\nSarah: No, they didn't.
+20	A: Did they try to finish it on time or not?\nB: Yes, they did.
+21	David: Did we finish?\nSarah: No, they didn't.
+22	A: Should they consider asking for another option?\nB: Yes, they should.
+23	"Did we meet him?" he asked. "Yes, they did," she said.
+24	David: Did I try to finish it on time or not?\nSarah: No, you didn't.
+25	A: Did I finish?\nB: Yes, you did.
+26	David: Should I meet him?\nSarah: No, I shouldn't.
+27	Should I finish?\nNo, you shouldn't.
+28	David: Should I consider asking for another option?\nSarah: Yes, you should.
+29	Did I try to finish it on time or not?\nYes, you did.
+30	David: Did I leave?\nSarah: Yes, I did.
+31	A: Did I find something to do later?\nB: Yes, you did.
+32	Should I quit?\nYes, you should.
+33	Did I meet him?\nNo, I didn't.
+34	"Did we think it was going to work?" he asked. "Yes, we did," she said.
+35	David: Did they finish?\nSarah: Yes, we did.
+36	David: Did we try to finish it on time or not?\nSarah: Yes, we did.
+37	A: Did they meet him?\nB: Yes, we did.
+38	David: Did we go somewhere after the movie was over?\nSarah: No, we didn't.
+39	"Did they figure it out?" he asked. "No, we didn't," she said.
+40	David: Did they figure it out?\nSarah: Yes, we did.
+41	A: Should they meet him?\nB: No, we shouldn't.
+42	A: Did they leave?\nB: No, they didn't.
+43	Should they figure it out?\nNo, they shouldn't.
+44	Should they quit?\nNo, they shouldn't.
+45	David: Did they think it was going to work?\nSarah: No, we didn't.
+46	A: Should they quit?\nB: Yes, they should.
+47	Did they find something to do later?\nNo, they didn't.
+48	Did they meet him?\nYes, they did.
+49	David: Should they consider asking for another option?\nSarah: Yes, they should.
+50	A: Should she finish?\nB: Yes, I should.
+51	A: Did she go somewhere after the movie was over?\nB: Yes, I did.
+52	Did she quit?\nYes, I did.
+53	Did she figure it out?\nNo, she didn't.
+54	A: Should she finish?\nB: No, I shouldn't.
+55	Did she arrive?\nYes, I did.
+56	David: Did she try to finish it on time or not?\nSarah: Yes, she did.
+57	David: Did she leave?\nSarah: Yes, she did.
+58	Did she finish?\nNo, she didn't.
+59	David: Should she figure it out?\nSarah: Yes, she should.
+60	Should she consider asking for another option?\nNo, she shouldn't.
+61	David: Did you try to finish it on time or not?\nSarah: No, you didn't.
+62	Did you quit?\nNo, you didn't.
+63	Did you finish?\nNo, you didn't.
+64	A: Should you figure it out?\nB: Yes, you should.
+65	Should you quit?\nYes, you should.
+66	David: Should you leave?\nSarah: No, I shouldn't.
+67	"Did you meet him?" he asked. "No, you didn't," she said.
+68	Did we go somewhere after the movie was over?\nNo, you didn't.
+69	David: Should you quit?\nSarah: Yes, you should.
+70	David: Did you finish?\nSarah: No, you didn't.
+71	A: Should you consider asking for another option?\nB: No, you shouldn't.
+72	A: Should you leave?\nB: Yes, you should.
+73	A: Did you figure it out?\nB: Yes, you did.
+74	David: Did you figure it out?\nSarah: No, you didn't.
+75	A: Should you meet him?\nB: Yes, you should.
+76	David: Should you figure it out?\nSarah: No, you shouldn't.
+77	Did you meet him?\nYes, you did.
+78	David: Should they consider asking for another option?\nSarah: Yes, we should.
+79	David: Should they quit?\nSarah: No, we shouldn't.
+80	Did you arrive?\nNo, we didn't.
+81	Did you figure it out?\nYes, we did.
+82	A: Did they go somewhere after the movie was over?\nB: No, we didn't.
+83	A: Should they leave?\nB: Yes, we should.
+84	"Did you meet him?" he asked. "Yes, we did," she said.
+85	David: Should they meet him?\nSarah: Yes, we should.
+86	A: Did I figure it out?\nB: Yes, you did.
+87	Should I quit?\nYes, you should.
+88	"Did I meet him?" he asked. "No, you didn't," she said.
+89	"Should I figure it out?" he asked. "Yes, you should," she said.
+90	Did I leave?\nNo, you didn't.
+91	David: Did I arrive?\nSarah: Yes, she did.
+92	A: Did I think it was going to work?\nB: No, you didn't.
+93	A: Did I finish?\nB: Yes, you did.
+94	Did I try to finish it on time or not?\nYes, you did.
+95	Should I finish?\nYes, you should.
+96	A: Should you quit?\nB: No, I shouldn't.
+97	David: Did you think it was going to work?\nSarah: Yes, I did.
+98	David: Should I meet him?\nSarah: No, I shouldn't.
+99	Did you figure it out?\nYes, I did.
+100	"Did you meet him?" he asked. "Yes, I did," she said.
+101	David: Should I figure it out?\nSarah: No, I shouldn't.
+102	Did I go somewhere after the movie was over?\nNo, I didn't.
+103	"Did you try to finish it on time or not?" he asked. "No, I didn't," she said.
+104	David: Did we meet him?\nSarah: Yes, they did.
+105	A: Should we consider asking for another option?\nB: Yes, they should.
+106	Did we arrive?\nYes, we did.
+107	Did we quit?\nYes, they did.
+108	Did we quit?\nNo, they didn't.
+109	David: Did we think it was going to work?\nSarah: Yes, they did.
+110	"Did we try to finish it on time or not?" he asked. "No, they didn't," she said.
+111	Did she arrive?\nNo, she didn't.
+112	David: Did she finish?\nSarah: No, she didn't.
+113	A: Should she meet him?\nB: Yes, she should.
+114	Did I go somewhere after the movie was over?\nYes, she did.
+115	Did she try to finish it on time or not?\nNo, she didn't.
+116	Did I leave?\nYes, she did.
+117	Did I quit?\nYes, she did.
+118	"Did I meet him?" he asked. "Yes, she did," she said.
+119	"Did I figure it out?" he asked. "Yes, she did," she said.
+120	David: Should I finish?\nSarah: Yes, you should.
+121	A: Should I meet him?\nB: No, you shouldn't.
+122	Did you go somewhere after the movie was over?\nYes, you did.
+123	David: Did you figure it out?\nSarah: Yes, you did.
+124	David: Did you quit?\nSarah: No, you didn't.
+125	David: Did you arrive?\nSarah: No, you didn't.
+126	A: Should I consider asking for another option?\nB: No, you shouldn't.
+127	Should you figure it out?\nNo, you shouldn't.
+128	David: Did you think it was going to work?\nSarah: No, you didn't.
+129	Did we figure it out?\nNo, they didn't.
+130	A: Did we quit?\nB: Yes, you did.
+131	David: Did we quit?\nSarah: No, you didn't.
+132	David: Should we leave?\nSarah: Yes, they should.
+133	"Did we try to finish it on time or not?" he asked. "No, they didn't," she said.
+134	A: Did we go somewhere after the movie was over?\nB: Yes, they did.
+135	David: Did we arrive?\nSarah: Yes, they did.
+136	Should we quit?\nNo, you shouldn't.
+137	A: Should you say that we will figure it out?\nB: Yes, I should.
+138	A: Did you say that they figured it out?\nB: No, I didn't say that.
+139	David: Should she say that we will meet him?\nSarah: No, I shouldn't.
+140	Should you say that they will consider asking for another option?\nYes, I should.
+141	"Did you say that we figured it out?" he asked. "Yes, I did," she said.
+142	"Did you say that I left?" he asked. "No, I didn't say that," she said.
+143	David: Did she say that they will try to finish it on time or not?\nSarah: No, I didn't say that.
+144	Did you say that I finished?\nYes, you did.
+145	"Should you say that we will meet him?" he asked. "Yes, you should say that," she said.
+146	"Did you say that she figured it out?" he asked. "No, you didn't," she said.
+147	A: Did you say that I will try to finish it on time or not?\nB: Yes, you said that.
+148	A: Did you say that you will find something to do later?\nB: No, you didn't say that.
+149	A: Did you say that I left?\nB: No, you didn't.
+150	"Did you say that I met him?" he asked. "Yes, you said that," she said.
+151	David: Should you say that they will meet him?\nSarah: No, we shouldn't.
+152	Did you say that she will think it was going to work?\nYes, you said that.
+153	A: Did you say that they figured it out?\nB: No, you didn't.
+154	Did they say that you met him?\nNo, they didn't.
+155	A: Should they say that they will figure it out?\nB: No, they shouldn't say that.
+156	David: Did they say that they will go somewhere after the movie is over?\nSarah: Yes, they did.
+157	Did they say that she finished?\nYes, they did.
+158	David: Did we say that she will try to finish it on time or not?\nSarah: No, they didn't.
+159	David: Should they say that they will leave?\nSarah: Yes, they should say that.
+160	David: Should I say that we will finish?\nSarah: Yes, you should say that.
+161	A: Should I say that I will consider asking for another option?\nB: Yes, you should.
+162	David: Should I say that I will finish?\nSarah: Yes, you should say that.
+163	Should I say that I will figure it out?\nNo, you shouldn't.
+164	A: Did I say that we quit?\nB: No, you didn't say that.
+165	David: Did I say that we quit?\nSarah: Yes, you did.
+166	A: Did I say that I will try to finish it on time or not?\nB: Yes, you did.
+167	Did I say that she will try to finish it on time or not?\nYes, you said that.
+168	A: Did I say that I will find something to do later?\nB: No, I didn't.
+169	Did I say that you finished?\nYes, you did.
+170	Should I say that you will meet him?\nYes, you should.
+171	Did they say that I will try to finish it on time or not?\nYes, we said that.
+172	Did they say that I will think it was going to work?\nYes, we said that.
+173	David: Did they say that you figured it out?\nSarah: Yes, we did.
+174	David: Did we say that we finished?\nSarah: No, we didn't.
+175	A: Did they say that you will go somewhere after the movie is over?\nB: No, we didn't.
+176	A: Should they say that you will leave?\nB: No, we shouldn't say that.
+177	David: Should they say that she will finish?\nSarah: Yes, we should.
+178	"Should they say that I will meet him?" he asked. "Yes, we should," she said.
+179	David: Should they say that you will leave?\nSarah: Yes, we should.
+180	A: Should they say that they will meet him?\nB: No, they shouldn't say that.
+181	A: Did they say that they left?\nB: No, they didn't.
+182	Did they say that I met him?\nYes, they said that.
+183	"Should they say that we will figure it out?" he asked. "Yes, they should say that," she said.
+184	"Did they say that I will try to finish it on time or not?" he asked. "Yes, we said that," she said.
+185	Should they say that we will finish?\nYes, they should.
+186	A: Should they say that you will leave?\nB: No, they shouldn't say that.
+187	A: Did they say that you will go somewhere after the movie is over?\nB: Yes, they did.
+188	Did she say that we will think it was going to work?\nNo, she didn't say that.
+189	A: Should she say that I will leave?\nB: Yes, she should.
+190	David: Did she say that you will find something to do later?\nSarah: Yes, she said that.
+191	David: Should she say that we will figure it out?\nSarah: Yes, she should say that.
+192	Should she say that she will meet him?\nNo, she shouldn't say that.
+193	David: Should she say that they will finish?\nSarah: Yes, she should.
+194	David: Did she say that we quit?\nSarah: Yes, I did.
+195	Did she say that she met him?\nNo, she didn't.
+196	David: Should you say that you will figure it out?\nSarah: Yes, I should.
+197	David: Should you say that you will leave?\nSarah: No, I shouldn't.
+198	David: Did you say that you finished?\nSarah: No, you didn't say that.
+199	David: Did you say that I will find something to do later?\nSarah: No, you didn't.
+200	Did you say that I quit?\nNo, you didn't.
+201	A: Did you say that we left?\nB: No, you didn't say that.
+202	Should you say that I will consider asking for another option?\nNo, you shouldn't.
+203	A: Did you say that she quit?\nB: Yes, you did.
+204	A: Did you say that you finished?\nB: Yes, you did.
+205	David: Did you say that they finished?\nSarah: Yes, you did.
+206	"Should you say that they will figure it out?" he asked. "Yes, you should say that," she said.
+207	Should you say that you will quit?\nYes, you should.
+208	Did you say that she will find something to do later?\nYes, you said that.
+209	"Did we say that we left?" he asked. "No, you didn't," she said.
+210	A: Should you say that she will figure it out?\nB: No, you shouldn't say that.
+211	Did you say that she will find something to do later?\nYes, you did.
+212	"Did you say that she will try to finish it on time or not?" he asked. "Yes, you did," she said.
+213	A: Did you say that we figured it out?\nB: Yes, we did.
+214	Should you say that you will figure it out?\nYes, we should.
+215	"Did you say that they will think it was going to work?" he asked. "Yes, we said that," she said.
+216	David: Should you say that you will figure it out?\nSarah: No, we shouldn't.
+217	David: Should they say that you will finish?\nSarah: No, we shouldn't.
+218	David: Did you say that she left?\nSarah: No, we didn't.
+219	David: Did you say that you will try to finish it on time or not?\nSarah: No, we didn't.
+220	David: Did you say that you figured it out?\nSarah: Yes, we did.
+221	Did you say that she will find something to do later?\nNo, we didn't.
+222	Should you say that you will meet him?\nNo, we shouldn't say that.
+223	A: Did I say that they left?\nB: Yes, you did.
+224	David: Should I say that they will finish?\nSarah: Yes, she should say that.
+225	David: Should I say that you will quit?\nSarah: Yes, she should.
+226	"Did I say that they will try to finish it on time or not?" he asked. "Yes, you said that," she said.
+227	Did I say that they quit?\nNo, you didn't.
+228	Should I say that we will quit?\nYes, you should.
+229	David: Did I say that she will think it was going to work?\nSarah: Yes, you did.
+230	"Did I say that you met him?" he asked. "No, you didn't," she said.
+231	A: Should you say that you will consider asking for another option?\nB: Yes, I should say that.
+232	"Did you say that you left?" he asked. "Yes, I did," she said.
+233	Should you say that I will consider asking for another option?\nNo, I shouldn't.
+234	Should you say that she will finish?\nNo, I shouldn't say that.
+235	Should you say that I will quit?\nYes, I should.
+236	Should you say that you will finish?\nNo, I shouldn't.
+237	David: Did you say that they will think it was going to work?\nSarah: Yes, I did.
+238	"Did you say that she will try to finish it on time or not?" he asked. "No, I didn't," she said.
+239	David: Did I say that they left?\nSarah: Yes, I said that.
+240	A: Did we say that I met him?\nB: No, they didn't.
+241	"Did we say that she will think it was going to work?" he asked. "Yes, they said that," she said.
+242	A: Did we say that she met him?\nB: No, they didn't.
+243	Should we say that you will leave?\nYes, they should.
+244	A: Did we say that you quit?\nB: No, they didn't say that.
+245	"Did we say that you will try to finish it on time or not?" he asked. "Yes, we said that," she said.
+246	Did we say that they will think it was going to work?\nYes, we said that.
+247	A: Did we say that I will go somewhere after the movie is over?\nB: Yes, they did.
+248	A: Did we say that you will go somewhere after the movie is over?\nB: No, they didn't.
+249	Should we say that you will finish?\nNo, they shouldn't say that.
+250	A: Did she say that she will try to finish it on time or not?\nB: No, she didn't say that.
+251	A: Did she say that I will find something to do later?\nB: Yes, she did.
+252	Did I say that I quit?\nYes, she did.
+253	Did she say that she finished?\nNo, she didn't.
+254	"Did I say that she left?" he asked. "No, she didn't," she said.
+255	David: Should she say that I will consider asking for another option?\nSarah: Yes, she should say that.
+256	Should she say that you will quit?\nNo, she shouldn't say that.
+257	A: Should I say that you will meet him?\nB: No, she shouldn't.
+258	A: Should I say that I will quit?\nB: Yes, she should.
+259	Should she say that they will meet him?\nNo, she shouldn't.
+260	David: Should I say that she will consider asking for another option?\nSarah: No, you shouldn't.
+261	David: Did you say that she met him?\nSarah: No, you didn't say that.
+262	Did I say that she quit?\nYes, you said that.
+263	A: Should you say that we will meet him?\nB: No, you shouldn't say that.
+264	Should you say that I will finish?\nNo, you shouldn't say that.
+265	David: Did you say that she finished?\nSarah: No, you didn't.
+266	A: Did you say that we left?\nB: No, you didn't.
+267	Did you say that she will try to finish it on time or not?\nYes, you said that.
+268	Did you say that we finished?\nNo, you didn't.
+269	"Did you say that we will think it was going to work?" he asked. "Yes, you said that," she said.
+270	"Should I say that we will meet him?" he asked. "Yes, you should," she said.
+271	Did we say that we quit?\nYes, you said that.
+272	"Should we say that we will meet him?" he asked. "Yes, they should," she said.
+273	A: Did we say that I quit?\nB: No, they didn't.
+274	David: Did we say that you met him?\nSarah: No, you didn't.
+275	David: Did we say that you left?\nSarah: No, you didn't say that.
+276	A: Did we say that you finished?\nB: No, you didn't.
+277	A: Did we say that we will find something to do later?\nB: No, you didn't.
+278	A: Should we say that she will consider asking for another option?\nB: Yes, you should.
+279	Should we say that you will quit?\nNo, you shouldn't.